Real-Time Distribution Grid State Estimation with Limited Sensors and Load Forecasting
Real-Time Distribution Grid State Estimation with Limited Sensors and Load Forecasting
复制标题
使用有限传感器和负载预测的实时配电网状态估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Tomlin
中科院分区：
文献类型：
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作者：
Roel Dobbe;D. Arnold;Stephan Liu;Duncan S. Callaway;C. Tomlin
分布式发电（DG）和电动汽车（EV）的高渗透率使配电网的潮流多样化，并带来不确定性，使配电系统运营商（DSO）的规划和控制更加复杂。约束违反的风险增加触发了需要增加预测与真实的时间状态估计。这在经济上和技术上都具有挑战性，因为它需要投资大量的传感器，并且这些传感器必须与通常较旧且较慢的监控和数据采集（SCADA）系统进行通信。我们解决配电网状态估计通过结合只有一组有限的传感器与负荷预测信息。它重新审视了最近一篇提出贝叶斯估计方案的论文中的开放问题。通过严格的建模，我们得到了平衡电力网络的估计。离线分析的负载聚合，预测精度和传感器的数量提供了具体的工程权衡，以确定所需的精度的传感器的最佳数量。该估计过程可以在真实的时间中用作控制问题的观测器，或者离线用于规划目的，以评估DG或EV对特定网络组件的影响。
High penetration levels of distributed generation (DG) and electric vehicles (EVs) diversify power flow and bring uncertainty to distribution networks, making planning and control more involved for distribution system operators (DSOs). The increased risk of constraint violation triggers the need to augment forecasts with real- time state estimation. This is economically and technically challenging since it requires investing in a large number of sensors and these have to communicate with often older and slower supervisory control and data acquisition (SCADA) systems. We address distribution grid state estimation via combining only a limited set of sensors with load forecast information. It revisits open problems in a recent paper that proposes a Bayesian estimation scheme. We derive the estimator for balanced power networks via rigorous modeling. An off-line analysis of load aggregation, forecast accuracy and number of sensors provides concrete engineering trade-offs to determine the optimal number of sensors for a desired accuracy. This estimation procedure can be used in real time as an observer for control problems or off-line for planning purposes to asses the effect of DG or EVs on specific network components.