Modeling Distributed Generation Adoption using Electric Rate Feedback Loops

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Modeling Distributed Generation Adoption using Electric Rate Feedback Loops

USAEE Austin, TX – November 2012

Mark Chew, Matt Heling, Colin Kerrigan, Dié (Sarah) Jin, Abigail Tinker, Marc Kolb, Susan Buller

Modeling Distributed Generation Adoption using Electric Rate Feedback LoopsUSAEE 2012 – Pacific Gas & Electric Company 2

Contents

Background/Motivation

Methodology

Results and Next Steps


Background (1/2)

What is DG?In this context, DG is generation (primarily solar PV) on the customer side of the meter, where most

of the power displaces grid-supplied energy.

How Much DG is in PG&E’s Service Area?•27% of nationwide rooftop systems are located within our service area, compared to 5% population

•69,000 rooftop PV systems installed as of July 31, 2012, growing at approximately 1,000/month

•693 MW -- 290 MW Res, 403 Non-Res solar DG, compared to over 20 GW max demand

What are the Key Drivers?•Declining costs of PV technology

•Availability of attractive ownership structures (lease, PPA)

•High percentage of customer base is green-minded

•High marginal customer rates, which customers can avoid paying through DG

•Supportive policy in California (eg. direct subsidies, Net Energy Metering (NEM), Virtual NEM)

•Political climate in California strongly supportive of DG


Background (2/2)Why is it significant to PG&E?•Continued growth without fundamental changes in rates will allow DG adopters to avoid paying for grid and other services that they receive

– In California, residential customers are charged in 4 tiers, with marginal rates increasing with increasing monthly usage

– Residential customers with the largest monthly usage are most incentivized to adopt DG; revenues lost from these adopters are much larger than costs avoided

– “Cost shift” refers to the increase in costs among non-adopters, when policy allows DG adopters to pay less than their share of costs to the utility they generate

•Because of present rate structure, a shrinking population high-use customers (those most likely to adopt) will cover these costs through higher rates

Why is a model needed?•In a decoupled environment, high rates drive DG adoption; DG adoption drives rates even higher

•Impacts of different policies are hard to intuitively predict because of the positive feedback dynamic

•The model guides PG&E’s leadership on how to best enable a sustained DG industry without unfairly harming non-adopters

Goal of analytical effort is to evaluate the impact of different DG policies


Contents


Methodology



• LVOEs depend on previous years’ rates; LCOEs are based on technology cost and performance assumptions.

• Adoption is based on Cost Effectiveness, which is based on the Levelized Cost of Energy (LCOE) of DG technologies and the Levelized Value (LVOE) produced by the DG units.

• Rates module uses adoption information to calculate the new rates, which in turn are fed back into the LVOE module to restart the loop

DG Model Structure

Adoption

RatesLVOE

LCOELevelized Cost of Energy

Levelized Value of Energy


Cost-Effectiveness Module

• Capital costs (including finance structure)

• O&M costs

• Fuel costs

• Electric efficiency

• Thermal efficiency

• Tax benefits

• Incentives

• Electric rates (forecast)

• Gas rates (forecast)

• Generation profiles

• Compensation mechanisms (e.g., NEM, FiT)

• Capacity factor

• Degradation rates

• Discount rate

LCOE InputsLVOE Inputs

Common Inputs

DG Technology Cost-

Effectiveness

Adoption Module


Customers Segmented to Forecast Adoption

Adoption Module

Adoption and Energy Impacts

to Rates

Inputs from Cost Effectiveness

Module

Regression Model on

Adoption from 2003-2010

Historic Cost Effectiveness

Projected Cost Effectiveness

• Adoption Behavior

• Usage

• Rate Type

• Income

• Homeownership

• Geography

Historic Customer Characteristics


DG Adoption Causes Rate Increase

Procurement CostProcurement Cost

Integration CostIntegration Cost

Interconnection CostInterconnection Cost

Capacity CostCapacity Cost

Rev.Required

Rev.Required

Customer Charge

Customer Charge

DemandCharge DemandCharge

Energy Charge Energy Charge

Number of CustomersNumber of Customers

Maximum KWMaximum KW

kWh consumption

kWh consumption

*

*

*

RATERATE

Although RRQ will decrease because of net avoided cost (expense), this does not offset the lost revenue from decreasing kW and kWh sales.

Rev. Collected

Rev. Collected

Incentives & Admin Cost

Incentives & Admin Cost

v

Cost of doing business

1a

1a

1b

2


Contents


Methodology



ResultsMain Insights from ModelThe DG model is being used to prepare for high DG scenarios

•The scenario that would create the greatest cost shift is “virtual net metering” – where all customers could count remotely located PV against their current consumption, under the current rate structure

•Because of rate structure, costs caused by DG are shifted to customers who are unable to lower their usage or adopt DG – a fairness issue

•Rate changes to address high bill impacts also significantly reduce cost shift from DG

Cost Shift$


Next Steps

Work with stakeholders on rate reform•Seek sustainable future with healthy DG market and customer choice

•Explore alternatives to Net Energy Metering (NEM) that provide fair compensation

•Reduce the highest rate tiers

•Make rate structure less volumetric, to reflect actual costs of service

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Thank You

Mark [email protected]

Modeling Distributed Generation Adoption using Electric Rate Feedback Loops

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