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Chaotic sine–cosine algorithm for chance-constrained economic emission dispatch problem including wind energy

机译:混沌正弦余弦算法,用于机会约束经济排放派出问题,包括风能

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摘要

One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine-cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.
机译:减少碳排放最有效的方法之一是将可再生能源集成到电力网络中。目前,风力涡轮机是所有可再生源之间的增长最快。随着风电场的整合到电网中,由于风能随机可用性,经济排放派遣(EED)问题正在变得越来越复杂。在这项研究中,提出了一种新的方法来解决包含风电场的EED问题。该问题被制定为处理风电随机特征的机会约束问题。提出了一种新型混沌正弦余弦算法(CSCA)以提供最佳的发电时间表,以尽量减少生成成本和发射。标准正弦余弦算法(SCA)的开发和勘探能力遇到了一些弱点。因此,混乱融入原始SCA以改善其性能。此外,将新的突变策略添加到SCA中。在本研究中,新算法基于三个互斥的方程。新技术应用于69公交车10单元和40单元测试系统,无动能。将CSCA进行的结果与由其他最近技术产生的结果进行比较。

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