AI 中文总结
针对分布式能源扩张带来的配电网安全评估难题，本文提出基于智能电表数据的自适应迭代算法，可在负载变化时实时识别跟踪电压极值节点，实现配电网安全的有效评估。
AI 中文摘要
分布式能源资源的快速扩张加剧了配电网运行的不确定性与波动性，可能引发电压越限、电压不平衡过度等电能质量问题。确保配电网可靠安全运行需要系统实时评估，但配电网内传感、测量与通信能力的限制导致系统状态感知不足。为实现配电网安全的更优实时估计，本文利用多数配电网已普及的智能电表数据，提出一种实时安全评估方法。假设可实时获取有限数量的电压幅值测量值，本文设计迭代算法自适应识别智能电表子集，其实时测量值可用于验证所有电压幅值保持在限值内。该算法迭代执行两步：(1)在给定有限电压幅值测量值的情况下，求解优化问题以确定最坏情况下的电压幅值；(2)利用这些问题的解与灵敏度信息更新测量集。在IEEE 123配电网馈线上的数值测试表明，所提算法能在负载随时间变化时，持续识别并跟踪电压幅值最高与最低的节点。
英文摘要
The rapid expansion of distributed energy resources is heightening uncertainty and variability in distribution system operations, potentially leading to power quality challenges such as voltage magnitude violations and excessive voltage unbalance. Ensuring the dependable and secure operation of distribution grids requires system real-time assessment. However, constraints in sensing, measurement, and communication capabilities within distribution grids result in limited awareness of the system's state. To achieve better real-time estimates of distribution system security, we propose a real-time security assessment based on data from smart meters, which are already prevalent in most distribution grids. Assuming that it is possible to obtain a limited number of voltage magnitude measurements in real time, we design an iterative algorithm to adaptively identify a subset of smart meters whose real-time measurements allow us to certify that all voltage magnitudes remain within bounds. This algorithm iterates between (1) solving optimization problems to determine the worst possible voltage magnitudes, given a limited set of voltage magnitude measurements, and (2) leveraging the solutions and sensitivity information from these problems to update the measurement set. Numerical tests on the IEEE 123 distribution feeder demonstrate that the proposed algorithm consistently identifies and tracks the nodes with the highest and lowest voltage magnitude, even as the load changes over time.
CommentsPublished in Proceedings of the 2024 IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 6493-6500
Journal refProceedings of the 2024 IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 6493-6500
DOI：10.1109/CDC56724.2024.10885867
