Economic Emission Dispatch with wind Farms using Opposition based Competitive Swarm Optimizer

Soumyabrata Das, S. Roga, Priti Das
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引用次数: 1

Abstract

With the increasing requisition of clean and green energy resources, the dispatch of a smooth electricity supply is a great challenge for system engineers. Thermal generating units consider the fuel and emission costs together while complying with economic load dispatch. On the other hand, green energy sources such as wind power plants reduce the cost of thermal power generators along with emission mitigation despite the wind speed uncertainties. Therefore in this paper, an economic emission dispatch optimization model is represented, which caters to the fuel cost, emission cost, and the wind power cost altogether. To solve such a complex optimization problem, a new algorithm called the Opposition-based Competitive Swarm Optimizer (OCSO) algorithm is used in this work. Three-fourths of the total swarm is updated in each iteration of the OCSO algorithm, which is a modified version of the Competitive Swarm Optimizer algorithm (CSO). The OCSO has the ability to improve the exploration property of the original CSO algorithm most effectively. To prove the efficacy of OCSO, several case studies are conducted and analyzed. The convergence results demonstrate the more extraordinary exploration ability of the algorithm in comparison to others.
基于对立竞争群优化的风电场经济排放调度
随着人们对清洁和绿色能源需求的不断增加,平稳的电力供应调度对系统工程师来说是一个巨大的挑战。火力发电机组在满足经济负荷调度的同时,要综合考虑燃料成本和排放成本。另一方面,尽管风速存在不确定性,但风力发电厂等绿色能源在减少排放的同时降低了火力发电机的成本。因此,本文提出了一个同时考虑燃料成本、排放成本和风电成本的经济性排放调度优化模型。为了解决这一复杂的优化问题,本文采用了一种新的基于对立的竞争群优化算法(OCSO)。OCSO算法是竞争群优化算法(CSO)的改进版本,每次迭代更新总群的四分之三。该算法能够最有效地提高原CSO算法的搜索性能。为了证明OCSO的有效性,进行了几个案例研究并进行了分析。收敛结果表明,与其他算法相比,该算法具有更出色的探索能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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