Optimal Allocation of FACTS Devices in Restructured Power Systems Integrated Wind Generation

A. Eladl, A. Elmitwally, S. Eskander, I. Mansy
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引用次数: 2

Abstract

This paper proposes an approach to optimally allocate flexible AC transmission system (FACTS) devices in market-based restructured power systems based on Expected Benefit (EB) and installation cost of FACTS devices (FDs). The aims of the approach are minimizing device investment cost, and the locational marginal pricing (LMP) differences between buses to maximize revenue. This revenue is defined as the difference between EB with and without FACTS installation. The expected cost includes operating cost not only under normal condition but also under contingencies along with their associated probabilities to occur. The proposed method accurately evaluates the annual cost and benefits obtainable by FACTS devices installation by formulating a large-scale optimization problem that contains power flow analyses for a large number of system states representing annual power system operations. The objectives for normal state are maximizing social welfare by minimizing LMPs differences between buses and minimizing of generations re-scheduling, while the objectives in case of contingency are maximizing social welfare as well as minimizing compensations paid for generations re-scheduling and load shedding cost. The overall problem is solved using both Particle Swarm optimization (PSO) for attaining optimal FACTS devices setting and allocation as main problem and optimal power flow to calculate the operating costs and benefits as sub optimization problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.
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风力发电系统重构中FACTS设备的优化配置
提出了一种基于预期效益(EB)和柔性交流输电系统(FACTS)设备安装成本的市场化重构电力系统柔性交流输电系统(FACTS)设备优化配置方法。该方法的目标是使设备投资成本最小化,并使总线之间的位置边际定价(LMP)差异最大化收益。这个收益被定义为安装了FACTS和没有安装FACTS的EB之间的差额。预期成本不仅包括正常情况下的运营成本,还包括突发事件及其发生概率下的运营成本。该方法通过对代表电力系统年运行的大量系统状态进行潮流分析,形成一个大规模优化问题,准确地评估FACTS设备安装的年成本和效益。在正常状态下,目标是通过最小化公交车间LMPs差异和最小化代际再调度来实现社会福利最大化;在突发情况下,目标是通过最小化代际再调度补偿和减载成本来实现社会福利最大化。采用粒子群算法(Particle Swarm optimization, PSO)实现FACTS器件的最优设置和分配,并将最优潮流计算作为子优化问题进行求解。在改进的IEEE 14总线测试系统和IEEE 118总线测试系统上验证了该方法的有效性。
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