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Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles

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  • Guo, Shiliang
  • Li, Pengpeng
  • Ma, Kai
  • Yang, Bo
  • Yang, Jie

Abstract

The growing number of electric vehicles (EVs) has resulted in increasing availability of battery storage capacities. The energy storage capacity of EVs is used to provide demand flexibility for the supply side. However, the different preferences of EV users will affect the charge and discharge decision of EVs. To overcome this problem, the concept of charging and discharging pressure is proposed to restrict the charging and discharging behavior of EVs. It is mainly dominated by the electricity price. Simultaneously, the charging and discharging time anxiety and state of charge (SoC) of EVs also affect the charging and discharging mode of EVs. This paper proposes a novel industrial microgrid (IMG) structure, which is mainly composed of power demand of industrial production, renewable energy sources (RES), energy storage systems (ESS), EVs and thermal power generation units. The aim of the proposed model is to minimize the operation cost of IMG and maximize the income of EV users. For the management of demand side, the strategy of time of use (ToU) price is adopted. In addition, considering the uncertainty of RES and industrial load, a robust optimization algorithm is proposed, and the operation of IMG under different uncertain scenarios is analyzed. Finally, the robust mixed integer quadratic programming (MIQP) of IMG is studied. The detailed simulation and comparison results verify the effectiveness of the proposed energy system under different charging and discharging pressures based on EVs.

Suggested Citation

  • Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s030626192201114x
    DOI: 10.1016/j.apenergy.2022.119846
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    1. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2015. "Value of flexible electric vehicles in providing spinning reserve services," Applied Energy, Elsevier, vol. 157(C), pages 60-74.
    2. Mahmud, Khizir & Town, Graham E., 2016. "A review of computer tools for modeling electric vehicle energy requirements and their impact on power distribution networks," Applied Energy, Elsevier, vol. 172(C), pages 337-359.
    3. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    4. Ruifeng Shi & Shaopeng Li & Changhao Sun & Kwang Y. Lee, 2018. "Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-22, August.
    5. 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.
    6. Park, Sung-Won & Cho, Kyu-Sang & Hoefter, Gregor & Son, Sung-Yong, 2022. "Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system," Applied Energy, Elsevier, vol. 305(C).
    7. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    8. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    9. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    10. Welzel, Fynn & Klinck, Carl-Friedrich & Pohlmann, Yannick & Bednarczyk, Mats, 2021. "Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model," Applied Energy, Elsevier, vol. 290(C).
    11. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
    12. Daryabari, Mohamad K. & Keypour, Reza & Golmohamadi, Hessam, 2021. "Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles," Applied Energy, Elsevier, vol. 290(C).
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    2. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    3. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    4. Bowen Zhou & Zhibo Zhang & Chao Xi & Boyu Liu, 2023. "A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles," Mathematics, MDPI, vol. 11(21), pages 1-33, November.
    5. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    6. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    7. Wisam Kareem Meteab & Salwan Ali Habeeb Alsultani & Francisco Jurado, 2023. "Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer," Energies, MDPI, vol. 16(16), pages 1-18, August.

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