Review of Power Generation Optimization Algorithms: Challenges and its Applications

A. Vijay, Wardah Afzal, M. Tariq, A. Mustafa, Aatika Shahzad
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Abstract

Optimizing the power system is challenging to address because power systems are enormous and complicated and can be impacted by several unanticipated occurrences. As a result, approaches to addressing these challenges should be an active main focus of this study. Energy users can optimize the power system to help the system run efficiently, cut down on peak load use, and minimize control adjustments. The new approaches and management techniques to ensure flexibility have emerged from the increasing trend of renewable energy integration with power generation uncertainty and availability. This paper contains a research study of different optimization algorithms. The pros and cons of recently used optimization techniques in power systems are briefly discussed in this paper. The algorithms that are discussed in this article are Power optimization algorithm (PSO), Genetic Algorithm (GA), Tabo Search Algorithm (TSA), Artificial Neural Network (ANN), Fuzzy Logic, Level Shifting Phase Disposition (LSPD), Simulated Annealing (SA) and Random Search Algorithm (RS). A thorough analysis of power optimization techniques, the difficulties with traditional approaches, and finally, the most modern optimization algorithms are covered. Overall, this review will strengthen the initiatives toward developing reliable and sustainable power systems using practical power optimization algorithms in future applications.
发电优化算法综述:挑战与应用
优化电力系统是一个具有挑战性的问题,因为电力系统庞大而复杂,并且可能受到一些意想不到的事件的影响。因此,应对这些挑战的方法应该是本研究的一个积极的主要焦点。能源用户可以优化电力系统,以帮助系统高效运行,减少高峰负荷的使用,并最大限度地减少控制调整。随着可再生能源发电的不确定性和可用性的增加,确保灵活性的新方法和管理技术已经出现。本文对不同的优化算法进行了研究。本文简要讨论了电力系统中常用的优化技术的优缺点。本文讨论的算法有功率优化算法(PSO)、遗传算法(GA)、禁忌搜索算法(TSA)、人工神经网络(ANN)、模糊逻辑、电平移相配置(LSPD)、模拟退火(SA)和随机搜索算法(RS)。深入分析了功率优化技术,传统方法的难点,最后介绍了最现代的优化算法。总的来说,这篇综述将加强在未来应用中使用实用的功率优化算法开发可靠和可持续的电力系统的主动性。
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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