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A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch
Applied Soft Computing ( IF 5.472 ) Pub Date : 2021-01-08 , DOI: 10.1016/j.asoc.2021.107088
Mohammad Nasir; Ali Sadollah; İbrahim Berkan Aydilek; Afshin Lashkar Ara; Seyed Ali Nabavi-Niaki

This paper represents a hybrid Firefly and Self-Regulating Particle Swarm Optimization (FSRPSO) algorithm to solve optimal Combined Heat and Power Economic Dispatch (CHPED) problem. Valve point effect on fuel cost function of pure generation units, electrical power losses in transmission systems and feasible operating zones are taken into account in the CHPED problem. The CHPED refers to minimize total costs of fuel for electricity and heat generation supply to load demand. The proposed FSRPSO attempts to determine the start of the local search process properly by checking the previous global best. Thus, the FSRPSO is able to exploit strong points of both Firefly Algorithm (FA) and SRPSO mechanisms in order to balance between exploration and exploitation phases. Besides, for the sake of validation the proposed hybrid method, the FSRPSO is examined on 21 well-known benchmarks, and also a real engineering case i.e., two power systems for evaluating its performance compared with the SRPSO, FA, PSO, and other state-of-the-art algorithms. The obtained optimization results show that the proposed FSRPSO provides fast, mature and reliable optimum solutions and outperform other compared algorithms in diverse categories of benchmarks along with the studied CHPED problem.



中文翻译:

FA和SRPSO算法相结合的热电联产调度

本文提出了一种混合萤火虫和自调节粒子群优化(FSRPSO)算法,以解决最优的热电联产经济调度(CHPED)问题。CHPED问题考虑了阀点对纯发电机组燃料成本函数的影响,传动系统中的电力损耗和可行的运行区域。CHPED是指将电力和热能供应的燃料总成本降至负荷需求的最低。提议的FSRPSO尝试通过检查以前的全局最佳方法来正确确定本地搜索过程的开始。因此,FSRPSO能够利用萤火虫算法(FA)和SRPSO机制的优点,以便在勘探和开发阶段之间取得平衡。此外,为了验证所提出的混合方法,FSRPSO在21个著名基准上进行了检验,同时也是一个实际工程案例,即两个电源系统,用于与SRPSO,FA,PSO和其他最新算法进行性能比较。获得的优化结果表明,所提出的FSRPSO提供了快速,成熟和可靠的最佳解决方案,并且在研究的CHPED问题上,在各种基准类别中均优于其他比较算法。

更新日期:2021-01-13
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