2012 ©
             Publication
Journal Publication
Title of Article An Improved DA-PSO Optimization Approach for Unit Commitment Problem 
Date of Acceptance 18 June 2019 
Journal
     Title of Journal Energies 
     Standard ISI 
     Institute of Journal MDPI AG (Basel, Switzerland) 
     ISBN/ISSN  
     Volume 2019 
     Issue 12 
     Month May
     Year of Publication 2019 
     Page 2335 
     Abstract Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mixed-integer part of obtaining the Unit Commitment problem. To verify the effectiveness of the iDA-PSO approach, it was tested on four different-sized systems (5-unit, 6-unit, 10-unit, and 26-unit systems). The unit commitment, generation schedule, total generation cost, and time were compared with those obtained by other algorithms in the literature. The simulation results show iDA-PSO is a promising technique and is superior to many other algorithms in the literature. 
     Keyword dragonfly algorithm; metaheuristic; particle swarm optimization; unit commitment 
Author
587040006-6 Mr. SIROTE KHUNKITTI [Main Author]
Engineering Doctoral Degree

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