基于场景概率分布不确定性和概率组合场景性能的微电网鲁棒经济优化
CSTR:
作者:
作者单位:

(上海电力大学自动化工程学院,上海 200090)

通讯作者:

郑鹏远(1975—),男,博士后,副教授,主要从事预测控制优化理论、电网优化及复杂工业过程优化控制的研究;E?mail:pyzheng@shiep.edu.cn

中图分类号:

TM734

基金项目:

国家自然科学基金(61573239)


Robust economic optimization of microgrid based on scenario probability distribution uncertainty and probability combination scenario performance
Author:
Affiliation:

(School of Automation Engineering, Shanghai University of Electric Power, Yangpu District, Shanghai 200090, China)

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    摘要:

    针对孤岛型微电网内可再生能源和负荷的不确定性,提出基于场景概率分布的不确定性和概率组合场景性能的微电网鲁棒经济优化方法。采用K?means聚类方法对大量历史数据进行预处理,构建数据驱动场景概率分布模糊集。在日前计划阶段引入二进制展开技术,对连续变量形式的概率组合系数进行离散化,简捷有效地参数化最恶劣场景搜索的密集度和搜索区间,使得最恶劣场景的搜索范围从不确定集边界有效扩展至不确定集内部,从而搜索出最恶劣的概率组合场景;通过优化最恶劣概率组合场景性能,计算出微电网运行日前优化解。接着在日内调度阶段利用新能源和负荷的实时测量数据,对部分日前计划优化解进行二次优化调整,提高微电网控制方案的经济性和鲁棒性。仿真算例验证所提方法的有效性。

    Abstract:

    Addressing the uncertainties of renewable energy and load within isolated microgrids, a robust economic optimization approach for microgrids is proposed based on scenario probability distribution uncertainty and probabilistic combined scenario performance. The K-means clustering method is employed to preprocess extensive historical data, constructing a fuzzy set of data-driven scenario probability distributions. In the day-ahead planning phase, the binary expansion concept is introduced to discretize the probabilistic combination coefficients in continuous variable form, simplifying and effectively parameterizing the intensity and search interval of the worst-case scenario search. This extends the search range of the worst-case scenario effectively from the boundary of the uncertainty set to its interior, enabling the search for the worst probabilistic combined scenario. By optimizing the performance of the worst probabilistic combined scenario, the day-ahead optimal solution for microgrid operation is calculated. Subsequently, in the real-time scheduling phase, real-time measurement data of renewable energy and load are utilized to perform secondary optimization adjustments on part of the day-ahead planning optimization solutions, enhancing the economic efficiency and robustness of the microgrid control scheme. Simulation examples demonstrate the effectiveness of the proposed method.

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徐晓旭,郑鹏远,秦海杰,等.基于场景概率分布不确定性和概率组合场景性能的微电网鲁棒经济优化[J].电力科学与技术学报,2024,39(4):187-200.
XU Xiaoxu, ZHENG Pengyuan, QIN Haijie, et al. Robust economic optimization of microgrid based on scenario probability distribution uncertainty and probability combination scenario performance[J]. Journal of Electric Power Science and Technology,2024,39(4):187-200.

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  • 在线发布日期: 2024-09-10
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