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Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources

Author

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  • Yang, Zhichun
  • Yang, Fan
  • Min, Huaidong
  • Tian, Hao
  • Hu, Wei
  • Liu, Jian
  • Eghbalian, Nasrin

Abstract

Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model.

Suggested Citation

  • Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025816
    DOI: 10.1016/j.energy.2022.125695
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    1. Salari, Azam & Zeinali, Mahdi & Marzband, Mousa, 2024. "Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior," Energy, Elsevier, vol. 288(C).
    2. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    3. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    4. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    5. Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
    6. Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    7. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).
    8. Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).
    9. Aras Ghafoor & Jamal Aldahmashi & Judith Apsley & Siniša Djurović & Xiandong Ma & Mohamed Benbouzid, 2024. "Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges," Energies, MDPI, vol. 17(17), pages 1-28, September.

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