In response to Assembly Bill 32, the state of California considered three types of carbon emissions trading programs for the electric power sector: load-based, source-based, and first-seller. They differed in terms of their point of regulation and in whether in-state-to-out-of-state and out-of-state-to-in-state electricity sales are regulated. In this paper, we formulate a market equilibrium model for each of the three approaches, considering power markets, transmission limitations, and emissions trading, and making the simplifying assumption of pure bilateral markets. We analyze the properties of their solutions and show the equivalence of load-based, first-seller, and source-based approaches when in-state-to-out-of-state sales are regulated under the cap. A numeric example illustrates the emissions and economic implications of the models. In the simulated cases, "leakage" eliminates most of the emissions reductions that the regulations attempt to impose. Furthermore, "contract reshuffling" occurs to such an extent that all the apparent emissions reductions resulting from changes in sources of imported power are illusory.In reality, the three systems would not be equivalent because there will also be pool-type markets, and the three systems provide different incentives for participating in those markets. However, the equivalence results under our simplifying assumptions show that load-based trading has no inherent advantage compared to other systems in terms of costs to consumers, contrary to claims elsewhere.
Microgeneration using solar photovoltaic (PV) systems is one of the fastest growing applications of solar energy in the United States. Its success has been partly fueled by the availability of net metering by electric utilities. However, with increasing solar PV penetration, the availability of net metering is likely to be capped. Households would then need to rely on distributed storage to capture the full benefits of their installed PV systems. Although studies of these storage systems to assess their benefits to the individual household have been examined in literature, the system-wide benefits have yet to be fully examined. In this study, the utility level benefits of distributed PV systems coupled with electricity storage are quantified. The goal is to provide an estimate of these benefits so that these savings can potentially be translated into incentives to drive more PV investment. An agent-based residential electricity demand model is combined with a stochastic programming unit commitment model to 2 determine these effects. A case study based on the California residential sector shows that at 10% penetration levels for households with a 4 kW solar PV panel with a 0.5 kWh battery, the daily systems cost savings per household could be over $5 a day in August.
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