Volume 9, Issue 2 (7-2019)                   JEM 2019, 9(2): 34-47 | Back to browse issues page

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Golshannavaz S, Esmaeeli M. Probabilistic Integrated Planning of Primary and Secondary Distribution Networks based on a Hybrid Heuristic and GA Approach. JEM 2019; 9 (2) :34-47
URL: http://energy.kashanu.ac.ir/article-1-1102-en.html
Urmia University
Abstract:   (2845 Views)
The integrated planning of distribution system reveals a complex and non-linear problem with discontinous variables. Even though, many researchers due to these technical and modeling complexities tend to optimize the primary and secondary distribution networks individually, their individual treatment decreases the accuracy of the results. Accordingly, the integrated planning of these networks is put forward to guarantee reliable and accurate results. Different approaches are put forth for the distribution of network planning studies. Genetic algorithm stands as one of the widely deployed approaches. Due to the existing uncertainties in probabilistic planning of large distribution networks, the conventional genetic algorithm is aligned with high computational burden and, thus, with probable loss of efficiency. To annihilate these issues, the ongoing study contributes to an improved approach ending in the reduction of computational efforts and accurate result. Inspired by a traditional approach for the placement of distribution transformers and its mixture with the genetic algorithm, a heuristic method is devised which reduces the search space sensibly. Accordingly, the optimal solutions are more swiftly attained in probabilistic planning of distribution networks. To represent the existing uncertainties, we have defined a set of scenarios based on parameters of probability distribution functions. The aggregated effects of these scenarios are introduced as the expected values of the investigated variables. Efficiency of the proposed approach is explored on two test systems within which obtained results are discussed in depth.
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Type of Study: Research | Subject: Electrical Engineering
Received: 2018/05/14 | Revised: 2019/10/26 | Accepted: 2018/09/30 | Published: 2019/06/3

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