Abstract
Rule curves are fundamental guidelines for operating a reservoir system. The objective of this paper is to find a suitable objective function and to propose a smoothing function constraint for searching the optimal rule curves by using genetic algorithms connected simulation model. The results show that an average water shortage is the optimal objective function for searching the optimal rule curves. It can represent the situations of water deficit and excess release. The results also indicate that a moving average applied to be the constraint of searching can reduce the variation of the upper and lower rule curves. Further, the developed model has been applied to determine the optimal rule curves of the Bhumibol and Sirikit Reservoirs (the Chao Phraya River Basin, Thailand). It is shown that the model gives the rule curves which are more mitigate the situations of water deficit and excess release than the existing rule curves. It is also concluded that the genetic algorithms connected simulation with the smoothing constraint is more effective than the model without constraint.
Highlights
Rule curves of a reservoir are basic monthly guides for long run of reservoir operation
To reduce the fluctuate of rule curve in order to obtain the optimal rule curves which are suitable in the practice, the moving average is chosen as a base of the smoothing function constraint for fitting the rule curves, for each curve can be present as xτ −2 + xτ −1 + xτ 3
The objective of this paper is to find the suitable objective function and to propose a smoothing function constraint for searching the optimal rule curves by using genetic algorithms (GAs) connected simulation technique
Summary
Rule curves of a reservoir are basic monthly guides for long run of reservoir operation. Chleeraktrakoon and Kangrang[7] applied the DP with a principle progressive optimality to determine the optimal rule curves using a magnitude of water shortage and excess release as the objective function. This paper proposes the smoothing-function constraint for fitting rule curves and presents the suitable objective function for determining the optimal rule curves using the genetic algorithm (GAs) with the simulation model. The reproduction including selection, crossover and mutation is performed for creating a new rule curve parameters in generation Reservoir simulation - decide release by the rule curve (chromosome) parameters - make balance the storage and record release - calculate the situation of water shortage and excess release and Reproduction - selection - crossover - mutation - create new rule curve parameters
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