cpuc’s distribution forecasting working group planning impacts from distr… · a short version...

CPUC’s Distribution Forecasting Working Group

on Distributed Energy Resource Allocation

Mark Quan

Itron Inc.

October 8, 2019

A SHORT VERSION OF HISTORY

“On August 14, 2014, the Commission opened Rulemaking (R.) 14-08-013 in order to establish

policies, procedures, and rules to guide California Investor-owned Utilities (IOUs) in developing their

Distribution Resource Plan (DRP) Proposals.”

This new code section requires the electrical corporations to file distribution resources plan proposals

by July 1, 2015. According to the Code, these plan proposals will “identify optimal locations for the

deployment of distributed resources.” It defines “distributed energy resources” as “distributed

renewable generation resources, energy efficiency, energy storage, electric vehicles, and demand

response technologies.”

The Code also requires the CPUC to “ review each distribution resources plan proposal submitted by

an electrical corporation and approve, or modify and approve, a distribution resources plan for the

corporation. The commission may modify any plan as appropriate to minimize overall system costs

and maximize ratepayer benefit from investments in distributed resources.”

(https://www.cpuc.ca.gov/General.aspx?id=5071)

AUTHORIZING DECISION

February 8, 2018:

D. 18-04-002

• “We direct Energy Division to develop a process and schedule for resolving the issues discussed

in this decision through the Distribution Forecasting Working Group.”

• The two main unresolved issues are the “DER forecast disaggregation methodologies, and using

alternative scenarios for resource planning purposes.”

ORDER …

• “The IOUs shall vet disaggregation methods through the Growth Scenario Working Group and

incorporate best practices in their planning process”

Mandate

DER DISAGGREGATION

» Disaggregation is the process by which a system

level forecast of DER for a given IOU service

territory is allocated at the circuit level.

» The goal of disaggregation methodologies is to

identify, to the extent possible, where new DERs

will be adopted.

» DERs studied are:

• Photovoltaic Generation

• Electric Vehicles

• Additional Achievable Energy Efficiency

• Energy Storage

• Load Modifying Demand Response

System

(IEPR)

Disaggregation

Methods

Circuit

THE ISSUE: DATA GRANULARITY VS . . .

System Region Substation

Data Stability

Model Accuracy

Power of Economic DataHigh Low

High Low

High Low

Circuit

Items to ForecastFew Many

Data Granularity Challenge

Customer

Issue

Load Shape ImpactsLow High

FORECASTING SPECIALIZATION

PNM Resources Weather Impact Analysis | ‹#›

FORECASTING SYSTEM LOAD CHANGES» Top-Down System Forecast

» Disaggregation to obtain load impacts

OFFICIAL REPORTS

https://drpwg.org/growth-scenarios/

Distribution Forecasting Working Group

(DFWG )April 18, 2018 to June 13, 2018

RANGE OF METHODS

Complexity

Adoption

Models

Proportional

Allocation

Propensity

Models

• Key adoption data

• Time series data

• Develop S-Curve

model

• Forward Looking

• Key adoption

characteristics

• Forward and/or backward

looking

• Develop cross-sectional

model

• Known utility data

(e.g. class sales,

customers)

• Backward looking

HighLow

METHOD: PROPORTIONAL ALLOCATION

System

(kWh)

Circuit 1

(kWh)

Circuit 2

(kWh)

Circuit 1 kWh = System kWh x Circuit 1 Ratio

Circuit N kWh = System kWh x Circuit N Ratio

System kWh = Circuit 1 kWh + … + Circuit N kWh

Circuit 1 Ratio = Circuit 1 Customers

System Customers

Circuit N Ratio = Circuit N Customers

System Customers

. . .Circuit N

(kWh)

. . .

. . .

» Disaggregates based on utility data for the circuit (load, energy, or number of

customers)

» Refinements using sector or rate class data

METHOD: PROPENSITY MODELS

Circuit 1 Score = f(Customers1, Income1, …)

Circuit N Score = f(CustomersN, IncomeN, …)

. . .

Circuit 1 Ratio = Circuit 1 Score

Total Score

Circuit N Ratio = Circuit N Score

. . .

Total Score = Circuit 1 Score + … + Circuit N Score

Total Score

» Disaggregates based on propensity score

» The scores (models) may be based on regression, machine learning or any

other method using cross section data with key identifying variables that

correlate with adoption.

METHOD: ADOPTION MODELS

Circuit 1 Adoption

Circuit 2 Adoption

Total Adoption

Time

Time

Time

Ad

op

tio

n U

nits

Ad

op

tio

n U

nits

Ad

op

tio

n U

nits

Circuit 1 Ratiot = Circuit 1 Adoptiont

Total Adoptiont

Circuit N Ratiot = Circuit 2 Adoptiont

. . .

Total Adoptiont

» Bottom-up adoption forecast is used

to develop ratios.

» S-Curve models are used to capture

adoption through time.

» S-Curve models need saturation and

adoption rate parameters.

RANGE OF METHODS

Complexity

Adoption

Models

Proportional

Allocation

Propensity

Models

• Photovoltaics

• Electric Vehicles• Electric Vehicles

• Load Modifying

Demand Resource

• Additionally

Achievable Energy

Efficiency

• Energy Storage

• Load Modifying

Demand Resource

HighLow

QUALITATIVE RISK

» Uncertainty: Relative uncertainty is defined as the range of possible

outcomes within DER technologies. A High rating indicates a relatively wide

range of possible outcomes. A Low rating indicates a relatively narrow range

of possible outcomes.

» Impact: Expected Impact is defined as the relative size of one DER against

another for planning purposes.

» Risk: Risk combines uncertainty and expected impact and should guide the

relative level of attention that different technologies receive in the planning

process.

Risk = f(Impact , Uncertainty)

UNCERTAINTY AND RISK

CONCLUSION

» Disaggregation Methods. “the DFWG vetted each

method and found that they are reasonable for

disaggregating the IEPR DER forecasts considering

the state of each of the DER technologies and the

available data”

» Uncertainty: “the DFWG recommends that these

qualifications be used to guide, not dictate, future

analysis and modeling efforts”

» Final Report submitted July 2, 2018.

Final Note: The Commission directed the IOUs to annually hold a one-day workshop to

present and discuss updates to their disaggregation methods. The Commission did not

determine that a longer working group process would be necessary unless there was a

major policy change.

TECHNOLOGY LEVEL SUMMARIES

PHOTOVOLTAICS


Allocation Method

• Adoption Model (BASS Model or S-Curve Model) at the zip code level.

• Proportional Allocation method to Circuit level.

Key Issues/Uncertainty:

Method: Good quality geographic data

Shapes: Good quality load shapes from utility and 3rd party sources

Lumpiness: Timing of large project beyond interconnection queue is

difficult.

Impact: Size of market may be large.

ELECTRIC VEHICLES


Allocation Method

• S-Curve Adoption Model (1 IOU) driven by a propensity model.

• Propensity Model (2 IOUs) at the zip code level.

• Proportional Allocation method to Circuit level.


Method: Early adoption makes identifying customer choice

characteristics difficult for the mass market.

Shapes: Charging patterns are evolving. Home charging versus station

charging will look different.

Lumpiness: Location and timing of commercial charging stations is difficult.

Impact: Size of market is expected to be large in the future.

ADDITIONAL ACHIEVABLE ENERGY EFFICIENCY


Allocation Method

• Proportional Allocation using sector (class) load at the circuit level.


Method: Location is of EE is known for more than 50% of past EE

programs.

Shapes: Lack of recent EE shape data studies.

Lumpiness: Timing and location of large EE project is impossible to

forecast accurately.

Impact: Large amount of AAEE forecast.

ENERGY STORAGE


Allocation Method

• Proportional Allocation method

• Allocate residential ES based on PV adoption (2 IOUs).

• Allocate non-residential ES based on load (2 IOUs).

• Allocate all ES based on load (1 IOU).


Method: Very early adoption phase. Lack of adoption data makes

modeling difficult.

Shapes: Operating profiles vary among customers based on customer

objectives.

Lumpiness: Timing and location of large EE project is impossible to

forecast accurately.

Impact: Very small amounts of ES in the forecast.

LOAD MODIFYING DEMAND RESPONSE


Allocation Method

• Proportional Allocation based on eligible customers (1 IOU)

• Propensity based on regression trend model (1 IOU)

• Customer level propensity model for nonparticipants (1 IOU)


Method: Opt-out TOU rates create large uncertainty in customer

behavior.

Shapes: Impact profiles from existing studies appear reasonable.

Lumpiness: Clusters of adoption may introduce uncertainty.

Impact: Very small amounts of LMDR in the forecast.

THANK YOU

www.itron.com

[email protected]

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