计及电动汽车接入的区域综合能源系统双层日前协调优化调度

Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles

规模化电动汽车(electric vehicles,EV)的接入对区域综合能源系统(regional integrated energy system,RIES)优化调度提出了新的挑战,文章提出了计及分布式电源和电动汽车源荷双向互动的RIES双层日前优化调度策略。首先,采用蒙特卡洛法模拟电动汽车无序充电,在此基础上利用分时电价引导负荷侧的电动汽车有序充电;然后,基于电力市场和碳交易市场,充分考虑源侧风光出力和负荷侧价格型需求响应等过程中的不确定性因素,以日系统经济成本和碳交易成本最低为目标进行系统优化调度,并采用改进粒子群算法进行求解;最后,通过仿真算例分析可得,文章提出的双层优化调度策略能够有效降低系统的负荷峰谷差,在实现电动汽车有序充电的同时,能够有效提升用户用电满意度、降低碳排放量以及增加系统经济效益。

Access to large-scale electric vehicles poses new challenges for the optimal dispatching of a regional integrated energy system(RIES).This study proposes a bi-level day-ahead scheduling strategy for a RIES while considering the integration of electric vehicles.First,the Monte Carlo method was used to simulate the disordered charging of electric vehicles.On this basis,the time-of-use price was used to guide the orderly charging of electric vehicles on the load side.Next,based on the electricity and carbon trading markets and fully considering the uncertainty factors in the process of wind power output on the source side and the demand-side response on the load side,the system was optimized and scheduled to minimize the daily economic and carbon trading costs.An improved particle swarm optimization algorithm was used to solve the problem.Finally,through the analysis of simulated examples,it was concluded that the bi-level optimal scheduling strategy proposed in this study can effectively reduce the peak-valley difference of the system load,improve the electricity consumption satisfaction of users,reduce carbon emissions,and increase the economic benefits of the system,while realizing the orderly charging of electric vehicles.