Genetic algorithm for optimal sizing and location of multiple distributed generations in electrical network

Ameri, Ahmed Al; Nichita, Cristian; Riouch, Tariq; Bachtiri, Rachid El

doi:10.1109/meps.2015.7477194

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…In [5], binary particle swarm optimization and shuffled frog leap (BPSO-SLFA) algorithms are used to minimize losses, improve voltage profiles, and enhance cost savings for different distribution systems. Research shows that the linear model combined with GA is efficient in reducing real power losses by finding the optimal location and size of DG units [6]. In [7], using the differential evolution meta-heuristic algorithm, the placement and sizing of distributed generation units has been done to reduce active power losses and it has been compared with the cuckoo search algorithm (CSA), simple genetic algorithm (SGA) and ant-lion optimization algorithm (ALOA).…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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One of the most important aims of the sizing and allocation of distributed generators (DGs) in power systems is to achieve the highest feasible efficiency and performance by using the least number of DGs. Considering the use of two DGs in comparison to a single DG significantly increases the degree of freedom in designing the power system. In this paper, the optimal placement and sizing of two DGs in the standard IEEE 33-bus network have been investigated with three objective functions which are the reduction of network losses, the improvement of voltage profiles, and cost reduction. In this way, by using the backward-forward load distribution, the load distribution is performed on the 33-bus network with the power summation method to obtain the total system losses and the average bus voltage. Then, using the iterative search algorithm and considering problem constraints, placement and sizing are done for two DGs to obtain all the possible answers and next, among these answers three answers are extracted as the best answers through three methods of fuzzy logic, the weighted sum, and the shortest distance from the origin. Also, using the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) and setting the algorithm parameters, thirty-six Pareto fronts are obtained and from each Pareto front, with the help of three methods of fuzzy logic, weighted sum, and the shortest distance from the origin, three answers are extracted as the best answers. Finally, the answer which shows the least difference among the responses of the iterative search algorithm is selected as the best answer. The simulation results verify the performance and efficiency of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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“…The locations of DGs are determined by using Index Vector Method (IVM) approach and Artificial Bee Colony (ABC) optimization algorithm has been employed to determine the optimum size , the proposed approach has been proved on standard 15-bus and 33-bus Radial Distribution Network (RDN) [14] .the distribution system reconfiguration (DSR), for considering network configuration impact that runs in offline mode with fixed loads, and optimal DG allocation and sizing issues are studied at the same time to find an optimal condition for distribution network depends on operational thresholds and reliability improvements, Nondominated Sorting Genetic Algorithm is used to fix these problems simultaneously [15]. a Genetic Algorithm (GA) optimization method proposed to find optimal sizing and location of multi distributed generations in electrical networks, it is tested on (14, 30 and 57) IEEE standard systems [16] . Optimal distributed generation allocation using evolutionary algorithms in meshed network, the proposed method is applied on a standard IEEE 30-bus system with various DG penetration limits, the results received show the choice of the appropriate optimization algorithm and the effect of the constraints considered for optimal sizing and placement of the DGs [17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Generation Placement and Sizing to Reduce Active Power Loss Using Ga and Aco Algorithm

Zakaria

El-Amary

Swief

et al. 2019

Journal of Al-Azhar University Engineering Sector

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In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniques are proposed to find optimal sizing and location for distributed generation in electrical networks. The objective function of the work relies upon a linearized model to compute the active power losses as a function of power generators. This strategy based on a strong coupling between active power and power flow taking into consideration the voltage angles. With the end goal to exhibit the adequacy of the proposed method, the proposed strategy is applied on IEEE 30-bus standard systems. Different maximum penetration level capacity of DG units with three ranges such as, 10%, 20% and 30% of maximum power load and various possible places of DG units among several types of DG (active, reactive or active and reactive power) are considered. Results show that the optimization tools employing GA and ACO are effective in reducing active power losses and cost loss by finding the optimal placement and sizing of DG units.

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