解除管制环境下电力系统最优潮流的多目标粒子群优化

2011 11th International Conference on Intelligent Systems Design and Applications Pub Date : 2011-11-01 DOI:10.1109/ISDA.2011.6121809

F. Zaro, M. Abido

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引用次数: 16

摘要

本文提出了一种多目标粒子群优化(MOPSO)技术，用于求解放松管制环境下的最优潮流问题。将OPF问题表述为同时优化燃料成本和车轮成本的非线性约束多目标优化问题。采用MVA-km法计算系统的轮转成本。该方法将该问题作为一个真正的多目标优化问题来处理。结果表明，该方法能够在一次运行中生成多目标OPF问题的真实且分布良好的Pareto最优解。此外，还验证了该方法的有效性及其解决多目标OPF问题的潜力。以IEEE 30总线系统为例，验证了该算法的适用性

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Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems

In this paper, a multi-objective particle swarm optimization (MOPSO) technique is proposed for solving the optimal power flow (OPF) problem in a deregulated environment. The OPF problem is formulated as a nonlinear constrained multi-objective optimization problem where the fuel cost and wheeling cost are to be optimized simultaneously. MVA-km method is used to calculate the wheeling cost in the system. The proposed approach handles the problem as a true multi-objective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal solutions of the multi-objective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multi-objective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm

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2011 11th International Conference on Intelligent Systems Design and Applications

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