A market-based method for reconfiguration of distribution network

Alemohammad, Seyed Hamidreza; Mashhour, Elaheh; Saniei, Mohsen

doi:10.1016/j.epsr.2015.03.014

Cited by 23 publications

(12 citation statements)

References 20 publications

(56 reference statements)

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“…From every pair of parents two new offspring are created, which need to keep the part of the parent's genetic material, preferably the best one. In most of the approaches that employ evolution algorithms for the network reconfiguration problem, this is usually the point at which the problems appear [5,8,10,11,16,17,19,20,22]. These problems are related to the methods used in the crossover process, which result in offspring that violate radiality constraint or have isolated parts of the network.…”

Section: Crossover Processmentioning

confidence: 99%

“…The proposed algorithm introduces several improvements related to the generation of the initial set of possible solutions as well as crossover and mutation steps in the genetic algorithm. Although genetic algorithms are often used in the optimal reconfiguration of a distribution networks [15][16][17][18][19][20][21][22][23][24], most of the approaches [16][17][18][19][20][21][22][23] don't provide an effective means of creating an initial population, as well as effective operators to implement a crossover and mutation process over the set of population individuals. Due to this, during the evolution process, a large number of generated individuals is often rejected and power flow calculations are often conducted for unfeasible individuals (network topologies), that don't provide the radial network topology or include the isolated parts of the network.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

et al. 2020

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This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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Section: Crossover Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

et al. 2020

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“…Once the fault is restored, the system base configuration can be restored. For illustration purposes, a modified IEEE 16-bus system (Alemohammad, Mashhour, & Saniei, 2015) divided into three separate distribution systems is considered. Figure 5 shows the topologies of each of the DSs and the total supply and demand profiles of the three distribution systems over a period of 24 h. As seen in the figures, distribution systems 1 and 3 have overproduction during the day, whereas the demand of the distribution system 2 is always higher than its supply.…”

Section: Assessing Energy Resilience: a Case Studymentioning

confidence: 99%

Energy resilience through self-organization during widespread power outages

Čaušević

Saxena

Warnier

et al. 2019

Sustainable and Resilient Infrastructure

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Resilience of power systems is highly impacted by factors such as increasing severity and frequency of weather events, but also smart grid advances that introduce major operational changes in power systems. Rapidly adapting to these changing circumstances and harnessing the potential of technological advances is the key to ensuring that power systems stay operational during disturbances, thereby improving resilience. This paper addresses this challenge by presenting an approach for improving resilience through local energy resource sharing across multiple distribution systems. The approach brings together the physical and the ICT layer of power systems through a self-organization approach that automatically alters the physical grid topology and forms local energy groups in order to mitigate the effects of widespread outages. Thereby, supply and demand are locally matched, and demand met is maximized during an outage. The results demonstrate that using the proposed approach, operational resilience of impacted distribution systems is improved.

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“…Analytic hierarchy process is used to determine the best sequence of switching. A discrete teaching learning‐based optimization algorithm is used in Pfitscher et al In Arash and Hossein, a market‐based method is proposed taking into account locational marginal prices at different connection points between distribution and transmission systems. In Sattarpour et al, optimal sizing and siting of DG units and smart meters problem is solved using GA.…”

Section: Introductionmentioning

confidence: 99%

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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A market-based method for reconfiguration of distribution network

Cited by 23 publications

References 20 publications

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Energy resilience through self-organization during widespread power outages

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

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