Stochastic Inflow Simulation for Searching Rule Curves

Abstract

Rule curves are basic monthly guidelines for long term reservoir operation. Generally, the optimal rule curves are searched by reservoir simulation model and optimization techniques. A traditional reservoir simulation does not consider the risk of reservoir operation caused by natural uncertainty from inflow. A stochastic simulation model embedded genetic algorithm model is developed for searching the optimal rule curves in this study. Synthetic inflows are used in the developed model for assessing the risk reservoir operation. Single and multi-reservoir systems are applied to assess the efficiency of the proposed technique. The developed model has been applied to determine the optimal rule curves of the Bhumibol and Sirikit Reservoirs (the Chao Phraya River Basin, Thailand) for multi-reservoir system and the Ubolratana Reservoir (the Chi River Basin, Thailand) for single system. The optimal rule curves of each system were used to assess by a Monte Carlo simulation. The results show that the situations of water shortage and excess release of the obtained rule curves are not significantly different from the situation of the curves searching by tradition simulation. It can be concluded that the stochastic simulation model embedded genetic algorithm provided the optimal rule curves as considering the risk of reservoir operation. Furthermore, the proposed model is applicable for both single and multi-reservoir systems.