Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.
Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada.
Sci Rep. 2022 Nov 7;12(1):18880. doi: 10.1038/s41598-022-21635-0.
Hydropower plants are known as major renewable energy sources, usually used to meet energy demand during peak periods. The performance of hydropower reservoir systems is mainly affected by their operating rules, thus, optimizing these rules results in higher and/or more reliable energy production. Due to the complex nonlinear, nonconvex, and multivariable characteristics of the hydropower system equations, deriving the operating rules of these systems remains a challenging issue in multi-reservoir systems optimization. This study develops a self-adaptive teaching learning-based algorithm with differential evolution (SATLDE) to derive reliable and precise operating rules for multi-reservoir hydropower systems. The main novelty of SATLDE is its enhanced teaching and learning mechanism with three significant improvements: (i) a ranking probability mechanism is introduced to select the learner or teacher stage adaptively; (ii) at the teacher stage, the teaching mechanism is redefined based on learners' performance/level; and (iii) at the learner stage, an effective mutation operator with adaptive control parameters is proposed to boost exploration ability. The proposed SATLDE algorithm is applied to the ten-reservoir benchmark systems and a real-world hydropower system in Iran. The results illustrate that the SATLDE achieves superior precision and reliability to other methods. Moreover, results show that SATLDE can increase the total power generation by up to 23.70% compared to other advanced optimization methods. Therefore, this study develops an efficient tool to extract optimal operating rules for the mentioned systems.
水力发电站是主要的可再生能源，通常用于满足高峰期的能源需求。水力发电水库系统的性能主要受其运行规则的影响，因此优化这些规则可以提高能源产量和/或提高可靠性。由于水力系统方程具有复杂的非线性、非凸性和多变量特性，因此推导出这些系统的运行规则仍然是多水库系统优化中的一个挑战。本研究开发了一种具有微分进化的自适应教学学习算法 (SATLDE)，以推导出多水库水力发电系统的可靠而精确的运行规则。SATLDE 的主要新颖之处在于其增强的教学机制，具有三个重要改进：(i) 引入了排名概率机制，以自适应地选择学习者或教师阶段；(ii) 在教师阶段，根据学习者的表现/水平重新定义教学机制；(iii) 在学习者阶段，提出了一种具有自适应控制参数的有效变异算子，以提高探索能力。所提出的 SATLDE 算法应用于十个水库基准系统和伊朗的一个实际水力系统。结果表明，SATLDE 比其他方法具有更高的精度和可靠性。此外，结果表明，与其他先进的优化方法相比，SATLDE 可以将总发电量提高多达 23.70%。因此，本研究开发了一种有效的工具，用于提取所述系统的最优运行规则。
