1. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P. R. China
2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, P. R. China
3. China Three Gorges Corporation, Beijing100038, P. R. China
4. Datang Yantan Hydropower Corporation, Nanning 530022, P. R. China
5. Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, P. R. China

The hydro unit economic load dispatch (ELD) is of great importance in energy conservation and emission reduction. Dynamic programming (DP) and genetic algorithm (GA) are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA’s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

This work was supported by the National Basic Research Program of China (973 Program, Grant No. 2013CB036406), the National Natural Science Foundation of China (Grant No. 51179044), and the Research Innovation Program for College Graduates in Jiangsu Province of China (Grant No. CXZZ12-0242).

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