Multiple DG placements in radial distribution system for multi objectives using Whale Optimization Algorithm

Prakash, D.B.; Lakshminarayana, C.

doi:10.1016/j.aej.2017.11.003

Cited by 92 publications

(48 citation statements)

References 29 publications

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“…As observed, the power loss is smaller in the cases of AEO and EAEO algorithms compared with the other values in the literature. Hybrid [48] 2598.0 (6) 111.03 ----BFOA [18] 2200.0 (6) 113.14 ---- 755.0 (14) 1073 (24) 1068 (30) 72.81 ------MINLP [35] 800 (13) 1090 (24) 1050 (30) 72.784 ------GAMS [49] 770.9 (14) 1096.9 (24) 1065.8 (30) 72.…”

Section: ) Case Study 2: Optimal-pf Dgmentioning

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: ) Case Study 2: Optimal-pf Dgmentioning

confidence: 99%

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

et al. 2020

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“…Many pieces of research added some objective functions besides power loss and voltage profile. The most known objective functions besides power loss and voltage profile are reactive power loss, voltage stability index, investment cost, operational cost, total harmonic distortion, and system load ability, which have been introduced as a weighted sum multi-objective function to determine best site and size of DG integrated with DN [43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

et al. 2020

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Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

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“…Several numbers of studies have developed various methodologies of incorporating DG, which typically applied to the weakest point in the networks. Most of the related literature implements voltage sensitivity [1][2][3][4][5][6][7], or loss sensitivity [6,[8][9][10][11][12][13] as the main factors in deciding on (optimal) placing and sizing of DG.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based DG location using Monte-Carlo simulation technique

et al. 2020

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Reliability targets have become essential to distribution network operator since Energy Regulator set minimum targets of reliability performance of the network and penalise if the targets not achieved. Thus, placement of distributed generation (DG) into network offers improvement in reliability performance. Typically, less attention is given to DG reliability-based placement compared to voltage-and loss-based placement. Therefore, this paper presents the placement of DG based on reliability performance, which applied to medium voltage (MV) and low voltage (LV) suburban distribution network. The typical Monte-Carlo simulation technique is used to assess both system and customer-related reliability performance. The placement of DG into MV and LV networks show improvement in terms of reliability performance.

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Multiple DG placements in radial distribution system for multi objectives using Whale Optimization Algorithm

Cited by 92 publications

References 29 publications

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

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

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Reliability-based DG location using Monte-Carlo simulation technique

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