Configuration optimization and benefit allocation model of multi-park integrated energy systems considering electric vehicle charging station to assist services of shared energy storage power station. (15th February 2022) - Jianwei, Gao; Fangjie, Gao; Yu, Yang; Haoyu, Wu; Yi, Zhang; Pengcheng, Liang
Abstract: Energy storage (ES) has a significant impact on increasing the use of clean energy and lowering carbon emissions. But the high cost of ES limits its large-scale development. Hence, considering the various scenarios and electric vehicles' uncertainties, this paper develops a three-layer planning and scheduling model for the electric vehicle charging station (EVCS) to assist the shared energy storage power station (SESPS) in serving multi-park integrated energy systems. To assess the model's effectiveness, environmental factors are established. And a profit distribution model based on the Shapley value is established to increase the cooperation enthusiasm of each participant. The results show that: (1) The established model reduced the cost of the multi-park by 55.32% and increased the profit of the EVCS by 775.37%. Comparing to self-operation, the EVCS assisted the SESPS reduced capacity by 26.40% and increased profit by 120.31%. (2) With the participation of ES equipment, the utilization rate of new energy reached 100% and the carbon emission was reduced by 55.77%. (3) The improved Shapley value reduced the SESPS's payback period from 2.13 years to 1.12 years, allowing SESPS operators to work more effectively with multiple participants. Therefore, the planning, scheduling, and income distribution model proposed in this paper can improve clean energy utilization, reduce carbon emissions, and increase participants' willingness to cooperate on the basis of maximizing each participant's benefits. It is a reference for complex energy system planning, scheduling, and revenue distribution, as well as a way of encouraging clean energy use. Highlights: A three-level configuration and optimization model is established. Monte Carlo and Latin Hypercube simulations are used to study typical scenarios. Multiple uncertainties of EVs are considered in EV cluster scheduling. Utility functions and environmental factors are used to improve the Shapley value.