Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Abstract

Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966–2016 (51 years) including the future inflow during 2017–2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.

Keywords

• Civil engineering
• Optimization techniques
• Reservoir rule curves
• Reservoir operation
• Genetic algorithm
• Tabu search algorithm