Reservoir System Management Research Articles

Dam and Reservoir System Management based on Genetic Algorithms

Indeed, there are many hydrology variables influence on the operating of dam and reservoir system. Thus, modelling of dam operation is a complicated issue due to the nonlinearity of such hydrological parameters. Hence, the identification of a modern model with a high capacity to cope with the operation of the dam is extremely important. The current research introduced good an optimization algorithm, namely Genetic Algorithm (GA) to find best operation rules. The main aim of the suggested algorithm is to minimize the difference between irrigation demand and water release value. The developed algorithm was applied to find operation rules for Timah Tasoh Dam, Malaysia. This research used significant evaluation indexes to examine the algorithms' performance. The results indicated that the GA method achieved low Vulnerability, high Resilience and Reliability. It has been demonstrated that the GA method will be a promising tool in dealing with the problem of dam operation.

Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System

The paper presents a functionality investigation of the key dam elements based on finite element analysis. A detailed analysis of filtration processes, dam strength, and the surrounding rock mass was conducted. Dam elements whose potential damage could jeopardize the normal functioning of the embankment dam have been identified. A particular emphasis was placed on the analysis of dam elements that have been identified as weak points. A numerical analysis of the impact of individual grout curtain zone failure on leakage under the dam body, a strength analysis of the overflow section, as well as the analysis of the slope stability that can compromise the functioning of the spillway have been performed. To analyze the partial stability of individual structural elements, a new measure of local stability was introduced as the remaining load-bearing capacity. As a case study, the Zavoj dam, which is a part of the Pirot reservoir system in the Republic of Serbia, was used. Investigation revealed that local damage to the grout curtain will not significantly increase leakage under the dam body, the overflow section is one of the most robust elements of the dam, but the slope above the spillway can compromise the functioning of the overflow and thus the safety of the entire dam. Based on the analysis of the results of the remaining load-bearing capacity, the dependence of the spillway capacity on earthquake intensity has been defined. The established relationship represents a surrogate model for further assessment of dynamic resilience of the complex multipurpose reservoir system, within the scope of the advanced reservoir system management.

When to start an adaptation strategy in response to climate change in reservoir system management

This paper presents a methodology for the identification of the time tipping point where historical operating rules of a reservoir system should be replaced by new alternatives due to climate change. The time tipping point is a time indicator of the failure probability change point of the historical operating rules for the reservoir system under future climate change, called the failure warning time. For this study, the primary purpose of the reservoir system is hydropower generation. We first estimate the probability density function of the annual benefit of hydropower generation. Then, based on the estimated probability density function and the given risk tolerance level of the reservoir system, we determine the risk-based warning threshold, and further identify the failure warning time of the historical operating rules via the probability change-point analysis technique. We apply the proposed methodology to the Xiluodu-Xiangjiaba-Three Gorges cascade reservoir system on the Yangtze River in China. The results indicate that the failure warning time for the cascade reservoir system will occur in at least another 10 years in the future under climate change. We also find that the failure response in the cascade reservoir system is determined by both the reservoir that has the first failure occurrence and its storage capacity. The developed methodology can be used to determine the time tipping point to launch adaptation strategies for the river basin management, and further can help policymakers cope with climate change.

Multi-mechanism ensemble interior search algorithm to derive optimal hedging rule curves in multi-reservoir systems

The operating rules, especially the hedging rules, which are known as useful tools for effective utilization of water from reservoir systems, are commonly applied for managing reservoir systems in severe drought periods. Extracting optimal operation policies for multi-reservoir systems is a challenging issue due to their non-linear, large-scale, and stochastic nature. Therefore, improving optimization techniques is a worthy effort in developing the operating rules for multi-reservoir systems. Hence, this research aimed to develop an efficient optimization method called multi-mechanism ensemble interior search algorithm (MEISA) to tackle various problems in the optimal management of reservoir systems. The MEISA promotes the two main operators of the interior search algorithm (ISA) (i.e., the composition and the mirror operators) to enhance the capability of global and local search. Besides, the proposed algorithm employs an enhanced opposition-based learning (EOBL) operator to improve the best solution and increase the convergence speed. The MEISA also uses an adaptive parameter to balance the global and local search in the proposed algorithm. The results of several test functions demonstrate that the MEISA outperforms other optimizers in terms of the solution accuracy, the quality, and the convergence rate. Finally, the results of optimizing the hedging rules for two real-world single- and multi-reservoir systems in Iran indicate that the MEISA is more suitable efficient in ameliorating irreversible impacts of severe drought periods as compared with other optimization methods. The results show that the MEISA has highly convincing performance in obtaining the optimal hedging rules for the efficient management of multi-reservoir systems.

GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging rules.

Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utilization and avoid stakeholder disputes. The aim of this paper was to build an optimization-simulation framework based on implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN) for addressing the issue of reservoir operation. Inflow scenarios were generated synthetically based on a monthly scale to be used as an input to a multi-objective genetic programming model to construct an optimal operating rules database. Such database was subsequently used simultaneously with the output of the inflow forecasting model to simulate monthly reservoir hedging rules using RNN. Our results demonstrate the effectiveness of the GA-ISO-RNN model for simulating and predicting optimal reservoir release with consistent accuracy. Results from both the training and testing phases clearly proved the usefulness of RNN in predicting optimal reservoir release with relatively higher values of the Nash-Sutcliffe model efficiency coefficient, correlation coefficient, and lower values of root mean squared error and mean absolute deviation. Furthermore, by comparing the historical releases and the output of the proposed model, the results show that the proposed model was less vulnerable than standard operating rules. The proposed methodology was applied to the Bigge reservoir in Germany, as it features an extensive management infrastructure, but this methodology can also be easily adopted in other similar cases.

Assessment of water quality using GIS techniques and water quality index in reservoirs affected by water diversion

This paper assesses the quality of the excess water diverted from the Gurara reservoir to the Lower Usuma reservoir, with declining water resources, based on the Nigerian Standard for Drinking Water Quality (NSDWQ) and World Health Organization (WHO) permissible limits. A near-laser analysis was carried out on different samples from each of the two reservoirs both in the dry and wet season using geographic information system (GIS) technique and water quality index (WQI). Samples collected were analyzed for pH Temperature, Taste, Odour, Electrical Conductivity (EC), Total Dissolved Solid (TDS), Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), Sodium (Na+), Bicarbonate (HCO3-), Chloride (CI-), Nitrate (NO3-), Calcium (Ca2+), Magnesium (Mg2+), Iron (F-) and potassium (K+). The obtained values were evaluated and compared with the standard permissible limits. The result shows a link between the two reservoirs. The physiochemical determinants are within the maximum permissible limits with some slight variations at the Lower Usuma reservoir. The diverted water from the Lower Usuma reservoir was rated as a good water type with index of 62.5% in the dry and 49.7% in the wet season by WHO’s permissible limits, whilst the Nigerian Standard for Drinking Water Quality rates it at 83.3% at the dry season and 69.0% at the wet season. The water from the Gurara reservoir was rated as an excellent type with index of 43.23 % in the dry season and 45.7% in the wet season by WHO’s permissible limits whilst the Nigerian standard for drinking water quality equally rates it at 61.7% and 65.5% in the wet season. According to the correlation matrix, predominant determinants amongst others show both positive and negative correlation at seasonal basis. The spatial variation map shows higher concentration along downstream section at both reservoirs. The study outlined that the surface water of the area is impaired by the anthropogenic activities and needs proper management plan to control further contamination and pollution of the surface water of the area. Further, the surface water of the receiving region needs a proper treatment before its consumptive uses. The application of GIS techniques and water quality index in the assessment of water quality in the context of reservoir systems revealed to be a valuable contribution statistically to the understanding and management of connected water bodies. Future analyses will concentrate on broader applications of this methodology in the context of the understanding and management of reservoir systems in Nigeria. This is, therefore, a call on water managers to put in place routine mechanisms of monitoring the water from reservoirs affected by water diversion, due to the ever-increasing anthropogenic activities around the upstream of reservoir watershed in the coming decades.

Learning and inference methodologies for hybrid dynamic Bayesian networks: a case study for a water reservoir system in Andalusia, Spain

Time series analysis requires powerful and robust tools; at the same time the tools must be intuitive for users. Bayesian networks have been widely applied in static problem modelling, but, in some knowledge areas, Dynamic Bayesian networks are hardly known. Such is the case in the environmental sciences, where the application of static Bayesian networks in water resources research is notable, while fewer than five papers have been found in the literature for the dynamic extension. The aim of this paper is to show how Dynamic Bayesian networks can be applied in environmental sciences by means of a case study in water reservoir system management. Two approaches are applied and compared for model learning, and another two for inference. Despite slight differences in terms of model complexity and computational time, both approaches for model learning provide similar results. In the case of inference methods, again, there were slight differences in computational time, but the selection of one approach over the other is based on the prediction needed: If the aim is just to go one step forward, both Window and Roll out approaches are similar, when we need to go more than one step forward; the most appropriate will be Roll out.

Exploring the Potential and the Boundaries of the Rolling Horizon Technique for the Management of Reservoir Systems with over-Year Behaviour

The paper presents a set of experiments on the Rolling Horizon Technique (RHT), applied to the management of a multi-reservoir, multipurpose water resources system with over-year behaviour. In the RHT, decisions on releases from reservoirs are taken for a number of time steps ahead (the Forecasting Horizon – FH) through an optimization model, based on the present water availability in reservoirs and on some forecasts of future inflows over the FH. Only the decision concerning the first time step (the current month) is then implemented, as new information on reservoir levels and forecasts becomes available so that the process can start again with updated information. The paper investigates how the quality of forecasts and the length of the FH impact on the effectiveness of the decisions taken. The evaluation exercise is carried out in two different demand scenarios considering some deficit-related indices, such as TSSD, the Sum of Squared Standardized Deficits, over an Operation Horizon (OH) of forty years. The scenarios are designed to evaluate the role of different demand levels (corresponding to higher/lower stress) on system’s behaviour. Results show that in systems with over-year behaviour, effective forecasts (here simulated through the best possible: actually occurred inflows from the current month up to the end of water year) seem to add little value to system’s operation compared to the most naive forecast (average monthly values), especially in contexts characterized by high pressure on resources. Where there is less pressure on resources, good-quality forecasts are more effective to improve operation. A FH of 24 months seems to enhance significantly system’s performances compared to a FH of 12 months, while FHs longer than two years produce no improvements in system’s performances. The paper also tries to define the limits of a finite-horizon operation technique such as the RHT by benchmarking it with an idealistic release schedule obtained by a model that performs allocation with Perfect Foresight over the whole Operation Horizon.

Towards an optimal integrated reservoir system management for the Awash River Basin, Ethiopia

Abstract. Recently, the Kessem–Tendaho project is completed to bring about socioeconomic development and growth in the Awash River Basin, Ethiopia. To support reservoir Koka, two new reservoirs where built together with extensive infrastructure for new irrigation projects. For best possible socioeconomic benefits under conflicting management goals, like energy production at three hydropower stations and basin wide water supply at various sites, an integrated reservoir system management is required. To satisfy the multi-purpose nature of the reservoir system, multi-objective parameterization-simulation-optimization model is applied. Different Pareto-optimal trade-off solutions between water supply and hydro-power generation are provided for two scenarios (i) recent conditions and (ii) future planned increases for Tendaho and Upper Awash Irrigation projects. Reservoir performance is further assessed under (i) rule curves with a high degree of freedom – this allows for best performance, but may result in rules curves to variable for real word operation and (ii) smooth rule curves, obtained by artificial neuronal networks. The results show no performance penalty for smooth rule curves under future conditions but a notable penalty under recent conditions.

Application of periodic autoregressive process to the modeling of the Garonne river flows

Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and to estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959–2010) for the Garonne river in the southwest of France. Results show that the accuracy of forecasts is improved in the robust model with respect to the unrobust model for the quarter-monthly flows. By reducing the number of parameters to be estimated, the principle of parsimony favors the choice of the robust approach.

Optimal Operation of Multireservoir Systems: State-of-the-Art Review

With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.

A hybrid analytic/rule-based approach to reservoir system management during flood

The article presents an approach to real-time operation of a water retention reservoir during flood. Water releases are calculated repetitively on the basis of two-variant inflow forecasts and some expert knowledge. During every intervention of the control algorithm, an optimization problem with the expected value type performance index is solved. In the paper, the basic characteristics of trajectories of outflows resulting from the application of this control method are analyzed. Afterwards, the results of the series of simulation experiments based on a large set of historical data concerning a big Polish reservoir are presented.

Reservoir system management and environmental flows

AbstractConsiderable attention has been paid to the downstream effects of reservoirs on the ecology of rivers, streams and wetlands. However, most reservoirs were constructed well before ecological concerns became prominent. Little attention has been given to the question of what extent existing structures and management systems can accommodate changes. The paper discusses this matter and a range of associated issues. It is concluded that many problems can be rectified by structural means but that such rectification will be very expensive. While based primarily on Australian systems, the conclusions and examination are considered to be globally applicable.

New control concepts for uncertain water resources systems: 1. Theory

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.

Évaluation des règles d'exploitation de systèmes de réservoirs

During the operating phase, the evaluation of the risks of failure generates additionnal information that is essential for water reservoir system management. This information throws light on the selection of a release policy for the mid-range operating horizon and helps to determine, at the beginning of each period of the short-range horizon, if the risks of failure resulting from the application of the current release policy remain acceptable. A method of evaluation of the risks of failure of a reservoir system, variables that are not explicitly accounted for in the determination of the release policy for the mid-range horizon, is described. The evaluation method uses a stochastic simulation model to reproduce the evolution of the system operated according to the current release policy. It estimates the risk of the various types of failure for a known state of the system at the beginning of a decision period and those that the system may reach at the beginning of the other periods of the evaluation horizon. It also computes the statistics of the various types of failure encountered and of the net benefits. The evaluation method is applied to a system of one reservoir and one powerhouse. Key words: reservoirs, failure, stochastic simulation, risk evaluation, release policy.

Multi-Purpose Reservoir System Management : Impact of Science on Society

Multi-Purpose Reservoir System Management : Impact of Science on Society