Value of Distributed Energy Resource (DER)capabilities.itron.com/efg/2019/08_PrasenjitShil.pdf ·...

Value of Distributed Energy Resource (DER)

Prasenjit Shil, Load Forecasting ManagerItron EFG Workshop, April 3, 2019

2Ameren Proprietary Information. All rights reserved.

Disclaimer

• Some of the information presented are proprietary to Ameren and cannot be presented

without prior permission from Ameren.

• This presentation may also reflect personal views, opinions and insights that do not

necessarily reflect those of Ameren and its subsidiaries.

About Ameren

Ameren Corporation

is a Fortune 500, fully

rate-regulated electric

and gas utility company

headquartered in

St. Louis.

We pride ourselves on

operating safely and

maintaining financial

strength while providing

reliable, reasonably

priced energy in

an environmentally

responsible fashion.

33

This integrated utility owns a mix of energy centers

with 10,200 megawatts of electric generation capacity.

It is the second largest gas distributor in Missouri.

This delivery-only utility is the second largest

distributor of electricity and third largest distributor of

natural gas in Illinois.

This subsidiary is dedicated to electric transmission

infrastructure investment and expanding Ameren’s

already robust system of high-voltage lines.

Service Territory

44


DER Valuation

• State Policy mandated

– California, NY, Illinois

• Develop a defendable and repeatable methodology to evaluate the

value of DER to the AIC distribution system

– Locational

– Compliance with the Future Energy Jobs Act

• The Illinois Commerce Commission to open an investigation to determine the

value of DER rebates once the total generating capacity of net metering

customers reaches 3%

• Provides monetary mechanisms to incentivize the installations of renewable

DER on the AIC grid.

Background and Objective


Valuation Elements

6

AC Orrell, JS Homer, Y Tang, "Distributed Generation Valuation and Compensation – White Paper", Feb 2018.

https://www.districtenergy.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=0103ebf1-

2ac9-7285-b49d-e615368725b2&forceDialog=0

➢ Attempts to identify all the values

that a DER provides to the system

1. Avoided energy cost

2. Avoided T&D Costs—CapEx and O&M

3. Increased reliability

4. Environmental benefits

5. Social and other elements

➢ Valuation Approaches:

➢ Fixed Valuation

➢ Locational Marginal Value

➢ Project Specific

https://www.districtenergy.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=0103ebf1-2ac9-7285-b49d-e615368725b2&forceDialog=0


Valuation of DER on AIC Grid

• Project Objective

– Evaluate the value of DER to the AIC distribution system, at the location at which

it is interconnected, taking into account the geographic, time-based benefits, as

well as technological capabilities and present and future grid needs.

• Value to the distribution system.

• Applies to solar and wind generation.

• Evaluated for a compensation rebate that is a onetime payout to the DER

owner.

• Defendable and repeatable methodology

• Building Blocks

– Capacity Value (deferred expenditure to distribution system capacity)

– Grid Support Services (Volt/Var support)

Project Objective, Scope and Building Blocks of DER Valuation


DER Valuation Framework – Case Study

✓ 100% AMI Deployed

✓ Leverage AMI data

✓ Over two years of historical SCADA data

✓ 163 distribution transformers

✓ Three single-phase substation voltage

regulators

✓ One switched capacitor bank and three fixed

✓ 25 MVA Substation bulk transformer


Overview of Information Flow


Forecast and Analytics Flow


Load Forecast Methodology: High Level Summary

• Case 1: Not enough AMI Data available for forecasting

– Test Feeder 1 did not have 100% AMI data for meaningful timeframe at the time of

forecast

• Use monthly billing data and calibrate to hourly forecast using test year hourly load research

data/AMI profile

• SAE/Econometric approach

• Case 2: Sizeable AMI Data available for forecasting

– Develop use case with AMI data for Test Feeder 2

• Econometric/Time series regression model

• Use of known information/trend variable about energy efficiency installations


Forecast Drivers

• Economy data at county level

• Actual Weather

– Case 1: CDD/HDD at various base points

– Case 2: Temperature, Dew point, Wind speed, Wind chill, Cloud cover, Temperature-humidity index (THI),

Heat Index, Cooling degree hour (CDH), Heating degree hour (HDH)

• Not all variable will be significant in the model

• Normal Weather

– Case 1: Normal billed and calendar CDD/HDD used by Ameren for the specific company/weather station

– Case 2: Its difficult to define normal weather for all the variables used for the Case 2. Hence, instead of

averaging weather variables over 10 years period, the methodology will simulate the model using previous

10 year’s weather data and average the resultant load for forecast purpose

• Others: – Case 1: Season, months and interaction terms

– Case 2: Time variables such as day of the week, month, holiday, season and interaction terms


1. SAE type model for Residential

2. Commercial and Industrial models

are built based on econometric/time

series regression models

3. Use transformer load share to

derive transformer load forecast

1. Use hourly load shape to convert

monthly forecasts into hourly

forecast

Case 1: Annual ForecastTest Feeder 1


Case 1: Hourly Forecast Test Feeder 1

0100,000200,000300,000400,000500,000600,000700,000

Hourly Res Profile (kW)

0.000000

0.000500

0.001000

0.001500

0.002000

0.002500

0.003000

0.003500

13

82

763

114

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52

51

90

62

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72

66

83

04

93

43

03

81

14

19

24

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34

95

45

33

55

71

66

09

76

47

86

85

97

24

07

62

18

00

28

38

3

Res Hourly Shape

-

500

1,000

1,500

2,000

2,500

14

39

87

71

31

51

75

32

19

12

62

93

06

73

50

53

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14

81

95

25

75

69

56

13

36

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00

97

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77

88

58

32

3

Res-Hourly Load (kW)

-

500

1,000

1,500

2,000

2,500

3,000

Res_peak (kW)


Case 1: Hourly Forecast for Test Feeder 1

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2018 Forecasted Hourly Load (KW)

DS1 DS2 DS3 lighting

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

kw

Hours

Forecasted Peak Day profile: 2018

DS1 DS2 DS3 lighting

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Annual Peak (KW) 3,516 3,592 3,644 3,689 3,733 3,772 3,809 3,844 3,992 4,083 4,145 4,208 4,275

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

KW

Annual Peak Forecast (KW): Test Feeder 1

kW


Case 1 Forecast: Comparison to SCADA

SCADA Data

Should we use Design-day weather information for

peak load forecasting?


Load Profiles at Feeder 1Example Day with Solar Overlay

1kW Solar in 2018 5kW Solar in 2028

Capacity benefit is the difference in time

value of money between these two lines

Capacity benefit is the difference in time

value of money between these two lines


• AMI data available for two years

• Hence hourly models were developed using AMI data

Case 2: Hourly Forecast for Test Feeder 2

• Models are Feeder level

• Allocate forecast share at the

transformer level

• What weather input should be used for

the peak forecast?

• No established process to normalize

weather variables such as square of

temperature, cloud cover, snow, rain or

heat index etc.• Simulation


Case 2: Hourly Forecast ProcessModel Comparison and Selection


• Simulate the champion forecast model with

historical weather data and forecasted economic

variables

• The models could be simulated for all hours or just for

Design-day conditions

Case 2: Hourly Forecast Process


Other Analytics

• Use individual transformer load share and

load shape from AMI data to create

forecast at the Transformer level

– Missing Data or “poor quality” data

• Mitigation strategy?

• Calculate hourly loadings on each

individual transformer

– Forecast number of hours each

transformer is overloaded

– Input to the engineering analysis

Identify Transformer Overloading

Data Quality—Uncleaned data with Outlier


1. Customer/zip level data available for energy efficiency projects

sponsored by AIC

1. Provides estimates of known load reduction over next 5-10 years

1. Could be introduced as a known variable in the model

2. Should we introduce SCADA data as an explanatory variable?

1. This could help to smooth the hourly load shape as well as peak

forecast

3. Outage information

4. Customers on real time price

5. Propensity of EV and solar adoption at geographic level

6. Calibration to the system load growth

Other Considerations and Next Step


Announcement

Why

• Load forecasting must adapt to integrate customer

adoption of emerging technologies and services or risk

divergence that compromises capital planning and grid

reliability

Approach

• Load Forecasting Interest Group as forum for utility

information exchange on load forecasting

• Convene experts from industry and academia

Value to Members

• Keep abreast of innovations and best practices in load

forecasting

• Incubate ideas for future research projects

EPRI Load Forecasting Interest Group

Not an advertisement for EPRI, but

an advertisement for the Load

Forecasting Interest Group

➢ EPRI Program 182

➢ Open to utilities

➢ Monthly or frequent webcast of

contemporary load forecast topics

➢ Hope you will participate and

volunteer to share all the exciting

projects

➢ Finalizing the detail—first

announcement to come late April

or early May

Thank you

Value of Distributed Energy Resource (DER)capabilities.itron.com/efg/2019/08_PrasenjitShil.pdf ·...

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