Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms

Mendes, Alexandre; Boland, Natashia; Guiney, Patrick; Riveros, Carlos

doi:10.1109/tpwrs.2012.2194516

Cited by 60 publications

(22 citation statements)

References 22 publications

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“…Distribution network reconfiguration is a nondifferentiable nonlinear constrained optimization problem . The solving approaches for the reconfiguration of the network are classified into three main groups: deterministic, heuristic, and stochastic (meta‐heuristic) approaches . Farivar et al introduced a conic quadratic model for handling reactive power in distribution network.…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Fathi

Seyedi

Mohammadi‐Ivatloo

2020

Int Trans Electr Energ Syst

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Summary Distribution network reconfiguration is a common method to achieve objectives such as minimization of power loss and voltage deviations. There exist several different algorithms to find the optimal configuration of network. Almost all of these algorithms consider the main operational constraints such as voltage, current, and power limits. Although it is crucially important to incorporate protective devices and their constraints in the reconfiguration problem, there are few works in network reconfiguration considering protection system. Accordingly, this paper presents a new reconfiguration algorithm considering protective constraints in the presence of distributed generation (DG) units. The objective function of this paper is power loss, which is minimized subject to both operation and protection constraints. In addition to the protection constraints, the effects of DG units on the reconfiguration are considered in this work. Uncertainties in loads and power generation of DG units are also investigated, using a new scenario‐based algorithm. Two test systems are utilized to illustrate the efficiency of the proposed method and to compare it with conventional approaches. Moreover, in order to evaluate the performance of new scenario‐based algorithm, it is compared with Monte Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Fathi

Seyedi

Mohammadi‐Ivatloo

2020

Int Trans Electr Energ Syst

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“…What is more, the available ACSs in smart MGs require deeper research studies to guarantee their successful deployment. Although, there have been remarkable efforts in networks' topological reconfigurations, these surveys have merely dealt with long‐term periods . The voltage profile improvement and power losses reduction are the two most important objectives in such studies.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive stochastic energy management system in reconfigurable microgrids

Golshannavaz

Afsharnia

Siano

2016

Int. J. Energy Res.

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Summary This paper addresses the joint stochastic energy and reserve scheduling problem in microgrids (MGs). The established approach proposes a novel high‐performance energy management system (EMS) making use of automatically controlled switches (ACSs). Accordingly, besides the optimal scheduling of active elements namely distributed generations (DGs) and responsive loads (RLs), the optimal topology of the network for each of the scheduling intervals is determined as well. Likewise, the effects of the reconfiguration process in probable variations of the scheduled energy patterns in DGs, RLs, and grid purchases are thoroughly assessed to highlight the alterations in unallocated capacities of these resources. Moreover, the uncertainties associated with both the load and wind speed forecasting errors are suitably accommodated through the reserve allocations. The proposed optimization procedure is formulated as a mixed‐integer non‐linear problem and resolved using a genetic algorithm (GA). The effectiveness of the projected framework is verified utilizing a typical MG, and the obtained numerical results are discussed in depth. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Generally these methods can be grouped into several categories; classic optimization technique [10][11][12][13], sensitivities analysis method [14], knowledge-based heuristic method [15][16][17][18], and Genetic Algorithms [19]. Sensitivities analysis method and knowledge-based heuristic method can provide practical results with short computing time but may not be global solutions.…”

Section: Introductionmentioning

confidence: 99%

Distribution network reconfiguration validation with uncertain loads – network configuration determination and application

Bai

Mavrocostanti

Strickland

et al. 2016

IET Generation, Transmission & Distribution

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Automatic load transfer (ALT) on the 11kV network is the process by which circuit breakers on the network are switched to form open points in order to feed load from different primary substations. Some of the potential benefits that may be gained from dynamically using ALT include maximising utilisation of existing assets, voltage regulation and reduced losses. One of the key issues, that has yet to be properly addressed in published research, is how to validate that the modelled benefits really exist. On an 11kV distribution network where the load is continually changing and the load on each distribution substation is unlikely to be monitored -reduction in losses from moving the normally open point is particularly difficult to prove. This paper proposes a method to overcome this problem and uses measured primary feeder data from two parts of the Western Power Distribution 11kV Network under different configurations. The process of choosing the different configurations is based on a heuristic modelling method of locating minimum voltages to help reduce losses.

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Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms

Cited by 60 publications

References 22 publications

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

A comprehensive stochastic energy management system in reconfigurable microgrids

Distribution network reconfiguration validation with uncertain loads – network configuration determination and application

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