Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants

•Investigating Wind Turbine (WT) effect on Multi-objective Distribution Feeder Reconfiguration (MDFR).•Considering the uncertainty of loads and WTs by Point Estimate Method through MDFR problem.•Proposing a novel self adaptive modification approach based on Teacher Learning Optimization (TLO).•Utili...

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Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 55; pp. 680 - 691
Main Authors: Kavousi-Fard, Abdollah, Niknam, Taher, khosravi, Abbas
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.02.2014
Elsevier
Subjects:
Algorithms
Applied sciences
Direct energy conversion and energy accumulation
Electric power generation
Electric power plants
Electrical engineering. Electrical power engineering
Electrical power engineering
Energy
Exact sciences and technology
Feeders
Mathematical models
Miscellaneous
Multi-objective Probabilistic Distribution Feeder Reconfiguration (MPDFR)
Natural energy
Non classical power plants
Optimization
Power networks and lines
Probabilistic methods
Probability theory
Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm
Uncertainty
Wind energy
Wind power plants
Wind Turbines (WTs)
Uncertainty
Multi-objective Probabilistic Distribution Feeder Reconfiguration (MPDFR)
Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm
Wind Turbines (WTs)
Performance evaluation
Costs
Self Adaptive Modified Teacher Learning
Output power
Demand forecasting
Optimization method
Multiobjective programming
Wind generator
Feeder Reconfiguration (MPDFR)
Wind power plant
Power losses
Reconfigurable architectures
Learning algorithm
Distribution network
Probabilistic approach
Pareto optimum
Reactive load
Forecasting management
Constrained optimization
Case study
Electrical network
Optimal solution
Optimization (SAMTLO) algorithm
Load management
Objective function
Effectiveness factor
Multi-objective Probabilistic Distribution
ISSN:0142-0615, 1879-3517
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Summary:•Investigating Wind Turbine (WT) effect on Multi-objective Distribution Feeder Reconfiguration (MDFR).•Considering the uncertainty of loads and WTs by Point Estimate Method through MDFR problem.•Proposing a novel self adaptive modification approach based on Teacher Learning Optimization (TLO).•Utilization of TLO to solve MDFR considering WT for the first time. This paper proposes an efficient probabilistic approach to investigate the multi-objective Distribution Feeder Reconfiguration (DFR) considering Wind Turbines (WTs). The proposed probabilistic method considers the uncertainty regarding the active and reactive load forecast errors as well as the WT output power variations concurrently. In this regard, 2m Point Estimate Method (PEM) as a proper probabilistic technique is utilized to properly model the uncertainty. The objective functions are the total active power losses, voltage deviation, total cost including the grid and WTs and the emission. In addition, a new optimization method based on Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm is proposed to solve the Multi-objective Probabilistic DFR (MPDFR) problem while the simultaneous effect of WTs is considered. In the proposed algorithm, a novel self adaptive modification phase is proposed to improve the overall ability of the algorithm for optimization applications. As the investigated problem is a kind of nonlinear constrained multi-objective optimization problem with conflicting objectives, the idea of Pareto-optimality is utilized to find the set of optimal solutions. The effectiveness and efficiency of the proposed method are demonstrated for two different test systems as case studies.
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ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2013.10.028