Combined economic emission dispatch problem using chaotic self adaptive PSO
Combined economic emission dispatch problem using chaotic self adaptive PSO
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基于混沌自适应PSO的组合经济排放调度问题
DOI：
10.1109/icpec.2013.6527689
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
K. Busawon
中科院分区：
文献类型：
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作者：
C. Rani;D. Kothari;K. Busawon
提出了一种混沌自适应粒子群优化算法（CSAPSO）来求解联合经济排放调度问题。工作的主要目的是通过考虑发电机的几个非线性特性，如阀点效应，禁止操作区和斜坡率限制，推导出一种简单有效的方法进行最优发电调度，以最大限度地减少电力网络的燃料成本和排放。在算法中引入混沌局部搜索算子，避免了早熟收敛。利用MATLAB软件进行了仿真研究，以显示所提出的优化方法的有效性。IEEE 30节点6机系统算例验证了该方法的适用性和可行性。基于CSAPSO的方法已被扩展到评估的燃料成本和排放之间的权衡曲线，根据双准则的目标函数。为了验证该算法的有效性，将其与文献中的其他算法进行了比较。实验结果表明，CSAPSO算法比其他算法具有更强的性能。
This research work presents a Chaotic Self Adaptive Particle Swarm Optimization (CSAPSO) algorithm in order to solve the Combined Economic Emission Dispatch (CEED) problem. The main purpose of the work is to derive a simple and effective method for optimum generation dispatch to minimize the fuel cost and emission of power networks by considering several non-linear characteristics of the generator such as valve point effect, prohibited operating zones and ramp rate limits. A chaotic local search operator is introduced in the proposed algorithm to avoid premature convergence. Simulation studies are carried out, using MATLAB software, to show the effectiveness of the proposed optimization method. The applicability and high feasibility of the proposed method is validated on IEEE 30 bus, six generator systems. The CSAPSO based approach has been extended to evaluate the trade-off curve between the fuel cost and emission according to the bi-criterion objective function. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the CSAPSO is more powerful than other algorithms.