Multiobjective Reservoir Operation Optimization Using Improved Multiobjective Dynamic Programming Based on Reference Lines

Abstract

Reservoir optimal operation (ROO) needs to coordinate various profit-making objectives, which is a typical multiobjective optimization problem (MOP) with complex constraints. With the development of multiobjective evolutionary algorithms (MOEAs) in the past decades, more and more research has focused on MOEAs to solve MOP. Considering that multiobjective ROO is also a typical multi-stage Markov decision-making problem, this paper introduces the application of multiobjective dynamic programming (MODP) for multiobjective ROO in detail. On this basis, an improved MODP with selection mechanism of non-dominated solutions based on reference lines (MODP-BRL) is proposed to improve the convergence efficiency of MODP. The experimental results show that the proposed MODP-BRL is a reliable and effective tool in solving multiobjective ROO. In addition, MODP-BRL has better performance in convergence effect and efficiency in comparison experiments with NSGAII, NSGAIII, and SPEA2. It is noteworthy that MODP and MODP-BRL are very sensitive to the discrete step. With the decrease of the discrete step (the higher the discrete precision), the computing time increases nonlinearly. The appropriate discrete step of the state variable is key presets to balance the superiority and computational efficiency of non-dominated solutions with the application of MODP and MODP-BRL.

Keywords

• Reservoir operation optimization (ROO)
• multiobjective optimization
• multiobjective dynamic programming (MODP)
• reference lines