解决电力系统经济负荷调度的新型混合算法

IF 4.3 3区 工程技术 Q2 ENERGY & FUELS International Journal of Energy Research Pub Date : 2024-11-21 DOI:10.1155/2024/8420107
Khairul Eahsun Fahim, Liyanage C. De Silva, Viknesh Andiappan, Sk. A. Shezan, Hayati Yassin
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引用次数: 0

摘要

过去曾有各种算法将经济负荷调度(ELD)考虑在内。然而,这些算法都集中在多个调整参数上,需要进行超参数调整。本文提出了一种独特的无参数混合算法,可明确评估测试系统和实际电厂系统的 ELD,并与运行限制相匹配。此外,早期的算法只能根据超参数选择提供最终燃料成本的估计值。这可能导致无法达到全局最小值。为了全面解决电力系统中的 ELD 问题,本文提出了一种名为混合 Jaya 优化算法的新方法,它利用了 Jaya 算法和基于教学的优化算法 (TLBO) 的优点。提出这一增强算法的目的是为了改善种群的多样性、局部搜索和全局搜索之间的平衡以及原始 Jaya 优化方法的早期收敛性。一种名为 TLBO 的元启发式优化技术模拟课堂教学过程来优化问题。TLBO 算法在探索阶段随机生成可能的解决方案,以发现最佳解决方案。然后,该算法利用利用阶段来完善基于搜索空间的参数调整,以提高所确定的最佳解决方案的质量。另一方面,Jaya 算法是一种元启发式优化算法,其灵感来源于自然界中的社会行为。候选方案通过基于群体的合作与竞争反复改进,每个方案都会根据群体中的最佳和最差答案调整自己的位置。混合 Jaya(Jaya-TLBO)结合了两种算法的优点,其性能优于单独使用的每种方法,并最大限度地降低了发电成本,提高了收敛解决方案的质量。为了测试混合 Jaya-TLBO 算法的有效性,我们在四个不同的测试案例中进行了测试,如电气与电子工程师协会(IEEE)的 6 个单元、13 个单元、20 个单元、40 个单元的 ELD 系统和印尼的 10 个单元的 ELD 系统。仿真结果表明,所提出的算法在成本最小化方面优于近期使用的其他著名算法。因此,电力系统规划人员可以利用这一技术找到最经济的负荷调度方式。
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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A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems

Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real-world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching–learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching–learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space-based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimization algorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population-based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya–TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya–TLBO algorithm is tested on four different test cases, such as an Institute of Electrical and Electronics Engineers (IEEE) 6-unit, 13-unit, 20-unit, 40-unit ELD system and an Indonesian 10-unit one. Simulation results show that the proposed algorithm is superior in cost minimization to other well-known algorithms that have been used recently. As a result, power system planners can utilize this technique to find the most economical load dispatch.

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来源期刊
International Journal of Energy Research
International Journal of Energy Research 工程技术-核科学技术
CiteScore
9.80
自引率
8.70%
发文量
1170
审稿时长
3.1 months
期刊介绍: The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability. IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents: -Biofuels and alternatives -Carbon capturing and storage technologies -Clean coal technologies -Energy conversion, conservation and management -Energy storage -Energy systems -Hybrid/combined/integrated energy systems for multi-generation -Hydrogen energy and fuel cells -Hydrogen production technologies -Micro- and nano-energy systems and technologies -Nuclear energy -Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) -Smart energy system
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