Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

Asrari, Arash; Lotfifard, Saeed; Payam, Mohammad Sadegh

doi:10.1109/tsg.2015.2468683

Cited by 105 publications

(63 citation statements)

References 24 publications

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“…{x} · {y} = [min(xy, xy, xy, xy), max(xy, xy, xy, xy)] (20) where {x} and {y} represent two sets. In Moore's theory, the interval calculation can be done by using only their bounds, namely x, x, yandy.…”

Section: Interval Analysis Methodsmentioning

confidence: 99%

“…In Moore's theory, the interval calculation can be done by using only their bounds, namely x, x, yandy. Based on this, the elementary operations are theoretically described in Equation (20). These operations represent add, subtract, multiply and divide in interval calculation respectively.…”

Section: Interval Analysis Methodsmentioning

confidence: 99%

“…To integrate DG in DN, the location and regulation for DG units are the main optimization problems in network reconfiguration [19][20][21][22]. Some representative methods were proposed in recent references.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

et al. 2017

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This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG) allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO). Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA). In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.

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Section: Interval Analysis Methodsmentioning

confidence: 99%

Section: Interval Analysis Methodsmentioning

confidence: 99%

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A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

et al. 2017

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“…Therefore, the multi-objective optimization model was established as follows: where ω is the output weight of the KELM and is optimized by IABC. The proposed multi-objective optimization method was based on the Pareto theory, which is depicted below [25,26].…”

Section: The Objective Function Of Pismentioning

confidence: 99%

“…The proposed multi-objective optimization method was based on the Pareto theory, which is depicted below [25,26].…”

Section: Prediction Processmentioning

confidence: 99%

A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Yue

et al. 2017

Energies

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Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.

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Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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Summary The distribution flexible alternating current system (D‐FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta‐heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D‐STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load‐balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D‐STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D‐STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33‐bus, a 69‐bus, and a real‐time 31‐bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.

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Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

Cited by 105 publications

References 24 publications

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

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