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Solution to profit based unit commitment using swarm intelligence technique

机译:使用群体智能技术解决基于利润的单位承诺的解决方案

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This paper presents an application of swarm intelligence technique for power system optimization. Unit commitment is an important task for the optimal allocation of the generating units for the economic operations of the power system network. Solution to the unit commitment (UC) problem in the conventional market and profit based unit commitment (PBUC) in restructured power market have been solved using swarm intelligent techniques and thereby finding the power output of each generator available in the power system network. The main objective of the unit commitment problem is to minimize the total production cost for the generation satisfying all its system constraints. In PBUC problem the objective is to maximize the profit of the generation companies with or without emission limitation. In this paper, proposed method is tested on IEEE 39 bus system with 10 generating units and efficiency of the proposed algorithm is compared with other swarm intelligence methods.
机译:本文提出了群体智能技术在电力系统优化中的应用。机组承诺对于为电力系统网络的经济运行优化发电机组的分配是一项重要的任务。使用群智能技术已经解决了常规市场中单位承诺(UC)问题的解决方案和重组电力市场中基于利润的单位承诺(PBUC)的问题,从而找到了电力系统网络中每个可用发电机的功率输出。机组承诺问题的主要目的是使满足其所有系统约束的发电总成本最小化。在PBUC问题中,目标是在有或没有排放限制的情况下最大化发电公司的利润。本文在10个发电机组的IEEE 39总线系统上对提出的方法进行了测试,并将该算法的效率与其他群体智能方法进行了比较。

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