文章基本信息

标题：Enhanced Evolutionary Algorithm Based Security-Constraint Unit Commitment with Reserve Requirements
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作者：M.Priya ; P.SundaraMallayan
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2016
卷号：5
期号：4
页码：4781
DOI：10.15680/IJIRSET.2016.0504025
出版社：S&S Publications
摘要：Nowadays, due to the rapid development in technology load unit in a power system has been drasticallyimproved. Eventually the generation also need to improvise to balance the load demand increments. Hence it isnecessary to optimize the generation unit with the load demand to provide reliable power supply to the customers. UnitCommitment problem provides the solution for the above generation and load demand balance in optimized way issue.Unit commitment is an important optimizing task in daily operational planning of power systems which can bemathematically formulated as a large scale on non- linear mixed -integer problem for which there is no exact solutiontechnique. It is difficult to use the Mathematical optimization technique for handling the non- linear mixed -integerproblem. The above problem can be resolved by using stochastic optimization techniques. The stochastic optimizationcan handle any complex nonlinear problem in an easy manner. Nowadays, the evolutionary based stochasticoptimization has been utilized drastically in solving the complex power system problems. One among the evolutionaryalgorithm is Particle Swarm Optimization algorithms (PSO) that are indeed capable of obtaining higher qualitysolutions efficiently in solving Unit Commitment Problems. In this work, the PSO based unit commitment method todetermine the better generation unitsschedule to balance the load demand with the minimized production cost bymaintaining the systems security constraints such as transmission line power flow limits and Bus voltage limits.
关键词：Unit Commitment (UC);Particle Swarm Optimization (PSO);Security Constraint Unit Commitment;(SCUC);Fuel Cost; Production Cost; Power Generation