2014
DOI: 10.1016/j.ijepes.2014.02.001
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Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Abstract: Newcastle University ePrintsCrossland AF, Jones D, Wade NS. Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing.This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License ePrints -Newcastle University ePrints http://eprint.ncl.ac.ukPlanning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing International Journal of Electrical Power … Show more

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Cited by 96 publications
(59 citation statements)
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“…However, modified Simulated Annealing Genetic Algorithm can sooner achieve a better global optimum solution. The reasons are: the suffix structure design of chromosome reduced the space of the solution, the self-adaptive genetic operator and double crossover and mutation improved 'premature convergence problem'; the introduction of Simulated Annealing Algorithm stretched the fitness and enhanced the local search ability [19]. …”
Section: Analysis Of Simulation Resultsmentioning
confidence: 99%
“…However, modified Simulated Annealing Genetic Algorithm can sooner achieve a better global optimum solution. The reasons are: the suffix structure design of chromosome reduced the space of the solution, the self-adaptive genetic operator and double crossover and mutation improved 'premature convergence problem'; the introduction of Simulated Annealing Algorithm stretched the fitness and enhanced the local search ability [19]. …”
Section: Analysis Of Simulation Resultsmentioning
confidence: 99%
“…Technical and financial parameters have been used for this study which have been accepted by DNOs for works previously published by the authors [24]. In the future, DNOs will need to recalculate results if parameters change.…”
Section: Discussionmentioning
confidence: 99%
“…Procedures for locating storage include genetic algorithms [23]. In this paper, a genetic algorithm is used as this has been shown to be able to determine storage locations in LV networks in a short time [24]. This attempts to minimise the total installation cost of storage within the network, but with a fixed penalty if that network exceeds voltage limits.…”
Section: Dno Owned Energy Storagementioning
confidence: 99%
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