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A Novel Approach for Unit Commitment Problem via an Effective Hybrid Particle Swarm Optimization

机译:有效混合粒子群算法求解机组组合问题的新方法

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This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods.
机译:本文提出了一种通过混合粒子群优化(HPSO)方案解决单元承诺(UC)问题的新方法。本文提出的HPSO是二进制粒子群优化(BPSO)和实编码粒子群优化(RCPSO)的混合。 UC问题由BPSO处理,而RCPSO解决经济负荷分配问题。两种算法同时运行,调整其解决方案以寻找更好的解决方案。 UC的问题表述考虑了最小的上下时间限制,启动成本和旋转储备,并定义为在满足所有相关限制的同时使总目标函数最小化。给出了十个发电机调度问题的问题表述,表示形式和仿真结果。结果清楚地表明,与其他现有方法相比,HPSO在解决UC问题方面非常胜任。

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