Wind-photovoltaic uncertainty-accounted collaborative optimal scheduling for active distribution network and multi-microgrid systems

Abstract

To tackle the challenges posed by the uncertainty of wind-photovoltaic(PV) power generation in power system scheduling, this paper proposes a coordinated optimal scheduling strategy for active distribution network and multiple microgrids, explicitly considering wind and PV uncertainties. The objective is to balance the conflicting interests of multiple stakeholders while enhancing the system's economic efficiency and operational reliability. Firstly, wind and PV output scenarios are generated using Latin Hypercube Sampling(LHS), and a scenario reduction is performed through Fuzzy C-Means(FCM) clustering to extract a representative set of typical scenarios. Secondly, a master-slave game model is established between the active distribution network and multiple microgrids. By dynamically adjusting electricity prices, the model guides the trading behaviors of microgrids to achieve a balance of interests among stakeholders. To solve this model, a hybrid improved Snow Ablation Optimization(ISAO) algorithm is proposed, which incorporates chaotic mapping and natural selection strategies to enhance global search capability and convergence speed. Simulation result verify that the proposed method effectively mitigates the impact of wind and PV output uncertainties and significantly improves the overall economic performance of the system.

Keywords

master-slave game / active distribution network / Latin Hypercube Sampling / multi-microgrid / snow ablation optimization algorithm