APPLICATION OF THE GENETIC ALGORITHM FOR OPTIMIZING OPERATION RULES OF THE LiYuTan RESERVOIR IN TAIWAN1

ABSTRACT: A procedure to apply genetic algorithm to optimize operation rules is proposed and applied to the LiYuTan Reservoir in Taiwan. The designed operation rules are operation zones with discount rates of water supply. The first step of the procedure is to predefine the shape of boundary curves of operation zones according to reservoir storage routing. Then, relatively fewer variables are used to describe the curves, and a last genetic algorithm (GA) is applied to optimize the curves. The procedure is applied to the newly built LiYuTan Reservoir for increasing domestic water demands. Shortage index is used to evaluate the performance of operation zones. A year is divided into 36 operational periods, with each month containing three operational periods. The shortage indexes calculated in operational periods are 9.81, 8.27, and 7.13, respectively, for the reservoir without operation rules, applying operation zones optimized by GA with encoding 36 storage levels for each curve, and adopting operation zones optimized by GA with encoding the curves with predefined shape. The average deficits for the three cases are 77.2, 43.6, and 33.3 (10⁴ m³/day), respectively. The results indicate that operation zones optimized by the proposed procedure have smaller shortage indexes and lower average deficits. In addition, the optimized operation zones have less variation and thus are more practical for operation. Conclusively, the proposed procedure utilizing GA to optimize operation zones with predefined shape can provide better and realistic outcomes through limited iterations.     

UNCERTAINTY BASED OPERATION OF LARGE SCALE RESERVOIR SYSTEMS: DEZ AND KAROON EXPERIENCE1

ABSTRACT: Reservoir operation involves a complex set of human decisions depending upon hydrologic conditions in the supply network including watersheds, lakes, transfer tunnels, and rivers. Water releases from reservoirs are adjusted in an attempt to provide a balanced response to different demands. When a system involves more than one reservoir, computational burdens have been a major obstacle in incorporating uncertainties and variations in supply and demand. A new generation of stochastic dynamic programming was developed in the 1980s and 1990s to incorporate the forecast and demand uncertainties. The Bayesian Stochastic Dynamic Programming (BSDP) model and its extension, Demand Driven Stochastic Dynamic Programming (DDSP) model, are among those models. Recently, a Fuzzy Stochastic Dynamic Programming model (FSDP) also was developed for a single reservoir to model the errors associated with discretizing the variables using fuzzy set theory. In this study the DDSP and the FSDP models were extended and simplified for a complex system of Dez and Karoon reservoirs in the southwestern part of Iran. The simplified models are called Condensed Demand Driven Stochastic Programming (CDDSP) and Condensed Fuzzy Stochastic Dynamic Programming (CFSDP). The optimal operating policies developed by the CDDSP and the CFSDP models were simulated in a classical model and a fuzzy simulation model, respectively. The case study was used to demonstrate the advantages of implementing the proposed algorithm, and the results show the significant value of the proposed fuzzy based algorithm.