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Long-term scheduling of large cascade hydropower stations in Jinsha River, China

Wang, Chao, Zhou, Jianzhong, Lu, Peng, Yuan, Liu
Energy conversion and management 2015 v.90 pp. 476-487
electric power, mathematical models, power generation, rivers, shipping, system optimization, water power, wet season, China, Yangtze River
The Jinsha River is the third longest river in the world. It consists of four large hydropower stations with total installed capacity 42,960MW lying on the upper stretches of the Yangtze River, which is the longest river in the word. Due to the great potential of large cascade hydropower stations on power generation, long-term scheduling of large cascade hydropower stations (LSLCHS) plays an important role in electrical power system. As more and more concentrations focused on the optimal operation of large cascade hydropower stations, the LSLCHS has been taken into a multi-dimensional, non-convex and non-linear optimization problem due to its complicated hydraulic connection relationships and varieties of complex constraints with considering its power generation, shipping and ecological characteristics. In order to solve this problem, a multi-population ant colony optimization for continuous domain (MACOR) is proposed in this paper. A Gaussian group selection strategy is applied to overcome premature convergence and ants with different characteristics are employed to enhance search ability, and circulatory solution correction strategy is presented to handle outflow, water level and output constraints. Furthermore, the efficiency and stability of MACOR are verified by its more desirable results in comparison to other latest works in numerical simulation, and it can be a viable alternative for solving those complicated optimal problems. With the applications in hydropower operation, LSLCHS can obtain more power generation benefit than other alternatives in dry, normal and wet year, and the compensation analysis reveals that joint operation of cascade hydropower stations can enhance the total power generation especially in wet season.