基于分布鲁棒保序筛选的主动配电网电压控制安全筛选
Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening
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中文总结 AI 辅助
针对主动配电网新电压控制策略预部署的安全评估难点，提出DR-CSS框架，经IEEE标准节点系统实验验证可识别所有不安全场景，支持更安全的策略部署决策。
中文摘要 AI 辅助
在主动配电网中部署新的电压控制策略，需在物理电网测试前提供其能满足物理极限的证据。该评估存在两点难点：一是仿真无法覆盖真实电网中所有扰动、建模误差及设备交互；二是历史测量数据反映现有控制策略下的运行状态，而新策略可能使电网进入不同运行工况。为应对这些挑战，本文提出分布鲁棒保序安全筛选（DR-CSS），这是一种策略无关的预部署框架，利用历史数据和标称仿真器对新控制策略进行逐场景筛选。针对每个新场景，仿真器预测整个电网的未来电压轨迹，DR-CSS通过历史仿真-现实误差在该预测周围构建保序安全区间；该区间会进一步扩大，以考虑新策略部署引发的闭环变化及其与其余控制器的交互。据作者所知，DR-CSS是电力系统中首个结合现有控制策略的历史数据与不完善仿真器，用于新策略预部署安全筛选的框架。在IEEE 33节点和IEEE 141节点系统上开展的实验对基于学习的电压控制策略的部署进行评估，结果显示DR-CSS可识别所有不安全测试场景；为减少对安全场景的不必要警告，本文针对不同运行工况调整安全区间，并在每个阶段后通过重新校准逐步引入新策略，这些扩展提升了安全筛选的信息价值，支持主动配电网中更安全的部署决策。
英文摘要
Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical grid. This assessment is difficult for two reasons. First, simulations cannot capture every disturbance, modeling error, and device interaction present in the real grid. Second, historical measurements reflect operation under existing control policies, whereas a new policy may drive the grid into different operating conditions. To address these challenges, we propose Distributionally Robust Conformal Safety Screening (DR-CSS), a policy-agnostic framework for pre-deployment, scenario-by-scenario screening of a new control policy using historical data and a nominal simulator. For each new scenario, the simulator predicts a future voltage trajectory for the whole grid; DR-CSS then constructs a conformal safety interval around this prediction using historical simulation-to-reality errors. The interval is further enlarged to account for closed-loop changes induced by the deployment of the new policy and its interactions with the remaining controllers. To the best of our knowledge, DR-CSS is the first framework in power systems to combine historical data from an existing control policy with an imperfect simulator for pre-deployment safety screening of a new policy. Experiments on the IEEE 33-bus and IEEE 141-bus systems evaluate the deployment of learning-based voltage control policies and show that DR-CSS identifies all unsafe test scenarios. To reduce unnecessary warnings on safe scenarios, we adapt the safety intervals to different operating conditions and gradually introduce new policies with recalibration after each stage. These extensions increase the informational value of the safety screening and support safer deployment decisions in active distribution grids.
发表机构
- RWTH Aachen University（亚琛工业大学）
- Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。
