Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Oloulade, Arouna; Adolphe, Moukengue Imano; François-Xavier, Fifatin; Arcadius, Ganye Sewlan Amedee; Badarou, Ramanou; Vianou, Antoine; Herman, Tamadaho

doi:10.11648/j.ijecec.20190501.13

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Moreover, Ciro et al (2016) highlighted that the NSGA-III is better adapted to solve problems with more than two objectives. Given the advancements of NSGA-III, it has been extensively applied to solve multi-objective problems in infrastructure deployment studies, such as offshore wind farm location selection (Mytilinou & Kolios, 2017); emergency rescue facility placement in chemical parks (Men et al, 2019); unified power flow controller placements in transmission networks (Arouna et al, 2019); and contaminant sensors placement in water distribution systems (Hu et al, 2020). However, NSGA-III has not been explored in the charging station deployment problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal planning of flood‐resilient electric vehicle charging stations

Zhang

et al. 2022

Computer aided Civil Eng

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This study is the first attempt to integrate flood resilience into the electric vehicle (EV) charging station planning process. Instead of fully avoiding flood-prone areas, an optimized placement considering the magnitude of flood inundations can minimize the impact of flood hazards and simultaneously maximize the socio-economic benefit of EV charging station networks. In this study, an integrated framework of the non-dominated sorting genetic algorithm-III (NSGA-III) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to optimize the charging station locations by maximizing the charging convenience, minimizing the impact of flood hazards, and minimizing the impact of existing charging stations. The NSGA-III is applied to solve the multi-objective location optimization of charging stations.TOPSIS is subsequently used to determine the best solution from the feasible candidates generated by the NSGA-III. A case study conducted in the Waikiki area demonstrates that the proposed optimization framework can effectively deal with the trade-off between the impact of flood hazards and the charging service of a charging station network. This study provides new insights into best practices for dealing with multiple conflicting objectives in EV charging station planning under climate change.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal planning of flood‐resilient electric vehicle charging stations

Zhang

et al. 2022

Computer aided Civil Eng

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“…These are alternating current transmission systems incorporating controllers based on power electronics. They are very fast devices made up of static switches generally allowing the control of power flow [8]. They can facilitate the control of the flow of energy, the improvement of the transfer of power, the increase of the reactive reserve of the generators, the reduction of the variations of tension and losses [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-point Analysis of Economic, Environmental, Static and Dynamic Dispatching of an Energy Mix in the Presence of STATCOM by the U-NSGA-III Genetic Algorithm

Oloulade¹,

Imano²,

Fifatin³

et al. 2020

EPES

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In an energy environment with multiple production sources, operators are generally confronted with the optimal choice of sources which minimizes polluting gas emissions, losses and marginal production costs while meeting the contractual requirements for maintaining voltage in the ranges required. The present work consisted of optimizing an energy mix in the presence of multi-STATCOM in an interconnected network. Indeed, the (DEE) is a concrete real time problem in electrical energy production systems. This paper shows the impact of STATCOM on static DEE (DEES) and on dynamic DEE (DEED) using the modern genetic algorithm of type U-NSGA-III, which is based on non-dominance sorting. The optimal positioning of two STATCOMs in the application network associated with dynamic dispatching has contributed to the reduction of the total production cost, toxic gas emissions, active losses and then to the improvement of the voltage profiles and the transit of power in the branches. It is observed that the combination of DEED with the optimal positioning of FACTS in an interconnected network constitutes an efficient technico-ecological means to act in the direction of reduction on the triplet consisting of (gas emissions, losses, production cost). The relevance of the results obtained compared to the real case of operating the CEB's interconnected network, justifies the performance of the algorithmic tools developed in the context of this work.

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