期刊名称：Bulletin of the Institute of Heat Engineering
印刷版ISSN：2083-4187
出版年度：2016
卷号：96
期号：2
页码：115
语种：English
出版社：Warsaw University of Technology
摘要：Due to the new technologies introduced in smart grids, it is hard to forecast future load demands with deterministic values.This makes it essential to consider load demand uncertainty in power distribution planning (PDP) approaches. The purposeof this paper was to find an approach that can solve optimal integrated power distribution long-term planning under loaddemand uncertainty. A single objective function was used that considers costs of low and medium voltage feeders, distributiontransformers (DT) and high voltage (HV) substations simultaneously. Imperialist competitive algorithm (ICA) was used tosolve the optimization problem. The proposed approach was applied to a semi-real hypothetical test-case with geographicalattributes. Normal distribution function was used to model load demand uncertainty and Monte Carlo simulation (MCS)technique was applied to solve optimal planning under uncertainty. MCS takes statistical data and gives statistical results. Atechnique was utilized to take a single solution from statistical results. Based on comparisons with deterministic approach,the proposed approach is capable of giving a robust solution.
其他摘要：Due to the new technologies introduced in smart grids, it is hard to forecast future load demands with deterministic values. This makes it essential to consider load demand uncertainty in power distribution planning (PDP) approaches. The purpose of this paper was to find an approach that can solve optimal integrated power distribution long-term planning under load demand uncertainty. A single objective function was used that considers costs of low and medium voltage feeders, distribution transformers (DT) and high voltage (HV) substations simultaneously. Imperialist competitive algorithm (ICA) was used to solve the optimization problem. The proposed approach was applied to a semi-real hypothetical test-case with geographical attributes. Normal distribution function was used to model load demand uncertainty and Monte Carlo simulation (MCS) technique was applied to solve optimal planning under uncertainty. MCS takes statistical data and gives statistical results. A technique was utilized to take a single solution from statistical results. Based on comparisons with deterministic approach, the proposed approach is capable of giving a robust solution.
关键词：Distribution network planning;Load demand uncertainty;Monte Carlo Simulation;Optimization
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