A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty
A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty
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DOI：
10.1109/access.2023.3262234
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Ren Hu;Qifeng Li
中科院分区：
文献类型：
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作者：
Ren Hu;Qifeng Li
针对不确定可再生能源和电力负荷（UREPL）的不确定储能设备多周期最优潮流（OPF- esduu）问题，提出了一种基于考虑数据相关性的新型策略采样（SS）数据驱动优化（DDO）方法。这种DDO方法仅依赖于不确定性样本来产生满足特定置信度的最优解，由于两种令人信服的学习算法：贝叶斯分层建模（BHM）和确定性点过程（DPP），该方法是有效的。同时考虑了局部总线信息和所有总线的空间相关性，BHM比现有的学习方法更准确地学习了交流潮流的凸逼近（CAACPF），将原来的非凸OPF-ESDUU转化为凸优化问题。DPP利用随机矩阵理论，通过测量每个样本的相对权重，考虑样本之间的相关性，找到一个小的有意义的样本集，显著减少了现有SS所需的数据样本。IEEE测试用例的实验分析表明，在考虑数据相关性后，BHM比现有学习方法更好地学习了CAACPF，准确率提高了13-90%；2)与现有的DDO相比，基于dpp的SS将采样效率提高了至少50%，从而提高了DDO的效率。
This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13–90% accuracy improvement, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least.