Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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“…The problem was established for reducing active power losses. Some similar results are identified in [81], [82], and [83].…”

Section: Genetic Algorithm (Ga)supporting

confidence: 88%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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“…After x is given, computing

{\overset{V}{true}}_{k, t}

satisfying Equations and for all periods

scriptT

is computationally intensive (Asrari et al, ). Difficulties in searching the nondominated solutions include tight constraints, non‐linearity, and heavy computational effort.…”

Section: Application In the Practical Decision Makingmentioning

confidence: 99%

“…In this section, we demonstrate that the proposed decision-making approach is successful for the practical application. We target a distribution network reconfiguration problem as a real-world application (Ahuja et al, 2007;Asrari et al, 2016;Eldurssi & O'Connell, 2015;Malekpor et al, 2013;Rao et al 2011;Sekizaki et al, 2017). In this example, there are M = 13 objectives.…”

Section: Application In the Practical Decision Makingmentioning

confidence: 99%

“…A reconfiguration problem of distribution networks in an electricity power system is one example of a real‐world application with Technical Difficulties 1–5 (Ahuja et al, ; Asrari et al, ; Eldurssi & O'Connell, ; Malekpor et al, ; Rao et al ; Sekizaki et al, ). The distribution network is the electricity network closest to the end consumers.…”

Section: Introductionmentioning

confidence: 99%

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A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

Sekizaki

Nishizaki

Hayashida

2019

Multi Criteria Decision Anal

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This paper proposes a step‐by‐step decision making approach that incorporates preferences of a decision maker into a many‐objective evolutionary algorithm. The algorithm is applied to a challenging many‐objective optimization problem (MaOP) involving integer objective values, tight constraints, heavy computational burden, and limited knowledge about the true Pareto front. The proposed method consists of five steps. (a) The entire Pareto front with high dimensions is roughly approximated by a small set of nondominated solutions. (b) On the basis of the approximated Pareto front, the decision maker specifies a preference. (c) Reference points are distributed through part of the objective space using the preference information. (d) The approximation accuracy of the Pareto front in the region of interest is improved by local search using the reference points. (e) The most preferred solution from a set of the nondominated solutions is chosen. The evolutionary algorithms employed in Steps (a) and (d) have an improved normalization procedure and selection mechanism for the MaOP involving integer objective value(s) and the tight constraints. Steps (b) and (e) use a graphical‐user‐interface‐based interactive tool we developed to efficiently narrow down to the preferred solution(s) among the high‐dimensional, nondominated solutions. This alleviates the cognitive burden on the decision maker and therefore aids the decision maker in handling MaOPs. In the computational experiment, we choose the real‐world application of reconfiguring an electricity distribution network. We demonstrate that the proposed approach can provide satisfying solutions to a decision maker.

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“…This smoothing requires the coordination of thousands or even millions of elements spread over a certain area. The coordination of these elements, to properly use the available resource, becomes a complex task, which is addressed by the smart grids and must be tackled from the multi-agent and distributed system point of view [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

SwarmGrid: Demand-Side Management with Distributed Energy Resources Based on Multifrequency Agent Coordination

et al. 2018

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This paper focuses on a multi-agent coordination for demand-side management in electrical grids with high penetration rates of distributed generation, in particular photovoltaic generation. This coordination is done by the use of swarm intelligence and coupled oscillators, proposing a novel methodology, which is implemented by the so-call SwarmGrid algorithm. SwarmGrid seeks to smooth the aggregated consumption by considering distributed and local generation by the development of a self-organized algorithm based on multifrequency agent coordination. The objective of this algorithm is to increase stability and reduce stress of the electrical grid by the aggregated consumption smoothing based on a frequency domain approach. The algorithm allows not only improvements in the electrical grid, but also increases the penetration of distributed and renewable sources. Contrary to other approaches, this objective is achieved anonymously without the need for information exchange between the users; it only takes into account the aggregated consumption of the whole grid.

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Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing

Cited by 64 publications

References 37 publications

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

SwarmGrid: Demand-Side Management with Distributed Energy Resources Based on Multifrequency Agent Coordination

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