Optimizing the reservoir operating rule curves by genetic algorithms

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness of two genetic algorithms (GAs), i.e., binary coded and real coded, to derive multipurpose reservoir operating rule curves. The curves are assumed to be piecewise linear functions where the coordinates of their inflection points are the unknowns and we want to optimize system performance. The applicability and effectiveness of the proposed methods are tested on the operation of the Shih‐Men reservoir in Taiwan. The current M‐5 operating curves of the Shih‐Men reservoir are also evaluated. The results show that the GAs provide an adequate, effective and robust way for searching the rule curves. Both sets of operating rule curves obtained from GAs have better performance, in terms of water release deficit and hydropower, than the current M‐5 operating rule curves, while the real‐coded GA is more efficient than the binary‐coded GA. Copyright © 2005 John Wiley & Sons, Ltd.     

Optimization and simulation of reservoir operation with sediment evacuation: a case study of the Tarbela Dam,Pakistan

Many reservoirs around the world are being operated based on rule curves developed without considering the evacuation of deposited sediment. Current reservoir simulation and optimization models fall short of incorporating the concept of sustainability because the reservoir storage losses due to sedimentation are not considered. This study develops a new model called Reservoir Optimization‐Simulation with Sediment Evacuation (ROSSE) model. The model utilizes genetic algorithm based optimization capabilities and embeds the sediment evacuation module into the simulation module. The sediment evacuation module is implemented using the Tsinghua university flushing equation. The ROSSE model is applied to optimize the rule curves of Tarbela Reservoir, the largest reservoir in Pakistan with chronic sedimentation problems. In the present study, rule curves are optimized for maximization of net economic benefits from water released. The water released can be used for irrigation, power production, sediment evacuation, and for flood control purposes. Relative weights are used to combine the benefits from these conflicting water uses. Nine sets of rule curves are compared, namely existing rule curves and proposed rule curves for eight scenarios developed for various policy options. These optimized rule curves show an increase of net individual economic benefits ranging from 9 to 248% over the existing rule curves. The shortage of irrigation supply during the simulation period is reduced by 38% and reservoir sustainability is enhanced by 28% through increased sediment evacuation. The study concludes that by modifying the operating policy and rule curves, it is possible to enhance the reservoir's sustainability and maximize the net economic benefits. The developed methodology and the model can be used for optimization of rule curves of other reservoirs with sedimentation problems. Copyright © 2008 John Wiley & Sons, Ltd.     

Intelligent control for modelling of real‐time reservoir operation

This paper presents a new approach to improving real‐time reservoir operation. The approach combines two major procedures: the genetic algorithm (GA) and the adaptive network‐based fuzzy inference system (ANFIS). The GA is used to search the optimal reservoir operating histogram based on a given inflow series, which can be recognized as the base of input–output training patterns in the next step. The ANFIS is then built to create the fuzzy inference system, to construct the suitable structure and parameters, and to estimate the optimal water release according to the reservoir depth and inflow situation. The practicability and effectiveness of the approach proposed is tested on the operation of the Shihmen reservoir in Taiwan. The current M‐5 operating rule curves of the Shihmen reservoir are also evaluated. The simulation results demonstrate that this new approach, in comparison with the M‐5 rule curves, has superior performance with regard to the prediction of total water deficit and generalized shortage index (GSI). Copyright © 2001 John Wiley & Sons, Ltd.     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimization of operation rule curves and flushing schedule in a reservoir

Flushing sediment through a reservoir has been practiced successfully and found to be inexpensive in many cases. However, the great amount of water consumed in the flushing operation might affect the water supply. To satisfy the water demand and water consumed in the flushing operation, two models combining the reservoir simulation model and the sediment flushing model are established. In the reservoir simulation model, the genetic algorithm (GA) is used to optimize and determine the flushing operation rule curves. The sediment‐flushing model is developed to estimate the amount of the flushed sediment volume, and the simulated results update the elevation‐storage curve, which can be taken into account in the reservoir simulation model. The models are successfully applied to the Tapu reservoir, which has faced serious sedimentation problems. Based on 36 years historical sequential data, the results show that (i) the simulated flushing operation rule curves model has superior performance, in terms of lower shortage index (SI) and higher flushing efficiency (FE), than that by the original reservoir operation; (ii) the rational and riskless flushing schedule for the Tapu reservoir is suggested to be set within an interval of every 2 or 4 years in the months of May or June. Copyright © 2003 John Wiley & Sons, Ltd.     

Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation

Genetic algorithms (GAs) are well known optimization methods. However, complicated systems with high dimensional variables, such as long-term reservoir operation, usually prevent the methods from reaching optimal solutions. This study proposes a multi-tier interactive genetic algorithm (MIGA) which decomposes a complicated system (long series) into several small-scale sub-systems (sub-series) with GA applied to each sub-system and the multi-tier (key) information mutually interacts among individual sub-systems to find the optimal solution of long-term reservoir operation. To retain the integrity of the original system, over the multi-tier architecture, an operation strategy is designed to concatenate the primary tier and the allocation tiers by providing key information from the primary tier to the allocation tiers when initializing populations in each sub-system. The Shihmen Reservoir in Taiwan is used as a case study. For comparison, three long-term operation results of a sole GA search and a simulation based on the reservoir rule curves are compared with that of MIGA. The results demonstrate that MIGA is far more efficient than the sole GA and can successfully and efficiently increase the possibility of achieving an optimal solution. The improvement rate of fitness values increases more than 25%, and the computation time dramatically decreases 80% in a 20-year long-term operation case. The MIGA with the flexibility of decomposition strategies proposed in this study can be effectively and suitably used in long-term reservoir operation or systems with similar conditions.     

Reservoir operation using grey fuzzy stochastic dynamic programming

This paper presents an optimal regulation programme, grey fuzzy stochastic dynamic programming (GFSDP), for reservoir operation. It is composed of a grey system, fuzzy theory and dynamic programming. The grey system represents data by covering the whole range without loss of generality, and the fuzzy arithmetic takes charge of the rules of reservoir operation. The GFSDP deals with the multipurpose decision‐making problem by fuzzy optimization theorem. The practicability and effectiveness of the proposed approach is tested on the operation of the Shiman reservoir in Taiwan. The current M5 operating rule curves of this reservoir also are evaluated. The simulation results demonstrate that this new approach, in comparison with the M5 rule curves, has superior performance with regard to the total water deficit and number of monthly deficits. Copyright © 2002 John Wiley & Sons, Ltd.     

Performance evaluation of implicit stochastic reservoir operation optimization supported by long-term mean inflow forecast

This study applies implicit stochastic optimization (ISO) to develop monthly operating rules for a reservoir located in Northeast Brazil. The proposed model differs from typical ISO applications as it uses the forecast of the mean inflow for a future horizon instead of the current-month inflow. Initially, a hundred different 100-year monthly inflow scenarios are synthetically generated and employed as input to a deterministic operation optimization model in order to build a database of optimal operating data. Later, such database is used to fit monthly reservoir rule curves by means of nonlinear regression analysis. Finally, the established rule curves are validated by operating the system under 100 new inflow ensembles. The performance of the proposed technique is compared with those provided by the standard reservoir operating policy (SOP), stochastic dynamic programming (SDP) and perfect-forecast deterministic optimization (PFDO). Different forecasting horizons are tested. For all of them, the results indicate the feasibility of using ISO in view of its lower vulnerability in contrast to the SOP as well as the proximity of its operations with those by PFDO. The results also reveal that there is an optimal choice for the forecasting horizon. The comparison between ISO and SDP shows small differences between both, justifying the adoption of ISO for its simplified mathematics as opposed to SDP.     

Optimal operation of reservoirs for downstream water quality control using linked simulation optimization

A methodology is developed for optimal operation of reservoirs to control water quality requirements at downstream locations. The physicochemical processes involved are incorporated using a numerical simulation model. This simulation model is then linked externally with an optimization algorithm. This linked simulation–optimization‐based methodology is used to obtain optimal reservoir operation policy. An elitist genetic algorithm is used as the optimization algorithm. This elitist‐genetic‐algorithm‐based linked simulation–optimization model is capable of evolving short‐term optimal operation strategies for controlling water quality downstream of a reservoir. The performance of the methodology developed is evaluated for an illustrative example problem. Different plausible scenarios of management are considered. The operation policies obtained are tested by simulating the resulting pollutant concentrations downstream of the reservoir. These performance evaluations consider various scenarios of inflow, permissible concentration limits, and a number of management periods. These evaluations establish the potential applicability of the developed methodology for optimal control of water quality downstream of a reservoir. Copyright © 2007 John Wiley & Sons, Ltd.     

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r² and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.     

Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

ABSTRACT

In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.     

Long‐term runoff forecasting by combining hydrological models and meteorological records

Reservoir operation is generally based on the inflows of the upstream catchment of the reservoir. If the arriving inflows can be forecasted, that can benefit reservoir operation and management. This study attempts to construct a long‐term inflow‐forecasting model by combining a continuous rainfall–runoff model with the long‐term weather outlook from the Central Weather Bureau of Taiwan. The analytical results demonstrate that the continuous rainfall–runoff model has good inflow simulation performance by using 10‐day meteorological and inflow records over a 33‐year period for model calibration and verification. The long‐term inflow forecasting during the dry season was further conducted by combining the continuous rainfall–runoff model and the long‐term weather outlook, which was found to have good performance. Copyright © 2005 John Wiley & Sons, Ltd.     

Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization

Abstract

An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.

Citation Lerma, N., Paredes-Arquiola, J., Andreu, J., and Solera, A., 2013. Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58 (4), 797–812.     

A stochastic model for optimal operation of inter-basin water allocation systems: a case study

This paper presents a new methodology for optimal operation of inter-basin water transfer systems by conjunctive use of surface water resources in water donor basin and groundwater resources in water receiving basin. To incorporate the streamflow uncertainty, an integrated stochastic dynamic programming (ISDP) model is developed. In the ISDP, the monthly inflow to the reservoir in the water donor basin, the water storage of the reservoir, and the water storage of the aquifer in the water receiving basin are considered as state variables. A water allocation optimization model is embedded in the main structure of ISDP and a new ensemble streamflow prediction model based on K-nearest-neighbourhood algorithm is also developed and linked to the ISDP. By using a new reoptimization process, the ISDP model provides monthly policies for water allocation to users in water donor and receiving basins. As water users can form a coalition to increase their benefits, several solution concepts in cooperative game theory, namely Nash–Harsanyi, Shapley, Nucleolus, Weak Nucleolus, Proportional Nucleolus, Separable Costs Remaining Benefits (SCRBs) and Minimum Costs Remaining Savings are utilized to determine the profit of each water user. In the last step, stakeholders make negotiation over these solution concepts using the Fallback bargaining theory to reach a unanimous agreement on the final distribution of the total benefit. The methodology is applied to an inter-basin water transfer project and the results show that the Shapley and SCRB solutions concepts can provide better distributions for the total benefit and the total benefit of water users is increased by a factor of 1.6 when they participate in a grand coalition.     

Optimal use of PP and PS time-lapse stacks for fluid–pressure discrimination

The combined use of time‐lapse PP and PS seismic data is analysed for optimal discrimination between pressure and saturation changes. The theory is based on a combination of the well‐known Gassmann model and the geomechanical grain model derived by Hertz and Mindlin. A key parameter in the discrimination process is the opening angle between curves representing constant changes in PP and PS reflectivity plotted against pressure and saturation changes. The optimal discrimination angle in the pressure–saturation space is 90° and this is used to determine optimal offset ranges for both PP and PS data. For typical production scenarios, we find an optimal offset range corresponding to an angle of incidence of 25–30°, for both PP and PS data. For gas we find slightly different results. This means that conventional survey parameters used in marine multicomponent acquisition should be sufficient for the purpose of estimating pressure and fluid saturation changes during production.     

A stochastic dynamic programming model with fuzzy storage states for reservoir operations

Application of stochastic dynamic programming (SDP) models to reservoir optimization calls for state variables discretization. Reservoir storage volume is an important variable whose discretization has a pronounced effect on the computational efforts. The error caused by storage volume discretization is examined by considering it as a fuzzy state variable. In this approach, the point-to-point transitions between storage volumes at the beginning and end of each period are replaced by transitions between storage intervals. This is achieved by using fuzzy arithmetic operations with fuzzy numbers. In this approach, instead of aggregating single-valued crisp numbers, the membership functions of fuzzy numbers are combined. Running a simulation model with optimal release policies derived from fuzzy and non-fuzzy SDP models shows that a fuzzy SDP with a coarse discretization scheme performs as well as a classical SDP having much finer discretized space. It is believed that this advantage in the fuzzy SDP model is due to the smooth transitions between storage intervals which benefit from soft boundaries.     

Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation

We present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy programming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10‐year period were used. Comparisons were made with the existing M‐5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction α among the users and constraints; (2) the hybrid GA‐SA performed much better than the current M‐5 operating rules. Analysis also found the hybrid GA‐SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright © 2007 John Wiley & Sons, Ltd.     

Advance first order second moment (AFOSM) method for single reservoir operation reliability analysis: a case study

Reservoir system reliability is the ability of reservoir to perform its required functions under stated conditions for a specified period of time. In classical method of reservoir system reliability analysis, the operation policy is used in a simple simulation model, considering the historical/synthetic inflow series and a number of physical bounds on a reservoir system. This type of reliability analysis assumes a reservoir system as fully failed or functioning, called binary state assumption. A number of researchers from various research backgrounds have shown that the binary state assumption in the traditional reliability theory is not extensively acceptable. Our approach to tackle the present problem space is to implement the algorithm of advance first order second moment (AFOSM) method. In this new method, the inflow and reservoir storage are considered as uncertain variables. The mean, variance and covariance of uncertain variables are determined using moment values of reservoir state variables. For this purpose, a stochastic optimization model developed based on the constraint state formulation is applied. The proposed model of reliability analysis is used to a real case study in Iran. As a result, monthly probabilities of water allocation were computed from AFOSM method, and the outputs were compared with those from Monte Carlo method. The comparison shows that the outputs from AFOSM method are similar to those from the Monte Carlo method. In term of practical use of this study, the proposed method is appropriate to determine the monthly probability of failure in water allocation without the aid of simulation.     

Reservoir-system simulation and optimization techniques

Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most important steps to be considered. Reservoirs with well-functional and reliable optimization models require very accurate simulations. However, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir’s parameters (elevation, surface-area, storage). Optimization techniques have shown high efficiency when used with simulation modeling and the combination of the two methods had given the best results in the reservoir management. The principal concern of this review study is to critically evaluate and analyze simulation, optimization and combined simulation–optimization modeling approach and present an overview of their utility in previous studies. Inferences and suggestions which may assist in improving quality of this overview in the future are provided. These will also enable future researchers, system analysts and managers to achieve more precise optimal operational system.     

Simulation and optimization model for irrigation planning and management

A simulation and optimization model was developed and applied to an irrigated area in Delta, Utah to optimize the economic benefit, simulate the water demand, and search the related crop area percentages with specified water supply and planted area constraints. The user interface model begins with the weather generation submodel, which produces daily weather data, which is based on long‐term monthly average and standard deviation data from Delta, Utah. To simulate the daily crop water demand and relative crop yield for seven crops in two command areas, the information provided by this submodel was applied to the on‐farm irrigation scheduling submodel. Furthermore, to optimize the project benefit by searching for the best allocation of planted crop areas given the constraints of projected water supply, the results were employed in the genetic algorithm submodel. Optimal planning for the 394·6‐ha area of the Delta irrigation project is projected to produce the maximum economic benefit. That is, projected profit equals US$113 826 and projected water demand equals 3·03 × 10⁶ m³. Also, area percentages of crops within UCA#2 command area are 70·1%, 19% and 10·9% for alfalfa, barley and corn, respectively, and within UCA#4 command area are 41·5%, 38·9%, 14·4% and 5·2% for alfalfa, barley, corn and wheat, respectively. As this model can plan irrigation application depths and allocate crop areas for optimal economic benefit, it can thus be applied to many irrigation projects. Copyright © 2003 John Wiley & Sons, Ltd.