Characteristic matching of stochastic scenarios and flexible resource capacity optimisation for isolated microgrids

Considering a large number of stochastic scenarios when optimising the flexible resource capacity for isolated microgrids not only improves the accuracy and credibility of the results but also enables the use of probabilistic statistical methods to obtain a scheme that balances robustness and costs. However, the consideration of massive scenarios can drastically lower the computational efficiency, and there is a lack of research on configuration methods that can result in a compromise. In this study, a characteristic‐matching method is proposed to enhance the computational efficiency of optimisation, and a statistics‐based prism filtering method is designed to obtain the compromise scheme for use by decision‐makers. Specifically, the massive scenario set is divided into four subsets. The characteristic‐matching method is proposed to obtain a near‐optimal solution, which is used as the initial iteration value to accelerate the computational speed. Then, a characteristic‐matching‐based bi‐level optimisation method is proposed to solve massive scenarios with high computational efficiency. Moreover, a prism filtering method is designed to select a compromise scheme with high economic benefits using scenario coverage, load curtailment, power curtailment and economy indicators. Simulation results verify the effectiveness of the proposed models and methods.