Optimal bidding strategy for virtual power plant in multiple markets considering integrated demand response and energy storage

As the energy landscape undergoes a profound transition with the widespread penetration of renewable energy, Virtual Power Plant (VPP) energy dispatching management emerges as a highly effective approach to manage and optimize energy scheduling. In this study, we propose a distributionally robust chance-constrained optimization framework to optimize the day-ahead bidding decisions. To effectively deal with the uncertainty associated with renewable energy generation, we establish a novel interval moment information ambiguity set, which dynamically captures the uncertain characteristics. Furthermore, we design an integrated strategy for energy storage and demand response, incorporating shedding potential contract parameters for controllable loads, thereby remarkably refining the demand-side management. On the market side, we develop a multi-market trading strategy involving both the electricity market and the ancillary service market to synergistically enhance the overall operational profitability. To efficiently tackle the chance constraint of supply-demand power balance, we employ the CVaR approximation transformation to convert the model into a tractable mixed-integer second-order programming (MISOCP) form. The results of numerical experiments prove that the proposed energy scheduling and bidding strategies increase the economic benefits by 28%, significantly reducing the peak load by 25.4% and simultaneously increasing the valley load utilization by 29.3%. Additionally, our solution method exhibits excellent applicability and computational efficiency in largescale scenarios, which markedly improves energy efficiency and reduces carbon emissions by 44.8%, thus ensuring system reliability and making a profound positive impact on environmental sustainability.