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

• Published in: Energy (Oxford) Vol. 239; p. 121840
• Main Authors: Chen, Tengpeng, Cao, Yuhao, Qing, Xinlin, Zhang, Jingrui, Sun, Yuhao, Amaratunga, Gehan A.J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-01-2022; Elsevier BV
• Subjects: Cumulative relative regret; Decision making; Decision theory; Distributed generation; Electric power demand; Electrical loads; Energy; Energy management; Energy sources; Heat transfer; Multi-energy microgrid; Optimization; Renewable energy sources; Robust energy management; Robustness; Thermal analysis; Thermal load demand response; Uncertainty; Cumulative relative regret; Uncertainty; Thermal load demand response; Robust energy management; Multi-energy microgrid
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.121840

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.
•A novel cumulative relative regret decision-making approach is proposed to reduce the conservatism of microgrid operation.•The proposed approach can decrease the operation cost of microgrid further.•The optimization model of microgrid is improved.•The delay of heating transmission and the fuzziness of heating comfort are investigated.