Risk-aware learning for scalable voltage optimization in distribution grids
Risk-aware learning for scalable voltage optimization in distribution grids
复制标题
配电网可扩展电压优化的风险意识学习
DOI：
10.1016/j.epsr.2022.108605
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Zhu, Hao
中科院分区：
文献类型：
--
作者：
Lin, Shanny;Liu, Shaohui;Zhu, Hao
分布式能源的实时协调对于调节配电网的电压分布是至关重要的。通过利用可扩展的神经网络(NN)体系结构，可以实现分散的DER决策，以解决缺乏实时通信的问题。通过考虑无功预测和电压偏差带来的潜在风险，提出了一种先进的学习型DER协调方案。这种风险通过条件风险值(CVaR)来量化，我们只使用最坏情况的样本，并提出了一种小批量选择算法来解决CVaR正则化损失最小化的训练速度问题。在IEEE 123节点测试案例上使用真实数据进行的数值测试表明，所提出的分散供电决策的风险感知学习算法的计算量和安全性都得到了改善，特别是在减少馈线电压违规方面。
Real-time coordination of distributed energy resources (DERs) is crucial for regulating the voltage profile in distribution grids. By capitalizing on a scalable neural network (NN) architecture, one can attain decentralized DER decisions to address the lack of real-time communications. This paper develops an advanced learning-enabled DER coordination scheme by accounting for the potential risks associated with reactive power prediction and voltage deviation. Such risks are quantified by the conditional value-at-risk (CVaR) using the worst-case samples only, and we propose a mini-batch selection algorithm to address the training speed issue in minimizing the CVaR-regularized loss. Numerical tests using real-world data on the IEEE 123-bus test case have demonstrated the computation and safety improvements of the proposed risk-aware learning algorithm for decentralized DER decision making, especially in terms of reducing feeder voltage violations.