Forecasting Distributed Generation Adoption With Decision-based Feedback Loops

 

 

 

Mark Chew

Principal - Distributed Generation, PG&E (San Francisco, CA)

with

Matt Heling, PG&E; Colin Kerrigan, PG&E; Dié (Sarah) Jin, PG&E; Abigail Tinker, UC Berkeley; Marc Kolb, PG&E;

Susan Buller, PG&E; and Liang Huang, PG&E.

 

In the past decade, Pacific Gas & Electric Company (PG&E) has experienced an unprecedented rate of distributed generation (DG) adoption, and this trend is expected to continue into the foreseeable future.  In this work, DG is defined as electrical generation on the customer side of the meter – primarily rooftop solar.  PG&E has 27% of US rooftop systems within its service territory[1], while serving only 5% of the US population.  Extrapolating from current trends, DG capacity is expected to be over 70,000 units and over 700 MW by the end of 2012, compared to a system peak demand of approximately 20 GW.

The cost of DG has declined substantially in recent years.  The value proposition for DG to PG&E customers has been especially attractive in an environment featuring high marginal electric rates and a net energy metering (NEM) program[2].  Other driving factors, such as the availability of financing, power purchase agreements, and Governor Brown’s statewide DG goal for 2020, ensure that DG will become increasingly significant into the future.  As more customers adopt DG, electric utilities must adapt their rate-making procedures to ensure that both DG adopters and non-adopters are fairly charged for their cost of service.

In a decoupled utility environment, if DG adoption causes lost revenues greater than avoided cost, then rates for non-adopters will rise, other things being equal[3].  Since the utility’s recovery of capital is independent of commodity sales, costs therefore must shift onto the remaining non-adopters.  Higher rates give remaining customers even greater incentive to adopt DG, so the decoupled utility must limit the cost shift[4] from adopters to non-adopters early on, to avoid an uncontrolled acceleration in cost shift later.

The feedback dynamic of this situation makes it difficult to intuitively estimate the impact of various policies and mitigation strategies on DG adoption.  By creating a model that quantifies the rise in rates and uses this information as a factor in subsequent years’ adoption, a utility company can gain insight into the effect of different policy proposals on the cost shift impacts and adoption of DG.  This paper covers the development and use of such a model created by PG&E to gain insight into this dynamic.

Methods

The core of the feedback model consists of three parts: cost effectiveness, adoption, and rates.

The chosen metric for participant cost effectiveness compares the levelized value of energy (LVOE) not purchased from the utility, to the levelized cost of energy (LCOE) from DG, via a ratio.  The cost effectiveness component considers changes in technology costs and performance over time, the evolving marginal costs of grid electricity, the presence or expiration of various incentives in PG&E’s service territory (including Federal and State tax benefits), and other factors that influence DG project economics.

The adoption section of the model is built on customer-level data.  PG&E’s population of customers is segmented based on rate schedule, energy usage, and likelihood to adopt DG.  For each segment, a regression is used to translate historical responsiveness to cost-effectiveness, among other factors, into a forward projection.

The rates section reflects current rate-making mechanisms, assuming no changes in policy. The output of the rates section is detailed rates down to tiered rates and time-of-use rates[5], and are fed into the cost effectiveness section.  Having a detailed rates model is significant because rates are also one of the few tools that a utility can use to influence the cost shift from DG adoption. Potential DG customers make their decision based on the value of DG given current and expected future rates, so a model that captures this aspect of the decision more accurately represents likely future behavior. By modeling rates with high precision, the model also quantifies the cost shift and identifies which customers are most affected by it.  Due to statutory limits on rate design in California, most of the cost shift is borne by customers whose usage is in the highest tiers, increasing the likelihood that they will then adopt solar if they can.

A system diagram of the model is shown in Figure 1.  In each year of the model, cost effectiveness is calculated for each rate class and segment, based on the ratio of LVOE to LCOE.  Adoption is then predicted based on the cost effectiveness, and rates are determined based on predicted adoption.  The rates are then fed into the cost effectiveness calculation in the next year of the model, completing the loop.

 

 

 

Figure 1: Block Diagram of DG Model

Results

Before the model was built, PG&E had examined the impact of DG on customers, but had difficulty comprehensively comparing the net cost shift impacts of different policy proposals.  The DG model enables the simulation of different scenarios and policy options, to see how they would play out.  “Scenarios” are a set of assumptions, while “policies” are the solution set – mostly rate policies.  By examining how the scenarios play out under each policy, PG&E can gain insight into the impact of DG on both adopters and non-adopters, weighing the positives and negatives of each policy.

Conclusions

PG&E seeks to support solar and DG adoption in a sustainable way.  The DG adoption model allows PG&E to gain insight into the impact of proposed policy changes and identify options that increase fairness and promote long-term sustainabilityof customer DG.  The model has been used to evaluate the impact of pursusing less volumetric rates and reduced high tier rates, along with an alternative to net metering as the keys to supporting DG adoption while fairly assigning the cost of grid usage.

 


[1] As of 1/1/12. Based on PG&E internal data and national totals in the Interstate Renewable Energy Council’s Annual Report, published August 2012.

[2] Under NEM policy, customers can count energy exported back to the grid by their DG systems against their energy usage during the same billing cycle.  Excess electricity in a given month is carried forward as a monetary credit and can be used to offset charges in other months.  Effectively, this policy credits their generated energy at the retail rate, which is high in California.

[3] PG&E operates in a decoupled environment, meaning that company profits are not tied to the volume of energy sold.  Instead, rates adjust based on expected sales volume, so that the allowed profit is reached.  Because of decoupling, shareholders are indifferent to DG installations.

[4] “Cost shift” is defined as the aggregate amount that non-adopters pay, to subsidize the DG adopters.  PG&E’s goal is to minimize this cost shift because with DG adoption, higher-income customers generally shift cost onto middle- and lower-income customers – a fairness issue.

[5] California residential rates have four tiers with higher usage meaning higher per kWh charges.  In addition, most customers installing PV go on a TOU rate, which is also tiered.

Click to view a printable version of this article.