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Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Author

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  • Jianying Li

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Minsheng Yang

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Yuexing Zhang

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianqi Li

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianquan Lu

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

Abstract

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Suggested Citation

  • Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3356-:d:1120440
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    References listed on IDEAS

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    1. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    2. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    3. Karimi, Hamid & Jadid, Shahram, 2020. "Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework," Energy, Elsevier, vol. 195(C).
    4. Yang, Zaoli & Li, Qin & Yan, Yamin & Shang, Wen-Long & Ochieng, Washington, 2022. "Examining influence factors of Chinese electric vehicle market demand based on online reviews under moderating effect of subsidy policy," Applied Energy, Elsevier, vol. 326(C).
    5. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    6. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).
    7. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    8. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    9. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F., 2021. "A novel microgrid support management system based on stochastic mixed-integer linear programming," Energy, Elsevier, vol. 223(C).
    10. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    11. Hou, Hui & Xue, Mengya & Xu, Yan & Xiao, Zhenfeng & Deng, Xiangtian & Xu, Tao & Liu, Peng & Cui, Rongjian, 2020. "Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load," Applied Energy, Elsevier, vol. 262(C).
    12. Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
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