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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiang Liao & Beibei Qian & Zhiqiang Jiang & Bo Fu & Hui He, 2023. "Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies," Energies, MDPI, vol. 16(13), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5216-:d:1188623
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    References listed on IDEAS

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    1. Amiri, Saeed Salimi & Jadid, Shahram & Saboori, Hedayat, 2018. "Multi-objective optimum charging management of electric vehicles through battery swapping stations," Energy, Elsevier, vol. 165(PB), pages 549-562.
    2. Qiongjie Dai & Jicheng Liu & Qiushuang Wei, 2019. "Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    3. Andu Dukpa & Boguslaw Butrylo, 2022. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts," Energies, MDPI, vol. 15(15), pages 1-14, August.
    4. Ruifeng Shi & Penghui Zhang & Jie Zhang & Li Niu & Xiaoting Han, 2020. "Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm," Energies, MDPI, vol. 13(11), pages 1-15, June.
    5. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    6. Suri, Girish & Onori, Simona, 2016. "A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries," Energy, Elsevier, vol. 96(C), pages 644-653.
    7. Javed, Muhammad Shahzad & Song, Aotian & Ma, Tao, 2019. "Techno-economic assessment of a stand-alone hybrid solar-wind-battery system for a remote island using genetic algorithm," Energy, Elsevier, vol. 176(C), pages 704-717.
    8. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    9. Shojaabadi, Saeed & Talavat, Vahid & Galvani, Sadjad, 2022. "A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers," Renewable Energy, Elsevier, vol. 193(C), pages 407-417.
    10. Hernández, J.C. & Sanchez-Sutil, F. & Muñoz-Rodríguez, F.J. & Baier, C.R., 2020. "Optimal sizing and management strategy for PV household-prosumers with self-consumption/sufficiency enhancement and provision of frequency containment reserve," Applied Energy, Elsevier, vol. 277(C).
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