A survey on distribution system feeder reconfiguration: Objectives and solutions

Tang, Lei; Yang, Fang; Ma, Jingran

doi:10.1109/isgt-asia.2014.6873765

Cited by 47 publications

(20 citation statements)

References 52 publications

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“…Many researchers are constantly facing new challenges with stochastic techniques. Some reviews on both reconfiguration and metaheuristics can be found in the scientific literature [12][13][14][15]. Nonetheless, we could not find a proper survey about the most relevant metaheuristic techniques directly applied to DNR in recent years.…”

Section: Introductionmentioning

confidence: 86%

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

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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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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Section: Introductionmentioning

confidence: 86%

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

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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“…As shown in [3], the annual failure rates can be stochastically modelled using a Poisson probability distribution function. Then, the probability of k failures in a zone z is given by (5), and the standard deviation (σ z ) for every stochastic parameter l z is given by (6)…”

Section: Uncertainty Characterisationmentioning

confidence: 99%

Robust optimisation applied to the reconfiguration of distribution systems with reliability constraints

López

Lavorato

Rider

2016

IET Generation, Transmission & Distribution

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This study presents a mixed-integer second-order conic programming (MISOCP) model for the robust reconfiguration of electrical distribution systems, considering the minimisation of active power losses and reliability constraints. The uncertainty at the reliability data is considered by using a linear and adjustable robust approach. The indices used to evaluate the reliability of the system are the system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI), both of which are calculated as functions of the switch status (open or closed). Under radiality constraints, the solution generated by the proposed model is robust in terms of the reliability, i.e. the SADI and SAIFI limits imposed by regulators are not violated even if the uncertain data changes stochastically. The use of an MISOCP model guarantees convergence to optimality by using existing convex optimisation solvers. To evaluate the robustness of each solution generated by the proposed model, a set of Monte Carlo simulations were deployed. Finally, all tests were executed in a 136-node real distribution system. (),v z,k dual variable of the SAIFI constraint (48), related to the binary decision variable ω z,k , used in the robust formulation p U (),z act z,k dual variable of the SAIDI constraint (49), related to the binary decision variable z actz,k , used in the robust formulation p U (),v z,k dual variable of the SAIDI constraint (49), related to the binary decision variable ω z,k , used in the robust formulation P S i active power generated at node i ∈ Ω b [kW]

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“…There are two types of switches: sectionalising switches which are normally closed and tie line switches which are normally open. Distributed network reconfiguration (DNR) is the process of changing the structure of the distribution network by changing the status of the sectionalising and tie switches to maintain the radial topology [1]. DNR is considered highly complex, non-linear, discrete, combinatorial and stochastic optimisation problem [2,3].…”

Section: Introductionmentioning

confidence: 99%

Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation

Atteya

Ashour

Fahmi

et al. 2017

CIRED - Open Access Proceedings Journal

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Recently, losses reduction gained a great deal of attention in distribution system due to low-voltage level and the high-current passing through the lines, pushing the distribution utilities to improve their profit margins on one hand by reducing the unnecessary operational cost, and improving their delivered power quality on the other hand by maintaining the system reliability, and the continuity of supply for varying load demand. Load balancing, voltage regulation, network reconfiguration and others are different techniques used to reduce the losses. This study addresses the distribution network reconfiguration to minimise the network losses. A new modified form of particle swarm optimisation (PSO) is used to identify the optimal configuration of distribution network effectively. The difference between the modified PSO (MPSO) algorithms and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The suggested MPSO is tested via 33 and 69 IEEE networks. A benchmark comparison has been conducted to prove the effectiveness of MPSO compared with previous optimisation techniques. 3 Networks description 3.1 33-Bus test distribution system The 33 IEEE network, 12.6 kV, as shown in Fig. 1, consists of 37 branches, 32 normally closed switches (sectionalising switches) and 5 normally open switches (tie line switches). Interactive power 24th International Conference & Exhibition on Electricity Distribution (CIRED)

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A survey on distribution system feeder reconfiguration: Objectives and solutions

Cited by 47 publications

References 52 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

Robust optimisation applied to the reconfiguration of distribution systems with reliability constraints

Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation

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