Efficient optimization technique for multiple DG allocation in distribution networks

Selim, Ali; Kamel, Salah; Jurado, Francisco

doi:10.1016/j.asoc.2019.105938

Cited by 115 publications

(52 citation statements)

References 39 publications

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“…Fuzzy and clonal selection algorithm has been developed in [10] for DG allocation. Recently, many nature-inspired optimization technique have been used in the DG allocation problem such as backtracking search optimization algorithm (BSOA) [11], bacterial foraging optimization algorithm (BFOA) [12], stud krill herd algorithm (SKHA) [13], whale optimization algorithm (WOA) [14], and chaotic sine cosine (CSCA) [15].…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

et al. 2020

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In this paper, improved single-and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris Hawks optimizer (HHO) is a new inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG at different operating power factors (p.f) with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) considering the operational constraints of distribution system. In IHHO, the performance of the conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with those obtained by other optimization techniques. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single-and multi-objective scenarios. INDEX TERMS Harris hawks optimizer, single-and multi-objective optimization, DG placement, distribution systems, power loss reduction, voltage deviation, voltage stability index. I. INTRODUCTION A. MOTIVATION AND INCITEMENT ALI SELIM received the B.Sc. and M.Sc. degrees in electrical engineering from Aswan University, Aswan, Egypt, in 2010 and 2016, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical Engineering, University of Jaén, Spain. He is also an Assistant Lecturer with the Electrical Engineering Department, Aswan University. His research interests include mathematical optimization, planning, and control of power systems, renewable energies, energy storage, and smart grids.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

et al. 2020

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“…In [25] at different load levels, the objective function to find optimal location and sizing of DGs is reducing real and reactive power losses which solved by using biogeography-based optimization (BBO) algorithm. An efficient optimization algorithm to optimally allocate the multiple DG units in distribution systems based on sine cosine algorithm (SCA) and chaos map theory is proposed in [26] using three objective functions.…”

Section: Nomenclature C Dgmentioning

confidence: 99%

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

Elattar

Elsayed

2020

IEEE Access

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Due to the great impact of the penetration and locations of distributed generators (DG) on the performance of the distribution system, this paper proposes a modified moth flame optimization (MMFO) algorithm. Two modifications are proposed in MMFO to enhance the exploration and exploitation balance and overcome the shortcomings of the original MFO. The proposed MMFO is used to find the optimal location and sizing of DG units based on renewable energy sources in the distribution system. The main objective function is to minimize the total operating cost of the distribution system by considering the minimization of the total active power loss, voltage deviation of load buses, the DG units cost, and emission. This multi-objective function is converted to a coefficient single objective function with achieving different constraints. Also, the bus location index is employed to introduce the sorting list of locations to accomplish the narrow candidate buses list. Based on the candidate buses, the proposed MMFO is used to get the optimal location and sizing of DG units. The proposed MMFO algorithm has been applied to the IEEE 69-bus test distribution system and the results are compared with other published algorithms to prove its effectiveness and superiority.

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“…These functions depend on the technical characteristics of the ESSs, including storage capacity, efficiency, cost, lifetime and power rating [2,3]. Therefore, key aspects like the optimal placement and capacity of ESS should not be neglected to maximize the benefits provided by the ESS [4]. Optimal planning of ESS in the power systems ensures overall reliability and performance while reducing the investment and operation cost [5,6].…”

Section: A Motivation and Incitementmentioning

confidence: 99%

Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

et al. 2020

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This paper suggests a method to place and size the battery energy storage system (BESS) optimally to minimise total system losses in a distribution system. Subsequently, the duck curve phenomenon is taken into consideration while determining the location and sizing. The locations and sizing of BESS were optimised using a metaheuristic algorithm with high exploration and exploitation ability which is known as the Whale Optimisation Algorithm (WOA). Meanwhile, the performance of WOA was validated using other algorithms, i.e., Particle Swarm Optimisation and Firefly Algorithm. The results demonstrated the capability of WOA to determine the optimal BESS location and sizing for all cases, with and without considering the duck curve issue for loss reduction. Besides that, the duck curve issue can be mitigated by appropriately optimising the energy storage system (ESS) to reduce the steep ramp of the duck neck and ducktail and to lift the duck belly. In conclusion, although less loss reduction was achieved as a tradeoff to fulfil the constraint on net load ramp limit, the required BESS sizing was much smaller than the case without those constraints and charging operation, which makes this solution economically viable.

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Efficient optimization technique for multiple DG allocation in distribution networks

Cited by 115 publications

References 39 publications

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

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