Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm

• Published in: Energy (Oxford) Vol. 153; pp. 706 - 718
• Main Authors: Feng, Zhong-kai, Niu, Wen-jing, Cheng, Chun-tian
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-06-2018; Elsevier BV
• Subjects: Constraint handling method; Ecology; Efficiency; Electric power generation; Feasibility; Feasibility studies; Fork/join framework; Genetic algorithm; Genetic algorithms; Hydroelectric plants; Hydroelectric power; Hydropower system operation; Multi-objective optimization; Multiple objective analysis; Optimization; Parallel computing; Performance enhancement; Reservoirs; Subpopulations; System effectiveness; Water deficit; Water resources; Constraint handling method; Parallel computing; Hydropower system operation; Multi-objective optimization; Fork/join framework; Genetic algorithm
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.04.075

Recently, with increasing attention paid to energy production and ecological protection, the hydropower reservoirs operation balancing generation benefit and ecological requirement is playing an important role in water resource and power systems. Thus, the parallel multi-objective genetic algorithm is introduced to effectively resolve this multi-objective constrained optimization problem with two competing objectives and numerous physical constraints. In the proposed method, the original large-sized swarm is decomposed into several smaller subpopulations that will be simultaneously evolved on several computing units, effectively enhancing the execution efficiency and population diversity. During the evolutionary process, the chaotic initialization method is used to enhance the quality of initial population, while the feasible space identification method and the modified domination strategy are designed to improve the feasibility of solution and convergence rate of individuals. The results from the Wu hydropower system of China show that the presented method can make full use of computationally expensive resources to improve the performance of population. For instance, compared with the traditional method, the presented method can make 69.23% and 27.44% improvements in the standard deviation of power generation and water deficit in normal year, respectively. Thus, this paper provides an effective tool to support the multi-objective operation optimization of hydropower system.
•A multi-objective model is presented for hydropower generation-ecology operation.•Parallel multi-objective genetic algorithm is introduced to resolve this model.•Parallel technology and swarm decomposition strategy enhance population diversity.•Space identification and modified domination strategies enhance solution feasibility.•Results in different cases demonstrate the effectiveness of the presented method.