| 引用本文: | 卢 芳,王振宇,刘宏达,谢 彪,宋紫薇.考虑光伏不确定性的主动配电网自适应鲁棒优化经济调度策略[J].电力系统保护与控制,2025,53(9):93-106.[点击复制] |
| LU Fang,WANG Zhenyu,LIU Hongda,XIE Biao,SONG Ziwei.Adaptive robust optimization economic dispatch strategy for active distribution networks considering photovoltaic uncertainty[J].Power System Protection and Control,2025,53(9):93-106[点击复制] |
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| 摘要: |
| 针对光伏出力随机性对主动配电网经济性的影响，提出一种基于高斯混合模型(Gaussian mixture model, GMM)的自适应鲁棒优化调度策略，以降低系统运行成本。首先，将光伏出力分为光照充足和光照不足两种情况，采用GMM对光伏出力历史数据进行聚类分析，生成不同光照条件下不同时刻光伏出力不确定集的均值与标准差，并基于拉依达准则构建了不同光照条件下的精确不确定性集。其次，建立了以配电网总调度成本最小化为目标的自适应鲁棒优化调度模型，充分考虑了光伏出力的不确定性，并运用仿射决策规则进行求解，增强了模型对光伏波动的适应性。最后，通过改进的IEEE33节点配电网系统进行仿真验证，结果表明，该模型在保证系统安全性的同时，相较于经典区间集和多面体集有效降低了运行成本，优化结果的保守性小。 |
| 关键词: 主动配电网 自适应鲁棒优化 高斯混合模型 不确定性 |
| DOI：10.19783/j.cnki.pspc.240667 |
| 投稿时间：2024-05-29修订日期：2024-11-12 |
| 基金项目:国家重点研发项目资助(政府间国际科技创新合作)(2019YFE0105400)；黑龙江省自然科学基金项目资助(LH 2022E039)；山东省自然科学基金项目资助(ZR202103030510) |
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| Adaptive robust optimization economic dispatch strategy for active distribution networks considering photovoltaic uncertainty |
| LU Fang1,WANG Zhenyu1,LIU Hongda2,XIE Biao1,SONG Ziwei1 |
| (1. College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. Yantai Research Institute of Harbin Engineering University, Yantai 264000, China) |
| Abstract: |
| To address the impact of photovoltaic (PV) output randomness on the economic performance of active distribution networks, an adaptive robust optimization dispatch strategy based on Gaussian mixture model (GMM) is proposed to reduce system operating costs. First, PV output is categorized into two scenarios: sufficient illumination and insufficient illumination. GMM is used to cluster historical PV output data, generating the mean and standard deviation of PV output uncertainty sets for different time periods under varying illumination conditions. Based on the PauTa criterion, accurate uncertainty sets are constructed for each lighting condition. Next, an adaptive robust optimization dispatch model is established with the objective of minimizing the total scheduling cost of the distribution network. The model fully considers the uncertainty of PV output and uses an affine decision rule for solving, enhancing its adaptability to PV fluctuations. Finally, simulations are conducted on an improved IEEE 33-node distribution network system. The results show that the proposed model ensures system security while effectively reducing operating costs compared to traditional interval and polyhedral sets, with lower conservatism in the optimization results. |
| Key words: active distribution network adaptive robustness Gaussian mixture model uncertainty |
