The Impact of Rate Design and Net Metering on the Bill Savings from Distributed PV for Residential Customers in California

Darghouth, Naim ; Barbose, Galen ; Wiser, Ryan

关键词: 32;    29;    CALIFORNIA;    DESIGN;    ELECTRIC UTILITIES;    ELECTRICITY;    MARKET;    METERING;    PRODUCTION;    WEATHER;   

DOI  :  10.2172/983121
RP-ID  :  LBNL-3276E
PID  :  OSTI ID: 983121
Others  :  TRN: US201014%%571

美国|英语

来源: SciTech Connect

【 摘 要 】

Net metering has become a widespread policy in the U.S. for supporting distributed photovoltaics (PV) adoption. 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). While net metering has played an important role in jump-starting the residential 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 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 varies under net metering, and how the bill savings under net metering compares to 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). 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.