A Simulation-Based Approach for the Planning of Radial Distribution Networks to Improve the Overall Performance and Reliability

P.H.N. Vasconcelos, A. D. De Souza, B. Venkatesh, G. Taranto
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Abstract

A methodology for the upgrade of active power distribution networks focused on reducing costs associated with customer interruptions and increasing the overall system reliability is presented. This problem has been addressed in the literature considering interventions on the system to improve performance in different aspects during the operation, such as reducing energy losses and operation costs. In general, optimization algorithms are employed to solve the problems of allocating sectionalizing devices and routing new distribution line segments. Thus, heuristic approaches - such as the Particle Swarm Optimization (PSO) algorithm, can be implemented. The proposed method also considers the annualized costs of the planning interventions, considering the expected useful lifetime of the to-be installed distribution devices. Moreover, a Monte Carlo simulation-based approach is combined with a Binary PSO (BPSO) algorithm, so multiple planning problems with binary decision variables can be considered concurrently. During simulations, the robustness of the model outcomes is tested by varying the system operative conditions. Two factors are considered to generate different scenarios: the external weather conditions and the loading level of the system. The IEEE Comprehensive Test Feeder is used to run simulations, with some modifications to make it active.
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基于仿真的径向配电网规划方法以提高整体性能和可靠性
提出了一种以降低用户中断成本和提高系统整体可靠性为重点的有功配电网升级方法。这一问题已经在文献中得到了解决,考虑了在运行过程中对系统进行干预以提高不同方面的性能,例如减少能量损失和运行成本。通常,优化算法用于解决分配分段设备和路由新配电线段的问题。因此,启发式方法-如粒子群优化(PSO)算法,可以实现。该方法还考虑了计划干预措施的年化成本,考虑了即将安装的配电设备的预期使用寿命。此外,将基于蒙特卡罗模拟的方法与二值粒子群算法相结合,使得具有二值决策变量的多个规划问题可以同时考虑。在仿真过程中,通过改变系统运行条件来检验模型结果的鲁棒性。考虑了两个因素来产生不同的场景:外部天气条件和系统的负载水平。IEEE综合测试馈线用于运行模拟,并进行了一些修改以使其有效。
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