Multipurpose Multireservoir System Research Articles

Implicit stochastic optimization for deriving operating rules for a multi-purpose multi-reservoir system

Implicit stochastic optimization for deriving operating rules for a multi-purpose multi-reservoir system

Application of MOMSA algorithm for optimal operation of Karun multi objective multi reservoir dams with the aim of increasing the energy generation

Multi-reservoir system operation is a challenging and complex task, because of the curse of uncertainties, nonlinearities, dimensionalities and conflicts among the various contradictory objectives. Contemporaneous optimization of such competing objectives needs improving the capability of existing optimization algorithms. This study reports the first application of the recently-introduced multi-objective moth swarm algorithm (MOMSA) in optimization of long-term operation of the large Karun multi-purpose multi-reservoir system. In this algorithm, the decision variables were defined in such a way that the algorithm synchronization capability has improved. Furthermore, a robust mechanism (crowding distance mechanism) with high exploratory capability was employed to select the most efficient solutions within the population. To evaluate the algorithm's efficiency, the results of MOMSA were compared with two well-known multi-objective algorithms of the non-dominated sorting genetic algorithm-II (NSGA-II) and the strength Pareto evolutionary algorithm-II (SPEA-II). To compare the capability of these algorithms, a set of statistical indices including the generational distance (GD), the spacing (S), the spread (Δ), and the maximum spread (MS), along with four reservoir's performance evaluation indicators of the reliability (Rel), the vulnerability (Vul), the resiliency (Res), and the sustainability index were employed. The results demonstrated the superior performance of the MOMSA in solving the Karun multi-reservoir system problem with GD = 259.64, S = 3574.69, Δ = 0.755 and MS = 18420.8. The MOMSA, with the optimized average annual energy production of 15669.7 GW, outperformed the NSGA-II (7569.1 GW) and SPEA-II (9026.3 GW) algorithms. Furthermore, the developed algorithm could supply the downstream demands with a sustainability index of 86.73%, while the corresponding values for the NSGA-II, the SPEA-II and the real operating conditions were 22.73%, 46.09% and 78.87%, respectively. In addition, among the utilized algorithms, the MOMSA demonstrated the best results for flood control purpose. The superior performance of MOMSA will ensure it as a robust algorithm in the optimization of complex and large-scale problems.

A hydrologically-driven approach to climate change adaptation for multipurpose multireservoir systems

Climate change can significantly affect water systems with negative impacts on many facets of society and ecosystems. Therefore, significant attention must be devoted to the development of efficient adaptation strategies. More specifically, the reoperation of water resources systems to keep the overall performance within acceptable limits should be prioritized to avoid, or at least delay as much as possible, costly infrastructural investments. This manuscript presents a hydrologically-driven approach to support the reoperation of multipurpose multireservoir systems. The approach is organized around 1) the use of a large ensemble of GCM hydro-climate projections to drive a climate stress test; 2) the bottom-up clustering of those hydrologic projections based on hydrologic attributes that are both relevant to the region of interest and interpretable by the operators; and finally, 3) the identification of adaptation measures for each cluster after developing a one-way coupling of an optimization model with a simulation model. The climate impact assessment is illustrated with the multipurpose multireservoir system of the Lievre River basin in Quebec (Canada). Results show that cluster-specific, adapted, operating rules can improve the performance of the system and reveal its operational flexibility with respect to the different operating objectives.

Simulation Of Reservoir Operation In A Multi Reservoir System

The present study aims to apply simulation software MIKE Basin for the operation of reservoirs of Mahanadi Reservoir Project (MRP) Complex. MRP complex is a multipurpose multi reservoir system. Simulation is a technique by which we emulate the behavior of a system. Simulation is a very powerful technique in analyzing most complex water resource system in detail for performance evaluation. Reservoir operation study has been done for data of 34 years. The results extracted from the study indicated that the performance of simulation model MIKE Basin is satisfactory.

Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, II: optimal solution and performance evaluation

This papers forms the second part of series on application of artificial neural network (ANN) for optimal operation of a multi-purpose multi-reservoir system. Optimal operating policies of a reservoir system are derived using Discrete differential dynamic programming (DDDP)-based ANN model. In ANN model development a feed-forward network with delta learning rule and back propagation algorithm is used. Neural networks have been trained using supervised learning approach. Water supply for irrigation, municipal and industrial use have been selected as objective of operation and other purposes are treated as binding constraints. Minimization of the sum of square of penalties incurred due to deviation of release from the target, is selected as the objective function. Damodar Valley (DV), a multi-purpose four reservoir system in India is used for this study. With different combination of input data, five types of ANN models are developed. Simulation has been done with 5 years (out of 1000 years) generated monthly inflow sequence as well as three types of observed historical monthly inflow sequences: maximum annual inflow year, 75% dependable inflow year and minimum annual inflow year. For simulation, total 360 monthly networks are trained and stored. ANN model: in which initial storage, current period’s inflow and previous period’s inflow are considered as input and optimal final state as output, yields lowest objective function value. Performances of the said model is computed based on modern reliability parameters, i.e., reliability, resiliency and vulnerability.

Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, I: initial solution and selection of input variables

In this two-part series research paper, the effect of different input variables for optimal operation of a multipurpose multi-reservoir system using artificial neural network (ANN) models has been thoroughly investigated. As an alternative to multi-reservoir stochastic model, dynamic programming-based ANN models are developed. Due to nonavailability of any specific criterion to select the number and type of input variables for such ANN model, an exhaustive study with various combinations of input variables for single-reservoir ANN models is done. Damodar Valley, a multi-purpose multi-reservoir system in India, is used for this study. Results obtained from discrete dynamic programming, an optimization technique, are used as training data for ANN models. With different combinations of input data, five types of ANN models are developed. ANN models are simulated with generated inflow sequence as well as different types of observed historical inflow sequences. For each simulation, 240 monthly networks are trained and stored. Four different stochastic dynamic programming (SDP) models of the system are developed considering one reservoir at a time approach. Linear regression analysis is performed between the results obtained from ANN models with results of the best SDP model. Objective function values as well as different regression parameters are compared to select preferred ANN model and its input variables.

Optimizing the Management Strategies of a Multi-Purpose Multi-Reservoir System in Vietnam

Resource shortages are having an increasingly severe impact as global trends like rapid population growth, urbanization, economic development, and climate change unfold. Moreover, rising living standards across many regions are also affecting water and energy resources. This entails an urgent requirement to improve water resources management. An important improvement is to transfer water between the different uses of the reservoir system. A compromise between the needs of hydropower generation and the water supply can be negotiated for the reservoir system to reduce the severity of water shortages. The Be River basin in Vietnam was selected as a case study to investigate. The combination of the generalized water allocation simulation model (GWASIM) and the bounded optimization by quadratic approximation (BOBYQA) algorithm was applied to optimize hydropower generation in various water shortage scenarios. The results present optimized hydropower generation policies for cascade reservoirs that would significantly improve the present operating policy in terms of both the water supply and hydropower generation. Moreover, multiple scenarios will provide flexibility to the reservoir operator by giving the relationship between water and energy. Given water supply conditions, the operator will be able to choose among several optimal solutions to ensure greater water resource efficiency in the Be River basin.

Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system

The derivation of joint operating policy is a challenging task for a multi-purpose multi-reservoir system. This study proposed an aggregation-decomposition model to guide the joint operation of multi-purpose multi-reservoir system, including: (1) an aggregated model based on the improved hedging rule to ensure the long-term water-supply operating benefit; (2) a decomposed model to allocate the limited release to individual reservoirs for the purpose of maximizing the total profit of the facing period; and (3) a double-layer simulation-based optimization model to obtain the optimal time-varying hedging rules using the non-dominated sorting genetic algorithm II, whose objectives were to minimize maximum water deficit and maximize water supply reliability. The water-supply system of Li River in Guangxi Province, China, was selected for the case study. The results show that the operating policy proposed in this study is better than conventional operating rules and aggregated standard operating policy for both water supply and hydropower generation due to the use of hedging mechanism and effective coordination among multiple objectives.

Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints

The supply of municipal water in Germany is often governed by policies that warrant occurrence-based reliabilities of 99.5 % or more. Such reliabilities have to be considered in the optimization of reservoir operation. A reliability-based optimization requires sufficiently long simulation periods of several thousand years of time series of inflow and demand. However, long simulation periods lead, especially for multi-objective parameterization–simulation–optimization (MOPSO), to unacceptable computational burden. Therefore, techniques need to be developed that increase the computational efficiency of MOPSO for such optimization problems. In this paper, a novel Monte Carlo recombination method (MCR) is proposed. MCR reduces the length of inflow time series significantly while preserving critical statistics, i.e., characteristics of probability distributions and variability of wet and dry conditions. It could be shown that simulations based on these shortened time series allow for highly efficient MOPSO and yield comparable Pareto-fronts and reliabilities. For the demonstration of the capabilities of MCR, it is integrated into a MOPSO framework for the optimization of a multi-purpose multi-reservoir system in the Eastern Ore Mountains, Germany. For this real-world application, synthetic time series of a length of 10,000 years are generated and reduced to 882 years, which results in a reduction of the computational burden by a factor of eleven. A validation of the results shows that the MOPSO framework allows for optimization of operational policies that yield reliabilities over 99.95 % on a monthly scale and up to 99.7 % on an annual timescale.

Evaluation of Stakeholder Utility Risk Caused by the Objective Functions in Multipurpose Multireservoir Systems

Optimal operation of reservoir systems is a challenging task in water resource management, involving stakeholders with different utilities. These utilities are conflicting, especially in a multireservoir system when the number of stakeholders increases compared to a single-reservoir system. A reservoir system with hydropower energy generation and supplying downstream demands is an example of these challenges. An appropriate objective framework can improve mitigation of stakeholder conflicts. This paper considers a 1-year optimal operation of a multireservoir system, with two parallel upstream reservoirs and a downstream reservoir in series. System objectives are hydropower energy generation as well as supplying downstream demands. Existing conflicts of this system are determined in single-objective reservoir operation models. The mathematical form of objective functions can limit utility risk in multiobjective problems. To overcome impacts of existing conflicts, a simulation model has been coupled with the weighting method and Nash equilibrium in multiobjective models. Results show that although the calculated objectives using the Nash equilibrium are less (worse) than the maximum (best) values of the objective function with the largest weight for the first (highest) objective priority using the weighting method, in two parallel reservoirs the values of the objective function are larger (better), about 70.83 and 54.17%, respectively, for hydropower energy generation and supplying downstream demands objectives. Moreover, these objectives calculated by the Nash equilibrium are 58.33 and 50% better, respectively, than same values by the weighting method in series reservoirs. Thus, utilities were significantly balanced and increased from the worst (minimum) utility values by the Nash equilibrium. The Nash equilibrium increases considerably the reliability value for all stakeholders at the same time.

Optimal operation of a multipurpose multireservoir system in the Eastern Nile River Basin

Abstract. The upper Blue Nile River Basin in Ethiopia is a largely untapped resource despite its huge potential for hydropower generation and irrigated agriculture. Controversies exist as to whether the numerous infrastructural development projects that are on the drawing board in Ethiopia will generate positive or negative externalities downstream in Sudan and Egypt. This study attempts at (1) examining the (re-)operation of infrastructures, in particular the proposed reservoirs in Ethiopia and the High Aswan Dam and (2) assessing the economic benefits and costs associated with the storage infrastructures in Ethiopia and their spatial and temporal distribution. To achieve this, a basin-wide integrated hydro-economic model has been developed. The model integrates essential hydrologic, economic and institutional components of the river basin in order to explore both the hydrologic and economic consequences of various policy options and planned infrastructural projects. Unlike most of the deterministic economic-hydrologic models reported in the literature, a stochastic programming formulation has been adopted in order to: (i) understand the effect of the hydrologic uncertainty on management decisions, (ii) determine allocation policies that naturally hedge against the hydrological risk, and (iii) assess the relevant risk indicators. The study reveals that the development of four mega dams in the upper part of the Blue Nile Basin would change the drawdown refill cycle of the High Aswan Dam. Should the operation of the reservoirs be coordinated, they would enable an average annual saving of at least 2.5 billion m3 through reduced evaporation losses from the Lake Nasser. Moreover, the new reservoirs (Karadobi, Beko-Abo, Mandaya and Border) in Ethiopia would have significant positive impacts on hydropower generation and irrigation in Ethiopia and Sudan: at the basin scale, the annual energy generation is boosted by 38.5 TWh amongst which 14.2 TWh due to storage. Moreover, the regulation capacity of the above mentioned reservoirs would enable an increase of the Sudanese irrigated area by 5.5%.

Performance Evaluation Model for Multipurpose Multireservoir System Operation

This paper describes the development of a performance evaluation based model for the operation of multipurpose multireservoir in a river basin system. The methodology developed in the present study has been evolved for (1) allocation of releases for multi-purpose from each reservoir, (2) fair allocation of mandatory flow releases in the river, and (3) the assessment of the system capability for multipurpose operation. The System Performance Index (SYSPI) has been introduced as a measure of the overall performance of the system. SYSPI is defined as a function of the performance indicator indices which are developed to measure each of the objectives of the multireservoir system, namely, reliability of water supply, hydropower production, revenue income, and spill prevention. The SYSPI is maximized using a search algorithm which is linked to the simulation module. The application of the developed methodology is demonstrated for the reservoirs on the Narmada River System, India. The application of the methodology should enable increasing the hydropower generation within the existing framework.

A simulation model for the operation of multipurpose multireservoir system for River Narmada, India

This paper presents a simulation model for the operation of multipurpose multireservoir systems. The simulation model developed in this study has been applied to the Narmada River Reservoir System in India. The model has been formulated for the monthly operation of reservoirs to meet industrial, domestic, irrigation, and hydropower generation water requirements. The simulation model consists of four modules: inflow generation, minimum flow releases, simulation, and performance evaluation. The single-site Thomas–Fiering and multisite Lane's Applied Stochastic Techniques (LAST) inflow generation models have been used for the generation of a monthly inflow series. Five performance indicator indices have been formulated for evaluating the performance of the multipurpose multireservoir system: Volume Reliability Index ( VRI), Time Reliability Index ( TRI), Hydropower Production Index ( HPI), Economic Benefits Index ( ECBI), and Spill Prevention Index ( SPPI). The trade-offs between the performance indicator indices have been obtained using the simulation model. The results from these trade-offs will assist decision makers in the operation of reservoirs, as well as with planning and comparing operation strategies.

Management of Multipurpose Multireservoir Using Fuzzy Interactive Method

In this paper a fuzzy interactive method is proposed for efficient management of multipurpose multireservoir problems. The proposed method provides an option to decision maker (DM) to work in an interactive manner to achieve the conflicting objectives as close to their desired values as is practically feasible. In each iteration, fuzzy membership functions of various objectives are framed and combined into a single objective using the product operator. The single objective nonlinear optimization model thus framed in each iteration is numerically solved using genetic algorithm. The solution provides the values of the objectives which can be actually achieved keeping in view their aspired values as provided by DM. At the end of each iteration, DM has the option to modify the aspired values of one or more objectives keeping in view the results obtained by the algorithm thus far. The algorithm is stopped when DM feels satisfied with the results. The working of the proposed method has been demonstrated on the mathematical model of a realistic multipurpose multireservoir system taken from literature.

Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system

This study presents a methodology to establish a set of optimal operation release rules which are tree-based rules for real-time flood control on a multipurpose multireservoir system. The derived rules can be used to determine the optimal real-time releases during flood periods. Steps of the proposed methodology involve: (1) collection of data, (2) building of flood database, (3) generation of optimal input–output patterns by running the flood control optimization model, (4) classification of training and testing data, (5) extraction of tree-based release rules for designed scenarios using the decision-tree algorithm (C5.0), (6) determination of optimal tree-based rules, (7) generation of the real-time forecast data by using the hydrological forecast model, (8) processing of reservoir real-time releases by simulating the reservoir real-time flood control operation, and (9) verification of the superior release rules through comparisons of tree-based rules, regression-based rules derived from a multiple-linear regression model and existing release rules. The developed methodology is applied to the Tanshui River Reservoir System in Taiwan to extract the decision trees for each scenario and then select the best ones with highest accuracy as the optimal tree-based rules. The derived optimal tree-based rules, regression-based rules and existing rules are compared by conducting the real-time operations in three historical typhoons, including Aere, Haima and Nock-ten in 2004. Results demonstrate that the solution using the derived tree-based rules have better performance than the regression-based rules and the existing rules in terms of reducing the peak stage at downstream control points, and meeting the target reservoir storage at the end of flood.

Assessing marginal water values in multipurpose multireservoir systems via stochastic programming

The International Conference on Water and the Environment held in Dublin in 1992 emphasized the need to consider water as an economic good. Since water markets are usually absent or ineffective, the value of water cannot be directly derived from market activities but must rather be assessed through shadow prices. Economists have developed various valuation techniques to determine the economic value of water, especially to handle allocation issues involving environmental water uses. Most of the nonmarket valuation studies reported in the literature focus on long‐run policy problems, such as permanent (re)allocations of water, and assume that the water availability is given. When dealing with short‐run allocation problems, water managers are facing complex spatial and temporal trade‐offs and must therefore be able to track site and time changes in water values across different hydrologic conditions, especially in arid and semiarid areas where the availability of water is a limiting and stochastic factor. This paper presents a stochastic programming approach for assessing the statistical distribution of marginal water values in multipurpose multireservoir systems where hydropower generation and irrigation crop production are the main economic activities depending on water. In the absence of a water market, the Lagrange multipliers correspond to shadow prices, and the marginal water values are the Lagrange multipliers associated with the mass balance equations of the reservoirs. The methodology is illustrated with a cascade of hydroelectric‐irrigation reservoirs in the Euphrates river basin in Turkey and Syria.

Multireservoir real-time operations for flood control using balanced water level index method

This paper presents a real-time simulation-optimization operation procedure for determining the reservoir releases at each time step during a flood. The proposed procedure involves two models, i.e., a hydrological forecasting model and a reservoir operation model. In the reservoir operation model, this paper compares two flood-control operation strategies for a multipurpose multireservoir system. While Strategy 1 is the real-time joint reservoir operations without using the balanced water level index (BWLI) method, Strategy 2 involves real-time joint reservoir operations using the BWLI method. The two strategies presented are formulated as mixed-integer linear programming (MILP) problems. The idea of using the BWLI method is derived from the HEC-5 program developed by the US Army Corps of Engineers. The proposed procedure has been applied to the Tanshui River Basin system in Taiwan using the 6 h ahead forecast data of six typhoons. A comparison of the results obtained from the two strategies reveals that Strategy 2 performs much better than Strategy 1 in determining the reservoir real-time releases throughout the system during flood emergencies in order to minimize flooding, while maintaining all reservoirs in the system in balance if possible. Consequently, the proposed model using the BWLI method demonstrates its effectiveness in estimating real-time releases.

A stochastic approach to analyze trade‐offs and risks associated with large‐scale water resources systems

Water resources development projects often involve multiple and conflicting objectives as well as stochastic hydrologic inputs. Multiobjective optimization techniques can be used to identify noninferior solutions and to construct a trade‐off relationship between conflicting objectives. This paper presents a methodology for analyzing trade‐offs and risks associated with large‐scale water resource projects under hydrologic uncertainty. The proposed methodology relies on the stochastic dual dynamic programming (SDDP) model to derive monthly or weekly operating rules for multipurpose multireservoir systems taking into account the stochasticity of the inflows, irrigation water withdrawals, minimum/maximum flow requirements for navigation, fishing, and/or for ecological purposes. In SDDP, release decisions are chosen so as to minimize the operating costs of a hydrothermal electrical system. Irrigation water demands and other operating constraints are imposed on the system through the SDDP model. The proposed methodology is illustrated with the Southeastern Anatolia Development project, commonly called GAP, in Turkey. The GAP is a multidimensional development project involving primarily the production of hydroelectricity and irrigation. Simulation results using 50 hydrologic scenarios show that the complete development of the irrigation projects would reduce the total energy output by 6.5% and will increase the risk of not meeting minimum outflow at the Syrian border from 5% to 25%.

Decision Support System for Surface Water Planning in River Basins

A decision support system (DSS) for the integration of hydrologic process modeling and risk evaluation of the surface water management alternatives in a river basin is developed. The DSS, named CTIWM, is aimed at supporting the testing and evaluation of water management policies and at facilitating integration of user-selected scenarios into planning strategies of the water resource system in the Chikugo River basin, a multipurpose multireservoir system. CTIWM uses a module library that contains compatible modules for simulating a variety of hydrologic processes. Different numerical models are invoked through a user interface menu, which facilitates communications between users and models in a friendly way. The source code was developed by using object-oriented programming techniques. The result shows that the use of DSSs may effectively improve the speed and quality of water management and give users more flexibility in analyzing different scenarios.

Operating Rule Optimization for Missouri River Reservoir System

This paper describes the application of deterministic optimization to the main stem Missouri River reservoir system and the development and testing of inferred optimal operating rules from these model results. An implicit stochastic optimization approach is taken and tested using a simplified simulation model. This work illustrates the applicability and limitations of applying deterministic optimization to development of strategic operating rules for large-scale water resource systems. For this multipurpose multireservoir system, simple data display and simulation modeling are found to be superior to classical regression techniques for inferring and refining promising operating rules from deterministic optimization results.