Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs. (15th July 2020)

• Record Type: Journal Article
• Authors: Yin, Hao; Wu, Fei; Meng, Xin; Lin, Yicheng; Fan, Jingmin; Meng, Anbo
• Is Part Of: Energy. Volume 203(2020)
• Publication Date: 2020-07-15
• Subjects: Hydro-thermal generation scheduling -- Cascaded reservoirs -- Water transport delay -- Crisscross optimization -- Valve-point effects; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117822
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Short-term hydrothermal generation scheduling (SHGS) considering various hydraulic and electric constraints is a complex non-convex optimization problem. The coupling connection of cascaded reservoirs and the valve-point effects of thermal units greatly increase the difficulty of finding optimal solution. This paper presents an efficient solution to the SHGS problem by using a novel crisscross optimization (CSO) algorithm, which generates the optimal scheduling results by applying two distinctive search operators, i.e., horizontal crossover and vertical crossover. The horizontal crossover is used as a global optimizer that can reduce the search blind spots in complex solution space through a cross-border search strategy. The vertical crossover is used to address the premature convergence problem by applying a unique dimensional crossover mechanism. Both search operators take turns to generate moderation solutions and CSO always maintains a population of historically best solutions by using a greedy strategy so as to speed up the convergence speed. To investigate the CSO's performance on the SHGS problem, three test systems widely adopted in the literature are used to do the short-term hydrothermal generation scheduling. The results reveal that the proposed algorithm has good robustness and outperforms other state-of-the-art methods in terms of solution quality. Highlights: A novel crisscross optimization approach is applied to solve the SHGS problem. The horizontal crossover generates offspring using a cross-border search mechanism. The vertical crossover can greatly alleviate the premature convergence problem. The vertical crossover probability is not sensitive to the optimal results. CSO has advantage over other pervious methods in terms of solution quality.