Economic Optimal Scheduling of Virtual Power Plant Based on Information Gap Decision Theory and Electric Vehicle V2G

In order to reduce the risk brought by the uncertainty of renewable energy output and electric vehicle travel to virtual power plant scheduling, this paper proposes an optimal scheduling model of virtual power plant incorporating electric vehicles based on in-formation gap decision theory. Firstly, a Monte Carlo load prediction model was estab-lished based on the behavior characteristics of EV users, and the Sigmoid function was introduced to quantify the dynamic relationship between user response willingness and VPP incentive price. Secondly, based on the VPP framework, wind power, photovoltaic power, gas turbines, energy storage systems and EV clusters with V2G capabilities are integrated to establish an economic optimization scheduling model considering mul-ti-source collaboration. Then, aiming at the uncertain parameters in the model, the in-formation gap decision theory is introduced, and a two-level decision-making mecha-nism with both risk aversion and opportunity seeking is constructed. Finally, a virtual power plant is tested with an example to verify the correctness and effectiveness of the proposed model and algorithm. The results show that the method can realize the load side peak balancing and valley filling effectively, and has advantages in economy and stability.