Revenue targeting for a prosumer with storage under gross and net energy metering policies

This paper deals with the revenue targeting for a prosumer with solar photovoltaic generation and storage. We consider inelestic prosumer load demand under time-varying prices for two types of billing policy, namely Net Energy Metering (NEM) and Gross Energy Metering (GEM). The storage enables to exploit the price fluctuations by shifting consumption to off-peak price and generation to peak price instants to get maximum revenue. We propose computationally efficient Dynamic Programming (DP) and Linear Programming (LP) methods to solve the revenue maximization problem. The complexity of both methods is compared with the Mixed Integer Linear Programming (MILP) method. The DP method has better performance under uncertainties and has the potential for processor-based implementation. A Model Predictive Control (MPC) scheme is used for real-time implementation with a rolling horizon. The load, generation, and prices are predicted using seasonal Auto Regressive Moving Average (ARMA) models.The results show that the value of storage exhibits a minimum at a certain selling to buying price ratio under NEM policy. Moreover, the fast charging batteries earn higher revenue than the slower ones. However, fast-charging batteries are also more vulnerable to uncertainties. Therefore, we advocate the appropriate derating of the battery power capacity to hedge against uncertainties. The techno-economic analysis reveals that the battery will become an attractive option for lower ratios of selling to buying price in near future.