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MOPSO approach to solve profit based unit commitment problem (PBUCP)

机译:MOPSO解决基于利润的单位承诺问题(PBUCP)的方法

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In this paper a new intelligent technique named multi-objective particle swarm optimization (MOPSO) algorithm used to solve profit based unit commitment (PBUCP). The Profit Based Unit Commitment problem is a nonlinear multi-objective optimization problem which involves the simultaneous optimization to maximize the generation companies (GENCOs) profit. The first function is the revenue while the second is the total cost. This optimization involves many constraints such as system power and reserve, unit generation limit, unit minimum ON OFF duration and ramping constraints. The used technique has been tested on IEEE-39 bus system with ten generating units over 24-h time horizon. The simulation results obtained are compared without another technique. The algorithm and simulation are realized with MATLAB 7.4 software.
机译:本文提出了一种称为多目标粒子群优化(MOPSO)算法的新智能技术,用于解决基于利润的单位承诺(PBUCP)。基于利润的单位承诺问题是非线性多目标优化问题,涉及同时优化以最大化发电公司(GENCO)的利润。第一个函数是收入,第二个函数是总成本。该优化涉及许多约束,例如系统功率和储备,单位发电限制,单位最小ON / OFF持续时间和斜坡限制。所使用的技术已在IEEE-39总线系统上进行了测试,该系统在24小时的时间内具有十个发电机组。无需其他技术即可比较获得的仿真结果。该算法和仿真是通过MATLAB 7.4软件实现的。

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