|Title||The Impact of Rate Design and Net Metering on the Bill Savings from Distributed PV for Residential Customers in California|
|Year of Publication||2010|
|Authors||Naïm R Darghouth, Galen L Barbose, Ryan H Wiser|
|Keywords||electricity markets and policy group, electricity rate design, energy analysis and environmental impacts department, net metering, photovoltaics|
Net metering has become a widespread policy in the U.S. for supporting distributed photovoltaics (PV) adoption.1 Though specific design details vary, net metering allows customers with PV to reduce their electric bills by offsetting their consumption with PV generation, independent of the timing of the generation relative to consumption – in effect, compensating the PV generation at retail electricity rates (Rose et al. 2009). Though net metering has played an important role in jump-starting the PV market in the U.S., challenges to net metering policies have emerged in a number of states and contexts, and alternative compensation methods are under consideration. Moreover, one inherent feature of net metering is that the value of the utility bill savings it provides to customers with PV depends heavily on the structure of the underlying retail electricity rate, as well as on the characteristics of the customer and PV system. Consequently, the bill-savings value of net metering – and the impact of moving to alternative compensation mechanisms – can vary substantially from one customer to the next. For these reasons, it is important for policymakers and others that seek to support the development of distributed PV to understand both how the bill savings benefits of PV varies under net metering, and how the bill savings under net metering compares to savings associated with other possible compensation mechanisms. To advance this understanding, we analyze the bill savings from PV for residential customers of California's two largest electric utilities, Pacific Gas and Electric (PG&E) and Southern California Edison (SCE).3 The analysis is based on hourly load data from a sample of 215 residential customers located in the service territories of the two utilities, matched with simulated hourly PV production for the same time period based on data from the nearest of 73 weather stations in the state. We focus on these two utilities, both because we had ready access to a sample of load data for their residential customers, and because their service territories are the largest markets for residential PV in the country.
This research is in press with Energy Policy. To see the published version, please click here.
|LBNL Report Number|| |