Sine-Cosine Algorithm for the Dynamic Economic Dispatch Problem With the Valve-Point Loading Effect

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2023-01-20 DOI:10.4018/ijsir.316801
Jatin M. Soni, K. Bhattacharjee
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Abstract

Dynamic economic dispatch (DED) deals with the allocation of predicted load demand over a certain period of time among the thermal generating units at minimum fuel cost. The objective function of DED becomes highly complex and nonlinear after considering various operating constraints like valve point loading, ramp rate limit, transmission loss, and generation limits. In this study, the sine-cosine algorithm has been presented to solve the DED problem with various constraints. The randomly placed swarm finds an optimum solution according to their fitness values and keeps the path towards the best solution attained by each swarm. The swarm avoid local optima in the exploration stage and move towards the solution exploitation stage using sine and cosine functions. The proposed technique has been tested in several test systems. The results obtained by the proposed technique have been compared with those obtained by other published methods employing the same test systems. The results validate the superiority and the effectiveness of the proposed technique over other well-known techniques.
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考虑阀点负荷影响的动态经济调度问题的正弦余弦算法
动态经济调度(DED)处理在一定时间内以最小燃料成本在火力发电机组之间分配预测的负荷需求。在考虑了各种操作约束(如阀点负载、斜坡速率限制、传输损耗和发电限制)后,DED的目标函数变得高度复杂和非线性。在本研究中,提出了正弦余弦算法来解决具有各种约束的DED问题。随机放置的群根据其适应度值找到最优解,并保持每个群获得的最佳解的路径。群在探索阶段避免了局部最优,并使用正弦和余弦函数进入求解阶段。所提出的技术已经在几个测试系统中进行了测试。将所提出的技术获得的结果与采用相同测试系统的其他已发表方法获得的结果进行了比较。结果验证了所提出的技术相对于其他已知技术的优越性和有效性。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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