Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators

Rastgou, Abdollah; Moshtagh, Jamal; Bahramara, Salah

doi:10.1016/j.energy.2018.03.030

Cited by 45 publications

(18 citation statements)

References 56 publications

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“…The population-based metaheuristic optimization algorithms divide into many types according to the type of inspiration [9]. These types include evolutionary algorithms such as genetic algorithm (GA) [10] and differential evolution (DE) [11]; swarm intelligence such as particle swarm optimization (PSO) [12], ant colony optimization (ACO) [13] and artificial bee colony optimization (ABC) [14]; physical algorithms such as gravitational search algorithm (GSA) [15] and wind-driven optimization (WDO) [16]; bio algorithms such as grey wolf optimizer (GWO) [17] and bacterial colony foraging optimization (BCFO) [18]; and others such as sine cosine algorithm (SCA) [19], differential search algorithm (DSA) [20] and harmony search (HS) [21]. New algorithms have been added recently to this list, such as artificial ecosystem-based optimization (AEO) [22], [23], Levy flight distribution (LFD) algorithm [24], turbulent flow of water-based optimization (TFWO) [25] and atomic search optimization (ASO) [26].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations

et al. 2020

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Optimal allocation of distributed generations (DGs) is vital to the proper operation of the distribution systems, which leads to power loss minimization and acceptable voltage regulation. In this paper, an Enhanced Artificial Ecosystem-based Optimization (EAEO) algorithm is proposed and used to solve the optimization problem of DG allocations to minimize the power loss in distribution systems. In the suggested algorithm, the search space is reduced using operator G and sine-cosine function. The G-operator affects the balance between explorative and exploitative phases. At the same time, it gradually decreases during the iterative process in order to converge to the optimal global solutions. On the other hand, the sine-cosine function creates different and random solutions. The EAEO algorithm is applied for solving the standard 33bus 69-bus, and 119-bus distribution systems with the aim of minimizing the total power losses. Multiple DG units operating at various power factors, including unity-, fixed-, and optimal-power factors, are considered. Both single and multiple objectives are considered to minimize the total voltage deviation (TVD), maximize the system stability, and reduce the total power losses. The obtained results are compared with those obtained by the AEO and other algorithms. The results demonstrate a significant reduction of total power losses and improvement of the voltage profile of the network, especially for the DGs operating at their optimal power factors. Comparisons show the dominance of the proposed EAEO algorithm against other analytical, metaheuristic, or hybrid algorithms. Moreover, the EAEO outperforms the original AEO algorithm with a faster convergence speed and better system performance.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations

et al. 2020

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“…In this paper, using the multi-objective natural aggregation algorithm, the location and size of the electric vehicle charging stations, renewable energy sources, battery energy storage system, and distribution network expansion schemes are determined. By using an improved harmony search algorithm, optimal location and size of distribution substations and feeders are investigated in the presence of distributed generators [7]. In this paper, in order to keep the radial structure of distribution network, the simultaneous satisfaction of the two constraints is evaluated: first, the determinant of the branch-node matrix must be zero, and second, the number of branches should be less than the number of nodes by one.…”

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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“…Currently, within the concept of modern distribution networks, environmental concerns play an increasingly important role. With deregulation of the power system and taxation on carbon dioxide (CO 2 ) emission in various countries, expansion‐planning actions should provide lower environmental effects [9]. In this regard, investment in renewable distributed generations (DGs) is a viable and effective option for meeting load and reducing CO 2 emissions through less dependence on fossil fuels [9, 10].…”

Section: Introductionmentioning

confidence: 99%

“…With deregulation of the power system and taxation on carbon dioxide (CO 2 ) emission in various countries, expansion‐planning actions should provide lower environmental effects [9]. In this regard, investment in renewable distributed generations (DGs) is a viable and effective option for meeting load and reducing CO 2 emissions through less dependence on fossil fuels [9, 10]. Also, reliability concerns are central when dealing with the DSEP problem, as practical studies show that most service interruptions take place at the distribution level [2].…”

Section: Introductionmentioning

confidence: 99%