2022
DOI: 10.1002/jnm.3000
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A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

Abstract: Due to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocatio… Show more

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Cited by 9 publications
(4 citation statements)
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References 24 publications
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“…This is due to their increasing complexity in the modern energy sector, meeting the objectives of these features becomes more difficult. In this sense, blockchain technology in conjunction with tools such as smart grids, could provide a more complete control and monitoring of DER in power grids in order to solve the problems related to aggravated grid congestion, and DER uncertainty and variability [14], [241], [242].…”
Section: Discussionmentioning
confidence: 99%
“…This is due to their increasing complexity in the modern energy sector, meeting the objectives of these features becomes more difficult. In this sense, blockchain technology in conjunction with tools such as smart grids, could provide a more complete control and monitoring of DER in power grids in order to solve the problems related to aggravated grid congestion, and DER uncertainty and variability [14], [241], [242].…”
Section: Discussionmentioning
confidence: 99%
“…The paper [2] presents a method for determining the best location for DGs by minimizing the overall energy loss. This method uses nonlinear programming and optimal power flow (OPF) techniques, taking into account operational Journal Green Energy Research and Innovation 1(1) (2024) [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] constraints and uncertainties in load and power generation resources in the IEEE 33-bus test system. The unit commitment (UC) problem is addressed in [3] using a novel evolutionary approach.…”
Section: Introductionmentioning
confidence: 99%
“…The objective of [16] is to reduce active and reactive power losses in power networks by implementing a genetic algorithm (GA) to maximize the capacity of DGs. Through simulation on standard IEEE 30-and 118-bus systems, this technique proves to be a promising strategy for sustainable energy systems.…”
Section: Introductionmentioning
confidence: 99%
“…Othman et al 17 represented binary PSO to reconfigure the distribution system as well as enhance the voltage stability. For power loss reduction of RDN, Mirsaeidi et al 18 placed distributed energy resources of optimal sized in large‐scale radial network. Hota and Mishra 19 .…”
Section: Introductionmentioning
confidence: 99%