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An improved TLBO algorithm to solve profit based unit commitment problem under deregulated environment

机译:一种改进的TLBO算法,解决了解管制环境下基于利润的单位承诺问题

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The TLBO (Teaching learning based optimization technique) is one of the recently developed algorithm and In this paper, an improved TLBO algorithm is proposed to address the profit based unit commitment problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi constrained non linear optimization problem. The PBUC involves determining the on/off states of the generating units while satisfying demand and generating unit constraints. Some of the recently developed algorithms like GA, PSO, BBO, ACO etc. are available to solve this complex problem, but so far there is no such ideal technique which can completely address this problem. In this scenario an attempt is made to solve this problem using an improved TLBO. The regular TLBO algorithm is improved by considering some of the factors like teaching factor, number of teachers, learning through tutorials, self motivated learning. The step-by-step procedure of how to apply the improved TLBO algorithm for PBUC problem is presented. The proposed algorithm is tested on IEEE 10 unit 10 hours load demand as input data for simulation using MATLAB R2009a version. From the results obtained it is observed that the improved TLBO algorithm is effective compared to traditional TLBO while handling the computation time and the dimension of the problem.
机译:TLBO(教学基于教学的优化技术)是最近发达的算法之一,本文提出了一种改进的TLBO算法,以解决放松管制下的基于利润的单位承诺问题。 PBUC问题是电力生产者的主要任务之一,是一个高度复杂的多约束非线性优化问题。 PBUC涉及确定生成单元的开/关状态,同时满足需求和生成单元约束。一些最近开发的算法,如Ga,PSO,BBO,ACO等,可以解决这个复杂的问题,但到目前为止没有这样的理想技术可以完全解决这个问题。在这种情况下,尝试使用改进的TLBO来解决这个问题。通过考虑教学因素,教师数量,通过教程学习,自我激励学习来提高常规TLBO算法。提出了如何应用改进的TLBO算法的PBUC问题的逐步过程。在IEEE 10单元上测试了所提出的算法作为使用MATLAB R2009A版本进行仿真的输入数据。从获得的结果,观察到改进的TLBO算法与传统TLBO相比,处理计算时间和问题的维度。

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