Research on the Convergence Condition of Iterative Solution for the Mathematical Model of Reservoir Power Generation Optimization
- Wuhan University, School of Water Resources and Hydropower Engineering, Power Generation Operation, China (firstname.lastname@example.org)
Discrete differential dynamic programming algorithm is widely used in reservoir power generation dispatching, but the problem of "dimensional disaster" still exists, and there are different degrees of limitations such as premature convergence and uncertainty of convergence. In the existing monographs and literature, there is little research on the algorithm itself. The iterative solution convergence conditions, initial parameters, and initial trajectory selection of the mathematical model for reservoir power generation scheduling optimization have important effects on the iterative process and results. The convergence conditions directly determine when the iterative process converges and its calculation results. In this paper, the solution convergence conditions are studied. Based on the calculation results of the mathematical model of reservoir power generation scheduling optimization, the method of iteratively solving the convergence conditions when different state quantities are used as control factors is systematically studied. Shuibuya Hydropower Station Scheduling results show that using this method to determine the termination step size can shorten the calculation time and obtain an optimization result close to the ideal value, avoid the randomness of the convergence process of the iterative solution, and improve the accuracy of the DDDP algorithm and the efficiency of the target value.
How to cite: Tan, A. and Chen, S.: Research on the Convergence Condition of Iterative Solution for the Mathematical Model of Reservoir Power Generation Optimization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1992, https://doi.org/10.5194/egusphere-egu2020-1992, 2020