Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant

In this paper, a virtual power plant (VPP) that consists of generation, both renewable and conventional, and controllable demand is enabled to participate in the wholesale markets. The VPP makes renewable energy sources (RES) and distributed generations controllable and observable to the system operator. The main objective is to introduce a framework that optimizes the bidding strategies and maximizes the VPP's profit on day-ahead and real-time bases. To achieve this goal, the VPP trades energy externally with a wholesale market, and trades energy and demand response (DR) internally with the consumers in its territory. That is, when generation exceeds demand, the VPP sells the excess energy to the market, and it buys energy from the market when the generation and reduction in demand due to DR scheme are less than the required demand in its territory. Both load curtailment and load shift are modeled. For the day-ahead internal VPP market, fuzzy optimization is proposed to consider the uncertainty in the RES. Comparison results with deterministic and probabilistic optimizations demonstrate the effectiveness of the fuzzy approach in terms of achieving higher realized profits with reasonable computation effort. It is also shown that considering uncertainties in the optimization can result in reduced dependence on the conventional generator.