Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles

Shiduo Jia, Xiaoning Kang, Jinxu Cui, Bowen Tian and Shuwen Xiao
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Shiduo Jia: Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China
Xiaoning Kang: Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China
Jinxu Cui: Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China
Bowen Tian: Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China
Shuwen Xiao: Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China

Energies, 2022, vol. 15, issue 15, 1-23

Abstract: After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation results show that the proposed strategy can better achieve a win-win situation between EV owners and microgrid operators, and the operation cost of the proposed strategy is reduced by 16.55% compared with that under the disorderly charging and discharging strategy, which verifies the effectiveness of the proposed model and algorithm.

Keywords: electric vehicles; V2G; integrated energy system; stochastic optimal scheduling; sand cat swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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