Optimal Operation of Residential Microgrids Based on a Robust Model Using Information Gap Decision Theory

doi:10.12096/j.2096-4528.pgt.24045

Abstract

Abstract:

Objectives Residential microgrids are an important part of the new-type power system, and their optimal operation is crucial for improving the utilization efficiency of renewable energy and reducing system operating costs. However, the strong uncertainties in the output of distributed power sources such as photovoltaics, as well as in loads, pose significant challenges to the optimal operation of residential microgrids. Therefore, a robust optimal operation model based on information gap decision theory (IGDT) is proposed. Methods Firstly, by fully considering the influence of time-of-use electricity prices, an optimal operation model for grid-connected residential microgrids is established with the objective of minimizing user costs. Then, on the basis of this model, a robust optimal operation model based on IGDT is established by comprehensively considering the influence of uncertain factors such as photovoltaic output, load, and electric vehicle charging on the optimal operation of residential microgrids. Finally, a hierarchical sequencing method combined with the entropy weight method is used to solve the model. Results Through Monte Carlo simulations, the effectiveness and reliability of the IGDT-based robust model in addressing uncertainties such as photovoltaic output and load are validated. Conclusions This model provides a feasible solution for the optimal operation of residential microgrids. It is of great significance for improving their economic performance and the on-site consumption rate of distributed renewable energy.

Key words: renewable energy, residential microgrid, uncertainty, information gap decision theory (IGDT), hierarchical sequencing method, entropy weight method, Monte Carlo simulation, electric vehicle

CLC Number:

TK 01

Zonglong LUO, Shilin LIU. Optimal Operation of Residential Microgrids Based on a Robust Model Using Information Gap Decision Theory[J]. Power Generation Technology, 2025, 46(4): 705-714.

Figures/Tables 7

Fig. 1 Photovoltaic output scenario reduction set

Fig. 2 Load scenario reduction set

Fig. 3 Load fluctuation interval

Fig. 4 Photovoltaic output fluctuation interval

Fig. 5 Fluctuation interval of electric vehicle discharge power during travel

Fig. 6 Optimal operation scheme of residential microgrid based on IGDT robust model

Fig. 7 Cost comparison between simulation scenarios and IGDT robust model

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