Thermal Unit Commitment Using Hybrid Binary Particle Swarm Optimization and Genetic Algorithm

2012 ◽  
Author(s):  
S. M. Hassan Hosseini ◽  
H. Siahkali ◽  
Y. Ghalandaran
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Thermal Unit ◽  
Swarm Optimization

scholarly journals Operation cost reduction in unit commitment problem using improved quantum binary PSO algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 1149 ◽  
Author(s):  
Ali Nasser Hussain ◽  
Ali Abduladheem Ismail
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Cost Reduction ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Total Power ◽  
Binary Particle Swarm Optimization ◽  
Unit Commitment Problem ◽  
Swarm Optimization ◽  
Operation Cost

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm.

scholarly journals A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

2021 ◽  
Vol 40 ◽  
pp. 03016
Author(s):  
Prasun Bhattacharjee ◽  
Rabin K. Jana ◽  
Somenath Bhattacharya
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Greenhouse Gas Emission ◽  
Particle Swarm ◽  
Wind Power Generation ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Optimal Cost

Although India presently holds the global fourth-biggest instated Wind Power Generation (WPG) capability, it necessitates advancing more rapidly to satisfy the rising energy requirement of its evolving economy while restraining the consequential greenhouse gas emission. To accomplish the impressive target of setting up 140 GW WPG competence by 2030 as proposed by the Government of India, a greater number of financially viable wind farms are required to function all over the country without further ado. This paper focuses on finding the optimal cost for WPG in the Tirumala area of Andhra Pradesh. Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) have been employed concurrently with four randomly chosen terrain conditions. The research outcomes demonstrate the superior capability of BPSO to attain the most optimal cost of energy.

scholarly journals The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 733 ◽  
Author(s):  
Gad Shaari ◽  
Neyre Tekbiyik-Ersoy ◽  
Mustafa Dagbasi
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
State Of The Art ◽  
Cost Minimization ◽  
Particle Swarm ◽  
Environmental Constraints ◽  
Thermal Unit ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Modified Pso

Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also developed in literature to improve the convergence. In this study, these methods are grouped as extended PSO, modified PSO, and PSO with other techniques. This study shows that PSO with other techniques are utilized more than any other methods. In terms of constraints, it was observed that there are only few studies that considered Transmission Line (TL), Fuel (F), Emission (E), Storage (St) and Crew (Cr) constraints, while Power Balance (PB), Generation limit (GL), Unit minimum Up or Down Time (U/DT), Ramp Up or Ramp Down Time (R-U/DT) and system Spinning Reserve (SR) were the most utilized constraints in UC problems considering wind/solar as a renewable source. In addition, most of the studies are based on a single objective function (cost minimization) and, few of them are multi-objective (cost and emission minimization) based studies.

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