Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

Xiang Liao, Beibei Qian (), Zhiqiang Jiang, Bo Fu and Hui He
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Xiang Liao: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Beibei Qian: School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Zhiqiang Jiang: Hydro-Intelligence Institute, Huazhong University of Science and Technology, Wuhan 430074, China
Bo Fu: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Hui He: Changjiang Engineering Group, Wuhan 430010, China

Energies, 2023, vol. 16, issue 13, 1-26

Abstract: Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

Keywords: algorithm; many-objective optimization; integrated energy station; energy dispatching; optimal design (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: 2023
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