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Enabling Data Infrastructure for Utility Sustainability

John Lacy

November 8, 2010

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Enabling Smart Grid components

1) Generation

2) Transmission & Distribution

3) Meters

4) Hardware

5) Consumers

6) Smart Infrastructure

Smart

Smart

Smart

Smart

Smart

– “The Enabler”

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Data, Data, Data and more Data

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Meter Data Volumes – Yesterday, Today, and Tomorrow.

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Beyond AMI: Example Data Flows

AMRPI

C&IInterval

AMIn

(Future)

MeteredFeeder Data

SCADA1-n

OMS

DMS

Second orSub-second

30 minutecollected hourly

30 minutecollected hourly

GISMobile

DispatchWMS

Feeder Amps and other SCADA Data

Circuit Analysis Tool

Distribution Planning

daily readscollected daily

5 minute reads continuously

15 minute/hourly

5 minute/hourly

Access to all data

Distribution Operations

Access to all data

Field Engineering

Access to all data

Asset Information

Near -real timetransformer kW, device loading, voltages, etc.

15 minute/hourly

Manual Substation

Reads

Monthly

HAN

15 minutecollected daily Asset Management/

Substations

Access to all data CBM

Multi-year history and reporting flexibility for analysis such as: PF/Voltage/Var DG Loss Load

Multi-year history and real and near real-time data for: Capacitor Operations DG Status

Multi-year history and real and near real-time data for: PF/Voltage/Var Analysis Capacitor Operations

Source devices will evolve with new technologies and

cost effectiveness

Various data granularity and delivery frequencies

that will increase over time

CIS Accounting

Financial Systems of Record

(data streams not included in this

scenario)

IED

Capacitor Control System

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Smart Infrastructure - “The Enabler”

• DATA MANAGEMENT INFRASTRUCTURE is ESSENTIAL

• Need a home for all this data – accessible & highly available

• Underpins and supports the entire Smart Grid

• Connectivity to disparate devices and data sources

• Data collection at original resolution – milliseconds to minutes

• Liberate data from secure, locked down proprietary systems

• Data analysis – ad hoc, event based, in real time

• Transformation to information for decision support

• Scalable – to millions of meters and values

• Extensible – to other databases, applications

• Communications – robust, fast, bidirectional

7

Smart Generation

• Multiple Control systems’ data aggregated to improve unit and plant performance

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Phase Angle Jumping - Grid Stability

5:00 PM 5:10 PM

Phase Angle difference Continuing to grow.

60⁰

120⁰

Diff

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Substation Dashboard Level 3 – Asset View

13 of top 15 Owner/Operatorsusing PI to Manage Wind Farms

Source: Emerging Energy Research

Value Propositions

1. Turbine Manufacturer Warranty Management– “Fox Watching the Hens”– “Bathtub Curve” Implications for LTSA Concept– Need: Focus on Top-Ten Sources

2. Increasing kWh produced – Wind Farm Operations– Turbine Availability– Turbine Operating Efficiency

3. Increasing value of kWh produced – Utility Operations– Improving utility integration (forecasting and scheduling)– Improving market value of power (real-time info to power

marketing/trading floor)– VAR/Grid Stability Management

4. “Intangibles” – Corporate Requirements– Enterprise Integration,– Separation of Process Control Network from User/Enterprise– Regulatory & Reporting– Technology Risk Management – Perception and Reality

Asset Management Value PropositionTurbine Production Example

HoursTurbineClass

(kW)Capacity

Factor AvailabilityAnnual Expected

Output (kWh)*

BusbarPrice/kWh

Annual CashFlow per WTG

8760 600 33% 100% 1,734,480 0.0425 $ 73,715.40

8760 1000 33% 100% 2,890,800 0.0425 $ 122,859.00

8760 1500 33% 100% 4,336,200 0.0425 $ 184,288.50

8760 2000 33% 100% 5,781,600 0.0425 $ 245,718.00

8760 2500 33% 100% 7,227,000 0.0425 $ 307,147.50

8760 5000 33% 100% 14,454,000 0.0425 $ 614,295.00

*Formula = Hours * Generator Capacity * Capacity Factor * Availability

•A typical utility scale wind farm may have 30 to 200 Turbines•Large owners (e.g. Iberdrola – 3500MWs) may have thousands of turbines•A single percentage point gain/loss of “in-market” availability (e.g. turbines available to operate when the wind is blowing) for

•Iberdrola Total Fleet would result•in a 1st Year ROI/loss of $4.3MUSD. •NPV over 5 Years = $13.5MUSD @ 18% Discount Rate•Based on US prices, power rate in Spain is .07 to .10/kWh produced

•For a Single Wind Farm of 150MWs:•In a 1st year ROI of $185,000•NPV over 5 years = $576,000 @ 18% Discount Rate

Asset Hierarchy (Tree):

• Allowing expansion/collapse• Project drill down capability

Geographic Status map:

• Color-coded•“Rollovers” showing windspeed & project output, # of faulted turbines or state change• Project drill down capability

Real Time KPI Dashboard:

• Availability, Capacity, etc• Color coded with drill down capability

YTD Financial Meters:

• Revenue vs Budget • Commercial Availability

Trend Data:

• Market Prices

Defined KPIs per region/project, color coded with drill down capability:

• Financial• Production• Operations

Historical Work order Detail:

• ???

Wind farm status:

• Color-coded•Project drill down capability

Active Faults Window:

• Detailed wind farm faults

MTD Power Generation Chart:

• Expected vs Delivered

Work order Detail:

• Historical view of work orders• Ability to drill down to item details

Real time Operations

• Turbine Graphic• Turbine Details

Power Curve Chart:

• Actual Output vs Manufacturer

Excel Based Reporting

Turbine Performance – DLES Example

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“Roll-up” Mechanism: Net KWh Roll-Up

Load on meter data

All AMI meters summed to one distribution transformer

All distribution transformers summed to circuit segment

Circuit segment summed up to feeder

Total feeders summed to sub

• Key Points• Each trend shown is aggregated load (kWh) up to the next higher

trend from an individual meter, transformer, line segment, breaker and sub.

• If you overlay the Distribution SCADA load, the difference would be losses or leakage

• The physical model (CIM) allowing the aggregation and roll-up of individual loads

• End to end visibility – integrating meter and distribution system(s) operational data

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Utility Landscape

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