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Multi-energy microgrid robust energy management with a novel decision-making strategy

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  • Chen, Tengpeng
  • Cao, Yuhao
  • Qing, Xinlin
  • Zhang, Jingrui
  • Sun, Yuhao
  • Amaratunga, Gehan A.J.

Abstract

Uncertainties in renewable energy sources and load demand have become a consequential issue which has led to a significant effect on the microgrid operation. In this paper, a novel cumulative relative regret decision-making strategy is proposed for the optimal energy management on a grid-connected multi-energy microgrid considering these uncertainties. The proposed strategy can ensure the robustness of the microgrid and reduce the conservatism of microgrid operation as compared with the traditional robust optimization method. Furthermore, the typical optimization model of microgrid energy management is improved by taking the demand response of the thermal load into account. The delay of heat transfer and the fuzziness of heating comfort are also investigated in order to obtain a more economic microgrid scheduling plan. Simulation results using different decision-making strategies are provided to verify the effectiveness of the proposed cumulative relative regret based robust optimization approach.

Suggested Citation

  • Chen, Tengpeng & Cao, Yuhao & Qing, Xinlin & Zhang, Jingrui & Sun, Yuhao & Amaratunga, Gehan A.J., 2022. "Multi-energy microgrid robust energy management with a novel decision-making strategy," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221020880
    DOI: 10.1016/j.energy.2021.121840
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    2. Lu, Xi & Xia, Shiwei & Gu, Wei & Chan, Ka Wing, 2022. "A model for balance responsible distribution systems with energy storage to achieve coordinated load shifting and uncertainty mitigation," Energy, Elsevier, vol. 249(C).
    3. Xiaohang Wu & Hasan Dinçer & Serhat Yüksel, 2022. "Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
    4. 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.
    5. Vahid-Ghavidel, Morteza & Shafie-khah, Miadreza & Javadi, Mohammad S. & Santos, Sérgio F. & Gough, Matthew & Quijano, Darwin A. & Catalao, Joao P.S., 2023. "Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system," Energy, Elsevier, vol. 265(C).
    6. Yang, Peiwen & Fang, Debin & Wang, Shuyi, 2022. "Optimal trading mechanism for prosumer-centric local energy markets considering deviation assessment," Applied Energy, Elsevier, vol. 325(C).
    7. Zhou, Kaile & Fei, Zhineng & Hu, Rong, 2023. "Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties," Energy, Elsevier, vol. 265(C).
    8. Ma, Ning & Fan, Lurong, 2023. "Double recovery strategy of carbon for coal-to-power based on a multi-energy system with tradable green certificates," Energy, Elsevier, vol. 273(C).
    9. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
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