Journal ArticleDOI
Unit commitment computation by fuzzy adaptive particle swarm optimisation
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TLDR
A fuzzy adaptive particle swarm optimisation (FAPSO) for unit commitment (UC) problem has been proposed and the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities.Abstract:
A fuzzy adaptive particle swarm optimisation (FAPSO) for unit commitment (UC) problem has been proposed. FAPSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable resource size and changing load demand, the fuzzy adaptive criterion is applied for the PSO inertia weight based on the diversity of fitness. In this method, the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities. Velocity is digitised (0/1) by a logistic function for the binary UC schedule. To improve knowledge, the global best location is also moved instead of a fixed one in each generation. To avoid the system to be frozen, stagnated/idle particles are reset from time to time. Finally, benchmark data and methods are used to show effectiveness of the proposed method.read more
Citations
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Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations
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A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem
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Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review
TL;DR: In this article, a literature survey of UC concept, objectives and constraints is provided, and different UC models developed for addressing RES impacts are also reviewed, and the necessity for alternative optimization approaches for UC solution is explored.
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Faster evolutionary algorithm based optimal power flow using incremental variables
TL;DR: In this article, the authors proposed an efficient approach for evolutionary algorithm based Optimal Power Flow (OPF), which uses the concept of incremental power flow model, based on sensitivities.
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Thermal unit commitment using binary/real coded artificial bee colony algorithm
TL;DR: In this paper, a binary coded ABC with repair strategies is used to obtain a feasible commitment schedule for each generating unit, satisfying spinning reserve and minimum up/down time constraints, and economic dispatch is carried out using real coded ABC for the feasible commitment obtained in each interval.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.
Journal ArticleDOI
Particle swarm optimization to solving the economic dispatch considering the generator constraints
TL;DR: In this paper, a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems is proposed, and the experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Journal ArticleDOI
A particle swarm optimization for reactive power and voltage control considering voltage security assessment
TL;DR: In this article, a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA) is presented.
Journal ArticleDOI
A genetic algorithm solution to the unit commitment problem
TL;DR: This paper presents a genetic algorithm (GA) solution to the unit commitment problem using the varying quality function technique and adding problem specific operators, satisfactory solutions to theunit commitment problem were obtained.