Ontario Smart Grid Assessment and Roadmap - Electricity · Smart Grid Capability Deployment of Status Quo Scenario ... Microgrid Impacts ... Ontario Smart Grid Assessment and Roadmap

© 2015 Navigant Consulting Ltd.

Ontario Smart Grid Assessment

and Roadmap

Prepared for:

Navigant

333 Bay Street

Suite 1250

Toronto, ON M5H 2R2

+1.416.777.2440

www.navigant.com

January 2015

Disclaimer

The work presented in this white paper represents our best efforts and judgments based on the

information available at the time this report was prepared. Navigant is not responsible for the reader’s

use of, or reliance upon, the report, nor any decisions based on the report. NAVIGANT MAKES NO

REPRESENTATIONS OR WARRANTIES, EXPRESSED OR IMPLIED. Readers of the report assume all

liabilities incurred by them, or third parties, as a result of their reliance on the report, or the data,

information, findings, and opinions contained in the report.

Ontario Smart Grid Assessment and Roadmap Page i

Table of Contents

Executive Summary ................................................................................................................... 1

Navigant’s Recommendations ...................................................................................................................... 5

1. Introduction ............................................................................................................................. 7

2. The Current State of Smart Grid Investments ............................................................... 10

2.1 Analysis Framework .............................................................................................................................. 12

2.2 Smart Grid Plans through 2020—Status Quo Scenario ..................................................................... 15

2.2.1 Smart Grid Capabilities Deployed ......................................................................................... 16

2.2.2 Magnitude of the Benefits and Costs ..................................................................................... 21

2.2.3 Uncertainty in Benefits and Costs .......................................................................................... 22

2.2.4 Distribution of Benefits and Costs .......................................................................................... 23

2.3 The Road Ahead ..................................................................................................................................... 25

3. Future Deployment Scenarios ........................................................................................... 26

3.1 Vision for a Modern Distribution System ........................................................................................... 26

3.2 Baseline Future Scenario ....................................................................................................................... 27

3.2.1 Smart Grid Capabilities Deployed ......................................................................................... 27

3.2.2 Magnitude of Benefits and Costs ............................................................................................ 30

3.2.3 Uncertainty of Benefits and Costs .......................................................................................... 31

3.2.4 Distribution of Benefits and Costs .......................................................................................... 32

3.3 Promising Smart Grid Capabilities ...................................................................................................... 33

3.3.1 Automated Voltage Control .................................................................................................... 34

3.3.2 Self-Healing Grids .................................................................................................................... 34

3.3.3 Enhanced Fault Prevention ..................................................................................................... 35

3.3.4 Green Button ............................................................................................................................. 35

3.4 Enhanced Future Deployment ............................................................................................................. 35

4. Smart Grid Policy Roadmap .............................................................................................. 39

4.1 Barriers to Achieving a Modern Grid .................................................................................................. 39

4.1.1 Technical Barriers ..................................................................................................................... 40

4.1.2 Commercial Barriers................................................................................................................. 41

4.1.3 Cultural Barriers ....................................................................................................................... 44

4.2 Smart Grid Roadmap Initiatives .......................................................................................................... 45

4.2.1 Make Grid Modernisation a Component of Municipal Energy and Regional Planning

Processes ............................................................................................................................... 47

4.2.2 Establish a Province-Wide Framework for Evaluating the Benefits of Smart Grid

Investments .......................................................................................................................... 49

4.2.3 Consider Different Cost Allocation Mechanisms that Enable Distributors to Allocate and

Recover Costs Associated with Smart Grid Investments that Deliver Benefits beyond

their Local Customer Base.................................................................................................. 50

4.2.4 Create a Long-Term Funding Mechanism for Distributor-Led Pilot Innovation Projects53

Ontario Smart Grid Assessment and Roadmap Page ii

4.2.5 Establish a Forum for Distributors to Share Experiences with Smart Grid Deployments

................................................................................................................................................ 53

4.2.6 Establish Innovation Catalyst Funds ..................................................................................... 55

4.3 Conclusions ............................................................................................................................................. 56

Appendix A. Methodology .................................................................................................. A-1

A.1 Benefit-Cost Framework ....................................................................................................................A-2

A.2 Computational Model ........................................................................................................................A-3

A.3 Research and Inputs ............................................................................................................................A-4

A.4 Scope of Benefit-Cost Analysis ..........................................................................................................A-5

A.5 Estimating Deployment Curves ........................................................................................................A-8

A.6 Types of Benefits and Costs .............................................................................................................A-10

A.7 Cost-Sharing and Double Counting of Benefits ............................................................................A-11

A.8 Uncertainty .........................................................................................................................................A-12

Appendix B. Smart Grid Capabilities ................................................................................ B-1

B.1 Advanced Power Flow Control ......................................................................................................... B-1

B.2 Advanced Metering Infrastructure (AMI) ........................................................................................ B-1

B.3 Advanced Metering Infrastructure, Enhanced (AMI Enhanced) .................................................. B-2

B.4 Automated Reactive (or VAR) Power Control ................................................................................ B-3

B.5 Automated Real-Time Load Transfer ............................................................................................... B-4

B.6 Automated Voltage Control ............................................................................................................... B-5

B.7 Distributed Energy Resources Monitoring and Control ................................................................ B-6

B.8 Dynamic Capacity Rating ................................................................................................................... B-6

B.9 Critical Peak Pricing ............................................................................................................................ B-7

B.10 Electric Vehicle Integration and Control ........................................................................................ B-7

B.11 Energy Storage System Integration and Control ........................................................................... B-8

B.12 Enhanced Fault Prevention .............................................................................................................. B-9

B.13 Fault Current Limiting ...................................................................................................................... B-9

B.14 Green Button ..................................................................................................................................... B-10

B.15 Microgrids (Automated Islanding and Reconnection) ............................................................... B-11

B.16 Notification of Equipment Condition ........................................................................................... B-12

B.17 Self-Healing Grid ............................................................................................................................. B-12

B.18 Time of Use Pricing ......................................................................................................................... B-13

Appendix C. Detailed Assumptions ................................................................................... C-1

C.1 Grid Characteristics ............................................................................................................................. C-1

C.2 Benefit Valuation Parameters ............................................................................................................ C-1

Appendix D. Smart Grid Capabilities with Promising Findings ................................ D-4

D.1 Automated Voltage Control ..............................................................................................................D-4

D.2 Self-Healing Grids .............................................................................................................................D-10

D.3 Enhanced Fault Prevention ..............................................................................................................D-17

D.4 Green Button ......................................................................................................................................D-21

D.5 Dynamic Capacity Rating ................................................................................................................D-24

D.6 Microgrids ..........................................................................................................................................D-28

D.7 Distributed Energy Resources Monitoring and Control ..............................................................D-32

Ontario Smart Grid Assessment and Roadmap Page iii

D.8 AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing ....................................................D-37

D.9 Energy Storage System Integration and Control ..........................................................................D-41

Ontario Smart Grid Assessment and Roadmap Page iv

List of Tables and Figures

Table 1. Smart Grid Capability Deployment of Status Quo Scenario .......................................................................... 20 Table 2. Smart Grid Capability Deployment for Baseline Future Scenario ................................................................ 29 Table 3. Smart Grid Capability Deployment of Enhanced Future Scenario ............................................................... 36 Table 4. Mapping of Initiatives to Barriers ...................................................................................................................... 47 Table 5: Smart Grid Capability Penetration Metrics ................................................................................................... A-9 Table 6: Benefit Types ................................................................................................................................................... A-10 Table 7: Mapping of Benefit Categories to Benefit Types ........................................................................................ A-10 Table 8: Mapping of Benefit Categories to Each Segment of the Electricity Sector .............................................. A-11 Table 9: Cost Categories ............................................................................................................................................... A-11 Table 10: Deployment Figures for Automated Voltage Control ............................................................................... D-6 Table 11: Deployment Figures for Self-Healing Grids ............................................................................................. D-12 Table 12: Self-Healing Grid Impacts ........................................................................................................................... D-13 Table 13: Deployment Figures for Enhanced Fault Prevention .............................................................................. D-18 Table 14: Enhanced Fault Prevention Impacts .......................................................................................................... D-18 Table 15: Deployment Figures for Green Button....................................................................................................... D-21 Table 16: Green Button Impacts................................................................................................................................... D-21 Table 17: Deployment Figures for Dynamic Capacity Rating ................................................................................. D-25 Table 18: Deployment Figures for Microgrids ........................................................................................................... D-29 Table 19: Microgrid Impacts ........................................................................................................................................ D-29 Table 20: Deployment Figures for Distributed Energy Resource Monitoring and Control ................................ D-34 Table 21: Deployment Figures for AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing ..................... D-38 Table 22: AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing Impacts ................................................. D-38

Figure 1: Modern Distribution Grid ...................................................................................................................................1 Figure 2. Net Present Value of Enhanced Smart Grid Investments ...............................................................................2 Figure 3. Range of Net Present Value ................................................................................................................................3 Figure 4. Distribution of Benefits and Costs of Smart Grid across Industry Segments ..............................................4 Figure 5. Barriers to the Development of a Smart Grid ...................................................................................................4 Figure 6. The Emerging Energy Cloud ..............................................................................................................................7 Figure 7. Sample of Ontario Utility Smart Grid Investments ....................................................................................... 11 Figure 8. Select Smart Grid Fund Projects ....................................................................................................................... 12 Figure 9. Smart Grid Technology Overlap ...................................................................................................................... 13 Figure 10. Overview of Analysis Framework and Process ........................................................................................... 14 Figure 11. Deployment of AMI and Time of Use Pricing through 2020 ..................................................................... 16 Figure 12. Deployment of Additional Smart Grid Capabilities through 2020 .......................................................... 17 Figure 13. Deployment of Microgrids and Monitoring and Control Capabilities through 2020 ............................. 18 Figure 14. Definition of Smart Grid Capabilities ............................................................................................................ 19 Figure 15. Comparison of One Megawatt with the Load of Residential Homes and Electric Vehicles .................. 20 Figure 16. Annual Benefits and Costs of Status Quo Scenario ..................................................................................... 21 Figure 17. Net Present Value of Status Quo Scenario .................................................................................................... 22 Figure 18. Range of Net Present Value of Status Quo Scenario ................................................................................... 23 Figure 19. Distribution of Benefits and Costs for Status Quo Scenario ....................................................................... 24 Figure 20. Smart Meter Deployment Timelines across Multiple Jurisdictions........................................................... 25 Figure 21. Illustrative Modern Grid ................................................................................................................................. 27 Figure 22. Deployment of Smart Grid Capabilities through 2035 ............................................................................... 28 Figure 23. Deployment of Microgrids and Monitoring and Control Capabilities through 2035 ............................. 28

Ontario Smart Grid Assessment and Roadmap Page v

Figure 24. Definition of Additional Smart Grid Capabilities ....................................................................................... 29 Figure 25. Annual Benefits and Costs of Baseline Future Scenario ............................................................................. 30 Figure 26. Net Present Value of Baseline Future Scenario ............................................................................................ 31 Figure 27. Range of Net Present Value of Baseline Future Scenario ........................................................................... 31 Figure 28. Distribution of Benefits and Costs of Baseline Future Scenario ................................................................. 32 Figure 29. Benefit-Cost Ratios for Smart Grid Capabilities .......................................................................................... 33 Figure 30. Annual Benefits and Costs of Enhanced Future Scenario .......................................................................... 37 Figure 31. Net Present Value of Enhanced Future Scenario ......................................................................................... 37 Figure 32. Range of Net Present Value of Enhanced Future Scenario......................................................................... 38 Figure 33. Distribution of Benefits and Costs of Enhanced Future Scenario .............................................................. 38 Figure 34. Overview of Ontario’s Network Planning Framework .............................................................................. 48 Figure 35. Illustrative Allocation of Smart Grid Benefits .............................................................................................. 52 Figure 36: Smart Grid Analysis Framework Development ....................................................................................... A-1 Figure 37: Deployment Curves to Benefits and Costs ................................................................................................ A-2 Figure 38: Illustrative Mapping of Assets to Capabilities and Capabilities to Impacts ......................................... A-3 Figure 39: Screenshot of Navigant’s Smart Grid Benefit-Cost Model ...................................................................... A-4 Figure 40: Benefit-Cost Analysis Timeframe ............................................................................................................... A-6 Figure 41: Relationship Between Smart Grid, Conservation, and Distributed Energy Resources ....................... A-7 Figure 42: Modeling of Smart Grid Capability Deployment Curves ....................................................................... A-8 Figure 43: Cost Sharing across Capabilities ............................................................................................................... A-12 Figure 44: Illustration of Uncertainty Analysis ......................................................................................................... A-13 Figure 45: Illustrative Uncertainty Analysis .............................................................................................................. A-13 Figure 46: Sample of Grid Characteristics .................................................................................................................... C-1 Figure 47: Energy Cost Benefit Valuation Parameters ............................................................................................... C-2 Figure 48: Capacity Costs Benefit Valuation Parameters ........................................................................................... C-2 Figure 49: Value of Loss Load Valuations .................................................................................................................... C-3 Figure 50: Ancillary Services Valuation Parameters ................................................................................................... C-3 Figure 51: Illustrative Placement of Automated Voltage Control Assets ................................................................ D-5 Figure 52: Automated Voltage Control Deployment Impact Curve ........................................................................ D-7 Figure 53: Annual Benefits and Costs of Automated Voltage Control Deployment through 2035 ..................... D-8 Figure 54: Net Present Value of Automated Voltage Control Deployment through 2035 .................................... D-8 Figure 55: Present Value of Benefits and Costs of Automated Voltage Control Deployment through 2035 ...... D-9 Figure 56: Distribution of Benefits and Costs from Automated Voltage across Industry Segments ................. D-10 Figure 57: Illustrative Placement of Self-Healing Grid Assets ................................................................................ D-11 Figure 58: Self-Healing Grid Realised Benefits over Time ....................................................................................... D-14 Figure 59: Annual Benefits and Costs of Self-Healing Grid Deployments through 2035 .................................... D-14 Figure 60: Net Present Value of Self-Healing Grid Deployment through 2035 .................................................... D-15 Figure 61: Present Value of Benefits and Costs of Self-Healing Grid Deployments through 2035 .................... D-16 Figure 62: Distribution of Benefits and Costs from Self-Healing Grid across Industry Segments ..................... D-17 Figure 63: Annual Benefits and Costs of Enhanced Fault Prevention Deployments through 2035 ................... D-18 Figure 64: Net Present Value of Enhanced Fault Prevention Deployments through 2035 .................................. D-19 Figure 65: Present Value of Benefits and Costs of Enhanced Fault Prevention Deployments through 2035.... D-20 Figure 66: Distribution of Benefits and Costs from Enhanced Fault Prevention across Industry Segments .... D-20 Figure 67: Annual Benefits and Costs of Green Button Deployment through 2035 ............................................. D-22 Figure 68: Net Present Value of Green Button Deployment through 2035 ............................................................ D-23 Figure 69: Present Value of Benefits and Costs of Green Button Deployment through 2035 ............................. D-23 Figure 70: Distribution of Benefits and Costs from Green Button across Industry Segments ............................ D-24 Figure 71: Available Capacity vs. Static Rating—Frequency Graph ...................................................................... D-25

Ontario Smart Grid Assessment and Roadmap Page vi

Figure 72: Annual Benefits and Costs of Dynamic Capacity Rating Deployments through 2035 ...................... D-26 Figure 73: Net Present Value of Dynamic Capacity Rating Deployments through 2035 .................................... D-26 Figure 74: Present Value of Benefits and Costs of Dynamic Capacity Rating Deployments through 2035 ...... D-27 Figure 75: Distribution of Benefits and Costs from Dynamic Capacity Rating across Industry Segments ....... D-27 Figure 76: Annual Benefits and Costs of Microgrid Deployments through 2035 ................................................. D-30 Figure 77: Net Present Value of Microgrid Deployments through 2035 ................................................................ D-30 Figure 78: Present Value of Benefits and Costs of Microgrid Deployments through 2035 ................................. D-31 Figure 79: Distribution of Benefits and Costs from Microgrids across Industry Segments ................................ D-31 Figure 80: Renewables Capacity and Percentage of Total Capacity ....................................................................... D-33 Figure 81: Monitored and Controlled Distributed Resource Facilities .................................................................. D-34 Figure 82: Annual Benefits and Costs of Distributed Energy Resource Monitoring and Control Deployment

through 2035 .................................................................................................................................................................. D-35 Figure 83: Net Present Value of Distributed Energy Resource Monitoring and Control Deployment through

2035 .................................................................................................................................................................................. D-35 Figure 84: Present Value of Benefits and Costs of Distributed Energy Resource Monitoring and Control

Deployment through 2035 ............................................................................................................................................ D-36 Figure 85: Distribution of Benefits and Costs from Distributed Energy Resource Monitoring and Control across

Industry Segments......................................................................................................................................................... D-37 Figure 86: Annual Benefits and Costs of AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing through

2035 .................................................................................................................................................................................. D-39 Figure 87: Net Present Value of AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing Deployments

through 2035 .................................................................................................................................................................. D-39 Figure 88: Present Value of Benefits and Costs of AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

Deployments through 2035 .......................................................................................................................................... D-40 Figure 89: Distribution of Benefits and Costs from AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

across Industry Segments ............................................................................................................................................. D-40 Figure 90: Energy Storage Deployment Assumptions ............................................................................................. D-42 Figure 91: Annual Benefits and Costs of Energy Storage Deployments through 2035 ........................................ D-43 Figure 92: Net Present Value of Energy Storage Deployments through 2035 ....................................................... D-43 Figure 93: Present Value of Benefits and Costs of Energy Storage Deployment through 2035 .......................... D-44 Figure 94: Distribution of Benefits and Costs from Energy Storage across Industry Segments ......................... D-45

Ontario Smart Grid Assessment and Roadmap Page 1

Executive Summary

Electricity networks are undergoing significant transformation. Clean, small, distributed energy and

demand-side resources are challenging the traditional axiom of electricity from large, remote power

generation facilities delivered over extensive transmission and distribution (T&D) infrastructure to

consumers. This transformation is the result of a number of diverse and disruptive technology

innovations. These innovations are changing the design of the electricity network, the flow of electricity

in the system, and are driving utilities to forge a complex set of new relationships with stakeholders (e.g.,

end users, energy services companies, generators).

The backbone of this transformation is a modern electricity distribution system, a smart grid. A modern

electricity distribution system is more complex, has greater redundancy, and allows for greater choice

over the manner in which users generate, deliver, and consume electricity. Such a system should be

capable of integrating distributed energy and demand-side resources, must be operated and maintained

at a lower net cost than the traditional infrastructure, and must deliver improved reliability to customers

who are becoming increasingly dependent on a high-quality, reliable power supply.

Figure 1: Modern Distribution Grid

Source: Navigant.

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Digital Relays

Two Way CommunicationsEnergy Storage

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DER Interface

Automated Circuit Breakers

Condition Sensor

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Automated Switch

Multipurpose Sensor

Regulating Inverter

Fault Current Limiter

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DRMS

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Ontario’s smart grid vision is to modernise the grid to meet the needs of an evolving electricity system

and digitally sophisticated consumers. The vision is to enhance efficiency and reliability, increase

automation, improve customer engagement, and integrate distributed energy resources. Becoming a

leader in smart energy solutions, including cutting-edge smart grid technologies and services, is also an

integral part of this vision. As a step toward achieving this vision, the Ministry of Energy engaged

Navigant to:

Establish a clearer understanding of the current state of smart grid investment in Ontario

Develop a replicable and transparent methodology to quantify the benefit and cost of smart grid

investments

Analyse the benefits and costs of current and potential future smart grid investments

Identify barriers to the investment in and adoption of smart grid technologies

Recommend actions that various stakeholders could take to realise the long-term value of a

modernised grid for the benefit of Ontario’s electricity system, consumers, and the economy

Navigant’s analysis yielded several important findings, as detailed below.

Finding 1: Smart grid investments, if continue to be made through 2035, have the potential to deliver a

net benefit of $6.3 billion.

Ontario has made substantial investments in smart grid, including smart meters. These investments,

combined with the additional deployment of smart grid capabilities over the next 20 years, will transform

the electricity grid and deliver substantial benefits to the province. Navigant estimates that the net

benefit, or net present value, of these investments is $6.3 billion, as seen in Figure 2. This result highlights

a compelling business case for smart grid deployment across the province.

Figure 2. Net Present Value of Enhanced Smart Grid Investments

Source: Navigant; all values in 2014 $.

$(2.0)

$(1.0)

$-

$1.0

$2.0

$3.0

$4.0

$5.0

$6.0

$7.0

2005 2010 2015 2020 2025 2030 2035 2040 2045

$B (

nom

inal

)

Deployment through 2035


$6.3B

$3.2B

Enhanced deployment through 2035 $5.3B

Navigant analysed three scenarios. The first

scenario examines smart grid deployment

through 2020, the second analyses the

continued deployment through 2035, and the

third scenario revisits the deployment

assumptions through 2035 to enhance the

value of the combined investment.


Finding 2: The uncertainty surrounding the benefits and cost of smart grid investments does not

undermine the positive business case.

As with any many transformative technologies, there is a degree of uncertainty around the expected

benefits and costs. Navigant analysed the range of potential net benefits based on the underlying

uncertainty of important assumptions. The analysis suggests that even with less favourable assumptions,

the business case for smart grid investments is positive. Navigant expects that the net present value will

range from $3.8 billion to $9.0 billion.

Figure 3. Range of Net Present Value

Source: Navigant analysis; all values in 2014 $ and reflect benefits and costs through 2045.

Finding 3: The distribution segment will incur the majority of the cost of smart grid investments, whereas

benefits will accrue across the various segments of the industry.

Navigant’s analysis suggests that distribution utilities will incur the vast majority of the costs, but the

benefits will accrue across all segments of the industry. While in Ontario, the customer or ratepayer

ultimately bears the majority of costs and receives the majority of the benefits, the origination of benefits

and costs across the segments of the industry is important from a regulatory framework and cost

allocation perspective. Under the current cost allocation model, the misalignment of benefits and costs,

particularly for the distribution segment, is a potential barrier to smart grid investment.

The frequency distribution

curve represents the

likelihood of occurrence for

each unique outcome. The

expected case represents

the geometric mean, and the

worst and best cases reflect

the 5th and 95th percentile

values, respectively.


Figure 4. Distribution of Benefits and Costs of Smart Grid across Industry Segments

Source: Navigant; all values in 2014 $ and reflect benefits and costs through 2045.

Finding 4: Several barriers impede the development of a smart grid in Ontario.

Navigant’s consultation with stakeholders and independent assessment highlighted a number of barriers

that, if left unaccounted for, will impede the deployment smart grid technologies in Ontario. Navigant

identified nine barriers that represent Navigant’s assessment of the most significant barriers to smart grid

deployment in Ontario, as detailed in Figure 5.

Figure 5. Barriers to the Development of a Smart Grid

Source: Navigant

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Environmental

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Generation Transmission Distribution Customers

It is important to note that

this analysis shows where

the benefits and costs

originate. How the sector

ultimately distributes these

benefits and costs depends

on factors such as tariff

formulation and regulatory

policy.


1 | Immature technology: The underlying technology for a number of smart grid capabilities is immature. This immaturity leads to higher costs, evolving functionality, and unstable operation or higher failure rates.

2 | Lack of interoperability: As communication protocols for smart network assets continue to evolve, the lack of standard protocols leads to additional system integration costs, extended project implementation timelines, and risk of vendor and/or technology lock-in.

3 | Diffuse benefits, concentrated costs: Smart grid investments generally deliver a range of benefits (e.g., reliability improvements, reduction in losses, reduced consumption, deferred traditional network reinforcement, etc.) across the multiple segments of the industry, whereas costs are borne primarily by one segment—in this case the distribution segment.

4 | Labour work force constraints: Smart grid investments place a heavier emphasis on information technology, advanced control systems, and data analytics—skillsets not generally found within distribution utilities today.

5 | Financial constraints: Substantial capital investment associated with the renewal of Ontario’s aging electricity infrastructure leaves distributors in a situation where they are financially constrained and may not be in a position to accommodate additional smart grid investments.

6 | Fragmented ownership of the distribution sector: The fragmented structure of Ontario’s distribution sector results in a lack of scale for some distributors, as well as fragmented ownership of the distribution assets required to take full advantage of some smart grid capabilities.

7 | Regulatory framework: The current regulatory construct in Ontario, including the framework for assessing smart grid investments and the lack of strong incentives or penalties associated with performance or quality of service, can negatively impact some distributors’ and stakeholders’ perception of smart grid investments.

8 | Lack of knowledge sharing: Information about what worked, what did not work, what challenges were overcome, and why smart grid projects were successful or unsuccessful is not effectively shared across the industry.

9 | Risk averse behaviour and guarded culture: In general, the municipalities or the provincial government shareholders that own the distribution utilities in Ontario have a relatively low appetite for risk, and view their utility as a low- or risk-free investment. This perception, combined with the required strong emphasis on safety and operational resiliency results in a culture that is guarded, risk averse, and tends to shy away from innovation.

Navigant’s Recommendations

In order to realise the value of a smarter electricity grid, Ontario needs to prioritise and address these

barriers. The actions that the government, regulator, and industry can take to address these challenges

are not simple and require a sector-wide effort. Jurisdictions around the world are making efforts to

address the barriers to the evolution the electricity sector. To remain a leading-edge jurisdiction Ontario

should do the same.

Navigant has identified six high-priority initiatives that will enable substantial improvements in the

efficiency and pace of smart grid deployment in Ontario:

Make grid modernisation a component of community energy and regional planning processes.

The Independent Electricity System Operator, distributors, and the government should leverage

the municipal energy planning and regional planning processes that exist in Ontario to shift the

scope of the discussion of grid modernisation initiatives from a single utility to municipalities

and the broader region. Grid modernisation initiatives identified through these processes could

be alternatives to traditional network reinforcements and enable wider deployment of distributed

energy resources. There is a tremendous opportunity to use these processes as a platform for

debate and to inform customers about the benefits associated with a smart grid.


Establish a province-wide framework for evaluating the benefits of smart grid investments.

A robust, province-wide, benefit-cost analysis framework for smart grid investments is a

necessary evolution in the Ontario Energy Board’s approach to evaluating grid modernisation

initiatives. As smart grid capabilities evolve from pilot demonstrations to business-as-usual

operation, a precise, transparent, and common framework will help promote the adoption of

smart grid technologies among distributors and establish clear guidelines for the types of benefits

and costs that utilities should consider.

Consider different approaches to cost allocation that enable the distributors to recover the

costs associated with broader system benefits from the wider sector. As illustrated in

Navigant’s analysis, smart grid investments are characterised by diffuse benefits. Some smart

grid projects may be justified on the basis of impacts that benefit a distributor’s local customers

only; others, however, are justified on the basis of broader system impacts that benefit all

customers. Enabling distributors to allocate a portion of the cost associated with broader system

benefits to the sector as a whole will allow distributors to pursue smart grid investments they

might not otherwise consider, but that could deliver meaningful benefits to the entire system.

Create a long-term funding mechanism for distributor-led pilot innovation projects.

Creating a long-term funding mechanism for distributor-led pilot innovation projects will

encourage distributors to take a more active role in and to have more accountability for smart

grid initiatives. Beyond providing a test bed for technology evaluation, it will encourage

distributors to identify opportunities and critical system deficiencies in their networks, and to

pursue innovative smart grid solutions. Distributors will be engaged in the vendor and

technology selection process, as well as take ownership and responsibility for the full life cycle of

smart grid projects.

Promote broader sharing of positive and negative experiences with smart grid investments.

Distributors would benefit substantially from additional opportunities to share plans and lessons

from smart grid deployments. A well-maintained online repository of smart grid projects, with

contact details for project managers, would help to facilitate one-on-one discussions between

distributors. Better sharing of information will improve the efficiency of smart grid investments.

It will reduce the duplication of projects and the disjointedness of initiatives, identify activity

areas and gaps, provide a better understanding of likely sources of benefits and costs, and reduce

implementation times and costs.

Establish innovation catalyst funds. Utilities need to foster a culture that is conducive to and

promotes innovation. To support the development of an innovative corporate culture,

distributors in Ontario should establish innovation catalyst funds. Shareholder, as opposed to

ratepayer backed, these catalyst funds should be available to internal teams to demonstrate

proof-of-concept for new ideas rapidly. Distributors should also consider taking additional steps

to promote innovation, including building and reporting on innovation metrics, appointing

innovation champions, and creating cross-functional innovation networks within their

organisation.

Provided that industry and government agree with the merits of the initiatives outlined above, work

should commence immediately to assign responsibility to the various parties for developing detailed

plans. Navigant believes that work on the initiatives could proceed in parallel and that careful planning

could mitigate the impact of interdependencies between the initiatives on the overall timing. To realise

the potential net benefit from the investment in smart grid, the sector should aim to make significant

progress on the initiatives identified above over the next two to four years.


1. Introduction

Electricity networks are undergoing significant transformation. Clean, small, distributed energy and

demand-side resources are challenging the traditional axiom of electricity from large, remote power

generation facilities delivered over extensive transmission and distribution (T&D) infrastructure to

consumers. This transformation represents the impact of diverse and disruptive innovations in

technology and business models. These innovations are, in turn, driving utilities to forge a complex set of

new relationships with stakeholders (e.g., end users, energy services companies, power generators, etc.)

that will dictate how electricity networks are designed, how electricity flows, and how information is

shared.

Jurisdictions around the world are grappling with how to characterise this transformation. The state of

New York introduced the concept of a “distribution system platform provider” as a way to describe the new

role of distribution utilities in an environment rich with distributed energy resources. Other jurisdictions

have referred to this transformation as “Utilities 2.0” a generic term that represents a modern, smarter,

more connected, and more distributed electricity utility. Navigant characterises this new paradigm as the

energy cloud, as shown in Figure 6.

Figure 6. The Emerging Energy Cloud

Source: Navigant

In the information technology (IT) world, the cloud represents a game-changing move from the localised

provision of computing power (on the desktop, in a department or division, or in a single enterprise) to

the use of centralised networked services that have the flexibility and capacity to meet growing

requirements for IT services in an on-demand and more cost efficient manner. In the electricity world,

the energy cloud represents the shift away from a centralised energy generation architecture toward a

networked and dynamic infrastructure that actively incorporates distributed energy and demand-side

resources and has the capability to integrate renewable and intermittent energy sources, storage, electric

vehicles (EVs), and other connected devices alongside traditional electricity assets. This nonlinear

ecosystem involves multiple inputs and relies on a high degree of communication and automation to

support two-way energy flows.


The backbone of this transformation is a modern electricity distribution system, a smart grid. A modern

electricity distribution system is more complex, has greater redundancy, and allows for greater choice

over the manner in which users generate, deliver, and consume electricity. Such a system should be

capable of integrating distributed energy and demand-side resources, must be operated and maintained

at a lower net cost than the traditional infrastructure, and must deliver improved reliability to customers

who are becoming increasingly dependent on a high-quality, reliable power supply.

Ontario has embraced this transformation, while at the

same time recognising the need to maintain and

refurbish significant elements of the traditional

electricity infrastructure. The deployment of advanced

metering infrastructure (AMI, or smart meters) was an

important enabling policy that set much of this change

in motion. The government has also demonstrated its

support for distributed energy and demand-side

resources through the feed-in tariff (FIT), micro-FIT,

and net metering programs; energy storage

procurements; funding for combined heat and power

(CHP, or cogeneration) projects; and the Conservation

First framework.1 Furthermore, the government’s

decision to establish a Smart Grid Fund has supported

the development of new and emerging energy

technologies.2

While the government and many sector stakeholders are aware of the various valuable smart grid

initiatives and there is a broad understanding of the benefits initiated by the smart meter investment, a

more concrete measurement of such benefits would allow Ontario to strengthen existing advantages and

better plan for the future. Thus, the Ontario Ministry of Energy engaged Navigant to develop an up-to-

date evaluation of smart grid investments, supported by a methodology that enables the province to

conduct future assessments in a systemic fashion.

Ontario’s smart grid vision is to modernise the grid to meet the needs of an evolving electricity system

and digitally sophisticated consumers. This will be achieved through enhanced efficiency, reliability,

automation, improved customer engagement, and renewable energy integration. Becoming a leader in

smart energy solutions, including cutting-edge smart grid technologies and services is also an integral

part of this vision. As a step toward achieving this vision, the Ministry of Energy tasked Navigant with

the following specific objectives:

1 Information on Ontario’s FIT and micro-FIT programs is available online at: www.energy.gov.on.ca/en/fit-and-

microfit-program/. Information on Ontario’s Energy Storage procurement is available online at:

www.powerauthority.on.ca/generation-procurement/energy-storage. Information on Ontario’s CHP

procurements is available online at: www.powerauthority.on.ca/procurement-archive/combined-heat-and-

power. Information on Ontario’s Conservation First framework is available online at:

http://www.energy.gov.on.ca/en/conservation-first/.

2 Information on Ontario’s Smart Grid Fund is available online at: www.energy.gov.on.ca/en/smart-grid-fund/.

Smart grid means the advanced information exchange

systems and equipment that when utilised together

improve the flexibility, security, reliability, efficiency

and safety of the integrated power system and

distribution systems, particularly for the purposes of:

(a) enabling the increased use of renewable energy

sources and technology, including generation

facilities connected to the distribution system;

(b) expanding opportunities to provide demand

response, price information and load control to

electricity customers;

(c) accommodating the use of emerging, innovative

and energy-saving technologies and system control

applications; or

(d) supporting other objectives that may be

prescribed by regulation.

Smart Grid Ontario Electricity Act, 1998

http://www.energy.gov.on.ca/en/fit-and-microfit-program/

http://www.energy.gov.on.ca/en/fit-and-microfit-program/

http://www.powerauthority.on.ca/generation-procurement/energy-storage

http://www.powerauthority.on.ca/procurement-archive/combined-heat-and-power

http://www.powerauthority.on.ca/procurement-archive/combined-heat-and-power

http://www.energy.gov.on.ca/en/conservation-first/


Establish a clearer understanding of the current state of smart grid investment in Ontario

Develop a replicable and transparent methodology to quantify the benefits and costs of smart

grid investments

Analyse the benefits and costs of current and potential future smart grid investments

Identify barriers to the investment in and adoption of smart grid technologies

Recommend actions that various stakeholders could take to realise the long-term value of a

modernised grid for the benefit of Ontario’s electricity system, consumers, and the economy

This report presents Navigant’s findings and recommendations and consists of four sections.

Section 1 introduction that highlights the context for Navigant’s work and its objectives.

Section 2 discusses the existing state and current plans (through 2020) for smart grid investments

in Ontario as well as the expected net benefit of these investments over time.

Section 3 analyses the potential net benefit of smart grid over the next two decades.

Section 4 summarises the findings from Navigant’s engagement with stakeholders on barriers to

smart grid investment in Ontario and presents Navigant’s proposed recommendations.

There are four appendices.

Appendix A describes Navigant’s methodology for analysing the benefits and costs of smart grid

investments.

Appendix B provides a detailed description of the smart grid capabilities considered, including

the benefits and assets associated with each capability.

Appendix C summarises important drivers of value in the benefit-cost analysis.

Appendix D provides detailed results for a select number of smart grid capabilities.


2. The Current State of Smart Grid Investments

Over the past decade, the Ontario government has encouraged innovation in the electricity distribution

sector and the adoption of smart grid technologies. From 2006 to 2014, Ontario’s electricity distributors,

or local distribution companies, installed more than 4.8 million smart meters in homes and businesses

across the province and transitioned the vast majority of customers with smart meters to time of use

(TOU) electricity rates.

Electricity distributors have made use of the capabilities of smart meters to reduce meter-reading costs,

improve customer communication and outage management, enhance fault localisation, optimise the

dispatch of service crews, provide energy awareness tools to their customers, and to monitor assets on

their networks in ways previously not available. These enhanced capabilities enable distributors to

improve a number of elements of their businesses, as described below.

Outage management and communication: Distributors are integrating smart meter data with

outage management systems to provide customers with real-time outage maps and other

important information, reducing inbound call volume and improving overall customer

satisfaction.

Fault location: Distributors are using the last gasp notifications from smart meters to alert

operators of potential outages, more rapidly dispatching crews and restoring power.

Service restoration: Distributors are using smart meter functionality to verify when power has

been restored to customers, avoiding service calls or direct notification from customers.

Asset monitoring and grid visibility: Distributors are using smart meter data to monitor the

loading on distribution transformers and other assets, enabling better assessments of equipment

condition and more efficiently planned future investments.

Customer awareness and response: Distributors are collaborating with innovators to provide

customers with access to uniform smart meter data, enabling the development of analytical tools

and services that provide customers with additional information on electricity usage.

The investment in smart meters and the underlying communications network serve as the foundation for

future deployments of advanced distribution automation technologies. Thus, with many of the

fundamental elements already in place, the incremental investments in distribution automation will

benefit from stronger business cases.


Figure 7 presents a selection of investments that distributors in Ontario have made that use the advanced

functionality of smart meters as well as other smart grid technologies.

Figure 7. Sample of Ontario Utility Smart Grid Investments

Source: Navigant

Announced in 2011, the Ontario government established the $50 million Smart Grid Fund to support

private sector investment in smart grid projects that test, develop, and bring to market the next

generation of smart grid solutions while building the smart grid industry in Ontario. The fund currently

supports 26 projects in areas such as energy storage, EV integration, microgrids, distribution automation,

cyber security, and smart meter data analytics. Figure 8 presents some of the projects that have received

funding.

In 2014, PowerStream and GE

launched a microgrid demonstration

at PowerStream’s headquarters in

Vaughan. This project integrates

wind and solar power, three types

of energy storage technologies, a

solar carport, a natural gas

generator, and an EV charging

station. In addition, this project

uses GE’s Grid IQTM Microgrid

Control System.

Microgrid Demonstration

The Green Button is an initiative,

adopted by many utilities, to provide

customers with better access to

their energy usage information.

Green Button allows customers to

access and share their electricity

data with mobile and web-based

data analytics applications. In

2012, MaRS partnered with the

Ministry of Energy to launch the

Ontario Green Button initiative.

London Hydro and Hydro One are

currently testing a number of Green

Button services.

Green Button—Connect My Data

This project, led by NRStor and

Temporal Power, is the first grid-

connected commercial flywheel

facility in Canada. The 2 MW

flywheel device, which stores

electricity as kinetic motion in a

spinning steel rotor, will provide

regulation service to Ontario’s grid.

Regulation is a key ancillary service

required to match scheduled

electricity generation to dynamic

consumption, balancing the grid in

real time.

Flywheel Energy Storage

This project, led by Hydro in the

Owen Sound area, focuses on the

integration of a distribution

management system with intelligent

electronic devices.

The project has four objectives; the

integration of distributed generation,

distribution automation, AMI-

enabled outage restoration, and

distribution system oversight.

Hydro One will evaluate

deployment to other parts of their

service territory.

Hydro One’s Smart Zone

Many utilities have adopted outage

solutions that incorporate many

distribution-level systems. These

solutions may integrate AMI, outage

management systems, customer

information systems, and

geographic information systems.

These solutions analyse smart

meter data, locate outages, provide

outage information to customers via

outage maps, communicate with

restoration crews, and ultimately

verify power restoration.

This self-healing grid is located in

the downtown core of Burlington,

serving approximately 4,500

customers. Burlington Hydro and

S&C Canada installed remotely

operated 27.6 kV switches at 55

locations. The automated switching

system increased the reliability and

resiliency of the grid in downtown

Burlington. Burlington Hydro is now

able to restore outages that would

previously take hours to locate in a

matter of minutes or seconds.

Automated Switching Outage Management

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRw&url=http://www.powerstream.ca/app/pages/Logos.jsp&ei=ZC_BVPzWC8W0ggTflYSICg&bvm=bv.83829542,d.eXY&psig=AFQjCNEl6s9Tee3ab6o1xZAPVeltjL-keg&ust=1422033103162451

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRw&url=http://www.nrstor.com/&ei=FCfBVOaHEYaqgwT0tYKoCw&bvm=bv.83829542,d.eXY&psig=AFQjCNEod8VtS_jZ0DTAWwgEMRGZ4t7BdQ&ust=1422030990003087

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRw&url=http://www.marsdd.com/clients-and-tenants/temporal-power/&ei=NyfBVJzZBsy0ggTDk4PIDA&bvm=bv.83829542,d.eXY&psig=AFQjCNFQdE9FKWn2lT6FUG-6yj9-kkImGA&ust=1422031025012364


Figure 8. Select Smart Grid Fund Projects

Sources: Ontario Ministry of Energy, Smart Grid Fund

Over the next five years, distributors will continue to invest in smart grid initiatives. The purpose of this

section of the report is to analyse the benefits and costs associated with these investments, as well as the

investment that Ontario has already made. The analysis provides a more concrete measurement of the

benefits of grid modernisation initiatives and sets the foundation for subsequent discussion on the

potential benefits of future smart grid investment in Ontario.

2.1 Analysis Framework

Navigant applied its comprehensive benefit-cost framework for smart grid investments and

accompanying model to derive the results presented in this report. Navigant’s framework for estimating

smart grid benefits was the basis for the approach recommended by the Electric Power Research Institute

(EPRI) in January 2010.3 Navigant and Summit Blue Consulting (now also Navigant) contributed to the

3 Electric Power Research Institute. January 2010. “Methodological Approach for Estimating the Benefits and

Costs of Smart Grid Demonstration Projects.”

Microgrid Research & Innovation Park

Renewable Energy Microgrid Testing Centre

Dynamic Pricing & Customer Feedback

Smart Meter Cyber Security

IBM Canada Research & Development Centre

Customer Opt-In Dynamic Pricing Programs

Intelligent Electric Vehicle Charging System

Advanced Energy Storage Demonstration

Intelligent Energy Storage Systems & Electric Vehicle Charging Stations

GE Grid IQ Innovation Centre

Distribution Transformer Monitoring

Consumer Engagement for the Smart Grid

GE Digital Energy

GE and the Ministry of Energy partnered to establish

the Grid IQ Innovation Centre in Markham. This project

has become a catalyst for the development of new and

advanced smart grid technologies. The Innovation

Centre has grown into a smart grid demonstration

centre that integrates research, testing, and simulation

facilities, enabling utilities and industry to tackle

challenging energy problems. The Grid IQ Innovation

centre positions Ontario as a global leader and

destination for industry and utilities looking to enhance

grid reliability and optimise the operation of their

networks.

Canadian Solar

Canadian Solar is creating a real-life microgrid

laboratory that looks at grid-connected and off-grid

microgrids characterized by high penetration of

renewable resources. The Microgrid Centre will focus

on developing, testing, and validating microgrid

components and control systems across various

microgrid configurations. This facility will simulate

unique characteristics of proposed microgrid sites

across Ontario, such that it will be able to address

distinct challenges relating to power quality, reliability,

environmental risks, and diesel dependence.

Grid IQ Innovation Centre Renewable Energy Microgrid Testing Centre

+13 more

projects

Distribution Monitoring and Controls Systems


development of the EPRI framework. Additionally, in 2012, the European Commission adopted a

benefit-cost framework largely based on the EPRI framework.4

Navigant’s benefit-cost framework acknowledges the complexity and interdependencies of smart grid

investments. As illustrated in Figure 9, the costs and benefits of smart grid and the costs and benefits

from conservation and demand management or distributed energy resources can overlap. In this

analysis, Navigant has included only the incremental costs and benefits associated with smart grid. For

example, in the case of distributed solar photovoltaics (PV), Navigant has excluded the cost of installing

solar panels but has included the costs of installing the necessary equipment to actively control and

monitor the installation.

Figure 9. Smart Grid Technology Overlap

Source: Navigant

Figure 10 illustrates a high-level structural diagram of the Navigant analysis framework and process. At

the core of Navigant’s framework are assumptions about the deployment of smart grid capabilities. Smart

grid capabilities define what the utility is trying to achieve (e.g., automated voltage control, self-healing

grid, enhanced fault prevention, etc.). The framework establishes a relationship between smart grid

capabilities and the assets or equipment that a utility must purchase and install in order to achieve this

capability. The framework also defines a specific set of impacts corresponding to each of the smart grid

capabilities. Navigant also adapted the framework to reflect the unique features of the Ontario electricity

system, including: grid characteristics, reliability metrics, demand and energy forecasts, electricity and

ancillary market prices, among others (see Appendix C for a detailed list). Based on these characteristics,

Navigant developed unique assumptions for Ontario that define the magnitude and nature of benefits,

and in which segment of the industry they originate.

4 European Commission. 2012. “Guidelines for conducting a cost-benefit analysis of Smart Grid projects.”


Figure 10. Overview of Analysis Framework and Process

Source: Navigant

Ontario’s electricity sector is complex, and presents unique challenges for evaluating the benefits and

costs of smart grid investments. The diverse size and number of distributors, the geography, system

conditions, and the mix of customers served mean that the benefits and costs of smart grid investments

may vary considerably.

A total of 73 distributors serve Ontario’s 4.8 million electricity customers; the three largest distributors

serve approximately 50% of all customers, while the three smallest serve less than 0.1%. In addition,

despite the fact that the majority of the population lives in urban centres such as Ottawa and the Greater

Toronto Area, a significant portion of the province is rural. All of these factors make the evaluation of

Ontario-wide smart grid deployment as well as any provincial planning process a challenging and

complex exercise. To address this issue, without developing a separate benefit-cost model for each

distributor, Navigant’s analysis and results are based on average system conditions and deployment

assumptions. This means that any utility-specific investments should be evaluated on a case-by-case

basis in order to reflect characteristics unique to each distributor. As such, these results are intended to

provide provincial planners and distributors with directional guidance to facilitate smart grid

investments decisions.

Navigant’s framework includes over 30 smart grid benefits. For the purposes of reporting, these are

grouped into three categories.

Economic/system cost: These benefits arise primarily from reduced system costs or increases in

productivity. Examples include eliminating or deferring the need to upgrade traditional

infrastructure, reducing manual operations (e.g., meter reads, switching operations), and

lowering the cost of integrating distributed energy resources.


Reliability and power quality: These benefits arise from a reduction in the number and/or

duration of system interruptions or poor power quality events.

Environmental: These benefits include the impact on climate change and human health resulting

from a reduction in the emissions of carbon dioxide, nitrogen oxide, sulfur oxide, particulate

matter, and other pollutants.

Navigant’s framework does not explicitly account for macroeconomic or societal benefits. However,

Navigant expects that investments in smart grid will yield some of these non-energy benefits. In terms of

macroeconomic benefits, investments in smart grid and grid modernisation will create opportunities for

new Ontario smart grid technology and solutions companies. These investments could also spur the

growth of local secondary industries, including EVs, energy storage, distributed generation, and

renewable energy. Furthermore, smart grid investment will directly and indirectly impact labour and

equipment supply chains in Ontario. In terms of societal benefits, smart grid investments can lead to

increased customer satisfaction, awareness, and ultimately choice.

This analysis evaluates the deployment of smart grid capabilities over two timelines: deployment

through 2020 and deployment through 2035. The analysis timeframe for each deployment scenario

extends 40 years from 2005 to 2045. This timeframe is selected to capture the early deployment of smart

meters in 2005, and also to provide an appropriate analysis timeframe for capabilities that are deployed

as late as 2035.

Appendices A, B, and C provide a detailed description of Navigant’s smart grid benefit-cost framework

and underlying assumptions.

2.2 Smart Grid Plans through 2020—Status Quo Scenario

This section presents the results for the Status Quo scenario. This scenario analyses the benefits and costs

associated with the investments in AMI combined with distributors’ currently planned smart grid

investments through 2020. The five-year period from today to 2020 reflects the typical investment-

planning horizon for Ontario’s electricity distributors.

Navigant conducted extensive research, using publicly available information in combination with a

questionnaire of Ontario distributors. Navigant’s questionnaire asked distributors to specify the current

state of smart grid deployment, their five-year investments plans, and the maximum potential

deployment on their networks. The questionnaire was conducted to develop a clear understanding of

where smart grid deployment has taken place, which capabilities have been targeted, and how these

trends will evolve in the future. In addition, the questionnaire gathered network characteristics

information not available through regulatory filings.

Navigant received responses to its questionnaire from distributors representing approximately 70% of

electricity customers in the province. The responses to the distributor questionnaire and Navigant’s

review of publicly available documents, including distribution system plans and annual reports, provide

a clear picture of the current state of smart grid deployment in Ontario and of the near-term state of the

distribution system in 2020.


In addition, Navigant consulted with industry stakeholders and gathered inputs and assumptions from a

wide range of sources, including public agencies, distributors, industry groups, and results and findings

from several smart grid projects across North America.

2.2.1 Smart Grid Capabilities Deployed

The deployment of smart grid capabilities in Ontario dates back to the deployment of AMI (or smart

meters) in 2005. As Figure 11 illustrates, the first wave of smart grid deployment also included time of

use rates, followed by the deployment of enhanced capabilities that leveraged the advanced metering

infrastructure (AMI enhanced). As discussed above, distributors have leveraged AMI for multiple

innovative uses.5

Figure 12 and Figure 13 present the current and expected deployment of additional smart grid

capabilities through 2020. Combined, Figure 11, Figure 12, and Figure 13 characterise distributors’ smart

grid investments to date and the planned deployment of additional capabilities over the next five years.

This scenario models no incremental deployment beyond 2020 in order to assess only the current and

near-term deployments.

Figure 11. Deployment of AMI and Time of Use Pricing through 2020

Sources: Navigant, Ontario Energy Board smart meter/time of use monitoring reports

A self-healing grid is an example of an additional smart grid capability that distributors in Ontario are

pursuing in this timeframe. A self-healing grid enables automated response to customer interruptions by

locating and isolating a fault and reconfiguring feeders equipped with automated switches to restore

service rapidly.

5 Several distributors are using smart meter data to monitor real-time loads on distribution equipment and to

improve grid visibility. Many are also incorporating data from smart meters into their outage management

systems to locate faults, inform customers of outages rapidly, and to optimise the deployment of work crews to

minimise the duration of outages and the cost of outage restoration.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2010 2015 2020

Tot

al E

lect

ricity

Cus

tom

ers

AMI

AMI Enhanced

Time of Use pricing


Other additional smart grid capabilities utilities are pursuing include automated voltage and automated

reactive power control. Automated voltage control allows a utility to remotely monitor and operate the

network at the lower end of the allowed voltage range. Projects in the United States have shown that

operating the network at the low end of the approved voltage range can reduce overall electricity use by

as much as 2.9% per annum.6

For most smart grid capabilities, the optimal deployment is not likely to be 100%. Each individual

investment should be assessed on a case-by-case basis based on technical suitability and its value to the

distributor and customers. For example, each distribution feeder circuit and the customers they serve

have unique characteristics. In some instances a distribution feeder circuit will be well-suited for self-

healing capabilities (e.g., the underground network in a major urban centre), while others may be well

suited for automated voltage and reactive power control (e.g., a long rural distribution feeder). This

process of suitability and selection and the need to make a business case for each individual investment,

internally and to the regulator, is likely to lead to deployment levels that are considerably lower than

100% for most capabilities.

Figure 12. Deployment of Additional Smart Grid Capabilities through 2020 7

Source: Navigant

Distributors are also pursuing investments such as the Green Button initiative (Connect My Data and

Download My Data), fault current limiting, enhanced fault prevention, equipment condition monitoring,

and real-time load transfer capabilities. While there is no formal policy or program in place in Ontario,

Navigant has included a voluntary critical peak pricing (CPP) program as one of the smart grid

capabilities deployed by 2020. There is potential for this program to encourage customers to reduce

electricity demand during critical peak periods.

6 U.S. Department of Energy. December 2012. “Application of Automated Controls for Voltage and Reactive

Power Management – Initial Results.” 7 The Automated Volt/VAR control deployment curve reflects the corresponding deployment curves for Automated

voltage control and Automated reactive power control.

0%

5%

10%

15%

20%

25%

30%

2005 2010 2015 2020

Tot

al E

lect

ricity

Cus

tom

ers

Green Button

Notification of equip. condition

Enhanced fault prevention Self-healing grids Fault current limiting Automated Volt/VAR control Auto. real-time load transfer

Critical peak pricing (voluntary)


Figure 13. Deployment of Microgrids and Monitoring and Control Capabilities through 2020

Source: Navigant

Navigant’s research also revealed that Ontario distributors expect to continue to invest in energy storage

and microgrids, with approximately 80 megawatts (MW) of storage likely connected to the distribution

system in Ontario by 2020, and up to 20 MW of load served by microgrids. These values represent 0.3%

and 0.1%, respectively, of Ontario’s peak system demand of approximately 25,000 MW.

EVs and distributed energy resources are also gaining prevalence in Ontario. While there are currently a

number of EV charging stations across Ontario, utilities are not actively monitoring and controlling these

stations as part of a broader optimisation of energy storage capabilities. The same applies to distributed

energy resources, such as rooftop solar PV. There are a growing number of distributed energy resources

in Ontario; however, utilities have limited visibility into the real-time production and impact of these

resources on their networks. Navigant’s research identified that by 2020, Ontario utilities will likely

actively monitor and control approximately 190 megawatts of distributed energy resources and 12 MW of

EV charging stations. These values represent 0.8% and 0.04% of peak system demand, respectively.

0

40

80

120

160

200

2005 2010 2015 2020

Meg

awat

ts (

MW

)

Distributed energy resources monitoring and control

Energy storage integration and control

Microgrids

Electric vehicle monitoring and control


Figure 14. Definition of Smart Grid Capabilities

Source: Navigant

AMI

Advanced metering infrastructure

allows utilities to automate meter

reading, improve metering accuracy,

reduce theft, and improve utility

operations.

AMI enhanced

Enhanced AMI uses smart meter data

to provide better outage

management, fault localisation,

customer communication, and asset

monitoring, enabling utilities to

improve operations and maintenance

practices.

Automated reactive power control


uses sensors and capacitor bank

controllers to operate distribution

lines more efficiently and lower line

losses.

Automated real-time load transfer

This capability involves real-time re-

configuration of feeders which helps

distributors to optimise loading of

distribution equipment and avoid

overloading.

Automated voltage control

Automated voltage control uses

sensors and voltage regulators

equipped to optimise voltage levels

on distribution lines to reduce

electricity usage and demand.

DER monitoring and control

DER monitoring and control systems

provide utilities with increased

visibility and control to optimise the

production from distributed

generation resources.

Critical peak pricing

An expansion of time-of-use pricing,

critical peak pricing establishes

premium tariffs during periods of

critical system conditions

encouraging customer response.

Electric vehicle integration and

control

Electric vehicle monitoring and

control systems enable more effective

integration of electric vehicles to the

grid. In the extreme, electric vehicles

are able to act as distributed energy

resources.

Energy storage integration and

control

The integration and control of energy

storage devices to the grid will

provide flexibility and enable

distributors to optimise grid

infrastructure and efficiently integrate

distributed generation. Enhanced fault prevention

Fault prevention uses high-resolution

sensors to detect low current faults

that are difficult to locate across the

distribution system.

Fault current limiting

This capability uses modern fault

limiting technology to prevent

damage to distribution equipment and

avoid distribution upgrades needed to

meet increasing demand.

Green Button

Green Button allows customers to

access and share electricity usage

data in a standardised format to help

them conserve energy and manage

their electricity bills.

Microgrids

Microgrids use control systems to

integrate loads and distributed

resources and can operate in a

connected or islanded manner,

providing increased resiliency and

integrating distributed generation.

Notification of equipment condition

This capability enables real-time

remote monitoring and analysis of

distribution equipment, improving

planning and asset management

practices.

Self-healing grids

Self-healing grids use sensors,

controls, and switches to

automatically locate and isolate

faults, reconfigure feeder circuits and

restore power.

Time of use pricing

Time of use rates leverage AMI (or

smart meters) to provide a time-

variant rates for electricity that reflect

changing costs, encouraging

customers to shift their electricity

consumption


Table 1 presents the expected deployment figures of each capability through 2020. For smart grid

capabilities measured by deployment in megawatts, Figure 15 provides a comparison of one megawatt

with the load of residential homes and electric vehicles.

Table 1. Smart Grid Capability Deployment of Status Quo Scenario

Smart Grid Capabilities 2020

AMI 5.4 million customers 99% of customers

AMI enhanced 3.3 million customers 60% of customers

Time of use pricing 5.0 million customers 93% of customers

Enhanced fault prevention 1,000 feeders 9% of customers

Self-healing grid 1,300 feeders 12% of customers

Fault current limiting 1,000 feeders 9% of customers

Automated voltage control 600 feeders 6% of customers

Automated reactive power control 600 feeders 6% of customers

Notification of equipment condition 700 transformers 18% of customers

Automated real-time load transfer 1,000 feeders 8% of customers

Energy storage 84 MW8 (0.3% of peak)

Microgrids 24 MW9 (0.1% of peak)

Electric vehicles 12 MW (3,600 electric vehicles)10

Distributed energy resources 195 MW11 (0.8% of peak)

Green Button 165,000 customers12 3% of customers

Critical peak pricing 150,000 customers13 3% of customers

Source: Navigant

Figure 15. Comparison of One Megawatt with the Load of Residential Homes and Electric Vehicles

8 Megawatts of installed capacity; percentage of peak is based on 25,000 MW peak. 9 Megawatts of load served; percentage of peak is based on 25,000 MW peak. 10 Electric vehicles served by charging stations that are actively monitored and controlled. 11 Distributed energy resources that are actively monitored and controlled; percentage of peak is based on 25,000 MW

peak. 12 Customers who actively use Green Button applications; assumes available to 1.65 million customers (30% of all

customers). 13 Customers who participate; assumes program is available to all residential and small commercial customers.

1 MW =

~ 350 ~ 300

=

Electric VehicleDetached, Single

Family Homes

Electric Vehicles


2.2.2 Magnitude of the Benefits and Costs

Navigant applied its framework to estimate the benefits and costs associated with smart grid deployment

in the Status Quo scenario. This includes the deployment of capabilities to date and planned deployment

through 2020. The results reflect no incremental investment in new smart grid capabilities beyond 2020,

although the results do reflect ongoing operations, maintenance, and replacement costs through 2045 for

existing capabilities deployed prior to 2020. Figure 16 summarises Navigant’s estimate of the annual

benefits and costs.

Figure 16. Annual Benefits and Costs of Status Quo Scenario

Source: Navigant; all values in nominal $.

Navigant estimated that between 2005 and 2045, the cumulative cost (in nominal dollar terms) to install,

operate, and maintain the smart grid assets deployed through 2020 is $8.3 billion. This includes the initial

up-front investments, ongoing operations and maintenance, and replacement costs when assets reach the

end of their useful life. Costs also includes approximately $2.0 billion associated with the initial smart

meter deployment.14

While the analysis represents the cost as a large concentrated investment, in Ontario, as in most regulated

jurisdictions, utilities recover capital investments from customers over the life of the assets,

commensurate with when the benefits are realised. For example, if a utility makes a $100 investment in

an asset that has a useful life of ten years, the utility is able to recover approximately $15 per year from its

customer base for that asset over each of the next ten years.15

14 The Auditor General estimated that the cumulative investment in smart meters was approximately $2.0 billion,

represented by the sum of the annual investments up to 2013. 15 This illustrative example assumes a 7% weighted average cost of capital.

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The cumulative environmental and reliability benefits associated with these investments are $0.8B and

$6.9 billion, respectively, through 2045. The cumulative economic benefits, which account for

approximately 60% of benefits, are approximately $11.1 billion through 2045.

Much like most energy technologies, smart grid investments are long-term investments. They are

characterised by large upfront capital costs, while the benefits accrue over many years into the future. As

illustrated in Figure 16 above, to date, only a fraction of the benefits from Ontario’s investments in smart

grid have been realised. As benefits continue to accrue, the net present value of these investments will

increase. Figure 17, below, illustrates the net present value of these investment over time. Navigant

estimates that the investments in the Status Quo scenario will reach a breakeven point in 2026, and that

by 2035 the net benefit will be $1.5 billion (2014 $).16 Navigant estimates that the net benefit of these

investments will be $3.2 billion by 2045 (2014 $).

Figure 17. Net Present Value of Status Quo Scenario


2.2.3 Uncertainty in Benefits and Costs

The benefits and costs presented above are point estimates based on Navigant’s comprehensive

framework, detailed assumptions, and quantitative modelling. However, Navigant recognises that there

is a degree of uncertainty in even the best available information around smart grid deployments, costs,

and benefits.

Navigant’s benefit-cost framework incorporates an uncertainty model that reflects varying degrees of

confidence around individual assumptions. Figure 18 presents the results of Navigant’s probability

modeling of the smart grid investments in the Status Quo scenario.

16 Net benefits represent a positive net present value. This analysis assumes a societal discount factor of 5%.

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The distribution of values around the expected outcome of $3.2 billion represents the likelihood of

occurrence of each unique outcome. From this analysis, Navigant estimates that the net present value

will be greater than zero and produce a net benefit with 97% confidence. The insert in Figure 18 shows

specific values for the best, worst, and expected cases. The best and worst cases reflect the 95th and 5th

percentile values, respectively. The expected case represents the geometric mean.

Figure 18. Range of Net Present Value of Status Quo Scenario


The benefit-cost ratio for the expected case is approximately 1.6 : 1, with best and worst case values of

2.1 : 1 and 1.2 : 1, respectively. Thus, even in the worst-case scenario—reflecting a highly unlikely

outcome in our uncertainty analysis—the result of these investments is strongly positive. These are

encouraging results that are directionally consistent with other publically available analyses of smart grid

investments.17

2.2.4 Distribution of Benefits and Costs

Figure 19, below, shows the distribution of benefits and costs across the segments of the Ontario

electricity sector. It is important to note that this analysis shows where the benefits and costs originate.

How the sector ultimately distributes these benefits and costs depends on factors such as tariff

formulation and regulatory policy. In Ontario, for the most part, customers are the ultimate recipient of

the majority of benefits and costs.

As an example, a reduced need for investment in generation assets counts as a benefit that originates in

the generation segment, even though generation owners are not likely to view this as a benefit as it may

result in less profit due to a reduced need for capital investment. This reduced investment eventually

makes its way to the customer segment through reduced energy charges on electricity bills. Similarly, the

17 See EPRI’s 2011 Technical Report: “Estimating the Costs and Benefits of the Smart Grid.” See Smart Grid Great

Britain’s “Smart Grid: A race worth winning” and “Making smart choices for smart grid development.” See

Energy Needs Ireland’s (ENI’s) “2013 Cost Benefit Analysis” and Energinet.dk’s “Smart Grids in Denmark.”


deployment costs attributed to the distribution segment would generally be recovered through electricity

rates.

The generation, transmission, and distribution segments exclusively accrue economic benefits that arise

from reductions in sector costs. These benefits accumulate from avoided or deferred generation capacity

and traditional transmission and distribution infrastructure. Benefits to the distribution sector also

accrue from developing efficiencies across distribution operations. Benefits to customers accrue from

reduced energy charges on electricity bills, improvements in reliability, and avoided emissions. 18

Figure 19. Distribution of Benefits and Costs for Status Quo Scenario19


18 Appendix A explains the types of impacts (e.g., avoided capacity, reduced line losses) that contribute to each

benefit category and each industry segment. 19 Figure 19 shows where costs and benefits originate. The distribution of these is ultimately defined by the

regulatory environment. In addition, a number of factors are not fully captured or addressed by this figure; the

distribution of benefits to each customer class (e.g., residential, commercial, and industrial) is not proportionate,

and the environmental benefits (as a result of avoided emissions) credited to customers could be considered a

societal benefit.

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2.3 The Road Ahead

In 2004, when the government announced the smart metering initiative, Ontario was one of the first

jurisdictions to commit to installing smart meters in every residence and small business. This decision

positioned Ontario as a leader and early adopter of smart grid technologies. Since then, a number of

jurisdictions around the world have embraced the use of smart meters and are rapidly catching up.

Figure 20 compares the smart meter deployment timelines of jurisdictions around the world with

Ontario.

Figure 20. Smart Meter Deployment Timelines across Multiple Jurisdictions

Sources: European Commission, Edison Foundation, Navigant

The smart grid applications that Ontario distributors will deploy over the next five years are new and

rely on new or emerging technology. This presents an opportunity for Ontario to continue to advance the

government’s vision for the province to be a leader in smart energy solutions, including cutting-edge

smart grid technologies and services.


3. Future Deployment Scenarios

Analysis in Section 2 demonstrates that the investments in smart grid capabilities presented in the Status

Quo scenario are expected to yield a net benefit to the province. This scenario highlighted the

investments that Ontario’s distributors have made to date and have planned for the next five years. What

about the next steps toward grid modernisation and future investments? Did the initial investments

capture the most valuable opportunities? Which capabilities and technologies will continue to provide

the most significant benefit? To address these questions and others, this section examines two potential

future deployment scenarios. The first one, referred to as the Baseline Future Scenario, is based on

responses to the distributor questionnaire. Navigant used the questionnaire responses to identify the

deployment potential of each capability through 2035 based on technology maturity, deployment to date,

planned deployment through 2020, and the maximum potential deployment estimated by each

distributor.

The second scenario, referred to as the Enhanced Future Scenario, revisits deployment assumptions of

individual smart grid capabilities, placing additional emphasis on the most cost-effective capabilities and

those that encourage further integration of renewable and distributed energy resources. This scenario

seeks to align with Ontario’s smart grid definition, while addressing the need to reduce sector costs and

increase the overall value of the investment.

Navigant does not intend for the future deployment scenarios to be a prescriptive path forward for the

industry. Rather, they illustrate the potential for future smart grid investments in Ontario and provide a

practical guide to help inform investment decisions and policy discussions.

3.1 Vision for a Modern Distribution System

As electricity distribution networks continue to evolve over the next 20 years, the exact end-state is

unknown. However, expectations are that it will be a far more automated, connected, technically

advanced, digitised, and transactive system. Figure 21, below, presents an illustrative example of what

the networks might look like. In this example, an advanced distribution management system serves as

the brain of the electricity network, providing visibility and automated control capabilities, and

integrating outage management and resource management tools. Automated switches, circuit breakers,

voltage regulators, and capacitor banks tie into the advanced distribution management system to manage

the network actively. Remote energy management systems, within a customer’s premises, part of an EV

charging station, or part of an energy storage system, interact with sensors on the network and price

signals to optimise electricity consumption and production.

The transition away from a centralised architecture toward a modern system, characterised by a

networked and dynamic infrastructure, arises partly as a need to meet the challenges of a system that

incorporates distributed energy and demand-side resources. This transition will create an electricity

system in which existing energy infrastructure is better monitored and utilised, distribution assets are

remotely operated or automated, increased grid visibility enables improved provincial planning and

forecasting, and customers and distributed resources provide distribution networks with greater

flexibility.


Figure 21. Illustrative Modern Grid

Source: Navigant

3.2 Baseline Future Scenario

3.2.1 Smart Grid Capabilities Deployed

The Status Quo scenario analysed the benefits and costs of planned smart grid investments through 2020,

assuming no additional deployment beyond this point. The Baseline Future scenario expands on the

previous scenario and models continued deployment of smart grid capabilities up to 2035, assuming no

additional deployment occurs beyond 2035. The time horizon for this analysis remains unchanged,

extending from 2005 to 2045. This timeframe captures the initial deployment of smart meters in 2005, and

extends to 2045 in order to provide an appropriate horizon to evaluate deployment occuring up to 2035.

Figure 22 and Figure 23 show the deployment curves for smart grid capabilities in Ontario through 2035.

Over the next 20 years the deployment of several smart grid capabilities is expected to expand

significantly. For example, the penetration of self-healing grid capability doubles from 2020 to 2035,

benefiting approximately one in four electricity customers in the province, and energy storage systems

approximately triple from 84 MW in 2020 to 240 MW by 2035.

In addition, this scenario includes the deployment of new smart grid capabilities, such as dynamic

capacity rating and advanced power flow control.

GenerationTran

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DER Interface

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Condition Sensor

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Multipurpose Sensor

Regulating Inverter

Fault Current Limiter

EMS

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EMS


Figure 22. Deployment of Smart Grid Capabilities through 2035

Source: Navigant

Figure 23. Deployment of Microgrids and Monitoring and Control Capabilities through 2035

Source: Navigant

0%

5%

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30%

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al E

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Green Button

Equip. condition monitoring

Enhanced fault prevention

Self-healing grids


Automated Volt/VAR control

Real-time load transfer

Critical peak pricing Advanced power flow

Dynamic capacity rating

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600

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Microgrids Electric vehicles integration and control


Table 2 shows the penetration of each of the smart grid capabilities deployed in this scenario by 2035.

These deployment figures are reflective of responses to the distributor questionnaire.

Table 2. Smart Grid Capability Deployment for Baseline Future Scenario

Smart Grid Capabilities 2035

AMI 6.4 million customers 99% of customers

AMI enhanced 3.9 million customers 60% of customers

Time of use pricing 6.1 million customers 93% of customers

Enhanced fault prevention 1,500 feeders 12% of customers

Self-healing grid 2,900 feeders 24% of customers

Fault current limiting 1,500 feeders 13% of customers

Automated voltage control 1,400 feeders 12% of customers

Automated reactive power control 1,400 feeders 12% of customers

Notification of equipment condition 1,000 transformers 25% of customers

Automated real-time load transfer 2,600 feeders 21% of customers

Dynamic capacity rating 170 feeders 1% of customers

Advanced power flow control 7,500 km20 3% of customers

Energy storage 240 MW21 (1% of peak)

Microgrids 95 MW22 (0.4% of peak)

Electric vehicles 80 MW (23,000 electric vehicles) 23

Distributed energy resources 630 MW24 (2.5% of peak)

Green Button 195,000 customers25 3% of customers

Critical peak pricing 175,000 customers26 3% of customers

Source: Navigant

Figure 24. Definition of Additional Smart Grid Capabilities

Source: Navigant

20 Kilometers of distribution line. 21 Megawatts of capacity percentage of peak is based on 25,000 MW peak. 22 Megawatts of load served; percentage of peak is based on 25,000 MW peak. 23 Electric vehicles whose charging stations are actively monitored and controlled. 24 Distributed resources that are actively monitored and controlled; percentage of peak is based on 25,000 MW peak. 25 Customers who actively use Green Button applications; assumes it is available to 1.95 million customers. 26 Customers who participate; assumes program is available to all residential and small commercial customers.


This capability allows distributors to

determine the thermal ratings of assets

based on real-time measurements of

ambient and asset conditions in order

to extend the life of equipment and

optimise distribution infrastructure.

Advanced power flow control

Using new and existing technologies,

distributors actively control power flows

on networks by dynamically changing

impedance of circuits to optimise the

control of distribution networks.


3.2.2 Magnitude of Benefits and Costs

Figure 25 summarises Navigant’s estimate of the annual benefits and costs associated with the smart grid

capability deployment through 2035 outlined above.

In this scenario, deployment costs peak in 2022 and 2023, as utilities increase capital spending on smart

grid and infrastructure modernisation. Under this scenario, Navigant estimates that the cumulative

investment from 2005 through 2045 will be $12.0 billion. This is inclusive of the $8.3 billion investment

estimated in the previous scenario for deployment through 2020, which is reflective of the Auditor

General’s estimated $2.0 billion for the initial smart meter investment. Hence, the investment associated

with the incremental deployment from 2020 to 2035 is $3.7 billion.

Navigant estimates that the incremental deployment of smart grid capabilities from 2020 to 2035 can

result in significant additional benefits. As shown in Figure 25, the annual benefits are estimated to reach

$1.0 billion in 2030, increasing to $1.5 billion in 2045. Cumulative benefits of this deployment scenario

through 2045 are expected to equal $28.9 billion and are composed of the following: reliability benefits

valued at approximately $12.8 billion, economic benefits valued at $15.2 billion (largely attributable to

avoided generation capacity, deferred traditional transmission and distribution infrastructure

investments, and avoided energy use), and environmental benefits valued at $0.9 billion.

Figure 25. Annual Benefits and Costs of Baseline Future Scenario


Navigant estimates that the incremental deployment of smart grid capabilities from 2020 to 2035 will add

approximately $2.1 billion of value, increasing the net benefit by 2045 from $3.2 billion to $5.3 billion

(2014 $). The effect of the large capital spending over 2020 to 2025 temporarily decreases the net present

value of the overall investment, but once future benefits begin to accrue the net present value of these

investments increases substantially, as is expected with most long-term investments. Figure 26 below

illustrates this point.

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Figure 26. Net Present Value of Baseline Future Scenario


3.2.3 Uncertainty of Benefits and Costs

Figure 27 presents the distribution of net benefits arising from deployment through 2035 from Navigant’s

uncertainty model.

Figure 27. Range of Net Present Value of Baseline Future Scenario


The best and worst case scenario are expected to yield a net benefit through 2045 of $7.4 billion and $2.9

billion (2014 $), respectively. Despite the uncertainty, the results suggest that the net present value will

be greater than zero with a confidence level of 99%.

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3.2.4 Distribution of Benefits and Costs

Figure 28 shows the breakdown of benefits and costs associated with the Baseline Future Scenario across

each segment of the industry.

Figure 28. Distribution of Benefits and Costs of Baseline Future Scenario27


Navigant’s analysis suggests that the distribution segment will continue to carry all of the costs, while

only materialising 20% of the benefits. As will discussed in Section 4, this misalignment between the

segments of the industry that incur the costs and the segments that benefit is a potential barrier to smart

grid investment.

27 This representation of costs and benefits does not address cost allocation matters and distribution of benefits

across customer classes, among others. See Section 2.2.4.

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3.3 Promising Smart Grid Capabilities

Navigant evaluated the investment case for each smart grid capability using the corresponding

capability’s benefit-cost ratio. The results are based on the deployment assumptions for the Baseline

scenario, and are reflective of Ontario-specific grid characteristics and deployment. Figure 29 presents

the benefit-cost results for each capability. Navigant grouped the AMI, enhanced AMI, time of use, and

critical peak pricing capabilities as one capability for simplicity, as they are all primarily enabled by the

smart meters. The confidence bars indicate the best and worst case range (95th and 5th percentile outcome

in the analysis).

Figure 29. Benefit-Cost Ratios for Smart Grid Capabilities

Source: Navigant

Eight of these applications are expected to be net beneficial to the electricity system in Ontario:




Microgrids


Self-healing grids

Green Button

AMI, Enhanced AMI, Time of use, Critical peak pricing

0 1 2 3 4 5 6 7 8 9




Advanced power flow control

Automated real time load transfer

Notification of equipment condition


DER monitoring and control

Microgrid


Self-healing grid


EV integration and control

Green Button

AMI, Enhanced AMI, Time of use, Critical peak pricing


Appendix D provides detail and results for each of these applications.28 Sections 3.3.1 to 3.3.4 provide a

high-level overview of Navigant’s findings with respect to four capabilities that deliver a benefit-cost

ratio greater than three and for which, even in the worst-case scenario, the benefits outweigh the costs.

The first, automated voltage control, is a significant driver of the economic benefits given its ability to

reduce peak demand and overall electricity consumption. The second and third, self-healing grids and

enhanced fault prevention, largely drive reliability benefits, as they significantly decrease the number and

duration of sustained and momentary outages. Finally, the fourth, Green Button—though still in the

pilot stage—will enable customers to better manage their electricity usage and is expected to deliver

savings to customers, and reduce demand and electricity consumption to the electricity system.

3.3.1 Automated Voltage Control

Historically, utilities have managed the network voltage by sending a crew to a distribution station to

change the tap position on a transformer manually. This work was almost always in response to a

customer complaint. With new technologies, advanced communications networks, and smarter

distribution management systems, utilities are now able to optimise voltage levels across distribution

feeders autonomously. Utilities can also take this a step further, either by lowering the voltage on a

distribution feeder to a minimum level on a constant basis with the goal of reducing energy consumption

or lowering the voltage at specific times with the objective of reducing peak load and helping defer

distribution capacity investments.

Under the Baseline Future Scenario, automated voltage control capabilities deploy to approximately 1,400

feeders by 2035. Navigant estimates that the net present value of this investment will be approximately

$405 million, with best and worst cases of $288 million to $500 million (2014 $). The benefit-cost ratio is

3.9 and may range from 2.7 to 5.6.

3.3.2 Self-Healing Grids

Self-healing grids use sensors, control systems, automated switches, automated circuit breakers, and

communication networks to locate and isolate faults, reconfigure feeders, and rapidly restore power to

customers. The largest driver for the adoption of self-healing technologies is the prospect of improving

grid reliability. Utilities are likely to target critical areas, such as high-density residential and commercial

areas, as well as areas serving critical loads, such as hospitals and major transit hubs.

Investments in self-healing grid capabilities will result in substantial reductions in the duration, number,

and extent of outages. Under the Baseline Future Scenario, self-healing grid capabilities will be deployed

to over 2,900 feeders by 2035. There is large uncertainty around the benefit-cost ratio, which though

expected to be 5.1 may vary from 3.5 to 7.8. Navigant estimates that the net present value of utility’s

investments in self-healing grids will be $3.6 billion, with best and worst scenario results of $4.6 billion

and $2.4 billion (2014 $).

28 Appendix D also provides detail on energy storage system integration and control. This capability has been

included because even though it does not yet yield positive results, in large part due to high cost uncertainty,

technology costs are expected to decrease over time and, as a result, are expected to deliver a positive business

case. An assessment of energy storage deployment in 2020 has shown to deliver a benefit-cost ratio of 1.3.


3.3.3 Enhanced Fault Prevention

Enhanced fault prevention relies on high-resolution sensors to detect previously hard to locate faults

precisely. The combined use of digital relays, communications systems, and high-resolution fault sensors

provides utilities an opportunity to monitor and respond to potential fault conditions rapidly.

Under the Baseline Future Scenario, enhanced fault prevention capabilities will be deployed to

approximately 1,500 feeders by 2035. As is the case with most reliability-driving capabilities, there is

uncertainty around the realisation of benefits. The benefit-cost ratio is 3.0 on an expected basis and may

vary from 1.3 to 5.5. Navigant estimates that the net present value of investments in enhanced fault

prevention in Ontario through 2045 to be $457 million, with best and worst cases of $783 million and $78

million (2014 $).

3.3.4 Green Button

Green Button allows customers to access and share electricity data in a standardised format. This

provides customers with access to innovative applications, products, services, and solutions that can help

customers conserve energy and better manage electricity bills.

In the Baseline Future Scenario, Green Button becomes available to one-third of the province,

approximately 1.95 million customers, by 2035. The applications, products, and services enabled by

Green Button are used actively by 10% of the customer base with availability, equivalent to 195,000

customers. This segment of customers is ultimately the driver for the magnitude of benefits. The benefit-

cost ratio is 3.3 on an expected basis and may vary from 1.7 to 7.5. Navigant estimates that the net

present value Green Button investments through 2045 to be $95 million, with best and worst cases of $166

million and $29 million (2014 $).

3.4 Enhanced Future Deployment

The Baseline Future Deployment scenario suggested that there is a strong business case for continued

investment in smart grid capabilities. It also identified smart grid capabilities that under current and

projected conditions are likely, on average, to deliver meaningful net benefits.

The Enhanced Future Deployment scenario presents an alternative deployment scenario that aligns with

the priorities and principles of Ontario’s policy, and results in a greater net benefit to the province.

Through adjustments to the deployment levels in the Baseline Future Deployment scenario, the Enhanced

Future Deployment scenario illustrates that an even greater benefit could be realised through a more

targeted and informed approach to investment planning.

Cost-effectiveness and value, in particular with respect to ratepayer impact, is of considerable importance

for the government; it has been identified as one of its policy priorities, most recently in the mandate

letter from the Premier to the Minister of Energy, as well as in the 2013 Long-Term Energy Plan.

Integrating distributed, clean energy resources has also been and continues to be a priority as seen in the

Green Energy Act and the current Energy mandate. In addition, while reliability is important, the

objective is to achieve the most value via a balanced approach, not one of improving reliability at any

cost.


With this in mind, Navigant increased the deployment of smart grid capabilities that deliver economic

benefits, as well as capabilities that support the integration of distributed energy resources. In parallel,

Navigant modestly increased the deployment of capabilities that improve the reliability and resilience of

the grid, with a view on maintaining a strong ratepayer value.29 Navigant reduced the deployment of

smart grid capabilities that the analysis showed had low benefit-cost ratios and held constant the

deployment of capabilities that are generally deployed in parallel to other net beneficial capabilities.30

The deployment of automated voltage control, Green Button, and CPP was increased significantly due to

their primarily economic benefits. Table 3, below, shows the adjusted deployment figures.

Table 3. Smart Grid Capability Deployment of Enhanced Future Scenario

Smart Grid Capabilities Baseline Enhanced Change

Enhanced fault prevention 1,500 feeders 1,600 feeders +6%

Self-healing grid 2,900 feeders 3,000 feeders +5%

Fault current limiting 1,500 feeders 1,400 feeders -5%

Automated voltage control 1,400 feeders 1,800 feeders +30%

Automated reactive power control 1,400 feeders 1,400 feeders –

Notification of equipment condition 1,000 transformers 1,000 transformers –

Automated real-time load transfer 2,600 feeders 2,600 feeders –

Dynamic capacity rating 170 feeders 200 feeders +18%

Advanced power flow control 7,500 km 6,300 km -16%

Energy storage 240 MW 240 MW –

Microgrids 95 MW 100 MW +5%

Electric vehicles 80 MW (23,000 electric

vehicles)

90 MW (26,000 electric

vehicles) +13%

Distributed energy resources 630 MW 730 MW +13%

Green Button 195,000 customers 260,000 customers +30%

Critical peak pricing 175,000 customers 585,000 customers +300%

Source: Navigant

29 Navigant modestly adjusted capabilities that deliver significant reliability benefits but small economic benefits.

For example, self-healing grids create the best benefit-cost case for deployment given a benefit-cost ratio of 5.1;

however, only a small fraction of these benefits actually reduce system costs since most of the benefits accrue as

improved reliability. As a result, Navigant increased the deployment of self-healing grids a modest amount. 30 For example, automated reactive power control is often deployed in parallel to automated voltage control. These

two capabilities are deployed as an integrated approach to manage reactive power and to optimise voltage levels

along distribution feeders.


Figure 30 presents the annual benefits and costs for this scenario. As a result of the adjusted deployment,

there is a small change in the relative proportion of the benefits derived from economic, reliability, and

environmental impacts. However, the overall value increased to $6.3 billion, or by $1.0 billion relative to

the Baseline Future Scenario. Figure 31 illustrates this increase, and compares the present value of all

three scenarios.

Figure 30. Annual Benefits and Costs of Enhanced Future Scenario


Figure 31. Net Present Value of Enhanced Future Scenario


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Figure 32 shows that the value of these investments may range from $3.9 billion to $9.0 billion, and Figure

33 shows the distribution of costs and benefits across each segment of the electricity sector.

This analysis further demonstrates that there is a significant potential net benefit to Ontario if distributors

are able to deploy smart grid capabilities effectively. Doing so, however, is not a foregone conclusion and

achieving these outcomes requires a sector-wide effort to reduce the barriers and actively pursue cost-

effective smart grid investments.

Figure 32. Range of Net Present Value of Enhanced Future Scenario


Figure 33. Distribution of Benefits and Costs of Enhanced Future Scenario


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4. Smart Grid Policy Roadmap

The analysis above highlights the potential benefit of investments in smart grid to Ontario. It also

highlights one of the major challenges to its deployment—diffuse and unevenly distributed benefits.

However, this is not the only impediment to widespread adoption. In this section, Navigant discusses

nine barriers that are currently hindering, to varying degrees, grid modernisation initiatives in Ontario.

Navigant consulted with electricity distributors, public agencies, and industry and applied its own

informed views and understanding of the electricity sector in the province and generally across North

America to identify the barriers. The list is not exhaustive; rather it intends to capture the most

significant barriers to advancing smart grid in Ontario.

This section also identifies a set of pragmatic actions that government and industry could take to reduce

the impact of the barriers or remove them altogether. This set of actions forms a roadmap. It articulates

an approach that government and industry could take to unlock additional benefits from smart grid

investments. Stakeholders across the sector will need to establish the specific implementation details for

each action.

4.1 Barriers to Achieving a Modern Grid

In an effort to understand and characterise the barriers to grid modernisation in Ontario, Navigant

consulted with electricity distributors, the Independent Electricity System Operator, the Ontario Energy

Board, the Ministry of Energy, the Smart Grid Forum, and equipment suppliers. The consultation

explored perceived barriers to wider and faster adoption of smart grid technologies within the electricity

distribution sector in Ontario. The barriers identified fall into three general categories, introduced below.

Technical: Technical barriers are those that impact a utility’s ability to design a cost-effective

smart grid investment. These barriers stem from the underlying equipment or solution. Issues

that relate to the relative immaturity of some smart grid technologies such as high costs, rapidly

evolving functionality, or the introduction of operational and technical challenges for a utility are

examples. Another example, discussed in Section 4.1.1.2 is the need for interoperability and

common communication protocols across new smart equipment as well as between new smart

equipment and existing legacy systems.

Commercial: Commercial barriers are those that impact a utility’s ability to implement cost-

effective smart grid investments. That is, those barriers that directly impact investments in grid

modernisation initiatives that through the design phase have a positive benefit-cost ratio to the

industry as a whole. These barriers generally stem from the regulatory or commercial structure

of the industry. Examples include the regulatory treatment of smart grid investments, the

fragmentation of distribution sector ownership, shareholder and corporate financial constraints,

or the availability of a qualified labour force.

Cultural: Cultural barriers persist across the design, planning, and implementation phases of a smart

grid investment opportunity. They relate to the natural collective or individual response to

transformative technologies. They can impact a utility’s decision whether or not to explore

innovative solutions to traditional network reinforcements or a utility’s decision to proceed with

an alternative that it might perceive as having higher risks. The adoption of smart grid


technologies entails not only an evolution of the electricity system, but also a transformation in

the sector’s response and approach to effecting change.

4.1.1 Technical Barriers

4.1.1.1 Immature Technology

The underlying technology for a number for smart grid capabilities is immature, which leads to:

Higher costs, as global manufacturing of smart grid equipment is not at a sufficient scale to

achieve efficiencies

Evolving functionality

Unstable operation or higher failure rates

These issues make it more costly and difficult for utilities to design grid modernisation initiatives, as the

cost may exceed the benefits or the operational or financial success of a particular investment may be too

uncertain. Electric utilities have high reliability and safety requirements for assets installed on their

network and rightfully so. Uncertainty around the capability of new technologies will limit their

deployment.

The technologies that underpin the capabilities described in this report have reached varying degrees of

maturity. For example, the technology underpinning automated voltage control (e.g., automated tap

changers, voltage regulators, etc.) is reasonably mature, although their use in this particular application is

relatively new. In contrast, the technology underpinning advanced power flow control for the

distribution system is still immature (e.g., distributed series reactance, flexible alternating current (AC)

transmission system devices, etc.).

4.1.1.2 Lack of Interoperability

Standard communication protocols for smart network assets are evolving. The lack of standard protocols

leads to:

Additional system integration costs

Extended project implementation timelines

Risk of vendor and/or technology lock-in

This lack of interoperability makes it more costly and difficult for utilities to pursue investments in smart

grid. The costs may exceed the benefits due to the need for substantial system integration efforts.

Additionally, the risk of proceeding with a particular solution or equipment provider may mean that the

ability to change course or take advantage of new solutions or equipment at a later date is hindered,

introducing additional risk. From an operational perspective, a lack of interoperability between smart

grid systems from different vendors may detract from the business case of the investment if their

capabilities cannot be combined to leverage joint functionality.

There are two levels of protocols to consider for smart grid device interoperability: communication

protocols and application-level protocols. The former refers to how a device connects to broader

communication systems, such as the Internet or another dedicated network. The latter refers to the

specification of data inputs or outputs from a device and the rules for exchanging this data.


Early smart grid equipment deployments used proprietary communication protocols. The industry,

recognising the limitations of this approach, is shifting toward open and standardised protocols that

provide interoperability between different vendors. The Internet protocol suite (which consists of

protocols used over the Internet), for example, allows vendors to select standardised components—such

as Ethernet or Wi-Fi—to incorporate into their smart grid solutions. Utilities can then connect these

devices to their existing communications infrastructure.

The utility industry already has a number of application-level protocols to increase interoperability across

devices from different vendors. For example, utilities use Distributed Network Protocol 3 (DNP3) on top

of Internet protocols to support two-way communications between control centers and remote terminal

units. DNP3 defines the security model for proper message authentication and encryption between end

points. The development of these standard protocols is evidence of the industry’s recognition of the

importance of interoperability.

While initiatives are underway, in the case of both communication and application-level protocols, the

industry has yet to reach a consensus on a set of common standards for smart grid equipment.

4.1.2 Commercial Barriers

4.1.2.1 Diffuse Benefits and Concentrated Costs

As illustrated by the results of Navigant’s analysis, smart grid investments have diffuse benefits and

concentrated costs. That is, the costs concentrate primarily in one segment of the industry—in this case

the distribution segment—whereas the benefits accrue across all segments including generation,

transmission, and the end-user. In addition, some benefits may accrue outside the sector to Ontario or

society as a whole (e.g., carbon emission reductions).

The alignment of the benefits and costs of grid modernisation initiatives across the segments of the

industry is important to understand. A misalignment means that investments, which on the whole may

be net beneficial to the sector, do not appear to be net beneficial to all the individual segments. Parties

that carry a disproportionate portion of the costs relative to the benefits may be less inclined to proceed

with individual investments. Policy and regulation can mitigate this issue to some extent, in particular if

there are mechanisms to allocate the costs of these particular investments to the various segments on a

more proportional basis to the benefits.

Smart grid investments usually deliver a variety of benefits (e.g., reliability improvements, reduction in

losses, reduced consumption, deferred traditional network reinforcement, etc.) across the segments of the

industry, and it is not necessarily the case that one type of benefit is dominant or delivers a majority of

the economic value for a project. Hence, for a project to be net beneficial a utility must combine a number

of different benefits to make the business case positive. Under the current industry structure and

regulatory framework for distributors in Ontario, there are limited mechanisms for distributors to

monetise the value of benefits that originate outside of the distribution segment.

4.1.2.2 Labour Force Constraints

The different nature of smart grid investments relative to traditional utility investments places a heavier

emphasis on IT, advanced control systems, and data analytics, skillsets that previously were not always

required within distribution utilities. The labour force within a distributor in Ontario may not have all of


these skills and capabilities, nor do distributors necessarily have the financial resources to expand their

labour force or contract for these skills and capabilities from third parties. As a result, distributors may

miss opportunities to pursue net beneficial grid modernisation initiatives or may not fully realise all of

the potential benefits.

The allocation of resources to other high-priority projects will generally take precedent over smart grid

initiatives. In addition, the inherent complexity and uncertainty of smart grid technologies may dissuade

distributors from making employees available for such projects. While it may not always be the case, this

barrier is more acute at small and medium distributors, which may be more workforce-constrained and

may prefer to allocate resources to operational projects better aligned with the traditional business of the

utility.

4.1.2.3 Financial Constraints

The distribution sector in Ontario is undergoing a period of renewal, replacing assets installed 20, 30, 40,

or even 50 years ago. This renewal requires substantial capital investment. At present, the funds for

these investments are not coming from utility shareholders injecting new capital into the sector but rather

from deferred earnings (i.e., municipal and provincial shareholders choosing to forgo dividends and

reinvest profit back into the organisation). This is certainly an acceptable approach, but there is a limit to

how much growth or innovation utilities can fund through these means. At some point the sector will

require new incremental capital.

The fact that Ontario’s distribution sector is going through a period of renewal at the same time that new

technologies are transforming the way the network operates presents a unique opportunity. Grid

modernisation initiatives can be coordinated with traditional asset replacement initiatives on a large

scale. It also, however, presents a challenge. Distributors in Ontario are either already—or quickly

approaching—a situation where they are financially constrained, as under their current ownership

structure, existing shareholders may be unable to contribute additional capital or reinvest more of the

utility’s earnings.

Without additional capital investment, distributors may not be able to execute on cost-effective grid

modernisation initiatives, even if they obtain approval to recover the cost of the investment through the

current regulatory framework. For example, assume a utility earns $100 in net profit and has $200 of

approved capital investments. Under this example, the utility can fund half of the approved capital

investment through the profit generated by the company but will have to raise an additional $40 in equity

and $60 in debt to maintain its existing capital structure. If the shareholder does not have the $40 of

equity to invest, the utility may be limited in the amount of capital investment it undertakes.31

4.1.2.4 Fragmented Structure of the Distribution Segment

The distribution sector in Ontario is fragmented. Hydro One Networks, the provincially owned

distribution company, serves approximately 1.2 million customers. There are 42 distribution utilities,

primarily owned by municipalities, which serve less than 25,000 customers. There are 22 distribution

utilities in Ontario that serve between 25,000 and 100,000 customers, and there are eight utilities in

31 The specific issues around obtaining approval to recover smart grid investment costs through the regulatory

framework are discussed in Section 4.1.2.5.


Ontario that serve more than 100,000 customers (excluding Hydro One Networks). This structure

presents two challenges to grid modernisation initiatives for some distributors:

A lack of scale which negatively impacts the cost effectiveness of certain smart grid investments

Limited access to and control of the network assets required for successful deployment of certain

smart grid capabilities

Small distributors may not have the scale to make certain grid modernisation initiatives cost-effective. A

number of grid modernisation initiatives build on infrastructure and systems that are presently non-

existent within small and medium utilities in Ontario, such as distribution management systems, outage

management systems, operational data stores, and geographic information systems. Furthermore, it

would generally not be cost-effective for a small utility to make the investment to obtain the capability

internally. A third party could provide these capabilities to smaller utilities as a service. However, under

the current regulatory framework there is no obligation for utilities to have access to or be able to provide

the capabilities that the systems enable. The Ontario Distribution Sector Review Panel spoke to the

advantages of larger utilities, noting that only “larger distribution utilities will have the resources and

capacity to deal with the impending changes in electricity generation and consumption, including

distributed generation, energy storage, and electric vehicles. It will also allow them to more quickly

adopt the Smart Grid technology that will be the foundation for the sector’s future development.”32

Another challenge, although exclusive to certain distributors, is who owns the distribution assets

required to serve all of the customers in a distributor’s service territory. A number of distributors in

Ontario are embedded within Hydro One Networks’ or another distributor’s service territory. These

embedded distributors do not own or operate all of the distribution assets that are required to serve their

customers, limiting their ability to pursue some smart grid investments. Several smart grid applications

require the distributor to have operational control over the distribution station and upstream feeders.

Embedded distributors may not have full control over those assets, and as such, their engagement in

smart grid projects is limited by the level of cooperation that they can achieve with the host distributor.

4.1.2.5 Regulatory Framework

Elements of the current regulatory construct in Ontario, while not explicitly creating barriers to smart

grid investments, make it harder for utilities to propose and gain acceptance for these types of initiatives.

The discussion that follows focuses on two aspects of the regulatory context:

The framework for assessing smart grid investments

The incentives or penalties associated with performance or quality of service

The Ontario Energy Board evaluates smart grid investments in the same manner as traditional

infrastructure investments. Utilities develop a business case and present it for approval to interveners

and, ultimately, the Ontario Energy Board. While it is important for utilities to develop and understand

the business case for all of their proposed investments, this approach is potentially limiting given the

complexity of grid modernisation initiatives. Although distributors, interveners, and the Ontario Energy

Board share a strong understanding of the framework for evaluating the traditional utility investments

(e.g., network expansion, asset replacement, etc.), there is less of a common understanding of the nature

32 Ontario Distribution Sector Review Panel. “Renewing Ontario’s Electricity Distribution Sector: Putting the

Consumer First.” December 2012. http://www.energy.gov.on.ca/en/ldc-panel/.

http://www.energy.gov.on.ca/en/ldc-panel/


and magnitude of the benefits and risks associated with smart grid investments. This creates an

environment that tends to favour traditional pole and wire solutions over innovative new approaches.

Additionally, under the current regulatory framework there are limited incentives to encourage

distributors to give sufficient consideration to smart grid investments. Utilities have a financial incentive

to expand their asset base, and if it is easier to obtain approval for traditional investments then that is

where utilities will focus their efforts. Furthermore, the lack of strong performance standards and the

weak penalties associated with poor service quality inhibit the need for innovation.

As a result, distributors make limited consideration for innovative and smart solutions as alternatives to

traditional investments, and the Ontario Energy Board and interveners do not often appeal for

consideration of more innovative approaches.

There is also a broader issue relating to how the regulatory structure in Ontario may need to evolve to

reflect the changing role of the distribution utility in an era of distributed energy resources. There is

considerable uncertainty around how the current regulatory framework and pricing models will be

adapted as the role of the distributor evolves from exclusively a distributor of electrons toward a

platform to the provision of a range of distributed services such as demand response, energy storage,

distributed energy resources, and other energy services.

4.1.3 Cultural Barriers

4.1.3.1 Lack of Knowledge Sharing

There is limited sharing of lessons learned from successful and unsuccessful smart grid investments. As

a result, utilities may be required to overcome the same obstacles that other utilities may have already

addressed or experience pitfalls that could have been avoided with more collaboration and knowledge

sharing. The reluctance to share information represents a significant barrier for wider and faster

adoption of smart grid technologies, as it introduces inefficiencies that increase the cost of smart grid

investments and delays deployment.

Distributors are inclined to keep detailed operational information confidential and are inherently

cautious of sharing quantitative data or lessons learned with each other. This is particularly true for

unsuccessful initiatives. This lack of sharing may arise out of a fear of being discredited, having

information shared with the regulator, or for other reasons.

There is also a general lack of knowledge amongst consumers about smart grid or grid modernisation

initiatives.

4.1.3.2 Risk Averse Behaviour and Guarded Culture

In general, the municipalities or the provincial government shareholders that own the distribution

utilities in Ontario have a relatively low appetite for risk and view their investment in the utility as low-

or risk-free. This perception, combined with the required strong emphasis on safety and operational

resiliency, results in a culture that is guarded, risk averse, and tends to shy away from innovation.


As it should be, safety of employees, customers, and the network are paramount. Taken to its extreme,

however, this can create an unwillingness to adopt new technologies even where they have demonstrated

success.

Amongst most utilities in Ontario and elsewhere around the world, there is limited willingness to

undertake high risk, high reward projects. High risk, high reward projects are those that could yield

significant benefits but have a meaningful chance realising limited value. This is partly due to the large

amount of capital that is required to undertake projects in the sector, but it also stems from the lack of

strong financial incentives in the regulatory framework.

Financial incentives are an important driver of innovation. While there are a number of significant

differences between the utilities sector and the technology sector, the comparison is interesting and

illustrates the potential impact of strong financial incentives.

Venture capital investors invest in a number of different technology companies, even if a number are

likely to fail. They do so, in part, because if one of the companies is a success the profit opportunity is

tremendous. In the utility sector, however, if a project or set of initiatives is successful, the company may

earn only a slightly higher return on its overall investment (1%-3%); however, this would typically only

last for a short period of time before the regulatory framework requires that rates be adjusted.

This guarded and risk averse culture oftentimes means that utilities do not like to be the first to deploy a

new technology or to adopt a new operating practice, even if the potential benefits are significant. They

may not even like to be second. Rather, they may aim for being third.

4.2 Smart Grid Roadmap Initiatives

If left unaddressed, the barriers identified above will translate into a multitude of missed opportunities.

As presented in the previous sections, the potential for continuing to develop a smart grid in Ontario is

clear. The net benefit from pursuing smart grid investments is expected to be as high as $6.3 billion. In

order to realise the smart grid opportunity, the sector as a whole will need to address these challenges.

This smart grid roadmap identifies actions the government, regulator, and industry can take to maximise

the potential of smart grid deployment and to realise the long-term benefits for the electricity system,

consumers, and the economy in Ontario. This roadmap addresses some of the major challenges in the

sector to enable substantial improvements in the efficiency and pace of cost-effective smart grid

deployment. Work on the recommended activities should begin immediately in 2015 with a goal to

complete within two to four years.

Navigant is cognisant that this work and these initiatives do not exist in a vacuum and that there are a

number of ongoing initiatives that have a direct or tangential impact on smart grid investment. For

example:

The Premier’s Advisory Council on Government Assets is reviewing and identifying

opportunities to maximise the value of Hydro One Networks and Ontario Power Generation,

stimulating additional discussion about the structure of the distribution sector


The Independent Electricity System Operator and the government are rolling out the new

Conservation and Demand Management Framework, which puts a greater emphasis on

distributors to deliver on aggressive targets

The Ontario Energy Board has established the Smart Grid Advisory Committee to provide

assistance on emerging smart grid issues and address regulatory gaps

The Ontario Energy Board has initiated a consultation process to consider stronger reliability

performance targets

In this context, Navigant’s proposals are actionable, pragmatic, and intended to inform and complement

the already robust discussion within the sector.

Navigant has identified six high-priority initiatives that should enable substantial improvements in the

efficiency and pace of smart grid deployment in Ontario.

Make grid modernisation a component of community energy and regional planning processes

Establish a province-wide framework for evaluating the benefits of smart grid investments

Consider different approaches to cost allocation that enable costs associated with broader system

benefits to be recovered from the sector more broadly

Create a long-term funding mechanism for distributor-led innovation pilot projects that have the

potential to deliver net benefits

Promote sharing of positive and negative experiences with smart grid investments

Establish catalyst funds within utilities to foster a culture of innovation

Table 4 summarises the relationship between the individual barriers and the proposed initiatives. The

proposed initiatives will impact all but two of the barriers: lack of interoperability and the fragmented

ownership of the distribution network. The lack of interoperability will be addressed in due time by the

industry, as there are a number of ongoing national and international processes.33 The fragmented

ownership of the distribution network is outside the scope of this engagement and has been addressed

extensively by the Ontario Distribution Sector Review Panel and the Premier’s Advisory Council on

Government Assets.34

33 Smart Grid Policy Center. May 2011. “Paths to Smart Grid Interoperability”.

National Institute of Standards and Technology (NIST). September 2014. “NIST Framework and Roadmap for

Smart Grid Interoperability Standards, Release 3.0”. 34 Ontario Distribution Sector Review Panel. December 2012. “Renewing Ontario’s Electricity Distribution Sector”.

Premier’s Advisory Council on Government Assets. November 2013. “Retain and Gain: Making Ontario’s Assets

Work Better for Taxpayers and Consumers”.


Table 4. Mapping of Initiatives to Barriers

Barriers

Initiatives

Grid modernisation in community and

regional planning

Province-wide benefit and cost

framework

Cost allocation mechanism for broader system

benefits

Funding mechanism for distributor-led

pilots

Enhanced knowledge

sharing

Catalyst funds

Immature technology

Lack of interoperability Stakeholders noted that there are national and international ongoing processes to address this issue.

Diffuse benefits,

concentrated costs

Resource constraints

Financial constraints

Fragmented ownership of

the distribution network See discussion below. Solutions to this challenge are beyond the scope of this engagement.

Regulatory framework

Lack of knowledge

sharing

Risk averse behavior and

guarded culture

Source: Navigant

4.2.1 Make Grid Modernisation a Component of Municipal Energy and Regional Planning Processes

Suggested Lead: Government or Independent Electricity System Operator

Objective: Increase customer awareness and promote integrated network planning

Barriers Addressed: Diffuse benefits, concentrated costs; regulatory framework; lack of knowledge

sharing

The Independent Electricity System Operator, distributors, and the government should leverage the

municipal energy planning and regional planning processes underway in Ontario to inform and, if

desired by end-users, increase demand for grid modernisation initiatives. Grid modernisation initiatives

identified through these processes could be alternatives to traditional network reinforcements and enable

wider deployment of distributed energy resources.

The Municipal Energy Plan program supports the efforts of municipalities to understand their local

energy needs and identify opportunities for energy efficiency and clean energy.35 The current municipal

energy planning process does not explicitly consider grid modernisation within the suite of options

available to address local energy needs. As part of the funding available through the Municipal Energy

Plan program, the government could introduce a requirement that communities consider the

opportunities or need for grid modernisation initiatives to support their broader energy goals. Through

this process, communities would develop a more fulsome understanding of the types of benefits

associated with grid modernisation initiatives and where specific capabilities could be a good fit with

local needs.

35 For more information on the Municipal Energy Plan program see: www.energy.gov.on.ca/en/municipal-energy


There is also an opportunity for the Independent Electricity System Operator, transmitters, and

distributors to incorporate the consideration of grid modernisation more directly within the regional

energy planning processes.36 There is a robust regional planning process in Ontario. The Independent

Electricity System Operator is the process lead for developing Integrated Regional Resource Plans and the

transmitter is the process lead for developing the Regional Infrastructure Plan. Individual distributors

are responsible for developing a Distribution System Plan, which can include smart grid technologies.

An overview of the regional planning process and the relationship between the Integrated Regional

Resource Plan, the Regional Infrastructure Plan, and the Distribution System Plan is provided below in

Figure 34. The Regional Infrastructure Plan is effectively the wires component of the regional plan and

only is required to incorporate distribution facilities if a regional need is the driving force.

Figure 34. Overview of Ontario’s Network Planning Framework

Source: Regional Infrastructure Planning, Process Planning Working Group, June 2013

At present, the regional elements of this process do not directly take into account grid modernisation.

When considering T&D investments, the current process focuses almost exclusively on traditional

network expansion to address capacity needs.37

There is a tremendous opportunity to use the regional planning process to shift the nature of the

discussion of grid modernisation initiatives from a single utility to the broader region. There is also an

opportunity to use these processes as a platform for debate and to inform customers about the benefits

36 For more information on the regional planning process see: hwww.powerauthority.on.ca/power-

planning/regional-planning. 37 The following is an excerpt from a presentation to stakeholders to introduce the T&D element of the regional

planning process in central Toronto: “Depending on the type of capacity need, options can be: New load station

(s) or transmission lines and stations.”


that smart grid investment could deliver in terms or being able to realise regional energy goals related to

distributed energy resources or conservation and demand management.

4.2.2 Establish a Province-Wide Framework for Evaluating the Benefits of Smart Grid Investments

Suggested Lead: Ontario Energy Board

Objective: Raise the profile of smart grid investments within a utility’s system plan and

create a consistent framework for evaluation and reporting

Barriers Addressed: Diffuse benefits, concentrated costs; regulatory framework; lack of knowledge

sharing

Smart grid investments are generally characterised by diffuse benefits, which are generally more difficult

to estimate and monetise than concentrated benefits. As this report illustrates, an appropriate cost-

benefit analysis of smart grid investments needs to recognise and account for a number of different

benefits. Some of these benefit streams are easier to evaluate, and there is likely to be consensus amongst

stakeholders as to the value. Other benefit streams, including some of those included in Navigant’s

analysis, are more difficult to evaluate, and stakeholders may take different views on the value that

different grid modernisation initiatives generate in these areas. Having these discussions is an important

part of the process to develop a common framework.

The Ontario Energy Board evaluates the merit of smart grid investments in a manner consistent with how

it evaluates the other types of investments that distributors make. Distributors must develop a business

case for smart grid investments and substantiate it with quantitative or qualitative evidence. Each

business case is unique, as each distributor is unique. At present, the Ontario Energy Board provides

limited guidance on the type of benefits that distributors should consider, how distributors should

evaluate and quantify impacts, how they should report costs, and ultimately how they should calculate

cost-effectiveness. The Ontario Energy Board has noted that it is engaging stakeholders to identify and

develop approaches and tools to support investment proposals, and acknowledged that as smart grid

capabilities evolve over time, its evaluation process and a future framework would evolve as well.38

A robust, province-wide, cost-benefit analysis framework for smart grid investments is a necessary

evolution in the Ontario Energy Board’s approach to evaluating grid modernisation initiatives. As smart

grid capabilities evolve from pilot demonstrations to business-as-usual operations a precise, transparent,

and common framework will help promote the adoption of smart grid technologies amongst distributors.

The conversation within the industry that would be necessary to reach that common framework would

be valuable in its own right, educating and informing the participants.

Additionally, a province-wide benefit-cost framework will enable a consistent methodology to track

cumulative costs, benefits, and the relative maturity and adoption of different technologies. This

framework would provide direction to all distributors looking to develop a business case for a potential

investment. In addition, this framework could also address the future need for measurement and

verification guidelines to quantify benefits following deployment.

38 For more information see the Ontario Energy Board’s Supplemental Report on Smart Grid (February 2013)


This framework should provide:

A consistent methodology that distributors could use for different types of smart grid

investments

Clear guidelines for the types of benefits and costs that should be considered

Benchmark values for the impact of standard smart grid applications

Common values for estimating province-wide benefits

Requirements for including distributor-specific benefits and costs

As the industry regulator, the Ontario Energy Board is the right entity to lead the adoption of an Ontario-

specific framework. The Ontario Energy Board could leverage the analysis and results of this report as

well as work from other jurisdictions. One such jurisdiction is Massachusetts. The Massachusetts

Department of Public Utilities requires that distribution companies file a Grid Modernization Plan,

including a business case, or benefit-cost analysis, as a central component. The Department of Public

Utilities explained to distributors that it “intends to look to the distribution company’s business case

analysis as the primary lens for deciding whether to accept, reject, or require modifications” to the plan.39

The Department of Public Utilities provided a template for the benefit-cost analysis that it expects the

utilities to use.40

An analogous example in Ontario would be the conservation and demand management cost-effectiveness

tool created by the Ontario Power Authority, now the Independent Electricity System Operator.

Distributors use this tool to determine the cost-effectiveness of proposed conservation and demand

management programs and portfolios using a consistent evaluation framework and uniform input

assumptions. This tool characterises a number of cost-effectiveness metrics including the type of benefit-

cost analysis test (e.g., total resource test vs. societal cost test) and levelised delivery cost metrics (e.g.,

cost per unit of peak demand or energy savings), which are commonly used to characterise conservation

and demand management programs.

4.2.3 Consider Different Cost Allocation Mechanisms that Enable Distributors to Allocate and

Recover Costs Associated with Smart Grid Investments that Deliver Benefits beyond their

Local Customer Base

Suggested Lead: Government

Objective: Address the challenge of diffuse benefits and enhance the fairness of the

allocation of smart grid investment costs

Barriers Addressed: Diffuse benefits, concentrated costs; financial constraints; regulatory framework

Currently the only cost-recovery mechanism for distribution investments in smart grid is through local

electricity delivery rates. In this context, the costs associated with smart grid investments are exclusively

borne by the customers of the distributor making those investments. As is usually the case with smart

grid investments, in addition to delivering local benefits, other benefits may accrue upstream in

39 Order in D.P.U. 12-76-B, June 12, 2014, at 17. 40 http://web1.env.state.ma.us/DPU/FileRoomAPI/api/Attachments/Get/?path=12-76%2FBusinessCaseSumTemp.pdf


generation and transmission or as societal benefits. This creates a situation where all investment costs

must be borne locally even if benefits are distributed.

Some smart grid projects may be justified on the basis of local benefits alone (e.g., deferred distribution

reinforcement, improved reliability, etc.). Other investments, however, are net beneficial based on the

broader system benefits (e.g., reduction in upstream losses, reduced generation capacity requirements,

etc.). While one of the Ontario Energy Board’s mandates is to “facilitate the implementation of smart grid

in Ontario,” it also has the mandate to “protect the interest of consumers with respect to prices.” In

fulfilling the latter, the Ontario Energy Board is careful to regulate the quantum of costs incurred, and it is

also careful to maintain a reasonable allocation of those costs. In instances where smart grid investments

are net beneficial based on broader system benefits, there is a potential for conflict between the Ontario

Energy Board’s multiple mandates. Additionally, distributors have less of an incentive to propose

investments that are net beneficial based on broader system benefits, as these investments may result in a

net increase in cost to the distributor’s customers.

Enabling distributors to allocate the portion of the cost associated with broader system benefits to the

sector as a whole and to reflect those in an all-inclusive business case would allow them to justify and, in

due course, pursue additional cost-effective smart grid investments.

Other jurisdictions have recognised this issue. In New York, the Public Service Commission (PSC)

initiated the Reforming our Energy Vision proceedings, which seek to align the state’s current regulatory

structure with its energy vision. As part of this vision, the PSC has stated that “benefits and costs need to

be understood along two dimensions: those that are monetised directly within the existing market

structure vs. those that are not, and how each benefit or cost accrues to different stakeholders within the

system.”

As an example, Figure 35 shows the business case for an illustrative smart grid investment. In this

example, the investment cost is $100 million and the expected system-wide benefits are valued at $140

million, an overall benefit-cost ratio of 1.4. The benefits are assumed to be split between the distribution

segment ($70 million) and the generation and transmission segment ($70 million). The example assumes

that the distribution utility proposing the investment serves 10% of customers and demand in the

province.

Under the current framework, since only $70 million of the benefit originates locally within the

distribution utility, if would be difficult for a distribution utility to move forward with this project on its

own, or for the Ontario Energy Board to approve it since the benefit-cost ratio for the distributors’

customers would only be 0.77 (70/100 + 10%*70/100). However, if the distributor had a mechanism to

allocate a portion of the costs to ratepayers across the sector, it would be more likely to proceed with the

investment, and the Ontario Energy Board would be more likely to approve it. For example, if a

mechanism existed to allocate costs in proportion to the benefits, the customers of the distribution utility

would incur a cost of $50 million and receive a benefit of $70 million (B/C = 1.4). The customers of the

generation and transmission segments, of which the distributor’s customers are a subset, would also

incur a cost of $50 million and receive a benefit of $70 million (B/C = 1.4).


Figure 35. Illustrative Allocation of Smart Grid Benefits

Source: Navigant

It would be important for the administrator of the allocation mechanism to develop a test to ensure that

the allocation does not harm the distributor’s customers or any other ratepayers. As an example,

distributors have an economic model, or a test, that they use to determine the amount that a customer

must contribute for a new connection. The amount is determined such that existing customers are no

worse off.

There are a number of existing mechanisms in Ontario that already achieve a similar outcome. These

existing mechanisms or a new mechanism could deliver the necessary cost allocation. As an example of

an existing mechanism, the Independent Electricity System Operator could amend the eligibility

requirements for the Industrial Accelerator Program to allow distributor investments in smart grid

infrastructure to qualify.41 The Independent Electricity System Operator would have to amend the rules

carefully to reflect the unique measurement and verification challenges associated with grid

modernisation initiatives and to ensure that there were no opportunities to recover the same costs twice.

Another example of an existing mechanism that could be used to achieve this initiative is an expansion of

the definition of the types of conservation and demand management initiatives by the government that

qualify for funding and toward the targets within the Conservation First Framework.42

A new mechanism example would be the government and the Ontario Energy Board creating a

mechanism similar to the Renewable Generation Connection charge. The Renewable Generation

41 The Industrial Accelerator Program provides incentives to large facilities to implement electricity conservation

programs. Although this program is meant exclusively for large facilities, there is fundamentally no difference

whether the electricity and demand reductions are delivered from a project involving a large industrial customer

installing an energy efficiency solution or an electricity distributor deploying a smart grid capability. 42 A distributor may determine that the most cost-effective way to achieve a conservation target might be through a

particular smart grid capability (e.g., automated voltage control) and would be allowed to include smart grid

initiatives as part of their Conservation and Demand Management plan.

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Connection charge allocates the costs incurred by distributors to expand their networks to connect

distributed generation to all customers across the sector.

4.2.4 Create a Long-Term Funding Mechanism for Distributor-Led Pilot Innovation Projects

Suggested Lead: Government

Objective: Increase distributors’ activity in and ownership of grid modernisation pilot

projects

Barriers Addressed: Immature technology; workforce constraints; financial constraints; risk averse

behaviour and guarded culture

Creating a long-term funding mechanism for distributor-led pilot innovation projects will encourage

distributors to take a more active role in as well as have more accountability for grid modernisation

initiatives. A more practical role beyond providing a test bed for technology evaluation, it will encourage

distributors to identify opportunities and critical system deficiencies in their networks and to pursue

innovative smart grid solutions. Distributors will be engaged in the vendor and technology selection

process, and take ownership and responsibility for the project management, delivery, deployment, and

ultimately success of the project. In addition, this funding could help distributors bring in additional

workforce resources that might ultimately allow them to tackle larger and more complex smart grid

projects in the future.

A fundamental requirement of funding should be the ability to demonstrate value for money.

Furthermore, funding criteria should include requirements for benefit tracking and knowledge

dissemination.

As network operators, distributors are in the best position to identify problems and develop, test, and

evaluate potential solutions that will lead to the development and deployment of smart grid technologies

across Ontario. While the Smart Grid Fund has provided a great opportunity for distributors to partner

with industry to test and deploy smart grid technologies, creating a long-term funding mechanism for

distributor-led innovation projects will encourage distributors to take a more active role in and to have

more accountability for smart grid initiatives.

The Low Carbon Networks Fund in the United Kingdom is an example of a distributor-led smart grid

fund. The Low Carbon Networks Fund allowed up to £500 million to support projects sponsored by the

Distribution Network Operators to try out new technology and operating and commercial arrangements.

Distribution Network Operators used the fund to explore how networks could facilitate the uptake of low

carbon and energy saving initiatives such as electric vehicles, heat pumps, energy storage, micro and

local generation, and demand-side management.

4.2.5 Establish a Forum for Distributors to Share Experiences with Smart Grid Deployments

Suggested Lead: Distributors

Objective: Knowledge transfer

Barriers Addressed: Immature technology; lack of knowledge sharing; Risk averse behaviour and

guarded culture


Ontario’s electricity sector is unique. Distributors vary in terms of their size, customers served, and

service territory. This means that the operational and network characteristics of each distributor are

distinct, and as such, many distributors are only similar to a limited number of other distributors.

While the Electricity Distributors Association represents all of Ontario’s distribution utilities, there are a

number of distributor organisations that aim to represent smaller distinct groups of distributors.

The Coalition of Large Distributors represents the six largest distributors in the province (not

including Hydro One)

The GridSmartCity Cooperative is a partnership of ten medium-size distributors

The Cornerstone Hydro Electric Concepts is an association of 14 small distributors

These organisations provide distributors a venue to exchange ideas and solutions on a number of issues,

as well as to create efficiencies of scale enabled through cooperation. For example, the GridSmartCity

Consortium that includes 32 members, including distributors (which make up the GridSmartCity

Cooperative), suppliers, academia, and government, was established specifically to support the

deployment of innovative smart grid solutions. Venues like this provide distributors an opportunity to

share plans and lessons from smart grid deployments.

Despite this, distributors are generally cautious in sharing information and are inclined to keep

operational knowledge confidential. This reluctance to share project information represents a significant

barrier to faster and wider adoption of smart grid technologies. Holding group meetings under the

Chatham House Rule would help to encourage candid and open discussion, as would barring

government, supplier, and regulatory staff from participating.

There is a surfeit of information about smart grid projects in Ontario in the public domain, and where it is

available it is severely limited and fragmented. As a result, the sector does not effectively disseminate

important lessons from smart grid investments. There would be substantial merit in facilitating a safe

venue for distributors to discuss best practices and lessons learned.

A well-maintained online repository of smart grid projects, with contact details for project managers,

would help to facilitate one-on-one discussions between distributors. As an example, the Electricity

Networks Association in the United Kingdom maintains a Smarter Networks Portal.43 The association’s

members (electricity and natural gas distributors and transmitters) and the regulator requested that it

establish the portal, which aims to achieve the following:

Provide an overview of the technical and commercial coverage of current and completed

electricity and gas smart grid projects

Identify activity areas and gaps

Provide an understanding of likely sources of benefit-cost data

Provide a listing of the latest news, smart grid events, and launches

Provide information of relevance to the next round of Low Carbon Networks Fund bids

Track the progress of projects and promote the sharing of information and learning

43 http://www.energynetworks.org/electricity/smart-grid-portal/ena-smarter-networks-portal.html


Coordinating and keeping track of smart grid projects would reduce the duplication of projects and the

disjointedness of initiatives, as well as collect lessons learned and assess the maturity of smart grid

development in Ontario. This information would shed light on the number and types of projects

deployed, deployment sites, funding availability, distributor partnerships with government and

academia, and the results and lessons learned from such projects.

4.2.6 Establish Innovation Catalyst Funds

Suggested Lead: Distributors

Objective: Promote a culture of innovation

Barriers Addressed: Risk averse behaviour and guarded culture; immature technology

The government and the regulator can only do so much to create an environment that is supportive of

smart grid and grid modernisation initiatives. Ultimately, the utilities will need to foster a culture that is

conducive to and promotes innovation.

The return on investment on innovation is significant. Meta-analysis conducted by Frontier Economics

concluded that the mean private rate of return on research and development investments is typically

around 30%, with median returns being slightly lower—typically 20 to 25%.44

To support the development of an innovative corporate culture, distributors in Ontario should establish

innovation catalyst funds. These funds should be available to internal teams to demonstrate proof-of-

concept for new ideas rapidly. The funds should exhibit a number of characteristics.

Capital for the funds should come from shareholders, not ratepayers

Access should come by way of an internal competition

They should be promoted and supported by the most senior levels of the organisation, with

finalists pitching their ideas directly to senior executives

Winners should be publicised within the organisation

The activities that the funds support should supplement ratepayer- or taxpayer-funded research

and development initiatives

Distributors should also consider taking additional steps to promote innovation, including:

Building and reporting on innovation metrics

Appointing innovation champions

Creating cross-functional innovation networks within their organisation

44 Frontier Economics. “Rates of return to investment in science and innovation”. London, UK. July 2014. p. 21.


4.3 Conclusions

Beyond the specific initiatives outlined above, smart grid deployment would benefit from further

consolidation in the distribution sector and the introduction of private capital. Together these would

resolve some of the challenges associated with financial constraints and fragmented ownership of the

network. It is also possible that the introduction of private capital into Ontario’s distributors could

improve the current culture of risk aversion.

Provided that industry and government agree with the merits of the initiatives outlined above, work

should commence immediately to assign responsibility to the various parties for developing detailed

plans. Navigant believes that work on the initiatives could proceed in parallel and that careful planning

could mitigate the impact of interdependencies between the initiatives on the overall timing. To realise

the potential net benefit from the investment in smart grid, the sector should aim to make significant

progress on the initiatives identified above over the next two to four years.

Navigant believes that these initiatives will help to alleviate some of the barriers to smart grid

investments that exist today. However, fundamentally, it is up to utility shareholders, leaders, and

managers to drive innovation within their organisations and to prepare their businesses and systems for a

future in which consumers may have alternatives to traditional electricity supply.

Ontario Smart Grid Assessment and Roadmap Page A-1

Appendix A. Methodology

Navigant developed the results presented in this report using a robust benefit-cost assessment

framework. This framework is consistent with the approach recommended by the Electric Power

Research Institute in January, 2010.45 Navigant and Summit Blue Consulting (now a part of Navigant)

contributed to the development of the Electric Power Research Institute framework.

Navigant employed this framework for a number of regional smart grid studies, notably in the US Pacific

Northwest and the United Kingdom.46 In addition, this model leverages feedback gathered over a

number of years and multiple engagements with utilities, governments, and industry associations.

Over the course of this engagement, Navigant gathered inputs and assumptions from a wide range of

sources, including stakeholders, regulatory filings, distribution system plans, and results and findings

from several smart grid projects across North America.

Figure 36: Smart Grid Analysis Framework Development

Source: Navigant

45 Electric Power Research Institute. Jan 2010. “Methodological Approach for Estimating the Benefits and Costs of

Smart Grid Demonstration Projects”. 46 The Bonneville Power Administration’s smart grid regional business case white paper can be found at:

http://www.bpa.gov/Projects/Initiatives/SmartGrid/Pages/default.aspx

The Smart Grid Great Britain’s smart grid analysis report can be found at:

http://www.smartgridgb.org/benefits-of-smart-grid/item/522-new-smartgrid-gb-report-shows-smart-grid-

development-to-deliver-%C2%A32-8-billion-to-gb-economy-by-2030.html

Benefit-cost framework

This framework is based on multiple relational matrices that map benefits and costs, to functions and stakeholders

Computational model

A robust, bottom-up model built to reflect regional-scale deployment of smart grid functionalities.

Research and Inputs

Gather grid characteristics, energy/demand forecasts, avoided costs. Review research reports, studies, Long Term Energy Plan, Ontario Energy Board filings, DSPs.


A.1 Benefit-Cost Framework

Navigant’s benefit-cost framework assesses smart grid investments using a methodology that

acknowledges the interdependencies of investments, the diffuseness of benefits, and the distribution of

costs across the electricity sector value chain.

Figure 37 illustrates the most fundamental relationship of this framework. The deployment curve for a

particular smart grid capability links to costs through assets such as a hardware, software, and operations

and maintenance costs. Similarly, benefits are linked through impacts such as reduced demand,

electricity savings, or reliability improvements.

A unique deployment curve characterises each smart grid capability. The overall shape of this curve then

determines the rate at which benefits and costs accrue. The framework uses the penetration of each

capability, defined by the point on the deployment curve, to determine the number of assets required. In

addition, the framework reflects asset replacement cycles, declining technology costs, and recurring

operations and maintenance costs.

Similarly, the framework captures benefits in proportion to the number of customers, or the percentage of

the network impacted by the smart grid capability. The framework incorporates unique impact

assumptions for each type of benefit and each smart grid capability.

Figure 37: Deployment Curves to Benefits and Costs

Source: Navigant

As illustrated in Figure 38, the relationships of assets, capabilities, and impacts are not one-to-one. In the

framework, assets may enable one or more capabilities, and capabilities enable one or more impacts (or

benefits).

Capacitor Bank

Automated Switch

Voltage Regulator

FaultSensor

Regulating Inverter


Figure 38: Illustrative Mapping of Assets to Capabilities and Capabilities to Impacts

Source: Navigant

A.2 Computational Model

Navigant developed a computational model that implements the benefit-cost analysis framework

described above. The model uses a rigorous bottom-up architecture. Navigant tailored the model’s

flexible platform to reflect the nuances of Ontario’s electricity system, including grid characteristics,

reliability metrics, demand and energy forecasts, electricity and ancillary market prices, renewables

penetration, among others. The development of a benefit-cost framework and robust computational

model allows for the periodic revision and updates to input assumptions, and analysis of alternative

deployment scenarios.

Several distinct features characterise Navigant’s model:

Bottom-up approach that reflects the individual costs and benefit of smart grid deployments

Captures the incremental benefits and costs attributed to smart grid investments

Benefits and costs attributed to stakeholders across the electricity system supply chain

Risks reflected through Monte Carlo (uncertainty) analyses

Cost-sharing relationship among smart grid capabilities that rely on the same assets

Reflects over 150 grid characteristics and impact valuations specific to Ontario


Figure 39: Screenshot of Navigant’s Smart Grid Benefit-Cost Model

Source: Navigant

A.3 Research and Inputs

Navigant administered an electronic smart grid deployment questionnaire to Ontario’s distributors to

gain a clear understanding of the current level of smart grid investment in Ontario and the potential for

future deployment. Navigant used the responses to the questionnaire to inform the modelling of past

and future deployment of smart grid capabilities.

In addition, Navigant reviewed regulatory filings, distribution system plans, the Ontario Energy Board’s

Reporting and Records-keeping Requirements, the Long Term Energy Plan, as well as other public

reports. The model reflects over 150 characteristics, valuations, and structure of the electricity grid in

Ontario. Among others, these include:

Number of residential, commercial, industrial customers

Number of transmitters and distributors, and control centres

Number of transmission and distributions substations

Kilometers of transmission and distribution overhead and underground line

Number of transformers

Number of feeders

Installed embedded renewable generation capacity


Average transmission and distribution resistive and no-load losses

Peak transmission and distribution resistive losses

Reserve margin

Number of capacitor banks and distribution grid switches

Momentary Average Interruption Frequency Index

System Average Interruption Frequency Index

Customer Average Interruption Duration Index

Carbon dioxide, nitrogen oxide, sulfur oxide, particulate matter intensity of generation and fuel

Energy and demand forecasts (residential, commercial, and industrial)

Navigant also consulted with a large number of stakeholders, public agencies, industry groups, and

distributors, through one-on-one meetings and group sessions. The objectives of these meetings were

threefold:

To obtain additional detail on Ontario-specific smart grid capability deployment plans

To inform stakeholders of the structure of the analysis framework, verify model assumptions and

results, and to share key takeaways from interim results

To engage stakeholders in discussion around the types of barriers that exist for broader smart

grid deployment as well as innovations and opportunities to mitigate those

A.4 Scope of Benefit-Cost Analysis

Four dimensions define the scope of Navigant’s benefit-cost analysis: geography, timeframe, technology,

and market segment.

Geography

Navigant limited the scope of the analysis to the province of Ontario. A significant portion of Ontario’s

service territory is rural, despite the fact that the majority of the population and electricity customers live

in urban centres such as Ottawa and Greater Toronto Area. The electricity system varies considerably

across the different geographies.

Two electricity transmission utilities and a total of 73 electricity distributors (or local distribution utilities,

distributors) serve Ontario’s 4.8 million electricity customers. Ontario’s distributors are wildly diverse in

terms of size. The three largest distributors serve approximately 50% of all customers. The three smallest

serve less than 0.1% of all customers.

The benefits and costs of smart grid investments vary considerably based on utility, geography, system

conditions, customers, etc. Modelling the unique characteristics across the province is challenging. As

such, this analysis considers average system conditions.


Timeframe

The analysis covers a timeframe that captures past and future smart grid investments. The analysis

period starts in 2005 and extends to 2045. This timeframe is selected to capture the early deployment of

AMI during the 2006 to 2013 period, and also to allow capabilities deployed as late as 2035 to accrue

sufficient benefits over the subsequent 10 years. Figure 40 shows the time horizon included in the

analysis.

Figure 40: Benefit-Cost Analysis Timeframe

Source: Navigant

Technology

Navigant used a definition of smart grid that is consistent with provincial legislation. The Ontario

Electricity Act, 1998, established the following definition for smart grid:

Smart grid means the advanced information exchange systems and equipment that when utilised together

improve the flexibility, security, reliability, efficiency and safety of the integrated power system and distribution

systems, particularly for the purposes of:

(a) enabling the increased use of renewable energy sources and technology, including generation facilities

connected to the distribution system;

(b) expanding opportunities to provide demand response, price information and load control to electricity

customers;

(c) accommodating the use of emerging, innovative and energy-saving technologies and system control

applications; or

(d) supporting other objectives that may be prescribed by regulation.

2012: The Ontario Energy Board released the Renewed

Regulatory Framework for Electricity Distributors (RRFE)

Analysis Timeframe

2000 2005 2010 2015 2020 ….. 2045

2006: Distributors start rolling

out smart meters across Ontario

2011: Ministry of Energy launches the Smart Grid Fund

2014 – 2015: Procurement of 50 megawatts of energy storage

2009: Distributors transition customers to time of use rates 2006 – 2035: Distributor investments

in smart grid technologies

2015 - 2020: Distributors identify smart grid deployment plans through 2020

2004: The Ministry of Energy

announces the launch of the

Smart Metering Initiative

2014: Advanced Energy Centre is launched


Despite the wide array of smart grid definitions used in the industry, a general theme in most is the

incorporation of two-way communications and automated intelligence where limited to none of either

existed previously. The vision of a modern electricity grid integrates telecommunication networks,

digital technology, and information management. This more intelligent and better-connected grid uses

new technologies and innovative solutions to streamline utility operations and maintenance practices,

improve reliability and resiliency, and to enhance the value and availability of distributed energy

(including electric vehicles) and conservation and demand management resources.

Figure 41: Relationship Between Smart Grid, Conservation, and Distributed Energy Resources

Source: Navigant

Clearly differentiating between the benefits and costs of smart grid and the benefits and costs of

conservation and demand management or distributed energy resources is difficult. For example, the

adoption of distributed renewable generation can theoretically be achieved without smart grid,

However, monitoring and control systems – part of a smarter grid – may enable further adoption of

distributed renewable generation by decreasing integration or balancing costs and maximising energy

production. A similar argument exists for conservation and demand management initiatives.

For this analysis, Navigant has included only the incremental benefits and costs associated with smart

grid. For example, in the case of distributed solar photovoltaics, we have excluded the cost of installing

solar panels on a home or business, but have included the cost of installing the necessary equipment to

actively control and monitor the installation, along with the incremental production and/or reduced

integration costs that active monitoring and control provides.

Market Segment

From a cost perspective, Navigant’s analysis focuses primarily on investments in the electricity

distribution network, from the customer meter to the transmission system (i.e., 115 kilovolts and above).

Additionally, the analysis includes province-wide initiatives to provide customers with price and

consumption information, and increased control over their energy consumption.


From a benefits perspective, Navigant’s analysis considers all aspects of the electricity system

(generation, transmission, distribution, and end-user). For example, a benefit may originate at the end-

user level, such as with a reduction in peak demand. The transmission and distribution stakeholders

perceive this reduction in peak load as a potential avoided need for new delivery capacity. The

generation stakeholder perceives this reduction in peak load as a potential avoided need for additional

generation capacity.

Navigant’s analysis does examine non-system benefits which would be accrued outside of the electricity

system. However, Navigant does expect that investments in smart grid will create non-energy benefits.

Investments in smart grid will create opportunities for smart grid technology and solution companies,

support the growth of secondary industries, and create supply chains impacts across the province.

A.5 Estimating Deployment Curves

As mentioned above, the framework characterises each smart grid capability through a deployment

curve. All deployment curves have an “s” shape.47 Each curve reflects the degree of penetration of each

capability on the grid. Four factors define the shape of a deployment curve, and a fifth factor is used to

position on the curve on a time line.

Initial penetration: Reflects the original capability penetration. All capabilities in the model

were set with an initial penetration of zero.

Final penetration: Reflects the final degree of penetration.

Years-to-saturation: Reflects the number of years from initial to final penetration. This variable

determines the width of the curve.

Curvature: Reflects the steepness of the curve.

Start year: Used to define the location of the curve on a time line.

Figure 42: Modeling of Smart Grid Capability Deployment Curves

Source: Navigant

47 An s (or sigmoid curve) is a type of logistic function generally used to represent a learning curve.

Start Year

Initial Penetration

Final Penetration

Years-to- Saturation

Curvature

Question 1 Units Response

- Grid / network characteristics

a. Name of utility (use dropdown ) Midland Power Utility Corporation

b. How many feeders are there in your service territory? # 26

c. How many transmission stations do you own? # 0

d. How many distribution substations are in your service territory # 6

e. How many distribution station transformers are in your service

territory?# 6

f. How many pole-mounted distribution transformers are in your

service territory?# 709

g. How many pad-mounted distribution transformers are in your


h. How many underground distribution transformers are in your


Question 2 Units Responses

- Smart grid functions - Volt/VAR optimisation

Definition

a. Are you currently pursuing volt/VAR optimisation functionality for

your network?yes / no no

b. By Dec 31, 2014, how many feeders will have this functionality? # 0

c. By Dec 31, 2019, under your current plans, how many feeders do

you anticipate will have this functionality?# 0

d. What is the maximum penetration for this function on your network

(i.e. what percentage of feeders in your network would be good

candidates for this functionality)?

% 0

Question 5 Units Responses

- Smart grid functions - Automated real time load transfer

Definition

a. Are you currently pursuing automated real time load transfer

functionality for your network?yes / no no

b. By Dec 31, 2014, how many feeders will have this functionality? # 0

c. By Dec 31, 2019, under your current plans, how many feeders do

you anticipate will have this functionality?# 0

d. What is the maximum penetration for this function on your network

(i.e. what percentage of feeders in your network would be good

candidates for this functionality)?

% 0%

Monthly Monitoring

Reports

Distributor Questionnaire

Each function is characterised through its deployment curve


A unique grid metric scales each smart grid capability penetration. These metrics are primarily grid

assets, such as feeders and transformers, but can also be customers or electric vehicles. For example,

deployment of the self-healing grid capability is measured by the number of feeders equipped with

automated switches or though the number of customers affected. Table 5 shows the metrics used for each

capability. For reporting purposes, Navigant grouped the deployment curves for all capabilities based on

customers or MW; these are shown by the Customers and Megawatt columns in the table.

Table 5: Smart Grid Capability Penetration Metrics

Smart Grid Capabilities Metric Customers Megawatt

Enhanced fault prevention Feeders ✓

Self-healing grid Feeders ✓

Fault current limiting Feeders ✓

Automated voltage control Feeders ✓

Automated reactive power control Feeders ✓

Notification of equipment condition Transformers ✓

Automated real-time load transfer Feeders ✓

Dynamic capacity rating Feeders ✓

Advanced power flow control km of line ✓

Energy storage MW ✓

Microgrids MW ✓

Electric vehicles MW, electric vehicles ✓

Distributed energy resources MW ✓

Green Button Customers, utilities ✓

Critical peak pricing Customers ✓

Time of use pricing Customers, utilities ✓

AMI Customers ✓

AMI enhanced Customers, utilities ✓

Source: Navigant


A.6 Types of Benefits and Costs

The analysis captures a total of 32 benefits across eight benefit categories. The definitions for each are

shown below.

Table 6: Benefit Types

Benefit category Definition

Reduced energy use Energy savings as a result of reductions in electricity consumption. Also includes

transmission and distribution line losses, transmission and distribution no load losses,

and avoided congestion costs.

Reduced capacity expansion Avoided capacity requirements as a result of reduction in peak load. Also includes

reduced peak transmission and distribution losses, increased utilisation of existing

transmission and distribution infrastructure, and reduced reserve margin.

Reduced ancillary services costs Savings from the avoided need to provide ancillary services, or from availability of more

flexible resources. This includes regulation service and spinning and non-spinning

reserve. Black start and reactive support/voltage control are not included.

Improved renewables integration Savings from the reduction of integration costs (e.g., balancing requirements) for

renewable resources, or increases in their capacity factor resulting in overall increases

in firm renewable generation capacity

Improved reliability Reduction in the number, extent, and duration of sustained (and momentary) outages.

Attributed to end-users, and evaluated using average values of customer interruption

costs for three types of customers, residential, commercial, and industrial.

Extended equipment life Savings from extending the useful life of (or deferring the need to replace) distribution

equipment.

Improved Utility Operations and

Maintenance

Savings to distribution utilities from avoided operations and maintenance costs, service

restoration and switching operation costs, metering services, reduced electricity theft,

and reduced call volume.

Reduced Emissions Reduced carbon dioxide, nitrogen oxide, sulfur oxide, and particulate matter emissions.

These may arise from avoided electricity generation or reduced truck rolls.

Source: Navigant

Each of these eight categories map to one of three benefit types, as shown in Table 7.

Table 7: Mapping of Benefit Categories to Benefit Types

Benefit category Economic Reliability Environmental

Reduced energy use 48 ✓

Reduced capacity expansion ✓

Reduced ancillary services costs ✓

Improved renewables integration ✓ ✓

Improved reliability ✓

Extended equipment life ✓

Improved Utility Operations and Maintenance ✓

Reduced Emissions ✓

Source: Navigant

48 The environmental benefits (e.g., avoided CO2, NOx, SOx, PM emission) corresponding to Reduced energy use are

reflected through the Reduced Emissions benefit category.


In addition, each benefit category is credited to a specific segment of the electricity sector, as shown

below.

Table 8: Mapping of Benefit Categories to Each Segment of the Electricity Sector

Benefit category Generation Transmission Distribution Customer

Reduced energy use ✓ ✓ ✓

Reduced capacity expansion ✓ ✓ ✓

Reduced ancillary services costs ✓

Improved renewables integration ✓ ✓

Improved reliability ✓

Extended equipment life ✓

Improved Utility Operations and Maintenance ✓

Reduced Emissions ✓

Source: Navigant

There are two categories of costs, described below.

Table 9: Cost Categories

Cost category Definition

Asset costs Refers to capital cost expenditures for new equipment, as well as the equipment’s

corresponding installation, integration and maintenance.

Recurring and start-up costs These are non-capital costs accrued annually, such as the operations and maintenance costs

associated with the smart grid capability. These include overhead costs (administration,

customer service, marketing, training, etc.), operations, and engineering costs.

Source: Navigant

A.7 Cost-Sharing and Double Counting of Benefits

Navigant’s benefit-cost framework avoids double counting benefits and costs. The relational maps that

trace individual benefits to capabilities provide the transparency necessary to avoid overlapping of

benefits streams across multiple capabilities. This transparency and the tracking system inherent to the

model, provide the user the ability to trace back the nature and magnitude of individual impacts for each

capability. This is especially important for an assessment of such a large portfolio of capabilities, where

there are many overlapping technologies and impacts.

The framework assumes that a single asset can support multiple capabilities. For example, as shown in

the figure below, pricing schemes such as time of use or critical peak pricing require the deployment

AMI. The model acknowledges that these two capabilities will leverage the same basic assets and does

not double count equipment costs, as shown below. Operations, maintenance, and start-up costs, which

are specific to each capability, are included separately for each capability.


Additionally, and according to deployment specifications, it may occur that different capabilities which

require the same assets may ‘share’ this asset even in cases where, in reality, deployment takes place in

different locations. This may result in an underestimation of costs.

Figure 43: Cost Sharing across Capabilities

Source: Navigant

A.8 Uncertainty

The model captures uncertainties around benefits and costs by attributing confidence intervals to every

individual benefit and cost. All costs, including equipment costs, recurring operations and maintenance,

and overhead associated with each asset and each capability, as well as the corresponding impacts, are

attributed an uncertainty band. Ultimately, the model aggregates all the uncertainties to create a

probability distribution curve. The model runs a Monte Carlo simulation to create a frequency

distribution curve and estimate the relative likelihood for the occurrence of each outcome. For example,

Figure 44 shows a model run with a sample of 150 iterations. Once the model computed the present

value of the benefits and cost for each iteration, it generates the distribution curves, shown on the right,

from the sample of results.


Figure 44: Illustration of Uncertainty Analysis

Source: Navigant

This report presented uncertainty analysis results with three cases: expected, best and worst. The

geometric mean is the expected case, and the best- and worst-case scenarios represent the 95th and 5th

percentiles, respectively.

Figure 45: Illustrative Uncertainty Analysis

Source: Navigant

$M (

pres

ent v

alue

)

Number of iterations (runs)

Ontario Smart Grid Assessment and Roadmap Page B-1

Appendix B. Smart Grid Capabilities

The analysis reflects a portfolio of 18 smart grid capabilities. A definition as well as a mapping of the

benefits expected for each capability is below.

B.1 Advanced Power Flow Control

Definition

In AC power systems, electricity flows preferentially along low-impedance pathways, causing challenges

for grid operation. Advanced power flow control allows utility operators to automatically redirected

current by altering the impedance of a line or transformer. This capability utilises phase angle regulating

transformers or flexible AC transmission system devices, which typically include series or shunt

compensation.

Benefit Calculations

Benefit

Reduced Transmission and Distribution Line Losses:

(annual energy)*(% resistive losses)*(% decrease in line loss factor)*(avoided energy costs)*(% deployment)

Reduced Transmission and Distribution Line Loss Coincident with Peak:

(annual demand)*(% peak resistive losses)*(% decrease in line loss factor across all hours)*(avoided demand costs)*(%

deployment)

Reduced Carbon Dioxide Emissions

(reduced transmission and distribution Line Losses)*(generation emissions intensity)*(emissions cost)

Reduced Pollutant Emissions


Assets

Key Assets

Phase angle regulating transformers

Static synchronous compensator

Static VAR compensator

B.2 Advanced Metering Infrastructure (AMI)

Definition

The automated meter reading capability allows utilities to read customers' meters remotely, which

reduces meter operations costs and meter reading errors that result from manual meter readings.

Additionally, with AMI, utilities may receive readings over shorter time intervals (e.g., hourly) providing

greater detail about customers' energy consumption. This helps to detect meter tampering and theft.



Benefit

Reduced meter reads

(reduction in meter read costs)*(reads/customer)*(customers)*($/read)*(% deployment)

Reduced electricity theft

(reduction in electricity theft)*(% load un-metered)*(retail rates)*(% deployment)


(avoided reads)*(km/read)*(fuel efficiency)*(emissions intensity)*(emissions cost)


(avoided reads)*(km/read)*(fuel efficiency)*(emissions intensity)*(emissions cost)

Assets

Key Assets

Advanced metering infrastructure (head end, smart meters, communications networks)

Customer information systems

Meter data management systems

B.3 Advanced Metering Infrastructure, Enhanced (AMI Enhanced)

Definition

The enhanced variant of automated meter reading and billing builds on the benefits captured through the

AMI Standard capability. Utilities achieve this capability by integrating the meter data available through

AMI with systems such as the outage management, distribution management, and billing. Some of the

benefits include:

Improved outage management through a decrease in the duration of outages;

Extension of distribution equipment life through the detection of equipment overload;

A decrease in outage and regular service call volume resulting in improved customer satisfaction;

and

A decrease in restoration cost as a result of deferred outage and field service trips.



Benefit

Reduced Frequency and Duration of Sustained Outages on Distribution Grid:

(avoided sustained outages)*(fixed & variable interruption costs)*(customers)*(% deployment)

Extended Life of Existing Grid Assets - Distribution

(increase in distribution equipment life)*(distribution equipment life) = incremental life

The incremental life is discounted and adjusted to determine the annuitized value of life savings, and then multiplied by %

deployment.

Reduced Cost of Service Restoration

(% outage trips/customer)*(customers)*(avoided outage trips) ]*(% deployment)*(cost of worker hours) +

(% service trips/customer)*(customers)*(decrease in service trips)*(% deployment)*(cost of worker hours)

Reduced Call Volume – Improved Customer Satisfaction

(% interruption-calling customers/ customer base)*(customers)*(SAIFI)*(% decrease in call volume)*(cost of call handling)*(%

deployment) +

(% regular-calling customers/customer base)*(customers)*(% decrease in regular call volume)*(cost of call handling)*(%

deployment)

Assets

Key Assets

Advanced metering infrastructure (head End, smart meters, communications networks)

Customer information systems

Meter data management systems

Outage management systems

Web portals

B.4 Automated Reactive (or VAR) Power Control

Definition

The current technique to implement automated reactive power control (or conservation voltage

reduction) is open-loop reduction without reactive power feedback using a device such as a capacitor

bank. The installation of AMI has led many utilities to implement closed-loop automated reactive power

control. This capability is often deployed in parallel to automated voltage control, and is used to improve

the power factor of feeders, reduce line losses, and better manage voltage levels along feeders.



Benefit




(annual demand)*(% peak resistive losses)*(% decrease in peak line loss factor)*(avoided demand costs)*(% deployment)

Reduced Cost of Manual Distribution Switching

(cap banks/feeder)*(feeders)*(switching ops per cap)*(% decrease in cap switching)*(cost of manual switching)*(%

deployment)


(avoided cap switch ops)*(km/switch)*(fuel efficiency)*(fuel intensity)*(emissions cost) +


Reduced Pollutants

(avoided cap switch ops)*(km/switch)*(fuel efficiency)*(fuel intensity)*(emissions cost) +

(reduced Transmission and Distribution Line Losses)*(generation emissions intensity)*(emissions cost)

Assets

Key Assets

Capacitor banks

Capacitor bank controllers

Automated VAR control software

B.5 Automated Real-Time Load Transfer

Definition

In places that may have more than one distribution feeder in the area, circuits may be switched and

electrical feeds rerouted to make the distribution more efficient or more reliable. This capability allows

for real-time feeder reconfiguration and optimisation to relieve load on equipment, improve asset

utilisation, improve distribution system efficiency, and enhance system reliability.


Benefit

Reduced Transmission and Distribution No Load Losses:





(avoided sustained outages)*(average cost of sustained outage service restoration)*(decrease in restoration costs)*(%

deployment)


Assets

Key Assets

Load tap changer controller

Voltage regulator (and controller)

Capacitor bank (and controller)

Automated switches

B.6 Automated Voltage Control

Definition

The current technique to implement automated voltage control (or conservation voltage reduction) is

open-loop reduction without voltage feedback using a device such as a load-tap-changer. These

approaches ultimately optimise reductions of voltage levels along distribution feeders in order to create a

reduction in electricity usage and demand by end users. The installation of AMI has led many utilities to

implement closed-loop automated voltage control, which integrates voltage reads from smart meters into

the logic of voltage controllers.


Benefit

Reduced End Use Consumption

(energy reduction)*(annual energy consumption)*(avoided energy costs)*(% deployment)

Reduced Transmission and Distribution Line Losses

(Reduced End Use Consumption)*(% of resistive line losses)*(avoided energy costs)*(% deployment)

Reduced End Use Peak Load

(peak reduction)*(annual peak load)*(avoided demand costs)*(% deployment)


(Reduced End Use Peak Load)*(peak resistive line loss factor)*(avoided energy costs)*(% deployment)


(Reduced End Use Consumption)*(generation emissions intensity)*(emissions cost)



Assets

Key Assets



Automated voltage control software

Multipurpose distribution circuit sensor


B.7 Distributed Energy Resources Monitoring and Control

Definition

Advanced monitoring and forecasting, and control systems can help to make distributed energy

resources more predictable and reliable. This may include mitigation of issues, such as voltage sag,

associated with intermittent renewable generation. These systems leverage power electronics to improve

inverter efficiency, optimise voltage output for maximum power tracking, and handling of harmonics

issues. Additionally, it may include enhanced prediction/automation of demand response resources.


Benefit

Reduced Renewable Integration Cost

(% decrease in renewable integration cost)*(renewable integration cost)*(% deployment)

Increased Renewable Capacity Factor

(% increase in capacity factor)*(baseline capacity factor)*(installed cost of firm renewable capacity)*(% deployment)


(Increase in renewable generation)*(generation emissions intensity)*(emissions cost)

Reduced Pollutants Emissions

(Increase in renewable generation)*(generation emissions intensity)*(emissions cost)

Assets

Key Assets



Capacitor bank (and controller)

Controllable/regulating inverter

Distributed energy resource management system and interface

B.8 Dynamic Capacity Rating

Definition

Utilities and suppliers base power equipment capacity ratings on thermal limits from current-induced

heating, but actual capacity can vary significantly due to variables such as ambient air temperature and

wind speed. Dynamic ratings can reduce the risk of overestimating actual capacity from relying on static

seasonal data to establish line ratings. This capability increases the utilisation of transmission and

distribution assets during the majority of the time when static ratings underestimating actual capacity.


Benefit

Increased Transmission and Distribution Capacity Utilisation

(baseline capacity)*(% increase in capacity utilisation)*(avoided capacity costs)*(% deployment)


Assets

Key Assets

Multipurpose distribution circuit sensor

Dynamic capacity rating software

B.9 Critical Peak Pricing

Definition

Critical peak pricing involves the introduction of time-varying rates which act as signals that should be

sufficiently strong to induce a demand response among customers. Critical peak pricing derives it

benefits from an expected consumer price elasticity. Critical peak pricing benefits include a reduction in

electricity consumption and a reduction in demand during peaking hours. A snapback effect is often

associated with critical peak pricing. Downstream benefits include a reduction in line losses and

emissions.


Benefit













Assets

Key Assets

All assets are leveraged from previously installed capabilities

B.10 Electric Vehicle Integration and Control

Definition

Electric vehicle integration and control strategies can both mitigate issues for grid operation and provide

value to end users. For example, smart chargers can help to manage the additional energy consumption

of electric vehicles on constrained grids by charging at night when energy demand and prices are low,

thus reducing pressure on the grid and saving consumers money (when time of use rates are used). In

some cases, electric vehicle control strategies would operate batteries as distributed energy storage

devices by supplying electricity back to the grid during peak hours.



Benefit





Reduced Spinning Reserve Cost:

(annual energy)*(% required for 10S OR)*(% decrease in 10S prices)*(10S prices)*(% deployment)

Reduced Renewable Integration Cost

(% decrease in renewable integration cost)*(renewable integration cost)*(% deployment)

Assets

Key Assets

Electric vehicle interface

Vehicle-to-grid infrastructure

B.11 Energy Storage System Integration and Control

Definition

Energy storage system integration and control technologies enable seamless integration with the grid by

minimising disturbances while maximising the value of the system. Energy storage systems are used to

mitigate the impacts of intermittent resources on the grid, defer the need of upgrades in capacity-

constraint areas, and to provide ancillary services. Integration and control systems may include sensors,

protective hardware, communications equipment, and control software.


Benefit





Reduced Regulation Cost:

(annual energy)*(% required for regulation)*(% decrease in regulation prices)*(10S prices)*(% deployment)

Reduced Spinning Reserve Cost:

(annual energy)*(% required for 10S OR)*(% decrease in 10S prices)*(10S prices)*(% deployment)

Reduced Renewable Integration Cost:

(% decrease in renewable integration costs)*(renewable integration costs)*(renewable electricity production)*(% deployment)


Assets

Key Assets

Distribution-sited storage device

Distributed energy resource management system

B.12 Enhanced Fault Prevention

Definition

Enhanced fault protection uses high-resolution sensors to precisely detect faults that may be difficult to

locate and can address them without full power reclosing, which can damage equipment over time.

Traditional distribution protective devices, such as relays, require high fault currents for activation and

therefore may not respond quickly to faults with insufficient current.


Benefit





deployment)

Extended Life of Existing Grid Assets:



deployment.

Assets

Key Assets

Outage management system

Fault sensor

Fault current limiter

Automated switches

B.13 Fault Current Limiting

Definition

This capability uses fault current limiters, which insert electrical resistance between sources of fault

current, to prevent damage to transmission and distribution equipment from short circuits. Short circuits

result in high currents that can stress transmission & distribution equipment, causing abrupt failure or

accelerated degradation over time.



Benefit




deployment.

Assets

Key Assets

Fault sensor

Fault current limiter

B.14 Green Button

Definition

In the context of smart grid, end-use behavioral change refers to electricity customers' avoided energy

consumption in response to smart grid-enabled data and information (e.g., consumption feedback,

targeted marketing, etc.) through the use of innovative applications and solutions that optimise energy

consumption. Green Button allows customers to access and share electricity data in a standardised

format. This provides customers with access to innovative applications, products, services, and solutions

that can help customers conserve energy and better manage electricity bills.


Benefit













Reduced Program Administration Cost

(% decrease in DSM program costs)*(DSM program costs)(% deployment)


Assets

Key Assets

Demand My Data standard development

Connect My Data web portal and apps

B.15 Microgrids (Automated Islanding and Reconnection)

Definition

Automated islanding and reconnection senses conditions on the grid and microgrid to sense when to

disconnect (isolate) the microgrid from the macrogrid at the interconnection to operate independently

and when to reconnect with the grid to operate in parallel. This capability can provide greater reliability

for the grid and the microgrid. In addition, the microgrid can operate under different conditions when

islanded in order to protect equipment and maintain operation of critical loads.


Benefit

Reduced End Use Peak Load:




Reduced Non-Spinning Reserve Cost:

(annual energy)*(% required for 10N OR)*(% decrease in 10N prices)*(10N prices)*(% deployment)

Reduced Frequency of Momentary Outages on Distribution Grid:

(avoided momentary outages)*(fixed customer interruption costs)*(customers)*(% deployment)





deployment)

Assets

Key Assets

Electric vehicle interface

Fault location, isolation, and service restoration software

Automated switches

Microgrid controllers and technologies

Automated recloser switches


B.16 Notification of Equipment Condition

Definition

This capability is the on-line monitoring and analysis of equipment, its performance, and its operating

environment to detect abnormal conditions (e.g., high number of equipment operations, temperature, gas

production, or vibration). Manual testing and maintenance of large amounts of equipment can be

expensive and may fail to identify critical issues prior to failure. Remote monitoring enables the

equipment to notify asset managers and operations automatically to respond to a condition that increases

a probability of equipment failure.


Benefit




deployment.

Assets

Key Assets

Distribution management system

Capacitor bank condition sensor

Transformer condition sensor

Voltage regulator condition sensor

B.17 Self-Healing Grid

Definition

Fault location, isolation, and service restoration (sometimes referred to by the acronym FLISR) utilises

sensors, controls, switches, communication systems. In the event of a fault, a self-healing grid re-

configures feeder circuits to isolate a fault, and deliver power to the un-faulted sections of feeder by

transferring their load to un-faulted feeders. Self-healing grids enable a much faster restoration of power

to customers by performing switching operations automatically instead of dispatching a field crew to

carry out manual operations.


Benefit

Reduced Frequency of Momentary Outages on Distribution Grid:

(avoided momentary outages)*(fixed customer interruption costs)*(customers)*(% deployment)





deployment)


Assets

Key Assets

Fault location, isolation and service restoration software

Automated sectionalising switches (line/tie)

Automated switches

Microgrid controllers and technologies

Automated recloser switches

B.18 Time of Use Pricing

Definition

Time of use prices are those that vary by the time of day or season. Time of use pricing derives its

benefits from an expected consumer response to variation between electricity prices in different periods.

The largest system benefits from time of use pricing are a reduction in electricity consumption, and a

reduction in demand during peak hours. Downstream benefits include a reduction in transmission and

distribution line losses, and a reduction in carbon dioxide and other pollutant emissions (as a result of

reduced energy generation emissions).


Benefit













Assets

Key Assets

All assets are leveraged from previously installed capabilities

Ontario Smart Grid Assessment and Roadmap Page C-1

Appendix C. Detailed Assumptions

C.1 Grid Characteristics

The analysis reflects over 100 grid characteristics specific to the electricity system in Ontario. Figure 46

presents a selection of these used in the analysis over the 2015 to 2020 period.

Figure 46: Sample of Grid Characteristics

Characteristic 2015 2016 2017 2018 2019 2020

Residential customers 4,619,340 4,683,395 4,746,955 4,812,291 4,878,385 4,943,386

Commercial customers 438,676 444,759 450,795 457,000 463,276 469,449

Industrial customers 58,114 58,920 59,719 60,541 61,373 62,190

Transmission utilities 3 3 3 3 3 3

Distribution utilities 73 73 73 73 73 73

Km. of distribution line 198,817 199,757 200,689 201,648 202,618 203,572

Number of feeders 11,017 11,069 11,121 11,174 11,228 11,281

Distribution substations 2,130 2,140 2,150 2,161 2,171 2,181

Reserve margin 18.3% 18% 18.6% 20% 20% 20%

Renewable capacity 7,442 8,330 8,697 9,338 10,162 10,699

Energy forecast (TWh) 144.6 146.9 146.9 149.1 152.4 155.0

Peak forecast (MW) 24,275 24,579 24,665 25,024 25,511 25,805

Sources: Long Term Energy Plan, Ontario Energy Board Electricity Distributor Yearbooks, Navigant’s distributor

questionnaire, other Navigant analysis

C.2 Benefit Valuation Parameters

This analysis evaluates over 30 types of benefits. The sections below summarise critical assumptions that

the framework uses to derive a monetary value from a system impact.

Energy Costs

Energy benefits arise as a result of reductions in electricity usage, reductions in line losses, and avoided

electricity congestion. The valuation of these benefits is determined from the avoided cost of energy and

the avoided cost of re-dispatched energy.


Figure 47: Energy Cost Benefit Valuation Parameters

Sources: Independent Electricity System Operator, Long Term Energy Plan, Navigant analysis

Capacity Costs

Capacity benefits arise as a result of reductions in peak demand, increased utilisation of transmission and

distribution infrastructure, and reductions in reserve margin. The values of avoided generation,

transmission, and distribution capacity are used to characterise these benefits.

Figure 48: Capacity Costs Benefit Valuation Parameters

Source: OPA CDM Cost Effectiveness Guide

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Value of Lost Load

The framework uses value of loss load to monetise improvements in reliability. Figure 49 presents the fixed

(cost per outage) and variable (cost per outage-hour) values for the industrial and residential classes used

in the analysis.

Figure 49: Value of Loss Load Valuations

Source: Hydro One, “Approach to Smart Grid”

Ancillary Services Costs

Ancillary services benefits are monetised through forecasts of the value of regulation, and spinning and

non-spinning reserve.

Figure 50: Ancillary Services Valuation Parameters

Sources: Independent Electricity System Operator, Navigant analysis

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Regulation Cost

Non-Spinning Balancing Reserve (INC) Cost

Spinning Reserve CostNon-Spinning Reserve Cost

Ontario Smart Grid Assessment and Roadmap Page D-4

Appendix D. Smart Grid Capabilities with Promising Findings

This section presents the findings for nine specific smart grid capabilities. Each capability delivered a

benefit-cost ratio greater than one. Energy storage has also been included since the results suggest that as

the price of storage devices decreases over time the business case for storage will become positive. The

results presented are based on the deployment assumptions specific to the Baseline scenario. The

functions include:


Self-healing grids (or fault location, isolation, and service restoration)


Green Button


Microgrids


AMI, AMI enhanced, time of use, and critical peak pricing


D.1 Automated Voltage Control

Capability Overview

Utilities must maintain adequate voltage levels across their networks. Generally, utilities use voltage

regulators, load tap changers, and —wherever appropriate— capacitor banks, to maintain desired voltage

levels. With modern telecommunication technologies and advanced distribution management systems,

utilities are able to automatically track, operate, and optimise voltage levels on feeders. By automating

the process and decreasing the voltage along a distribution feeder, utilities achieve a number of

objectives. Among these are reducing electricity consumption and demand, deferring traditional

infrastructure upgrades and avoiding manual switching operations.

The assets used for automated voltage control include voltage regulators, load tap changers (and their

corresponding controllers), voltage control software, AMI, advanced distribution management systems,

and supervisory control and data acquisition systems working in a closed loop manner. Figure 51

presents a representative system diagram of a distribution network with the corresponding assets.


Figure 51: Illustrative Placement of Automated Voltage Control Assets

Source: Navigant

The voltage standard in Ontario for a single-phase residential customer allows for a range of 110 to 125

volts.49 It is usual for utilities applying voltage control practices to use a safety margin to ensure the end-

of-line voltage never falls below the range. Voltages outside of the 110 to 125 volt range can potentially

damage customer equipment.

At the customer end, many types of equipment can reduce energy consumption when supplied by

voltages closer to the lower end of the voltage range. Resistive and inductive loads will react differently

to reductions in voltage. Similarly, loads with and without a thermal cycle will also behave differently at

lower voltages.

For example, an incandescent light bulb is a simple resistive load without a thermal cycle. A decrease in

voltage translates proportionally to a reduction in the current flowing through the wire filament,

dimming the light bulb. In addition, a light bulb does not have a thermal cycle because it behaves

entirely independent from a time-variant cycle, meaning that its behavior will not change other than due

to a reduced voltage. In contrast, a water heater, though a resistive load, has a thermal cycle. At lower

voltages, a water heater will run at a lower power rating and, hence, will take longer to heat water to a

specified temperature and use more energy. In the case where an automated controller maintained the

desired power rating for a water heater, it will offset any energy savings by operating at a higher power

setting.

49 The 110 to 125 volt range is for a nominal voltage of 120V and is based on CSA Standard CAN3-C235-83


In addition to the type of load served, length and health are also important characteristics for selecting

cost-effective feeders for automated voltage control deployment. The length of the feeder could limit the

range of controllability, as the steady state voltage at one end may be significantly lower than the steady

state voltage at the other end. Reconditioning investments on a feeder with poor health could make the

investment less cost-effective.

Distributors can deploy voltage control practices in a number of ways to achieve energy savings and peak

load reductions. The discussion below considers two types of automated voltage control practices:

Optimised automated voltage control: Optimised voltage control is a more advanced type of

control enabled through the use of two-way communications and automated controls. This is a

more dynamic and actively controlled form that enables utilities to lower line voltage with the

goal of reducing energy consumption and peak demand in parallel.

Dispatchable automated voltage control: Utilities activate dispatchable automated voltage

control only under peak load conditions and exclusively to reduce peak system loading. Utilities

employ dispatchable voltage control as a demand response mechanism. This is a valuable

resource for deferring capacity expansions and upgrades.

Deployment and Impact Assumptions

Navigant modelled the deployment shown in Table 10 below. The model deploys both types of

automated voltage control to an equal number of distribution feeders. In 2035, Navigant assumed that

12% of all feeders (approximately 1,400 feeders) in the province will be equipped for automated voltage

control.

Table 10: Deployment Figures for Automated Voltage Control

Function 2020 2035

Dispatchable 320 feeders 695 feeders

Optimised 320 feeders 695 feeders

Total 640 feeders (~6% of feeders) 1,390 feeders (~12% of feeders)

Source: Navigant

Navigant assumed that optimised voltage control reduces energy consumption by 2.5% and peak

demand by 2%.50 Navigant assumed that dispatchable voltage control reduces peak demand by 3% and

has no effect on energy consumption.51

Early deployments of automated voltage control capabilities will likely target attractive, healthy feeders

with large loads and high power factor. These early deployments will yield a larger impact than

deployment to an average feeder. In contrast, feeders with poor health will likely require significant

50 Northwest Energy Efficiency Alliance. December 2007. “Distribution Efficiency Initiative.” Pacific Northwest

National Laboratory. January 2010. “The Smart Grid: An Estimation of the Energy and CO2 Benefits”, US

Department of Energy. December 2012. “Application of Automated Controls for Voltage and Reactive Power

Management – Initial Results.” and Pacific Northwest National Laboratory. July 2010. “Evaluation of

Conservation Voltage Reduction (CVR) on a National Level.” 51 Navigant analysis of Northwest Energy Efficiency Alliance 2007


investment to be attractive for deployment. As such, Navigant models the impact of early deployments

to be greater than the impact from the later deployments (see Figure 52). Also shown is the impact curve

representative of an automated voltage control program evaluation (or conservation voltage reduction)

evaluation in the United States.52 The Ontario curve represents Navigant’s estimate for the province, and

the dashed line represents a strictly proportional relationship. Relative to the U.S. curve, Navigant

adjusted the Ontario curve to reflect a more conservative assumption about the timing of benefits relative

to costs.

As shown in Figure 52, deployment to the initial 30% of feeders in Ontario captures approximately 50%

of the potential benefit, whereas the last 30% (from 70% to 100%) captures less than 20% of the potential

benefit.

Figure 52: Automated Voltage Control Deployment Impact Curve

Sources: Pacific Northwest National Laboratory, Navigant

Results

Figure 53 shows the annual benefits and costs. Nearly 70% of the benefits arise from reduced capacity

expansion. Utilities have generally considered automated voltage control as an investment to reduce

energy consumption. However, the expected benefits show that although energy benefits are significant,

capacity benefits are larger, accounting for over 70% of all benefits.53 The results show that deployment

of dispatchable voltage control, which would be required only during peaking periods, is more cost-

effective than optimised voltage control. However, optimised voltage control, which would deliver a

reduction in energy use, may be a cost-effective way for utilities to meet energy efficiency targets.

52 Pacific Northwest National Laboratory estimated the incremental benefit associated with increased deployment

across the United States. The report determined that conservation voltage reduction deployment to 40% of

feeders would capture 80% of the potential benefit. 53 Without energy benefits voltage control investment still has an expected benefit-cost ratio of 1.0.

0%

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United StatesOntario


An approach that combines voltage control as a dispatchable resource during peak periods and optimises

energy reduction impacts during the remainder of the year creates an ideal deployment scenario for

automated voltage control.

Figure 53: Annual Benefits and Costs of Automated Voltage Control Deployment through 2035


Navigant estimates that investments in automated voltage control capabilities will have a benefit-cost

ratio of 3.9 with a net present value of $405 million (Figure 54). The best- and worst-case scenarios for the

net present value—both yielding positive results—are $500 million and $288 million, respectively.

Figure 54: Net Present Value of Automated Voltage Control Deployment through 2035


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Navigant estimates that the present value of the benefits and costs will be $545 million and $141 million,

respectively. The distribution below shows a large range around the valuation of benefits. The

uncertainty around benefits is associated with the expected reduction in energy consumption and

reduction in peak demand.54 For example, generally reported values for peak reduction are 2%.

However, Pacific Northwest National Laboratory’s evaluation determined that the impact may vary from

0.5% to 4.0% based on a number of factors such as the base voltage, peak load and customer mix.

Navigant’s estimate of the costs ranges from $102 million to $174 million, with a smaller level of

uncertainty.55

Figure 55: Present Value of Benefits and Costs of Automated Voltage Control Deployment through

2035


Figure 56 shows the expected distribution of benefits and costs across the industry segments. Although

from a system-wide perspective automated voltage control is an attractive investment, benefits and costs

do not distribute evenly across the segments. Notably, the distribution segment carries all of the costs,

and only a small fraction of the benefits.

54 Navigant assumes a +/- 50% uncertainty range around the impacts from energy savings and peak reduction. A

Pacific Northwest National Laboratory (ref. no. 19596) evaluation on 24 prototypical feeders representative of all

distribution feeders in the United States determined that the estimated peak reduction varies from 0.5% to 4.0%,

based on a number of feeder characteristics. 55 Costs are much better understood because this function primarily consist of voltage regulators, load tap changes,

distribution management systems, and supervisory control and data acquisition systems, all of which have costs

that are relatively well understood.


Figure 56: Distribution of Benefits and Costs from Automated Voltage across Industry Segments


D.2 Self-Healing Grids

Capability Overview

Self-healing grids utilise sensors, controls, switches, and communication systems to isolate, reconfigure,

and potentially de-energise faulted segments on the distribution systems. The industry also often refers

to self-healing grid capability as fault location, isolation, and service restoration.

Self-healing grid capabilities are an advanced component of distribution automation. In general,

distribution automation refers to a largely controllable and intelligent distribution system. Self-healing

grids result in rapid restoration of power. Customers benefit from a more reliable and resilient grid,

through reductions in the number and duration of outages. Utilities benefit from avoided restoration and

switching operations costs.

Utilities across many jurisdictions have deployed various configurations of self-healing capabilities.

Utilities may pursue two different types of operating schemes: remotely controlled operations, which

require validation from an operator, or fully automated control. Systems requiring manual validation

typically lag in response time. 56

Self-healing grids incorporate hardware and software, telecommunications, and grid assets. The grid

assets include automated re-closer switches, sectionalising switches, fault sensors, automated circuit

breakers, and digital protective relays. Additionally, other centralised control requirements include;

controller software, two-way communications infrastructure, distribution management systems, outage

56 U.S. Department of Energy, “Fault Location, Isolation, and Service Restoration Technologies Reduced Outage

Impact and Duration”, December 2014, and “Reliability Improvements from the Application of Distribution

Automation Technologies – Initial Results”, December 2012.

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management systems, advanced metering, and supervisory control and data acquisition systems. Figure

57 contains a representative system diagram of a distribution network with the corresponding assets

required for self-healing grids.

A self-healing system is triggered when a fault occurs, commonly caused by natural events or equipment

failure. The fault is located using fault sensors which then communicates this information to the central

control server. The control system may then trigger switches to open upstream and downstream from

the fault, to isolate the fault successfully. If the feeder circuit topology allows sectionalising switches may

transfer load from the un-faulted, de-energised sections of the faulted feeders to healthy feeders supplied

from neighboring substations. Eventually, only loads served by the faulted section of the feeder remain

de-energised.

In practice, self-healing operations may not be as clear as the example above. For example, radial circuits

connected to a single substation will not be able to transfer un-faulted sections to another feeder.

Additionally, networked feeder circuits may not necessarily be able to transfer loads to working-feeders

if the power source is unable to meet load requirements.

Figure 57: Illustrative Placement of Self-Healing Grid Assets

Source: Navigant

The largest driver for the adoption of self-healing grid capabilities is the prospect of improving reliability.

Utilities track and measure grid reliability through a number of reliability indices. Navigant’s benefit-

cost framework reflects the impacts from both sustained and momentary outages.


The following three indices are inputs to the model:

System Average Interruption Frequency Index (SAIFI): Used to measure the average number of

sustained outages experienced by customers. Sustained outages generally last longer than one

minute, although the exact duration threshold is dependent upon utility practices. The Ontario

Energy Board requires utilities to track SAIFI.

Customer Average Interruption Duration Index (CAIDI): Used to measure the average duration

of sustained outages experienced by customers. The Ontario Energy Board requires utilities to

track CAIDI.

Momentary Average Interruption Frequency Index (MAIFI): Used to measure the average

number of momentary outages experienced by customers. Momentary outages last less than one

minute. The Ontario Energy Board does not require utilities to track MAIFI.57

Evaluation results from U.S. Department of Energy Smart Grid Investment Grant funded projects provide

an indication of the impact of self-healing grid capabilities on system reliability.58 Remote-switching

projects saw a 35% decrease in the number of customers interrupted, whereas automated-switching

projects saw a 55% reduction. Similarly, the impact on customer-minutes of interruption was 47% and

53%, respectively. Utilities measured large improvements in grid reliability, and although self-healing

capability deployment will realise significant benefits, the result is highly dependent on a number of

factors—for example, the frequency of severe weather events, customer densities, grid infrastructure and

resilience, and the location and number of installed switches, which may result in a partial or full feeder

outage.


Navigant modelled the deployment shown in Table 11 below.

Table 11: Deployment Figures for Self-Healing Grids

Function 2020 2035

Total 1,000 feeders 2,900 feeders

Source: Navigant

Navigant assumed that self-healing grid capabilities reduce MAIFI by 8%, SAIFI by 19%, and CAIDI by

21%. Navigant also assumed a 35% reduction in service restoration costs for distributors. These impacts

are summarised in Table 12.

57 Although the Ontario Energy Board does not require distributors to track MAIFI, a number of them do so for

internal purposes. Navigant estimated the provincial average from a sample of MAIFI indices for distributors

representing 50% of customers in the province. 58 U.S. Department of Energy 2014.


Table 12: Self-Healing Grid Impacts

Metric Reduction

MAIFI59 8%

SAIFI60 19%

CAIDI42 21%

Service restoration costs61 35%

Sources: See footnotes; Navigant analysis

From a provincial perspective, Navigant expects that distributors will initially target critical high-density

industrial and commercial areas, and areas serving critical loads such as hospitals and transit hubs. In

contrast, small towns and low-density suburban areas are less attractive.

Navigant estimates that the deployment level in 2020 will yield approximately 10% of potential benefits.

Over the following five years, through 2025, deployment will takes place in larger metropolitan and

economic centres and an additional 17% of potential benefits are realised. By 2035, deployment captures

approximately 29% of benefits.

Early deployments of self-healing grids in the United States have realised significant benefits to

customers. Coincidentally, early deployments have presented utilities with operational and system

integration challenges. Before deployment across the wider grid, extensive operational experience with

self-healing functionality will be required. Once utilities develop operational expertise, deployment will

reach larger critical loads.

59 California Energy Commission. March 2009. “The Value of Distribution Automation,” and Navigant analysis. 60 California Energy Commission, 2009.

Illinois Commerce Commission. January 2011. “Evaluating Smart Grid Reliability Benefits for Illinois”

Navigant Research. 2013. “Distribution Automation.” 61 NSTAR 2010


Figure 58: Self-Healing Grid Realised Benefits over Time

Source: Navigant

Results

Figure 59 shows Navigant’s estimate of the annual benefits and costs associated with self-healing grid

capability deployment through 2035. A total of 90% of benefits relate to improved reliability. The

balance relates to a reduction in the cost of service restoration.

Figure 59: Annual Benefits and Costs of Self-Healing Grid Deployments through 2035


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Based on Navigant’s assumptions above, self-healing grid capabilities could reduce the provincial

average of CAIDI and SAIFI by 6% and 2.5%, respectively. The impact on MAIFI is more modest, at 2%

reduction. These values are reflective of a provincial average, and it is not to say that that all customers

will see a reduction of this magnitude. Customers served by feeders with self-healing capabilities may

experience a 20% reduction in the number of outages, whereas other customers served by feeders without

self-healing capabilities will see no impact.

Navigant estimates that the deployment of self-healing grid capabilities in Ontario will have a benefit-

cost ratio of 5.1 and a net present value of $3.6 billion (Figure 60). Navigant estimates that the present

value of the benefits and costs will be $4.5 billion and $0.8 billion, respectively (Figure 61).

Figure 60: Net Present Value of Self-Healing Grid Deployment through 2035



Figure 61: Present Value of Benefits and Costs of Self-Healing Grid Deployments through 2035


The wide range around benefits is due to the uncertainty around the reduction in momentary and

sustained outages. While the model uses an expected regional average, at a utility level, the range of

improvements in reliability will vary significantly across feeder circuits. A number of factors influence

the degree of reliability improvements. These include: feeder health, circuit configuration (radial, looped,

networked), the number and load of customers served, the number of switches, seasonal weather

patterns, and localised conditions. In addition, successful load-transfer operations are dependent on the

ability of a neighboring feeder to carry the additional load. Severely capacity-constrained circuits will not

capitalise from automated switching. Such systems may require substation upgrades to condition them

for self-healing deployment.

Figure 62 shows the distribution of benefits and costs across the industry segments. Although the

business case for a self-healing grid is attractive, the distribution of benefits presents a challenge for

distributors. The distribution segment carries all of the costs but only a disproportionately small fraction

of benefits.


Figure 62: Distribution of Benefits and Costs from Self-Healing Grid across Industry Segments


D.3 Enhanced Fault Prevention

Capability Overview

Enhanced fault prevention relies on advanced distribution technologies that increase the ability to avert

faults across the distribution system, reflecting a preventative approach to system monitoring and

awareness. Enhanced fault prevention leverages the use of fault current limiters, fault sensors, digital

relays, automated breakers, automated switches, and communications systems.

Enhanced fault prevention, as the name suggests is preventative and avoids the occurrence of faults and

interruptions (although some post-fault operations may also be required).

The combination of digital relays, communications systems, fault sensors, and fault current limiters

provides utilities an opportunity to reduce the number of outages on their systems. For example, fault

current limiters are able to dynamically increase their impedance and as a result limit the amount of

current flowing through the system. During normal system operating conditions fault current limiters

operate with low or negligible electrical impedance. In the event of a high-current fault, the fault current

limiter controllers trigger the device to rapidly increase its impedance and limit the amount of current

flowing through the distribution system, thereby preventing damage to equipment.


Feedback from utilities suggests the use of fault prevention for outage avoidance is growing among a

limited number of utilities, although absent for the rest of utilities. Despite this, responses to the

distributor questionnaire suggest significant adoption figures within this group of select utilities.

Navigant modelled the deployment, shown in Table 13 below.

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Table 13: Deployment Figures for Enhanced Fault Prevention

Function 2020 2035

Enhanced fault prevention 1,100 feeders 1,500 feeders

Source: Navigant

Navigant assumed that enhanced fault prevention capabilities reduce SAIFI by 11%. Navigant also

assumed a 9% reduction in service restoration costs for distributors. These impacts are summarised in

Table 14.

Table 14: Enhanced Fault Prevention Impacts

Metric Reduction

SAIFI62 11%

Service restoration costs63 9%

Sources: See footnotes; Navigant analysis

Navigant assumed that the realised benefits will be almost proportional to annual deployment figures.

Results

Figure 63 shows the annual benefits and costs for the full deployment timeframe. A total of 85% of

benefits relate to improved reliability. The balance relates to a reduction in the cost of service restoration.

Figure 63: Annual Benefits and Costs of Enhanced Fault Prevention Deployments through 2035


62 California Energy Commission 2009, and Navigant analysis 63 NSTAR 2010

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Navigant estimates that the deployment of enhanced fault prevention in Ontario capabilities through

2035 will have a benefit-cost ratio of 3.0 and a net present value of $457 million (2014 $). Navigant

estimates that the present value of the benefits and costs will be $685 million and $228 million (2014$),

respectively.

Figure 64: Net Present Value of Enhanced Fault Prevention Deployments through 2035


Figure 65 shows the distribution of the present value of benefits and costs. Benefits vary significantly as a

result of uncertainty surrounding the impact of investments on reliability and the value of loss load for a

given customer mix, primarily since deployment may be limited to a small group of utilities.


Figure 65: Present Value of Benefits and Costs of Enhanced Fault Prevention Deployments through

2035


This capability shares a number of similarities with the self-healing grid capability. Customers are the

largest beneficiary, and the distribution segment is only credited a small fraction of the benefits, arising

from avoided service restoration operations and extended equipment life. Figure 66 shows the

distribution of the present value of the benefits and costs across the industry segments.

Figure 66: Distribution of Benefits and Costs from Enhanced Fault Prevention across Industry

Segments


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D.4 Green Button

Capability Overview

Green Button allows customers to access and share electricity data in a standardised format. This

provides customers with access to new applications, products, services, and solutions that can help

customers conserve energy and better manage electricity bills.

The Green Button initiative was launched in 2012 as a partnership between the Ministry of Energy and

the MaRS Discovery District. The initial phase of the Green Button initiative, called Download My Data,

enables customers to download their electricity usage from their distributor’s website. Approximately

60% of the Ontario’s electricity customers have access to the Download My Data service. The next phase,

called Connect My Data, allows customers to securely share their electricity consumption data with web

or mobile apps. Currently, London Hydro and Hydro One are conducting pilots that introduce Connect

My Data services to their customers.


Navigant has modeled the deployment of the Green Button capability as the roll out of the Connect My

Data services. The inherent assumption is that the availability of Connect My Data services follows the

Download My Data roll out. The impact assumptions are exclusively based on Connect My Data

services. In addition, the impacts are evaluated based on the fraction of customers that actively employ

Green Button services as opposed to total availability. The model assumes that 10% of the customer base

with availability will actively use Green Button. While this may be considered an aggressive assumption,

the ultimate driver of benefits is the actual number of customers that use Green Button, as shown in the

last row Table 15. The deployment assumptions are listed below:

Table 15: Deployment Figures for Green Button

Deployment 2015 2020 2035

Availability (%) 3.8% 29.5% 30%

Usage (%) 0.38% 2.95% 3%

Availability (customers) 190,000 1,600,000 1,800,000

Usage (customers) 19,000 160,000 180,000

Source: Navigant

The modeling assumptions are based on the availability of Green Button to residential and general

service <50 kW customers. The impact assumptions are in Table 16:

Table 16: Green Button Impacts

Benefit Residential GS<50kW

Electricity consumption 1% reduction 2% reduction

Demand 3% reduction 3% reduction

Source: Navigant


In addition, there is a corresponding benefits stream that arises from transmission and distribution losses,

avoided emissions, and a reduction of 1% in the program administration costs of the province’s

conservation program.

Results

Figure 67 shows the annual costs and benefits. As shown, approximately half of all benefits are derived

from the value of avoided capacity. This is as a result of the assumption of a 3% demand reductions for

customers.

Since the Green Button capability leverages some of the existing AMI, a fraction of the annual costs

attributed to Green Button (approximately 40%) are derived from incremental operations and

maintenance costs of AMI. While the attribution of AMI costs to Green Button may not be realistic, the

model assumes that any capability that leverages a given asset is responsible for a fraction of the asset’s

costs. The model attributes a fraction of costs equivalent to the number of assets required for that

particular capability divided by the cumulative number of assets required by all capabilities.

Figure 67: Annual Benefits and Costs of Green Button Deployment through 2035


Navigant estimates that the deployment of Green Button in Ontario will create a benefit-cost ratio of 3.3

and deliver a net present value of $95 million (2014 $), and may range from $166 million to $29 million.

Given the early maturity and pilot stage of Green Button in Ontario, the degree of uncertainty is large, as

is shown by the benefit’s frequency distribution curve (shown in Figure 68). Benefits are expected to

range from $207 million to $80 million. Given the degree of uncertainty around benefits, as well as

around costs, it is important to understand the underlying assumption of the Navigant model and how

those impact the overall business case of Green Button. As discussed above, cost sharing among

capabilities is one such assumption.

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Figure 68: Net Present Value of Green Button Deployment through 2035


Figure 69: Present Value of Benefits and Costs of Green Button Deployment through 2035


Figure 70 shows the distribution of costs and benefits across each segment of the electricity sector.


Figure 70: Distribution of Benefits and Costs from Green Button across Industry Segments


D.5 Dynamic Capacity Rating

Capability Overview

Utilities and suppliers establish power equipment capacity ratings based on thermal limits from current-

induced heating, but actual capacity can vary significantly due to variables such as ambient air

temperature and wind speed. Dynamic ratings use real-time sensing equipment to monitor line

conditions and to dynamically rate equipment capacity. This capability can reduce the risk of

overestimating and underestimating actual capacity from relying on static seasonal or annual data to

establish line ratings.

This capability increases the utilisation of transmission and distribution assets when static ratings

underestimate actual capacity and can be used to unlock capacity and reduce congestion on the grid. To

date, this capability has generally targeted transmission lines as a result of greater scale as supposed to

distribution networks where the costs for deploying sensing equipment across feeders and laterals is not

justified.

Figure 71 provides an illustration of the potential benefits of dynamic line rating. The orange area reflects

the available capacity based on a static rating of 85 megavolt amperes. The red area represents the risk

that the static rating overestimates the real-time rating. The green area represents the available, and

currently unused, capacity. In this example, during 60% of the time the available capacity would be

approximately 135 megavolt amperes. This represents an increase in capacity of 50 megavolt amperes, or

nearly 60%.

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Real deployments for transmission systems have resulted in increases in capacity utilisation in the range

of 15% to 30% for 90% of the time.64

Figure 71: Available Capacity vs. Static Rating—Frequency Graph

Source: Oncor - Dynamic Line Rating (Final Report – August 2013)


The deployment assumptions are based on the feedback from the distributor questionnaire. The

deployment assumptions are shown in Table 17 below. The model reflects deployment beginning in

2020. Despite this, there may be pilot projects or niche opportunities prior to 2020 for which dynamic

capacity ratings are highly beneficial and justify deployment.

Table 17: Deployment Figures for Dynamic Capacity Rating

Function 2020 2035

Dynamic capacity rating 0 feeders 170 feeders

Source: Navigant

The model assumes an increase in capacity utilisation of 15% during 90% of the time.

Results

Figure 72 shows the annual benefits and costs for the deployment of dynamic capacity ratings. The

benefits arise exclusively from avoided investments in capacity infrastructure. Despite this, given the

limited deployment, this application has no material impact on the overall results of the analysis.

64 The Valley Group. 2010. Dynamic Line Ratings for Optimal and Reliable Power Flow. For more information see:

https://www.ferc.gov/EventCalendar/Files/20100623162026-Aivaliotis,%20The%20Valley%20Group%206-24-

10.pdf


Figure 72: Annual Benefits and Costs of Dynamic Capacity Rating Deployments through 2035


Figure 73 shows the uncertainty analysis of the net present value. Navigant estimates that the net present

value will be $2.0 million yielding a benefit-cost ratio of 1.4, and with best and worst case scenarios of

$8.3 million and $-4.5 million, respectively. Figure 74 shows the uncertainty analysis of the benefits and

costs. Benefits are expected to be $7.9 million and costs are expected to be $5.7 million. As expected, due

to the current degree of technology maturity, the impacts and corresponding benefits are largely

uncertain.

Figure 73: Net Present Value of Dynamic Capacity Rating Deployments through 2035


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Figure 74: Present Value of Benefits and Costs of Dynamic Capacity Rating Deployments through 2035


Figure 75 show the distribution of costs and benefits across the different segments of the electricity sector.

All benefits are attributed to the distribution segments as a result of avoided infrastructure investments.

Figure 75: Distribution of Benefits and Costs from Dynamic Capacity Rating across Industry Segments


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D.6 Microgrids

Capability Overview

Microgrids use control systems to integrate loads and distributed resources; they can operate in a

connected or islanded manner providing increased resiliency and improving a distributor’s ability to

integrate distributed generation

Automated islanding and reconnection senses conditions on the grid and microgrid to sense when to

disconnect (isolate) the microgrid from the macrogrid at the interconnection to operate independently

and when to reconnect with the grid to operate in parallel. This capability can provide greater reliability

for the grid and the microgrid. In addition, the microgrid can operate under different conditions when

islanded in order to protect equipment and maintain operation of critical loads.

Each microgrid design is unique, and hence it is difficult to compare or benchmark the benefits and costs

from one project to another. For example, different types of microgrid projects might integrate

distributed generation, combined heat and power, energy storage, wind, and solar, among others. In

addition, each microgrid incorporates a distinct mix of customers: residential, commercial, industrial,

hospitals, fire departments, water treatment plants, and schools, among others; hence the value of

reliability can vary significantly. The regulatory framework in which a microgrid operates would also

determine the benefit stream available to the microgrid, the type of ownership structure, and services that

can be provided to the macro-grid or microgrid users.

The New York State Energy Research and Development Authority (NYSERDA) evaluated the feasibility

of microgrid development in New York State in a recent report.65 This work assessed the effect of a

number of factors on the valuation of microgrids; these included the regulatory structure, technical and

regulatory aspects of microgrid interconnections, the types of projects that might be implemented,

operation during emergency situations, funding mechanisms, and the current business case for

development based on feasibility studies for a number of sites. NYSERDA concluded that:

“Based on the sites analysed and modelling used, this study found that the deployment of

microgrids in support of critical infrastructure is usually not feasible based on a benefit-cost

analysis. This is primarily due to the robust backup generation available at most of the critical

facilities and the high costs of the electrical, communication and controls infrastructure of the

microgrid.

The cost-effectiveness of a microgrid improves if the system can economically operate on a more

frequent basis, rather than solely as back up generation in the event of emergencies.”

In this analysis, the cost of additional generation capacity is excluded from the benefit-costs framework,

such that if the microgrid were to require, for example, gas-fired generation or solar photovoltaics these

costs would not be included in this analysis.

65 NYSERDA. December 2014. Microgrids for Critical Facility Resiliency in New York State.



The deployment assumptions are based on the feedback from the distributor questionnaire. The

deployment assumptions are shown in Table 18 below. These assumptions do not reflect that, for

example, a particular microgrid of 20 MW in size might be connected in a particular year, but rather the

model assumes a continuous deployment curve.

Table 18: Deployment Figures for Microgrids

Application 2020 2035

Microgrids 24 MW 95 MW

Source: Navigant

Appendix A explained that the model reflects cost sharing of assets between different smart grid

capabilities such that it does not double count equipment costs. Microgrids are the only exception to this

assumption since no assets should be shared between a microgrid and a different capability.

The model reflects the following impact assumptions.

Table 19: Microgrid Impacts

Application Impact

Peak demand 10% reduction

Ancillary services 10% availability

Source: Navigant analysis

Results

Figure 76 shows the annual benefits and costs for the deployment of microgrids. Approximately 50% of

benefits arise from improved reliability. The balance of benefits is a result of reduced system costs from

improved utility operations, reduced costs of ancillary services, and avoided capacity infrastructure.


Figure 76: Annual Benefits and Costs of Microgrid Deployments through 2035



value will be $30 million yielding a benefit-cost ratio of 1.6, and with best and worst case scenarios of $46

million and $19 million, respectively. Figure 78 shows the uncertainty analysis of the benefits and costs.

Benefits are expected to be $81 million and costs are expected to be $51 million.

Figure 77: Net Present Value of Microgrid Deployments through 2035


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Figure 78: Present Value of Benefits and Costs of Microgrid Deployments through 2035


Figure 79 shows the distribution of costs and benefits across the different segments of the electricity

sector. Most benefits are credited to customers as reliability improvements.

Figure 79: Distribution of Benefits and Costs from Microgrids across Industry Segments


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D.7 Distributed Energy Resources Monitoring and Control

Capability Overview

Advanced monitoring and control systems can help to make distributed energy resources more

predictable and reliable, and enable greater levels of integration. This may include mitigation of issues

such as voltage sags/surges and harmonics that are often associated with intermittent renewable

generation. Additionally, it may include enhanced prediction/automation of demand response resources.

Monitoring and control systems provide innovative solutions that may reduce renewables integration

costs and increase their overall energy output. These systems leverage power electronics to improve

inverter efficiency, optimise voltage output for maximum power tracking, and the handling of harmonics

issues.

In Ontario, as renewables become a more significant component of the generation portfolio, there is an

increasing need to address issues related to their intermittent nature. For example, solar power plants

can lead to large instantaneous voltage surges as a result of cloud cover. These events can damage

distribution system equipment such as inverters and transformers. Similar effects can arise from wind

power. While voltage surges may occur over longer timeframes, they can carry a much greater power

shift. Small fluctuations in wind speed can result in megawatt-scale power swings.

Part of the strategy for integrating large amounts of renewables over the coming years must include the

monitoring and control of distributed energy resources. As part of this undertaking, utilities may look to

integrate weather data, sound/light sensor devices, monitoring of controllable inverters (maximum power

point tracking), and may additionally enable dynamic line monitoring in order to unlock transmission

and distribution capacity.


Figure 80 shows the build-up of renewables generation expected to connect to the transmission and

distribution network in Ontario through the 2025. The primary axis shows the penetration of wind, solar,

and biomass in megawatts, and the secondary axis shows the penetration as a percentage of total

generation capacity.

Notably, in the Long Term Energy Plan, the governments planned for over 4,000 MW of new wind

capacity between 2013 and 2025. The corresponding increase for solar is approximately 2,500 MW. At its

peak, wind will account for 16% of the provincial generation capacity. At such high penetration rates,

there is also a corresponding increase in reserve requirements and balancing costs. A number of factors

influence the need for balancing requirements, including: wind penetration, variability and distribution

of wind resources, and the degree of grid integration (through interconnections). In general, under

conditions of high wind penetration there is a greater need for system flexibility.


Figure 80: Renewables Capacity and Percentage of Total Capacity

Sources: Long Term Energy Plan, Navigant

With more widespread deployment of wind power plants, greater system flexibility is needed to

accommodate the increased frequency of ramping events and the corresponding magnitude of those

events. It follows that more dispatchable resources are required to match the intermittency of wind.

The system could accommodate the increased uptake of renewables through transmission and

distribution infrastructure expansion and increased balancing services, and/or from the adoption of

monitoring and advanced control systems.

As part of the questionnaire, Navigant asked distributors to provide the number of distributed energy

resources they anticipated to connect, monitor, and control over several timeframes. Figure 81 shows the

results.

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Figure 81: Monitored and Controlled Distributed Resource Facilities

Source: Navigant

By 2020, utilities anticipate to have control of over one-third of all distributed resource facilities. In

addition, distributors provided the following feedback:

» A number of utilities are actively looking at remote monitoring but not control

» Despite not being capable of controlling such facilities, utilities can disable larger projects in the

event of adverse impacts

» Utilities believe that future penetration of generation will ultimately be dependent upon

government policies and market conditions

Table 20 reflects the model assumptions.

Table 20: Deployment Figures for Distributed Energy Resource Monitoring and Control

Function 2020 2035

Distributed energy resource monitoring and control 195 MW 635 MW

Source: Navigant

Results

Figure 82 shows the annual benefits and costs associated with the deployment of distributed energy

resource monitoring and control capabilities through 2035. Nearly all benefits arise from improved

renewables integration and only a fraction from reduced emissions. In comparison to other smart grid

capabilities, the magnitude of the captured benefits is small.

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Figure 82: Annual Benefits and Costs of Distributed Energy Resource Monitoring and Control



Figure 83 presents the range of net present value associated with the investment. The net present value is

expected to be $7 million yielding a benefit-cost ratio of 1.3, and with best and worst cases of $32 million

and -$18 million (2014 $).

Figure 83: Net Present Value of Distributed Energy Resource Monitoring and Control Deployment

through 2035


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Figure 84 shows the present value of the benefits and costs. The benefits are expected to be $28 million,

ranging from $12 million to $50 million (2014 $), and the costs are expected to be $22 million, ranging

from $16 million to $28 million (2014 $).

Figure 84: Present Value of Benefits and Costs of Distributed Energy Resource Monitoring and

Control Deployment through 2035


Figure 85 presents the breakdown of benefits and costs across the industry segments. The distribution

segment has been attributed all deployment costs and benefits resulting from reduced integration and

balancing costs. Benefits associated with increases in the capacity factor for distributed renewables

generation accrue to the generation segment, while emission reductions accrue to society.


Figure 85: Distribution of Benefits and Costs from Distributed Energy Resource Monitoring and

Control across Industry Segments


D.8 AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

Capability Overview

This application includes the following capabilities: AMI, AMI Enhanced, time of use pricing, and critical

peak pricing. These capabilities have been grouped together because they are all primarily enabled by

the deployment of smart meters, more so than any other capabilities.

In addition, an independent analysis of each of these functions would not be appropriate. For example,

the deployment costs of time of use and critical peak pricing are small. To a large extent, they only reflect

overhead costs to roll out each particular pricing program. These costs, in comparison to AMI costs, are

negligible. In contrast, AMI reflects most costs associated with smart meters, meter data management

and repository (MDMR), and stranded costs. As a result, the benefit-cost ratios for time of use and

critical peak pricing are significantly high, whereas the benefit-cost ratio for AMI is less than one. Based

on the benefit-cost ratio, AMI should not be deployed since it is not cost effective, yet AMI Enhanced, time

of use, critical peak pricing, among other capabilities need the foundation of AMI. AMI is a fundamental

element that acts as an enabling technology for incremental deployments of smart grid capabilities. These

capabilities have been grouped together because AMI serves as their foundation for deployment. This

section will highlight the positive business case for this group of AMI-enabled capabilities.


The deployment assumptions for each capability are shown in Table 21 below.

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Table 21: Deployment Figures for AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

Application 2020 2035

AMI 5.4 million customers 5.8 million customers

AMI Enhanced 3.3 million customers 3.5 million customers

Time of use 5.1 million customers 5.5 million customers

Critical peak pricing 150,000 customers 66 175,000 customers

Source: Navigant

The impact assumptions for each particular capability are shown below.

Table 22: AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing Impacts

Application Impact

AMI Meter reading costs 60% reduction

Electricity theft 50% reduction

AMI Enhanced

CAIDI 5% reduction

Extended life of distribution assets 15% improvement

Reduced call volume Outages: 25%

Regular service: 5%

Reduced service trips Outage service calls: 8% reduction

Field service calls: 30% reduction

Time of use

Energy consumption Res.: 1% reduction

Comm.: 0.5% reduction

Peak demand Res.: 3% reduction

Comm.: 0.8% reduction

Critical peak pricing Peak demand Res.: 18% reduction

Comm.: 18% reduction

Source: Navigant

Results

Figure 86 shows the annual benefits and costs. The largest contributors are reduced energy consumption,

avoided capacity, improved utility operations and maintenance and improved reliability.

66 Customers who participate. Assumes program is available to all residential and small commercial customers.


Figure 86: Annual Benefits and Costs of AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

through 2035



value will be $1.2 billion yielding a benefit-cost ratio of 1.3, and with best and worst case scenarios of $3.0

billion and $-0.5 billion, respectively. Figure 88 shows the uncertainty analysis of the benefits and costs.

Benefits are expected to be $4.9 billion and costs are expected to be $3.6 billion.

Figure 87: Net Present Value of AMI, AMI Enhanced, Time of Use, and Critical Peak Pricing

Deployments through 2035


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Figure 88: Present Value of Benefits and Costs of AMI, AMI Enhanced, Time of Use, and Critical Peak

Pricing Deployments through 2035


Figure 89 show the distribution of costs and benefits across the different segments of the electricity sector.

Figure 89: Distribution of Benefits and Costs from AMI, AMI Enhanced, Time of Use, and Critical

Peak Pricing across Industry Segments


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D.9 Energy Storage System Integration and Control

Capability Overview

Energy storage system integration and control technologies enable the seamless integration of utility-

scale storage devices with the grid by minimising disturbances and maximising the value of the system.

Integration and control systems may include sensors, protective hardware, communications equipment,

and control software.

Navigant’s framework reflects benefits that arise from the use of storage devices by distribution utilities

such as for capacity and transmission and distribution investment deferral, ancillary services, and firming

and shaping of intermittent generation. The framework does not reflect benefit streams that arise from

the use of storage devices by commercial, industrial, or other private uses.

The range of energy storage technologies and the numerous benefit streams associated with each provide

a degree of flexibility that is new to the electricity system. A study by the Sandia National Laboratory

characterised a total of 17 benefit streams for utility-related applications.67 The importance of these

streams arises from the potential for aggregating storage applications in order to create attractive utility

projects. Common benefit streams expected for distribution system storage applications include:

electricity and demand shifting, regulation, operating reserve, voltage support, deferral of distribution

capacity, substation back-up power, and renewables firming. Several other benefits are also traceable

back to the transmission system or commercial needs. These include congestion management,

transmission capacity deferral, energy and/or demand-charge arbitrage, and improved power quality and

reliability.


Navigant’s framework models the penetration of energy storage based on questionnaire feedback.

Navigant asked nine questions to calibrate the deployment curve. These questions asked utilities to

forecast the number of megawatts of storage they anticipated to connect to their networks over several

timeframes. The responses of utilities provided the following findings:

Large and medium-size utilities are actively planning to connect storage devices to their

networks

As an aggregate, utilities anticipate have approximately 84 MW of storage connected to their

networks by 2020

Utilities will actively control all of the storage capacity connected to their networks

Accordingly, the analysis assumes deployment of distribution-connected storage devices starting in 2015

and ramping up through to 2035. Navigant assumes that by 2035 storage capacity will nearly triple to

240 MW, relative to 2020 figures. Figure 90 shows the storage figures through 2035:

67 Sandia National Laboratories (SNL). February 2010. “Energy Storage for Electricity Grid: Benefits and Market

Potential Assessment Guide”.


Figure 90: Energy Storage Deployment Assumptions

Source: Navigant

Navigant’s benefit-cost framework accounts for three primary storage benefits.

Peak shifting: Storage used as a means of deferring the need for additional generation capacity.

Storage would be located at a congested distribution substation and would be available

exclusively for peak shaving during peak hours.

Ancillary services (regulation and spinning reserve): During non-peaking hours, storage is

available for ancillary services. Using energy storage devices for frequency regulation and

spinning reserves reduces the need and cost of resources generally used to provide these services.

For example, if used for frequency regulation, storage provides two main advantages compared

to conventional generation: a much faster response time, and the ability to deliver load up and

down for the same amount of capacity.

Renewables integration: Energy storage is used as a means of firming the intermittent

production from renewable energy generators. As a result, firm renewable capacity displaces the

need for new generation capacity.

Additional business drivers may also include avoidance of negative prices when the load cannot match

supply and deferral of transmission and distribution capacity expansion triggered by new distributed

generation.

Results

Figure 91 shows the annual benefits and costs associated with the deployment of distribution connected

energy storage systems in Ontario through 2035. Approximately 60% of benefits arise from reduced

capacity expansion, the balance from reduced costs of ancillary services, and integration of renewables.

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Figure 91: Annual Benefits and Costs of Energy Storage Deployments through 2035


Figure 92 presents the range of net present value. The net present is expected to be -$288 million, with

best and worst cases of $610 million and -$1,205 million (2014 $). The results suggest that energy storage

is not presently cost effective. The benefit-cost ratio is 0.7, on an expected basis, and may vary from 0.2 to

1.3.

Figure 92: Net Present Value of Energy Storage Deployments through 2035


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Figure 93 shows the range of present values of benefits and costs. The benefits are expected to be $695

million, ranging from $253 million to $1,250 million (2014 $), and the costs are expected to be $983

million, ranging from $497 million to $1,610 million (2014 $).

Figure 93: Present Value of Benefits and Costs of Energy Storage Deployment through 2035


Figure 94 shows the distribution of benefits and costs across industry segments. The distribution

segment accrues all costs and only a fraction of benefits. Avoided generation capacity benefits accrue to

the generation segment, while cost savings from ancillary services and renewable integration accrue to

the transmission segment.


Figure 94: Distribution of Benefits and Costs from Energy Storage across Industry Segments


A number of factors drive the results for energy storage. Two such factors are the life of the storage

assets and the analysis period. The estimated useful life for an energy storage system is 15 years. As a

result, energy storage deployments beyond 2015 trigger a replacement cycle beyond 2030. The benefits

associated with the replacement cycle will not be fully accrued over the life of those assets since the assets

will outlast the end of the analysis period in 2045.68 This in turn yields lower than expected benefit-cost

ratios.

Revisiting the benefits and costs for an illustrative energy storage device installed in 2015 over its 15-year

life yields a benefit-cost ratio of 1.1, with worst and best scenarios of 0.3 and 3.0, respectively.

Additionally, another factor that affects the results for energy storage systems is the cost of storage

devices. As storage prices decrease over time it is expected that the business case for deployment will

improve. A business case assessment for the deployment of energy storage in 2020 will yield better

results than those presented in this report. A preliminary assessment suggests that the benefit-cost ratio

for a storage device installed in 2020, analysed over its 15-year life, will increase to 1.3.

68 For example, a storage device deployed in 2025 will not be replaced until 2040. This original device will accrue

benefits over its full life. In contrast, the replacement—deployed in 2040—will only accrue benefits over 5 years.

The benefit-cost ratio for the replacement will be less than that for the original device.

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Documents

Ontario Smart Grid Assessment and Roadmap - Electricity · Smart Grid Capability Deployment of Status Quo Scenario ... Microgrid Impacts ... Ontario Smart Grid Assessment and Roadmap