Allocation and sizing of distribution transformers and feeders for optimal planning of MV/LV distribution networks using optimal integrated biogeography based optimization method

Yosef, Mohamed; Sayed, Mahmoud M.; Youssef, Hala

doi:10.1016/j.epsr.2015.06.022

Cited by 21 publications

(21 citation statements)

References 42 publications

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“…Although a dozen studies have been documented for distribution network planning [6,19,20], or transformer optimal sizing [10][11][12][13][14][15][16], the OTR problem has been largely overlooked. In fact, so far, due to the limited number of DT replacement, typically, the OTR problem is optimised manually by trial and error.…”

Section: Literature Reviewmentioning

confidence: 99%

Distribution transformer relocation problem: an integer programming solution

Hosseini

Hajiaghapour-Moghimi

Hajipour

et al. 2020

IET Generation Trans & Dist

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The short-term expansion planning of the private utilities, as well as the emerging technologies such as photovoltaic panels (PVs), plug-in hybrid electric vehicles (PHEVs), cryptocurrency mining, and storage elements spread, make the long-term load estimation of distribution transformers (DTs) noticeably imprecise. In response, the number of overload and underload transformers is growing in recent years. The utilities normally analyse the loading of their DTs annually to determine the DTs, which should be replaced. It is a common practice for utilities to relocate these DTs to reduce the investment needed to purchase new transformers. Therefore, the utility needs a systematic algorithm to determine the optimal schedule to relocate/replace the DTs with minimum required time and cost. This paper introduces a two-stage procedure to obtain the optimal schedule to replace DTs. In the proposed algorithm, the maximum permitted number of work-hours per day for field workers is considered, which converts the problem to a daily-task based optimisation problem. Also, an useful life investigation of the in-service DTs is considered in the relocation problem. A real case study in Qom province, Iran, has been studied to prove the effectiveness of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Distribution transformer relocation problem: an integer programming solution

Hosseini

Hajiaghapour-Moghimi

Hajipour

et al. 2020

IET Generation Trans & Dist

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“…The main component of cost is investment cost of new transformers and lines. To solve the allocation problem, both classical methods and artificial intelligence techniques can be applied . Some classical methods applied to the transformer allocation problem are geographic information system (GIS)‐based method, Voronoi diagram, and dynamic programming (DP) .…”

Section: Introductionmentioning

confidence: 99%

“…In the last decade, artificial intelligence techniques have been successfully applied to many difficult power system problems. Some of these methods applied to the transformer allocation problem are imperialist competitive algorithm (ICA), genetic algorithm (GA), non‐dominated sorting GA‐II (NSGA II), biogeography‐based optimization (BBO), and automata learning …”

Section: Introductionmentioning

confidence: 99%

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Optimal MV/LV transformer allocation in distribution network for power losses reduction and cost minimization: A new multi‐objective framework

Khandani

Askarzadeh

2020

Int Trans Electr Energ Syst

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Among distribution network planning (DNP) problems, medium voltage (MV) to low voltage (LV) transformer allocation is one of the most important and challenging ones. This article presents a multi‐objective optimization framework for solving MV/LV transformer allocation problem considering two goals: power losses and investment cost. Transformer allocation problem has been solved in three scenarios: (1) base case, (2) mid‐term planning, and (3) long‐term planning. In order to effectively and efficiently solve this nonlinear problem, multi‐objective crow search algorithm (MOCSA) and multi‐objective particle swarm optimization (MOPSO) have been applied and compared in terms of Pareto front. Simulation results show that solving transformer allocation problem in multi‐objective framework results in a set non‐dominated solutions which provide a trade‐off between losses and cost. Moreover, MOCSA produces more promising results than MOPSO.

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“…In this paper, the Minimum Spanning Tree method is used for routing feeders on both the MV and LV sides of the distribution system. A biogeography-based optimization is employed to find the optimal location and rating of distribution transformers and substations, as well as the type and route of medium and low voltage feeders based on uniform or nonuniform load density [12]. The Imperialist Competitive Algorithm (ICA) is used to find an optimal route of mediumvoltage (MV) feeders at the presence of load forecasting uncertainties in multistage mode [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Feeder Routing and DG Placement Using Kruskal’s Algorithm

Kamyab¹

2020

EJEE

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In this paper, the optimal feeder routing along with optimal distributed generator placement is formulated as an optimization problem. In this problem, the total cost of capital recovery, supply interruption and energy losses are minimized. Also, line loading capacity and bus voltage constraints are applied. By proposing a novel method to code the solutions of the optimization problem with the facilitation obtained from the utilization of the Kruskal's algorithm, it is guaranteed that graphs of all solutions would always be spanning trees. The main result of the implementation of this method within meta-heuristic algorithms is to limit the search space to radial networks leading to snap quicker answers with higher degree of optimality. A distribution network with a 24 load points and 42 candidate branches is used as a baseline to indicate the effectiveness of the proposed method which was tested using three meta-heuristic algorithms including genetic algorithm, particle swarm optimization and simulated annealing.

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Allocation and sizing of distribution transformers and feeders for optimal planning of MV/LV distribution networks using optimal integrated biogeography based optimization method

Cited by 21 publications

References 42 publications

Distribution transformer relocation problem: an integer programming solution

Distribution transformer relocation problem: an integer programming solution

Optimal MV/LV transformer allocation in distribution network for power losses reduction and cost minimization: A new multi‐objective framework

Optimal Feeder Routing and DG Placement Using Kruskal’s Algorithm

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