Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于混合场景集性能和时间相关性微电网鲁棒优化算法

来源:电工电气发布时间:2025-01-24 10:24 浏览次数:48

基于混合场景集性能和时间相关性微电网鲁棒优化算法

单婷婷,郑鹏远,钱帮远
(上海电力大学 自动化工程学院,上海 200090)
 
    摘 要:针对孤岛型微电网内可再生能源、负荷的不确定性以及降低碳排放量等问题,提出了一种基于数据驱动和时间相关性的微电网鲁棒优化算法。通过聚类方法生成了典型场景,进而构建包括典型场景、极端场景以及预测场景的混合场景集。在日前阶段,将混合场景集加入约束,以混合场景集所对应的微电网运行成本的概率加权指标作为目标函数,并且在成本中引入阶梯式碳交易模型,得到预调度解。在鲁棒检验阶段引入时间相关性,加快其检验速度以及效率,确保所有场景可行。在日内阶段,利用测量的新能源和负荷数据,对日前阶段的优化解进行优化调整,得到微电网设备的最优出力,从而提高微电网的经济性和鲁棒性, 仿真结果验证了该方法的有效性。
    关键词: 数据驱动;混合场景集;时间相关性;鲁棒优化
    中图分类号:TM73     文献标识码:A     文章编号:1007-3175(2025)01-0009-10
 
Robust Optimization Algorithm for Microgrids Based on Hybrid Scenario
Set Performance and Temporal Correlation
 
SHAN Ting-ting, ZHENG Peng-yuan, QIAN Bang-yuan
(School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)
 
    Abstract: To address the uncertainties in renewable energy and load within islanded microgrids and to reduce carbon emissions, this paper proposes a data-driven and temporal correlation robust optimization algorithm for microgrids. Typical scenarios are generated by clustering method, and then a hybrid scenario set including typical, extreme, and predicted scenarios is constructed. In the day-ahead stage, the hybrid scenario set is added with constraints, and the probability-weighted index of the microgrid operation cost corresponding to the hybrid scenario set is used as the objective function, and a stepwise carbon trading model is introduced into the cost to obtain the pre-dispatch solution.Temporal correlation is introduced in the robust testing phase to speed up its testing as well as efficiency to ensure that all scenarios are feasible.In the intraday stage, the measured new energy and load data are used to optimize and adjust the optimization solution in the preday stage to obtain the optimal output of the microgrid equipment, so as to improve the economy and robustness of the microgrid, and the simulation results verify the effectiveness of the method.
    Key words: data-driven; hybrid scenario set; temporal correlation; robust optimization
 
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