A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

Abstract

With the goal of pursuing carbon neutrality, this study is aimed to investigate effectively managing distributed renewable energy. Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES). Time–space distribution characteristics of EVs and the responsiveness of EV users are analyzed and the available capacity calculation model is constructed. In the first stage, the bidding strategy and unit combination of the VPP are determined based on the predicted values of WP, PV, and load. Combining with the pre-decided decision from the first stage, the output of each unit is determined by considering an uncertainty set in the second stage, for achieving the best operational benefit under worst-case scenarios. Robust control parameters are introduced into the model and used to adjust conservativeness. The model is solved by applying the C&CG algorithm. Additionally, a carbon trading mechanism is leveraged to balance the system economy and environmental friendliness. The results show that the optimal scheduling solution helps to reduce fluctuations caused by uncertainties, balancing the economic benefits and operational risks of the VPP. Besides, guidance is provided for VPP operators to formulate reasonable incentives for EV users and the construction of energy storage systems. When the carbon trading price of the VPP proposed in the paper is set at 160 ¥/ton, economic and environmental sustainability can be effectively balanced.