—The cascade reservoir dispatching based on the ecology and water supply is a new topic in recent years. This paper present the Ecology and Water-supply Based Multi-objective Optimal Dispatch Model (EWSB-MODM) with three objectives: ecological benefit, water supply benefit and power generation benefit. Then, by using the Monthly Frequency Computation method and considering the ecological factors (includes the breeding of Chinese sturgeon and the four major Chinese carps, preventing saltwater intrusion, avoiding dissolved gas supersaturation and the water level control of the Dongting and Poyang Lake), this paper defines the suitable ecological flow for the Three Gorges Cascade (TGC), which is taken as the ecological benefit criterion. Meanwhile, with a view to the situation of water demands and the local water shortage risks appearing in the upper and middle reaches of the Yangtze River, the water requirement is determined, and it is converted into the water supply rate as the criterion of the water supply benefits. Finally, this paper imports the multi-objective differential evolution algorithm (MODE) to solving the engineering case of the Three Gorges cascade. Results show that the model and method proposed in this paper can gain non inferior solution set which is uniform distributed and meets the ecological and water supply benefits, providing a scientific basis for reservoir schedulers to make the reasonable decision.
—Ecology, water supply, power Generation, multi-objective optimal dispatch.
Xuemin Wang and Chao Wang are with the School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 43007, China (e-mail: email@example.com, firstname.lastname@example.org).
Jianzhong Zhou is with the Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China (e-mail: email@example.com).
Cite: Xuemin Wang, Jianzhong Zhou, and Chao Wang, "Ecology and Water Supply Based Multi-objective Optimal Dispatch Model and Its Case Study in Yangtze basin," International Journal of Modeling and Optimization vol. 5, no. 3, pp. 241-245, 2015.