A stochastic optimization method for economic microgrid operation based on "source-load operation" mode classification - ZHOU Hang; MA Lihong; LI Zhongzhong; WANG Haisheng; LI Sifan; ZHANG Changjun; FU Linbei (Zhejiang dianli, 2025)
The volatility of renewable energy output and the randomness of user-side electricity consumption behavior have led to increasing source-load uncertainties， posing significant operational risks to microgrids. To address this issue， an economic operation decision-making method for microgrids based on source-load operation mode classification is proposed to achieve economic operation under uncertain conditions. First， a multivariate time series representation based on information granules is used， and the traditional affinity propagation （AP） is improved with spatial weighted matrix distance （SWMD）. A multivariate time series clustering algorithm is applied to quantify correlations between variable dimensions， enabling the classification of microgrid operation modes. Second， a source-load probabilistic forecasting model for the target day is established using similar-day data under the same operation mode， and typical source-load forecasting scenarios are obtained through scenario generation and reduction. Finally， the adaptive weighted particle swarm optimization （AW-PSO） is employed to solve the stochastic optimization model for microgrid operations. Case study results show that the proposed method effectively handles source-load uncertainties while reducing operational costs.