Knowledge-Based Approach for Reservoir System Optimization

Abstract

This paper addresses a knowledge-based approach for reservoir system optimization. The approach takes a more detailed consideration of each turbine in a hydropower plant than the traditional constant output coefficient method. To use this approach, a knowledge expression and a knowledge function are defined for hydropower plant operation. The knowledge expression is extracted by dynamic programming to save all possibly optimal situations of unit commitment, and further, the knowledge function is formulated based on a two-dimensional interpolation of the knowledge expression. Through the use of the knowledge expression and the knowledge function, computer memory requirements can be reduced and unnecessary computations can be avoided in the reservoir operation optimization. To overcome the decomposition schemes in time and in space and guarantee finding the global optimum (in a discrete sense) with an extended time horizon, up to 400 CPU cores are used to run a parallel dynamic programming model, which applies the knowledge-based approach, to estimate the maximum energy production of the Three Gorges Project (TGP) and the Gezhouba Project (GZB) cascade hydropower plants in China in the year of 2010, with 1 day as the time step and 365 days as the time horizon. The case study results show that the maximum energy production of the TGP-GZB system would be