Real-time volt/var optimization for distribution systems with photovoltaic integration

Yan Chen, B. Luckey, J. Wigmore, M. Davidson, Andrea Benigni
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引用次数: 6

Abstract

This paper presents a two-stage optimization approach to mitigate the rapid voltage fluctuations and minimize the power losses of distribution systems due to the high penetration of photovoltaic (PV) generation. The first stage is a day-ahead optimal strategy which aims to minimize the total voltage deviations and power losses within the constraints of the daily maximum allowable number of operations of the on-load tap changers (OLTCs) and shunt capacitors (SCs). The second stage is a real-time inverter reactive power control to compensate for the uncertainties of PV output and load demand. As a part of the real-time control, an artificial neural network (ANN) approach is used to estimate the system states. In both stages, the optimization problems are formulated as nonlinear optimization problems and solved with direct search algorithms. The real-time optimization method is tested using a Hardware-In-the-Loop (HIL) simulation platform. A modified IEEE 34-node test feeder is applied to demonstrate the effectiveness of the proposed approach.
光伏集成配电系统的实时电压/无功优化
本文提出了一种两阶段优化方法,以缓解由于光伏发电的高渗透率而导致的快速电压波动和最大限度地减少配电系统的功率损耗。第一阶段是日前优化策略,其目的是在有载分接开关(oltc)和并联电容器(sc)的每日最大允许操作次数的约束下,将总电压偏差和功率损失最小化。第二阶段是逆变器无功功率实时控制,以补偿光伏输出和负荷需求的不确定性。作为实时控制的一部分,采用人工神经网络(ANN)方法对系统状态进行估计。在这两个阶段,优化问题都被表述为非线性优化问题,并使用直接搜索算法求解。在硬件在环仿真平台上对实时优化方法进行了测试。应用改进的IEEE 34节点测试馈线验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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