Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic

Castro, Jose Raul; Saad, Maarouf; Lefebvre, S.; Asber, Dalal; Lenoir, Laurent

doi:10.3390/en9020107

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Conventional algorithms convert the multi-objective problem to single-objective problem, including linear weighting, ε-constraint and so on [11]- [13]. The linear weighting approach assigns a weight to different subobjectives, and then multiple sub-objectives are linearly weighted into a single objective.…”

Section: A Conventional Algorithmsmentioning

confidence: 99%

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Liu

Zhang

Fang³

et al. 2021

IEEE Access

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This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of ε-greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).INDEX TERMS multi-objective reactive power optimization, particle swarm optimization, ε-greedy strategy, Pareto archive algorithm, voltage control.

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Section: A Conventional Algorithmsmentioning

confidence: 99%

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Liu

Zhang

Fang³

et al. 2021

IEEE Access

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“…The weighted-sum technique is probably the most widely used MOO technique in the industry. For example, it is used in [20,22,24,25].…”

Section: The Weighted-sum Technique and The Efficient Curvementioning

confidence: 99%

“…In [21], the multiple objectives of the VVC problem to be minimized electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid, and a fuzzy function is used to combine the multiple target functions. Voltage variation on pilot buses, reactive power production ratio deviation, and generator voltage deviation are minimized in [24] and [25]. In both cases, the multiple objectives are addressed using weighted-sum of Pareto optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

An Operational Approach to Multi-Objective Optimization of Volt-VAr Control

Raz¹,

Beck²

2020

Preprint

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Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, we propose a general formulation of the multi-objective Volt-VAr optimization problem. We consider the applicability of various multi-optimization techniques and discuss the operational interpretation of these solutions. We demonstrate the methods using simulation on a test feeder.

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“…Badania przeprowadzono na podstawie ogólnodostępnych modeli elementów sieci elektroenergetycznej. Istnieje wiele publikacji dotyczących sterowania przełącznikami zaczepów [3, [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Fakt ten potwierdza istniejące nadal zainteresowanie tematem.…”

Section: Wprowadzenieunclassified

Construction of a Simulation Model of the HV/MV Transformer Tap-Changer Controller – Assumptions and Requirements

Korpikiewicz¹

2018

PAR

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Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic

Cited by 13 publications

References 26 publications

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

An Operational Approach to Multi-Objective Optimization of Volt-VAr Control

Construction of a Simulation Model of the HV/MV Transformer Tap-Changer Controller – Assumptions and Requirements

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