USA-Argentina Collaboration: Smart Grid …...USA-Argentina Collaboration: Smart Grid Applications in Distribution Systems September 17, 2013 1 PRESENTER: MARCELO A. ELIZONDO, RESEARCH

USA-Argentina Collaboration: Smart Grid Applications in Distribution Systems

September 17, 2013 1

PRESENTER: MARCELO A. ELIZONDO, RESEARCH ENGINEER Electricity Infrastructure Group, Energy and Environment Division Buenos Aires, Argentina

PNNL-SA-98361

USA-Argentina Collaboration

Smart Grids (SG): Advances in communications and computation to address power systems challenges US DOE and PNNL expertise developing SG technologies in both transmission and distribution SG technology to address Argentina’s needs Summary of smart grid activities at PNNL on distribution systems. Several PNNL staff contributing. Marcelo’s background in USA and Argentina

UNSJ, San Juan, Argentina. Power system engineer. CONICET, Argentina. Scholarship

UNSJ, Argentina. PhD, power systems engineering. Carnegie Mellon University, USA. 2-year visitor.

Mercados (ME-C), Argentina. Consultant engineer. PNNL, USA, research engineer (Electric Infrastructure Group)


Outline

PNNL and its Electricity Infrastructure Group Four examples of developments

Grid FriendlyTM Appliances Transactive energy Demonstration projects, extensive experience Modeling the smart grid: GridLAB-D

Open discussion: opportunities in Argentina


Who is PNNL?


The Pacific Northwest National Laboratory is a DOE Office of Science laboratory in Richland, WA. Operated since 1965 by Battelle, a global non-profit research and development organization committed to science and technology for the greater good.

Mission: Transform the world through courageous discovery and innovation. Vision: PNNL science and technology inspires and enables the world to live prosperously, safely, and securely. Values: Integrity, creativity, collaboration, impact and courage provide the foundation for all we do.

PNNL employs nearly 5,000 staff and has an annual operating budget of $1.1 billion. PNNL has over 100 people working in Electric Infrastructure – over 50 Power System Engineers.

Slide from: Paul Skare, PNNL

PNNL draws upon core capabilities, facilities, and investments in Electric Infrastructure

5

Power system operation, planning and security

Power markets

Demand response

Renewable integration

Advanced analytic methods, HPC-based simulations, visualization

Staff Capabilities

Live PMU data from all three interconnections

PMU data archive

PowerNET lab

EMS/DMS displays

T&D-level data displays

Platform for tool evaluation, operator training

Physical Control Center (EIOC)

Live security data streams

Visual analytics

Co-located with classified assets that accelerate threat recognition and appropriate response

Emergency Response

Public / Private

Cyber Security / Resilience Center (EICC)

Networking and data management

Advanced analytic methods and HPC approaches for real-time modeling and simulation

Visualization and decision support

Next Generation EMS

Next Generation Simulation

Future Power Grid Initiative

Slide from: Paul Skare, PNNL

September 17, 2013

Outline





Grid Friendly™ Appliances Provide Fast, Autonomous Reliability Resource

Autonomously detects under-frequency events and sheds load 150 new Whirlpool clothes dryers, 50 retrofitted water heaters No one noticed in hundreds of curtailment events! Can displace spinning reserves and increase reliability Reacts within 1/2 second Delays & randomizes service restoration to avoid grid shock Low cost: no communications required


Slide from: Robert Pratt, PNNL

“When the inevitable occurs … people get stuck in elevators and high-value uses of power are shut off along with all the lowest priority uses of energy. It's the meat-ax approach to interrupting power flows.” Dr. Vernon Smith, 2002 Nobel Prize Winner, Economics

Home Energy Management Systems

Aggregate demand response: manage peak loads & supply ancillary services, especially for renewables

Advise customers on how to reduce energy bills, be “green” End-use consumption & renewable usage feedback Efficiency & demand response opportunities & savings estimates

Host supporting applications Disaggregate AMI data into end uses Enable graphical user interface (GUI)

Basis for utility program Measurement & Verification

Control strategies Behavioral changes Retrofits & replacements

Appliance & equipment purchases Programs, financing & incentives Diagnostics for major equipment & appliances

September 17, 2013 8 Slide from: Robert Pratt, PNNL

IT Infrastructure Supports New Opportunities to Increase Efficiency of the Overall Energy System (Source to End Use)

Pervasive information technology will enable us to comprehensively examine each kWh, each Btu

Generation and T&D efficiencies

Energy efficiency opportunities

Smart grid (demand response) opportunities

Identify & quantify opportunities to increase overall efficiency & cost effectiveness of the entire system from source to end-use sink

“No stone goes unturned … no kWh goes unexamined”



Outline






Transactive Control & Coordination (TC2)

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TC2 blends notions of power markets with control of energy systems into a transactive network

that manages large-scale deployments with (eventually) billions of distributed assets using distributed decision-making creating a virtual control system with the smooth, stable, predictable response required by operators.

Markets (5-min – day)

Control (msec – min)

The fundamental purpose is to: Reduce peak loads (minimize new capacity, maximize asset utilization)

Coordinate new distributed smart grid assets (demand response, distributed generation & storage)

Fully engage all viable value streams, from all types of resources, at all levels of the system, on a level playing field

Seamlessly integrate their use in conjunction with traditional grid assets

Despite their being largely owned & operated by consumers & 3rd parties

September 17, 2013

Linking Multiple Values/Benefits is Key to Cost Effectiveness

Long-term objective is to simultaneously achieve combined benefits via multi-objective control

Reduce peak loads (minimize new capacity, maximize asset utilization) Generation transmission, and distribution (~$800, $150, & $250 /kW-ea.)

Minimize wholesale prices & power production costs Reduce transmission congestion costs Free up transmission for renewables by stabilizing dynamically-constrained paths Provide ancillary services, ramping, & balancing

Especially as renewables increase demand

Managing distribution voltages when rooftop solar PV system output fluctuates rapidly

Slide from: Robert Pratt, PNNL September 17, 2013 12

3. Price-Discovery Mechanism

1. Price-Responsive Device Control

Indoor Temperature

$/kWh

Tair

Bid

Price

Tset

Load (kW)

Price ($/kWh)

Demand Curve (customer bids)

Pclear

Qclear

Rated Node Capacity

Node Supply Curve

Pwhole-sale

Precise, stable control of congested grid nodes derives from (1) customer price-responsive controls that (2) express their available flexibility to (3) a price discovery mechanism

Transactive Control from Interaction of Price Discovery & Customer Bidding Algorithms

Price ($/kWh)

Load (kW)

Charge battery

Discharge battery

Water heater

AC

2. Customer Price-Flexibility Curve

Price ($/kWh)

Load (kW)

Charge battery

Discharge battery

Water heater

AC

Max Load

Base Load

Hierarchical Network of Transactive Nodes Parallels the Grid Infrastructure

Transactive Node: Aggregates flexible net demand from nodes it supplies (below it)

Delivery + Consumption – Production

Expresses need for power as feed-back to node(s) supplying it (above it)

as function of price/incentive signal

Quantity & price at each node resolved via price-discovery process

market clearing, for example

Implements price-responsive controls for its own flexible assets

responsiveness is voluntary, set by owner response is automated on behalf of owner

$

MW

$

MW

Generation

$

MW

$

MW

Transmission

$

MW

$

MW

Transmission

$

MW

$

MW

Distribution

$

MW

$

MW

Distribution

Hierarchical, nodal representation of power flow in grid

Transactive nodes localize price/incentive

to manage flow of power


Outline





0 6 12 18 24

ancillary services distribution congestion transmission congestion

wholesale cost

Johnson Controls . IBM

$

MW

Invensys

Olympic Peninsula Demonstration (ca. 2006-07)

Market

Internet broadband communications to/from EIOC

Clallam PUD & Port Angeles n = 112, 0.5 MW DR Clallam County

PUD Water Supply District 0.2 MW DR

Sequim Marine Sciences Lab 0.3 MW DR 0.5 MW DG Slide from: Robert Pratt, PNNL


Olympic Peninsula Demo: Key Findings

Significant demand response obtained: 15% reduction of peak load for year Up to 50% reduction for consecutive days Response to wholesale prices, T & D congestion Short-term response could provide regulation services Common signal for Res–Com–Inst. bldgs. & distributed gen.

Customers will accept dynamic pricing if they are offered: Opportunity for significant savings (~10%) “No-lose” proposition compared to their current (flat) rate Control over their degree of response Technology that automates it A simple, intuitive user interface

More Comfort

More Savings


18 2

Transactive Energy – A Growing Community

Academia University of Washington, Cornell, U.C. Davis, Cal Tech, Northwestern . . .

Slide from: Ronald Melton, PNNL

Pacific Northwest Demonstration Project

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What: • $178M, ARRA-funded, 5-year

demonstration • 60,000 metered customers in 5

states Why: • Develop communications and

control infrastructure using incentive signals to engage responsive assets

• Quantify costs and benefits • Contribute to standards

development • Facilitate integration of wind

and other renewables Who: Led by Battelle and partners including BPA, 11 utilities, 2 universities, and 5 vendors


Transactive Control Feedback Loop

New incentive signals and feedback signals are generated on an event-driven basis. The most recently available information is used. Each signal responds to changes in the other, and the values converge.

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History of Load & Cost Forecasts for Current Time


NW Region “Influence Map”--Topology

21

Cut Plane

Flowgate


Transactive Node Inputs & Outputs

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Transactive Node

• Aggregate input • Calculate incentive

& load estimate • Take local action

Input Incentive Signals Output Incentive Signal

Input Load Estimates Output Load Estimate

Control signals for node assets

Local data & info


gridSMART® Demonstration Summary

First real-time market at distribution feeder level with a PUC-approved tariff (Ohio PUC) Value streams:

Energy purchase benefit: function of PJM LMPs Capacity benefits: distribution feeder & system gen/trans limitations, e.g., peak shaving Ancillary services benefits: characterized, but not part of the tariff

Uses market bidding mechanism to perform distributed optimization – transactive energy

200-300 homes bidding on 4 feeders Separate market run on each feeder “Double auction” with 5-minute clearing

HVAC automated bidding Smart thermostat and home energy manager Homeowner sets comfort/economy preference Can view real-time and historical prices to make personal choices

23 Slide from: Steve Widergren, PNNL

September 17, 2013

gridSMART® RTP in Action

24

Reduce feeder capacity to

engage end-use

Price rises to price cap

HVAC units drop-off Units rebound when

capacity returns to normal

Outline





Smart grid analyses field projects technologies control strategies performance metrics costs / benefits

Time scale: seconds to years

Open source Users/contributors from government vendors utilities academia

Vendors can add or extract own modules

A Unique Tool to Design the Smart Grid

Power Systems Loads Markets Unifies models of the key elements of a smart grid:

26

GridLAB-D is a DOE-funded, open-source, time-series simulation of all aspects of operating a smart grid from the substation level down, in unprecedented detail.

Simultaneously solves 1) steady-state & dynamic 3-phase power flow, 2) end use load behavior in 1000s of homes, and 3) double-auction markets.

Technologies: demand response, distributed generation & storage, inverters, VVC, microgrids.

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Simulation Method

Time (t)

Sta

te (x

)

Time-series of steady states of resolution ∆x with variable time-step of resolution ∆t

∆x

∆t

September 17, 2013

RTP Simulation with GridLAB-D

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Prototypical Distribution Feeders

A set of 28 distribution feeder models that are representative of those seen in North America. Openly available and constructed from 575 distribution feeders, located at 151 substations. 17 utilities contributed data.

5 PUDs 4 MUNs 7 IOUs 1 REA

These feeders can be used to extrapolate simulation results to a national level. This same process can be done on a smaller regional scale.


Conservation Voltage Reduction Analysis on a National Level

Many empirical studies indicate a reduction in distribution system voltage reduces energy consumption.

How CVR achieves this energy reduction has been a topic of debate. Using GridLAB-D it was possible to show the mechanism by which energy reduction is achieved.

With an analytic basis for analysis, it was possible to extrapolate these results to a national level.

When extrapolated to a national level a complete deployment of CVR provides a 3.0% reduction in annual energy consumption for the electricity sector.

80% of this benefit, or a 2.4% reduction, can be achieved if deployed on 40% of the feeders => targeting strategies for deployment.

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Perc

ent o

f Tot

al B

enef

it

Percent of Total Feeders

Percent Total Benefits vs. Percent Total Number of Feeders in the United States


Evaluation of General Electric’s Coordinated Volt-VAR Control System for American Electric Power

Eight distribution feeders were first simulated in GridLAB-D to estimate how well the system would perform.

Annual Energy Reduction for Substation 1

Then a field demonstration was conducted with extensive data collection. GridLAB-D simulations closely matched actual performance.

With the validated GridLAB-D models it is possible to create an optimized deployment schedule for the technology.


Potential Impacts of Primary Technologies in the Smart Grid Investment Grants Technology Load

Reduction SAIDI

Improvement Energy Savings

DR – instantaneous reduction 25% - 50%

DR – 6-hr load reduction 15% - 20%

Thermal storage (10% of bldgs. on commercial feeder) up to 5%

Fault detection/restoration 21% - 77%*

Distribution & outage management systems 7% - 17%

Reclosers/sectionalizers 2% - 70%

Volt/VAR optimization 2% - 4%

Residential PV (<6% penetration) 0.1% - 3%

* %-points; result highly dependent on initial value

Summary

PNNL experience Grid FriendlyTM Appliances Transactive energy Demonstration projects, extensive experience Modeling the smart grid: GridLAB-D

Significant opportunities and advancements to engage distribution system, demand response, and distributed generation to attain multi-thread benefits Opportunities in Argentina?


Thank you


Marcelo A. Elizondo, PhD Research Engineer

[email protected]

www.pnnl.gov

PNNL-SA-98361

Documents

USA-Argentina Collaboration: Smart Grid …...USA-Argentina Collaboration: Smart Grid Applications in Distribution Systems September 17, 2013 1 PRESENTER: MARCELO A. ELIZONDO, RESEARCH