A discrete Teaching–Learning-Based Optimization algorithm to solve distribution system reconfiguration in presence of distributed generation

Lotfipour, Arash; Afrakhte, Hossein

doi:10.1016/j.ijepes.2016.03.009

Cited by 92 publications

(30 citation statements)

References 26 publications

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“…The first group we call two-step binarization methods, which use an intermediate space from where the binarization is mapped. The second group we call continuous-binary Metaheuristic discretizations Teaching-learning Distribution system [73] operator transformation, where the metaheuristic operator is adapted to a binary problem. When we analyze the operator adaptation, we found methods that transform the algebraic operations and methods that use a probability for performing the transition in the search space.…”

Section: Resultsmentioning

confidence: 99%

“…This method has been used in [73] to solve distribution system reconfiguration in the presence of distributed generation using a teaching-learning metaheuristic.…”

Section: Metaheuristic Discretizationmentioning

confidence: 99%

“…This procedure allows us to map to discrete values between 1 and . This has been applied to smart grids using teaching-learning-based optimization [73].…”

Section: Metaheuristic Discretizationmentioning

confidence: 99%

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Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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Section: Resultsmentioning

confidence: 99%

“…This method has been used in [73] to solve distribution system reconfiguration in the presence of distributed generation using a teaching-learning metaheuristic.…”

Section: Metaheuristic Discretizationmentioning

confidence: 99%

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Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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“…The objective function of the problem is considered as minimization of power losses cost and reactive resources installation and reactive power purchasing cost as well as voltage deviations minimization in the distribution network [10], [18] and [26].…”

Section: B Objective Functionmentioning

confidence: 99%

“…where, f 1 is the total power loss, R i is i th branch resistance, The objective function of voltage profile improvement due to reduction of voltage deviations is defined as follows [26]. The objective function of the problem must be optimized in the following equality and inequality constraints.…”

Section: B Objective Functionmentioning

confidence: 99%

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

et al. 2020

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In this paper, multi-objective reactive power compensation based on optimal and simultaneous allocation of capacitors and distribution static compensator (DSTATCOM) in radial distribution networks based on fuzzy decision making is proposed with the aim of reducing power losses cost, reactive investment cost and reducing voltage deviations considering power loss reduction factor (PLRF) to identify candidate buses. Optimal location and size of reactive resources is determined based using multi-objective improved golden ratio optimization algorithm (MOIGROM) inspired by Fibonacci series and improved using nonlinearly inertia weight strategy. This method is performed on a 13 bus distribution network, 69 and 118 buses IEEE standard networks in various scenarios including capacitor allocation only, DSTATCOM allocation only and optimal and simultaneous capacitor and DSTATCOM allocation (hybrid allocation). The simulation results showed that the hybrid allocation has less power losses and voltage deviations in different scenarios using fuzzy decision making based on Pareto front set. Also the impact of different loading conditions on problem solution cleared that with increasing the load level, especially during the heavy load, the power losses and voltage deviations of the networks increase and vice versa. In addition, the advantage of the MOIGROM method over the other methods is confirmed. INDEX TERMS Radial distribution network, optimal capacitor and DSTATCOM allocation, power loss reduction factor, fuzzy decision making, multi-objective improved golden ratio optimization algorithm.

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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.

show abstract

A discrete Teaching–Learning-Based Optimization algorithm to solve distribution system reconfiguration in presence of distributed generation

Cited by 92 publications

References 26 publications

Putting Continuous Metaheuristics to Work in Binary Search Spaces

Putting Continuous Metaheuristics to Work in Binary Search Spaces

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

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

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