Multistage Dynamic Interaction Strategy-Driven Collaborative Optimization of Regional Integrated Energy System with Electric Vehicle Cluster

With the increasing adoption of new energy vehicles, a pressing challenge has emerged regarding how to effectively engage electric vehicles (EVs) in regional integrated energy system (RIES) scheduling. To address this issue, this study develops an RIES scheduling model incorporating an EV-RIES multistage dynamic interaction strategy (EMDIS). The proposed model and strategy allow the EV clusters interactive energy to achieve dynamic energy allocation, and each EV is divided as different dynamically, which alleviates the system pressure caused by large-scale EV charging by regulating EVs charging and discharging behaviors, while enabling flexible EV discharging through EV-RIES interactions. Furthermore, to solve the constructed high-complexity and highly nonlinear RIES scheduling model, a robust and convergence-stable multiobjective marine-species eel and grouper optimizer (MOMEGO) is proposed. Experimental results demonstrate that the MOMEGO exhibits superior robustness and higher universality, and compared with the benchmark scenario (without the strategy), the proposed EMDIS increases the energy producer’s daily revenue by $920 EVs integrated, while reducing the overall economic cost of RIES by 2%. The obtained outcomes contribute to enhancing the flexibility of EV participation in RIES scheduling and improving the operational efficiency of RIES.