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The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers' aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.
Tushar et al. (Thu,) studied this question.