计及风光不确定性的主动配电网及多微电网协同优化调度

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

摘要

为应对风能、光伏发电的不确定性给电力系统优化调度带来的挑战，提出了一种计及风光不确定性的主动配电网及多微电网协同优化调度策略，旨在平衡多运营主体的利益冲突，提升系统的经济性和可靠性。首先，基于拉丁超立方采样(LHS)生成风光出力场景，并通过模糊C均值(FCM)聚类进行场景缩减，提取典型场景集。其次，构建了主动配电网与多微电网之间的主从博弈模型，通过动态调整电价引导多微电网的购售电行为，实现利益均衡。为求解该模型，本文提出了一种混合改进的雪消融优化算法(ISAO),通过引入混沌映射和自然选择策略，提升算法的全局搜索能力和收敛速度。仿真结果表明，所提方法能够有效应对风光出力的不确定性，并显著提升系统的经济效益。

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.

关键词

主从博弈 / 主动配电网 / 拉丁超立方采样 / 多微电网 / 雪消融优化算法

Key words

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

基金资助

梯级水电站运行与控制湖北省重点实验室开放基金(2024KJX04)