An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties

Energy ◽  
2013 ◽  
Vol 50 ◽  
pp. 232-244 ◽  
Author(s):  
Bahman Bahmani-Firouzi ◽  
Ebrahim Farjah ◽  
Rasoul Azizipanah-Abarghooee
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Adaptive Learning ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Emission Dispatch ◽  
Economic Emission Dispatch ◽  
Self Adaptive

scholarly journals MSSA-PSO: A Hybrid Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 1786-1795
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Particle Swarm ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

This paper center a multi objective based half and half procedure to fathom EED (Economic Emission Dispatch) issue incorporates wind power with hydro-warm units. The half breed procedure is the joined execution of both the modified salp swarm streamlining algorithm (MSSA) with counterfeit astute AI (artificial intelligence) strategy helped with particle swarm optimization (PSO) system. In this, the MSSA is used to advancing the blend of the warm generators dependent on the breeze power vulnerability and siphoned stockpiling units. PSO-ANN is used to catch the vulnerability occasions of wind power so the framework is guaranteed the high use of wind power. Along these lines, arrangement of the proposed enhancement approach will be limited the all out expense. To approve the proposed technique viability, the six and ten producing units warm framework is contemplated with fuel and discharge cost as two clashing targets to be upgraded simultaneously. The proposed strategy is executed in MATLAB working stage and the outcomes will be analyzed with thinking about age units and will contrasted with IMFO-RNN systems. The correlation comes about uncovers the nature of the proposed approach and broadcasts its capacity for dealing with multi-target improvement issues of intensity frameworks.

Economic emission dispatch using an advanced particle swarm optimization technique

2019 ◽  
Vol 16 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Hamid Rezaie ◽  
Mehrdad Abedi ◽  
Saeed Rastegar ◽  
Hassan Rastegar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Content Type ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

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