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.     

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.     

Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks

By taking advantage of the close relationship between quality and quantity of water, we investigated the potential improvements of the in-reservoir water quality through the optimization of reservoir operational strategies. However, the few available techniques for optimization of reservoir operational strategies present some limitations, such as restrictions on the number of state/decision variables, the impossibility considering stochastic characteristics and difficulties for considering simulation/prediction models. One technique which presents great potential for overcoming some of these limitations is applied here and investigated for the first time in such complex system. The method, named stochastic fuzzy neural network (SFNN), can be defined as a fuzzy neural network (FNN) model stochastically trained by a genetic algorithm (GA) based model to yield a quasi optimal solution. The term “stochastically trained” refers to the introduction of a new loop within the training process which accounts for the stochastic variable of the system and its probabilities of occurrence. The SFNN was successfully applied to the optimization of the monthly operational strategies considering maximum water utilization and improvements on water quality simultaneous. Results showed the potential improvements on the water quality through means of hydraulic control.     

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.     

Identifying the relationships between trophic states and their driving factors in the Shihmen Reservoir,Taiwan

Eutrophication has become a crucial issue for water resource management in recent years. In addition, reservoir trophic states are varied with environmental and water quality variables. The objectives of this study were to apply the DFA model to examine which water quality variables significantly affect variations of trophic state index (TSI) factors (i.e. total phosphorus (TP), chlorophyll-a (Chl-a), and Secchi disk transparency (SD)) and use classification and regression tree (CART) to determine the trophic states of the Shinmen Reservoir based on the levels of TSI factors during spring 2001–winter 2009. Results showed that the optimal DFA model contained one common trend (the underlying processes influencing trophic states, which can be rainfall intensity or runoff volume) and 7 explanatory variables. Turbidity (TB), pH, and dissolved oxygen (DO) influence concentrations of TP, while ammonium nitrogen (NH₃-N), organic nitrogen (O-N), and nitrate nitrogen (NO₃-N) control variations of Chl-a, and TB is related to SD. The CART model can specify trophic states only using two dominant driving factors, i.e. TP and Chl-a. The results of the CART illustrated that eutrophication could be occurred in the Shihmen Reservoir if TP is greater than 31.65 μg/L or if Chl-a is greater than 5.95 μg/L while TP concentration is less than 31.65 μg/L. Runoff nonpoint source pollution resulted from heavy storms may be the important factor affecting reservoir trophic states. Establishing vegetative filter strips along the riparian zone may able to effectively reduce this pollution in a reservoir. The integrated DFA and CART serves as good-fit relationships among trophic states, TSI factors, and water quality variables and provide control strategies for managing water quality in the Shihmen Reservoir.     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Risk-based drought early warning system in reservoir operation

This paper introduces a risk-based decision process integrated into a drought early warning system (DEWS) for reservoir operation. It is to support policy making under uncertainty for drought management. Aspects of posterior risk, chances of option occurrences and the corresponding options to given chances, are provided to help decision makers to make better decisions. A new risk index is also defined to characterize decision makers’ attitudes toward risk. Decision makers can understand the inclination of attitude associated with any specific probability through accuracy assessment, and learn to adjust their attitudes in decision-making process. As a pioneering experiment, the Shihmen reservoir in northern Taiwan was tested. Over the simulation period (1964–2005), the expected overall accuracy approximated to 77%. The results show that the proposed approach is very practical and should find good use for reservoir operations.     

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.     

Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design

Classical optimization methodologies based on mathematical theories have been developed for the solution of various constrained environmental design problems. Numerical models have been widely used to represent an environmental system accurately. The use of methodologies such as artificial neural networks (ANNs), which approximate the complicated behaviour and response of physical systems, allows the optimization of a large number of case scenarios with different set of constraints within a short period of time, whereas the corresponding simulation time using a numerical model would be prohibitive. In this paper, a combination of an ANN with a differential evolution algorithm is proposed to replace the classical finite‐element numerical model in water resources management problems. The objective of the optimization problem is to determine the optimal operational strategy for the productive pumping wells located in the northern part of Rhodes Island in Greece, to cover the water demand and maintain the water table at certain levels. The conclusions of this study show that the use of ANN as an approximation model could (a) significantly reduce the computational burden associated with the accurate simulation of complex physical systems and (b) provide solutions very close to the optimal ones for various constrained environmental design problems. Copyright © 2008 John Wiley & Sons, Ltd.     

A combined simulation–genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project,Thailand

A combined simulation–genetic algorithm (GA) optimization model is developed to determine optimal reservoir operational rule curves of the Nam Oon Reservoir and Irrigation Project in Thailand. The GA and simulation models operate in parallel over time with interactions through their solution procedure. A GA is selected as an optimization model, instead of traditional techniques, owing to its powerful and robust performance and simplicity in combining with a simulation technique. A GA is different from conventional optimization techniques in the way that it uses objective function information and does not require its derivatives, whereas in real‐world optimization problems the search space may include discontinuities and may often include a number of sub‐optimum peaks. This may cause difficulties for calculus‐based and enumerative schemes, but not in a GA. The simulation model is run to determine the net system benefit associated with state and control variables. The combined simulation–GA model is applied to determine the optimal upper and lower rule curves on a monthly basis for the Nam Oon Reservoir, Thailand. The objective function is maximum net system benefit subject to given constraints for three scenarios of cultivated areas. The monthly release is calculated by the simulation model in accordance with the given release policy, which depends on water demand. The optimal upper and lower rule curves are compared with the results of the HEC‐3 model (Reservoir System Analysis for Conservation model) calculated by the Royal Irrigation Department, Thailand, and those obtained using the standard operating policy. It was found that the optimal rule curves yield the maximum benefit and minimum damages caused by floods and water shortages. The combined simulation–GA model shows an excellent performance in terms of its optimization results and efficient computation. Copyright © 2007 John Wiley & Sons, Ltd.     

Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models

Pumping optimization of coastal aquifers involves complex numerical models. In problems with many decision variables, the computational burden for reaching the optimal solution can be excessive. Artificial Neural Networks (ANN) are flexible function approximators and have been used as surrogate models of complex numerical models in groundwater optimization. However, this approach is not practical in cases where the number of decision variables is large, because the required neural network structure can be very complex and difficult to train. The present study develops an optimization method based on modular neural networks, in which several small subnetwork modules, trained using a fast adaptive procedure, cooperate to solve a complex pumping optimization problem with many decision variables. The method utilizes the fact that salinity distribution in the aquifer, depends more on pumping from nearby wells rather than from distant ones. Each subnetwork predicts salinity in only one monitoring well, and is controlled by relatively few pumping wells falling within certain control distance from the monitoring well. While the initial control area is radial, its shape is adaptively improved using a Hermite interpolation procedure. The modular neural subnetworks are trained adaptively during optimization, and it is possible to retrain only the ones not performing well. As optimization progresses, the subnetworks are adapted to maximize performance near the current search space of the optimization algorithm. The modular neural subnetwork models are combined with an efficient optimization algorithm and are applied to a real coastal aquifer in the Greek island of Santorini. The numerical code SEAWAT was selected for solving the partial differential equations of flow and density dependent transport. The decision variables correspond to pumping rates from 34 wells. The modular subnetwork implementation resulted in significant reduction in CPU time and identified an even better solution than the original numerical model.     

Optimization and uncertainty analysis of operational policies for multipurpose reservoir system

This paper presents optimization and uncertainty analysis of operation policies for Hirakud reservoir system in Orissa state, India. The Hirakud reservoir project serves multiple purposes such as flood control, irrigation and power generation in that order of priority. A 10-daily reservoir operation model is formulated to maximize annual hydropower production subjected to satisfying flood control restrictions, irrigation requirements, and various other physical and technical constraints. The reservoir operational model is solved by using elitist-mutated particle swarm optimization (EMPSO) method, and the uncertainty in release decisions and end-storages are analyzed. On comparing the annual hydropower production obtained by EMPSO method with historical annual hydropower, it is found that there is a greater chance of improving the system performance by optimally operating the reservoir system. The analysis also reveals that the inflow into reservoir is highly uncertain variable, which significantly influences the operational decisions for reservoir system. Hence, in order to account uncertainty in inflow, the reservoir operation model is solved for different exceedance probabilities of inflows. The uncertainty in inflows is represented through probability distributions such as normal, lognormal, exponential and generalized extreme value distributions; and the best fit model is selected to obtain inflows for different exceedance probabilities. Then the reservoir operation model is solved using EMPSO method to arrive at suitable operational policies corresponding to various inflow scenarios. The results show that the amount of annual hydropower generated decreases as the value of inflow exceedance probability increases. The obtained operational polices provides confidence in release decisions, therefore these could be useful for reservoir operation.     

Development of a real-time forecasting model for turbidity current arrival time to improve reservoir desilting operation

ABSTRACT

Among various strategies for sediment reduction, venting turbidity currents through dam outlets can be an efficient way to reduce suspended sediment deposition. The accuracy of turbidity current arrival time forecasts is crucial for the operation of reservoir desiltation. A turbidity current arrival time (TCAT) model is proposed. A multi-objective genetic algorithm (MOGA), a support vector machine (SVM) and a two-stage forecasting technique are integrated to obtain more effective long lead-time forecasts of inflow discharge and inflow sediment concentration. The multi-objective genetic algorithm (MOGA) is applied for determining the optimal inputs of the forecasting model, support vector machine (SVM). The two-stage forecasting technique is implemented by adding the forecasted values to candidate inputs for improving the long lead-time forecasting. Then, the turbidity current arrival time from the inflow boundary to the reservoir outlet is calculated. To demonstrate the effectiveness of the TCAT model, it is applied to Shihmen Reservoir in northern Taiwan. The results confirm that the TCAT model forecasts are in good agreement with the observed data. The proposed TCAT model can provide useful information for reservoir sedimentation management during desilting operations.     

Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba,Ecuador

Abstract

Reservoir operation is studied for the Daule Peripa and Baba system in Ecuador, where El Niño events cause anomalously heavy precipitation. Reservoir inflow is modelled by a Markov-switching model using El Niño–Southern Oscillation (ENSO) indices as input. Inflow is forecast using 9-month lead time ENSO forecasts. Monthly reservoir releases are optimized with a genetic algorithm, maximizing hydropower production during the forecast period and minimizing deviations from storage targets. The method is applied to the existing Daule Peripa Reservoir and to a planned system including the Baba Reservoir. Optimized operation is compared to historical management of Daule Peripa. Hypothetical management scenarios are used as the benchmark for the planned system, for which no operation policy is known. Upper bounds for operational performance are found via dynamic programming by assuming perfect knowledge of future inflow. The results highlight the advantages of combining inflow forecasts and storage targets in reservoir operation.

Editor D. Koutsoyiannis; Associate editor I. Nalbantis     

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.     

Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.     

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.     

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.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

Evaluation of stochastic reservoir operation optimization models

This paper investigates the performance of seven stochastic models used to define optimal reservoir operating policies. The models are based on implicit (ISO) and explicit stochastic optimization (ESO) as well as on the parameterization–simulation–optimization (PSO) approach. The ISO models include multiple regression, two-dimensional surface modeling and a neuro-fuzzy strategy. The ESO model is the well-known and widely used stochastic dynamic programming (SDP) technique. The PSO models comprise a variant of the standard operating policy (SOP), reservoir zoning, and a two-dimensional hedging rule. The models are applied to the operation of a single reservoir damming an intermittent river in northeastern Brazil. The standard operating policy is also included in the comparison and operational results provided by deterministic optimization based on perfect forecasts are used as a benchmark. In general, the ISO and PSO models performed better than SDP and the SOP. In addition, the proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.