Optimal power flow for distribution networks using gravitational search algorithm

Radosavljević, Jordan; Jevtić, Miroljub; Arsić, Nebojša; Klimenta, Dardan

doi:10.1007/s00202-014-0302-5

Cited by 24 publications

(8 citation statements)

References 23 publications

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“…In addition, better results are obtained with the use of the GSA. Moreover, the GSA has proven to be a highly effective tool for solving the various optimisation problems in electric power systems [17][18][19]. The application of this algorithm enables the determination of optimal solutions for different objective functions, taking into consideration all technical and economic constraints which are typical for underground cable lines.…”

Section: Introductionmentioning

confidence: 99%

Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm

Perović

Tasić

Klimenta

et al. 2017

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm

Perović

Tasić

Klimenta

et al. 2017

IET Generation, Transmission & Distribution

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“…P t Grid , must be added to the objective function as a quadratic penalty term. In this term, a penalty factor multiplied by the square of the difference between the actual value and the limiting value of the dependent variable is added to the objective function, and any unfeasible solution obtained during the optimization process is ignored (Radosavljević et al 2014). The new expanded objective function to be minimized becomes:…”

Section: Calculation Of the Active Power From (To) The Utilitymentioning

confidence: 99%

Energy and operation management of a microgrid using particle swarm optimization

Radosavljević¹,

Jevtić²,

Klimenta³

2015

Engineering Optimization

Self Cite

102

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This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical gridconnected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.

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“…GSA is a novel technique that depends on the second law of motion and newton law of gravity. This algorithm emanates under the populationbased algorithm having agents of different masses [3] and has been applied to the number of the applications including power engineering [4][5][6][7], image processing [8][9][10], communication [11][12][13], controls [14][15][16], biology [17,18], and computer science [19]. Every agent in GSA is simulated as a matter and problem search space as the universe where each agent subject to the gravitational force.…”

Section: Introductionmentioning

confidence: 99%

An improved gravitational search algorithm for solving an electromagnetic design problem

Khan

Ling

2020

J Comput Electron

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Gravitational Search Algorithm (GSA) is a novel optimization technique that depends upon the law of motion and law of gravity of masses interaction between the agents. GSA has shown outstanding performance but has a drawback of having a slow exploitation process due to the fitness function influence on masses of the agents. As a result, after each iteration, the masses are getting heavier and restricts the movement. For that reason, masses cancel out the gravitational forces on each other and restricting them from quickly exploiting the optimum. To overcome this limitation, an improved GSA (IGSA) with a modified exploitation strategy is proposed in this paper. The primary aim of modification is to enhance the proficiency of the algorithm in terms of faster convergence and avoid premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. Simulation results confirmed that the proposed method has outstanding results in solving the Loney's Solenoid design problem and solution stability is much better as compared to the Standard gravitational search algorithm and various other state-of-the-art techniques previously applied for solving this problem.

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Optimal power flow for distribution networks using gravitational search algorithm

Cited by 24 publications

References 23 publications

Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm

Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm

Energy and operation management of a microgrid using particle swarm optimization

An improved gravitational search algorithm for solving an electromagnetic design problem

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