Development of Future Rule Curves for Multipurpose Reservoir Operation Using Conditional Genetic and Tabu Search Algorithms (original) (raw)
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Future optimal rule curves are required for operating reservoir under uncertainty situation. This study applied the genetic algorithm (GA) connected a reservoir simulation model with smoothing function and adjusting of expert participation to search for future optimal reservoir rule curves for the Ubolrat Reservoir, which is located in the Northeast of Thailand, in period 2015-2064. The future optimal rule curves considered the impact of climate change with the PRECIS model under two emission scenarios, A2 and B2, and future land use maps using the CA Markov model. The future streamflow into the reservoir was determined using the Soil and Water Assessment Tool (SWAT) model. The results showed that the average future streamflow for the A2 and B2 were increased in comparison to the baseline year (1997-2014) due to the increase of average rainfall and temperatures, including widest land use change from rice and forest area to the sugarcane. The smoothing function applied to be the constraint can reduce the fluctuation of the upper and lower obtained rule curves from the GA. Further, the future rule curves can mitigate the frequency of water shortage situations and the releases of excess water during the increasing streamflow impacted by both climate scenarios and land use changes situation more than the existing and historic rule curves. Moreover, adjusted future rule curves with the expert participation were acceptable to operate the reservoir. Hence, it is necessary to have a process of analyzing and forecasting the
KSCE Journal of Civil Engineering, 2017
The uncertainties of climate and land use changes have directly impacted the inflows and water resource management in reservoirs. The optimal reservoir rule curve is a tool for the mitigation of droughts and floods, which are situations that occur often. This study applied the Genetic Algorithm (GA) to connect with a reservoir simulation model to search optimal reservoir rule curves during the period 2014-2064 for Lampao Reservoir located in the northeast of Thailand. It considered the impact of climate change with the PRECIS model under two emission scenarios: A2 and B2, and created future land use maps using the CA Markov model, including an assessment of the future inflow into the reservoir using the hydrologic model SWAT in the Upper-Lampao Basin, which is the headwater area of the reservoir. The results showed that the new rule curves were improved by the GA connected simulation model and can mitigate the frequency of water shortage situations and the releases of excess water during inflow changes in the future, including a situation where the water demand increased due to the expansion of irrigation areas.
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This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for antici...
Application of Optimization Techniques for Searching Optimal Reservoir Rule Curves: A Review
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This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and ...
Knowledge-Based Systems, 2019
Obtaining successful operation rules for dam and reservoir systems is crucial for improving water management to meet the increase in agricultural, domestic and industrial activities. Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. The main purpose of an operation rule is to minimize the gap between water supply and water demand patterns. To examine the optimized model performance, the simulation of a dam and reservoir system is usually carried out for a particular period utilizing the generated operation rule. During the simulation procedure, although reservoir inflow and evaporation are stochastic variables that are required to be forecasted during simulation, they are considered deterministic variables. This study attempts to integrate a forecasting model for reservoir inflow and evaporation with the operation rules generated from optimization models during the simulation procedure. The present study employs several optimization models to generate an optimal operation rule and two different forecasting models for reservoir inflow and reservoir evaporation. The three different *Revised Manuscript (Clean Version) Click here to view linked References optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). Two different forecasting models have been developed for reservoir inflow and evaporation using the radial basis function neural network (RBF-NN) and support vector regression (SVR). It is necessary to analyze the proposed simulation procedure for examining the operation rule to comprehend the analysis under different optimal operation rules and levels of accuracy for both hydrological variables. The suggested models have been applied to generate optimal operation policies and reservoir inflow and evaporation forecasts for the Timah Tasoh dam (TTD) located in Malaysia. The results show that the major findings regarding the model performance during the simulation period indicate the necessity to pay attention to evaluating the optimized model performance by considering the results of the forecasting model for both the hydrological variables of reservoir inflow and reservoir evaporation rather than the deterministic values.
Operation of reservoirs, often for conflicting purposes, is a difficult task. The uncertainty associated with reservoir operations is further increased due to the ongoing hydrological impacts of climate change. Therefore, various artificial intelligence techniques such as genetic algorithms, ant-colony optimization, fuzzy logic and mathematical optimization methods such as Linear programming, Dynamic Programming are increasingly being employed to solve multi-reservoir operation problems. For doing optimization, objective function is formulated which is subjected to various constraints. Constraints include continuity equation, reservoir storage constraints, release constraint and overflow constraint. Monthly data for the study are used of year 2007 to 2011. Genetic algorithm is based on Darwin's theory of Survival of the fittest. GA reduces the difference between releases and demand and returns the value of the fitness function / Objective function. In 2007, using Genetic Algorithm the generation of power can be increased 9.22% through optimal releases. There is 7.14% increase in optimal reservoir release. Further include the study of to optimize the monthly releases from the reservoir i.e. to minimize the sum of the squared difference between monthly release of water from the reservoir and downstream demands for Ukai Reservoir Project. DP is a quantitative technique which converts one big/large problem having many decision variables into a sequence of problem each with a small number of decision variables. DP reduces the difference between releases and demand and returns the value of the Objective function. After that the difference between actual releases and optimal releases i.e. Maximum Absolute Error is calculated for a month of July, August, September and October for each year. Also for evaluation of models developed by using dynamic programming the Root Mean Square Error and Correlation coefficient is calculated for all models. And also net additional available water for every year is also carried out.
Three Level Rule Curve for Optimum Operation of a Multipurpose Reservoir using Genetic Algorithms
Water Resources Management, 2021
Finding optimal policies for real-life reservoir systems operation (RSO) is a challenging task as the available analytical methods cannot handle the arbitrary functions of the problem. Most of the methods employed are numerical or iterative type and are computer dependent. Since the computer resources in terms of memory and CPU time are limited efficient algorithms are necessary to deal with the RSO problems. In this paper we present a Genetic Algorithms (GA) optimized rule curve (RC) model for monthly operation of a multipurpose reservoir which maximizes hydropower produced while meeting the irrigation demands with a given reliability. Instead of the usual single target storage for each period the proposed model considers three sets of target storages, namely dry, normal, and wet storages, based on the beginning of the period storage level. The reservoir considered is Bhadra Multipurpose Reservoir, in the state of Karnataka, India, which supplies water to irrigation fields through two canals while generating hydropower with turbines installed at each of the canal heads and at the river bed. Optimization ability and robustness of GA-RC approach are ascertained through simulation with a different inflow sequence for which global optimum is computed using Dynamic Programming. Further, a 15 year real-time simulation of the reservoir using historical inflows and demands showed significant improvement in the benefit, i.e. power produced, without compromising on the irrigation demands throughout the operation period.
JAWRA Journal of the American Water Resources Association, 2003
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...