Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric
Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric
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DOI：
10.1109/tpwrs.2018.2807623
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发表时间：
2017-06
影响因子：
6.6
通讯作者：
C. Duan;W. Fang;Lin Jiang;L. Yao;J. Liu
中科院分区：
文献类型：
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作者：
C. Duan;W. Fang;Lin Jiang;L. Yao;J. Liu
机会约束最优潮流（OPF）已被公认为是一个有前途的框架，以管理风险的可变可再生能源（VRE）。在VRE不确定性存在的情况下，本文讨论了一种分布鲁棒机会约束近似交流最优潮流。在建议的最优潮流制定的潮流模型结合了一个精确的交流潮流模型在标称操作点和近似的线性潮流模型，以反映系统的响应下的不确定性。分布鲁棒公式中采用的模糊集是以经验分布为中心的Wasserstein球。建议的OPF模型最小化的期望的二次成本函数w.r.t.最坏情况下的概率分布，并保证满足的机会约束的任何分布的模糊集。整个方法是数据驱动的，因为模糊度集是由历史数据构建的，而不需要对概率分布的类型进行任何假设，并且更多的数据导致更小的模糊度集和更少的保守策略。此外，特殊的问题结构，建议的问题制定开发一个有效的和可扩展的解决方案的方法。IEEE 14和118节点系统的案例研究进行了近似交流模型的准确性和必要性和有吸引力的功能相比，其他方法来处理不确定性的分布鲁棒优化方法。
Chance constrained optimal power flow (OPF) has been recognized as a promising framework to manage the risk from variable renewable energy (VRE). In the presence of VRE uncertainties, this paper discusses a distributionally robust chance constrained approximate ac-OPF. The power flow model employed in the proposed OPF formulation combines an exact ac power flow model at the nominal operation point and an approximate linear power flow model to reflect the system response under uncertainties. The ambiguity set employed in the distributionally robust formulation is the Wasserstein ball centered at the empirical distribution. The proposed OPF model minimizes the expectation of the quadratic cost function w.r.t. the worst-case probability distribution and guarantees the chance constraints satisfied for any distribution in the ambiguity set. The whole method is data-driven in the sense that the ambiguity set is constructed from historical data without any presumption on the type of the probability distribution, and more data leads to smaller ambiguity set and less conservative strategy. Moreover, special problem structures of the proposed problem formulation are exploited to develop an efficient and scalable solution approach. Case studies are carried out on the IEEE 14 and 118 bus systems to show the accuracy and necessity of the approximate ac model and the attractive features of the distributionally robust optimization approach compared with other methods to deal with uncertainties.