Reservoir Operation Rules Research Articles

Development of reservoir operation rules using seasonal climate forecasts

ABSTRACT This study verified the suitability of using precipitation forecasts in defining operation rules for the Cruzeta reservoir in Rio Grande do Norte, Brazil. The operation rules were developed through reservoir operation simulation–optimization, using a genetic algorithm. The performance indicators were analyzed in five operation scenarios: standard operating policy (SOP), current reservoir rationing rule (C), rationing without forecast (R), rationing with forecast (RF), and rationing with perfect forecast (RPF). The SOP scenario better met the total demand but made the system very susceptible to supply collapse. The results of the RF and RPF scenarios showed better compliance with the priority demands and the total demand during the dry periods. Changing from RF to RPF scenario, there is a small improvement in the evaluation indexes. The use of rules integrating the seasonal weather forecast is thus recommended.

Assessing Reservoir Performance under Climate Change. When Is It Going to Be Too Late If Current Water Management Is Not Changed?

Climate change is modifying the way we design and operate water infrastructure, including reservoirs. A particular issue is that current infrastructure and reservoir management rules will likely operate under changing conditions different to those used in their design. Thus, there is a big need to identify the obsolescence of current operation rules under climate change, without compromising the proper treatment of uncertainty. Acknowledging that decision making benefits from the scientific knowledge, mainly when presented in a simple and easy-to-understand manner, such identification—and the corresponding uncertainty—must be clearly described and communicated. This paper presents a methodology to identify, in a simple and useful way, the time when current reservoir operation rules fail under changing climate by properly treating and presenting its aleatory and epistemic uncertainties and showing its deep uncertainty. For this purpose, we use a reliability–resilience–vulnerability framework with a General Circulation Models (GCM) ensemble under the four Representative Concentration Pathways (RCP) scenarios to compare the historical and future long-term reservoir system performances under its current operation rule in the Limarí basin, Chile, as a case study. The results include percentiles that define the uncertainty range, showing that during the 21st century there are significant changes at the time-based reliability by the 2030s, resilience between the 2030s and 2040s, volume-based reliability by the 2080s, and the maximum failure by the 2070s. Overall, this approach allows the identification of the timing of systematic failures in the performance of water systems given a certain performance threshold, which contributes to the planning, prioritization and implementation timing of adaptation alternatives.

Research on water resources dispatch model based on improved genetic algorithm – water resources dispatch model

Abstract According to the research on reservoir water resources scheduling and distribution, the aim is to balance the water supply and demand in each period, and consider the total water supply and the annual external water withdrawal of the reservoir in each period as water rights. The decision-making variables are provided for the water supply of the reservoir in this paper, so that water demand of the water-receiving area can be better met to alleviate the water shortage at various stages and realize the effective use of water resources. Moreover, through the constraints of reservoir operation rules and other constraints, a mathematical model is established for optimal operation of water resources in the reservoir system. Meanwhile, optimized genetic algorithms are applied to solve the model according to the characteristics of the model. After simulation tests, compared with the traditional linear binary algorithm used in the reservoir, the improved genetic algorithm studied in the paper improves the accuracy of data calculation and data convergence, which proves that the research results of the paper provide theoretical and practical significance for improving the level of reservoir water resources management and solving the problem of optimal water resources scheduling.

Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

Reservoir sedimentation reduces the gross storage capacity of dams and also negatively impacts turbine functioning, posing a danger to turbine inlets. When the sediment delta approaches the dam, further concerns arise regarding sediments passing through turbine intakes, blades abrasion due to increased silt/sand concentration, choking of outlets, and dam safety. Thus, slowing down the delta advance rate is a worthy goal from a dam manager’s viewpoint. These problems can be solved through a flexible reservoir operation strategy that prioritize sediment deposition further away from the dam face. As a case study, the Mangla Reservoir in Pakistan is selected to elaborate the operational strategy. The methodology rests upon usage of a 1D sediment transport model to quantify the impact of different reservoir operating strategies on sedimentation. Further, in order to assess the long-term effect of a changing climate, a global climate model under representative concentration pathways scenarios 4.5 and 8.5 for the 21st century is used. The reduction of uncertainty in the suspended sediments concentration is achieved by employing an artificial neural networking technique. Moreover, a sensitivity analysis focused on estimating the impact of various parameters on sediment transport modelling was conducted. The results show that a gradual increase in the reservoir minimum operating level slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of a sustainable reservoir use for the societal benefit.

Deriving representative reservoir operation rules using a hidden Markov-decision tree model

Reservoirs have been widely used to regulate streamflow to meet both human and natural water requirements. This study applies a hidden Markov-decision tree (HM-DT) model to derive representative reservoir operation modules under various operation conditions (i.e., inflow, storage, as well as unknown factors) and their transitions (the dynamic change of operation rules) that reflect the impacts of seasonality, long-term non-stationarity, and extreme events on reservoir operation. The representative operation modules can be applied to reservoirs in the same region that are not observed; the capability for simulating dynamic operation behaviors improves the predictive accuracy as compared to regular decision trees. Using a number of reservoirs located in the same region for training, the HM-DT model can derive a limited number of representative operation modules in the form of decision trees (DT), and the transitions between different operation schemes in response to changing operation conditions. The application of the HM-DT model is demonstrated through a case study of the Upper Colorado River basin, where eight representative operation modules are determined for 50 reservoirs located in the region, and the modules are validated with 11 reservoirs in the same region. The eight operation modules are classified into three types (i.e. nearly constant release, release as a piece-wise function of inflow, and release almost identical to inflow). The identified operation modules and the transition patterns between operation modules can be used to better understand real-world operation behaviors, improve future operations, and build realistic reservoir operation components in basin-scale hydrological models.

Joint operation of parallel reservoir with interconnected tunnel

Reservoir operation is strongly influenced by water balance conditions in the catchment area. The amount of inflow, reservoir capacity, and water requirement are major factors in arranging the reservoir operation rules. The purpose of this research is to obtain the extent of the parallel reservoir operation reliability that performed simultaneously by utilizing water transfer from wet to dry watershed. Simulation method used to operate the parallel reservoir with interconnecting tunnel between. From simulated results on three seasons, there are obtained that the water potential to be transferred to the dry watershed. By utilizing this water potential, further simulation for 25 years will come to see the reliability of the joint operating rule in this location.

Research on the Flood Classification for the Lower Reaches of Jinshajiang River

The characteristics of floods in a river and their classification are of great significance for the flood control, risk reduction and water resources utilization as well. This paper investigates the classification of floods in the lower basin of the Jinsha River based on flood area composition analysis and Fuzzy C-means Clustering Algorithm. The results show that the floods in the lower basin of the Jinsha River are divided into upstream type, interval type and tributary type according to their regional compositions; the three types of floods are divided into two categories according to their intensity and morphological indicators using fuzzy C-means. The flood classification of the lower reaches of Jinshajiang River in this study laid a foundation for the formulation of the joint operation rules of the cascade reservoirs in the lower reaches of Jinshajiang River, and the flood control standard of Yibin City could be raised to prevent 100-year flood approximate through the joint operation of the cascade reservoirs, additionally, it is of great importance to recognize the flood inflow as well as their control for the Three Gorges Reservoir.

The interplay between reservoir storage and operating rules under evolving conditions

Reservoir storage helps manage hydrological variability, increasing predictability and productivity of water supply. However, there are inevitable tradeoffs, with control of high frequency variability coming at the expense of robustness to low frequency variability. Tightly controlling variability can reduce incentives to maintain adaptive capacity needed during events that exceed design thresholds. With multiple dimensions of change projected for many water supply systems globally, increased knowledge on the role of design and operational choices in balancing short-term control and long-term adaptability is needed. Here we investigated how the scale of reservoir storage (relative to demands and streamflow variability) and reservoir operating rules interact to mitigate shortage risk under changing supplies and/or demands. To address these questions, we examined three water supply systems that have faced changing conditions: the Colorado River in the Western United States, the Melbourne Water Supply System in Southeastern Australia, and the Western Cape Water Supply System in South Africa. Moreover, we parameterize a sociohydrological model of reservoir dynamics using time series from the three case studies above. We then used the model to explore the impacts of storage and operational rules. We found that larger storage volumes lead to a greater time before the shortage is observed, but that this time is not consistently used for adaptation. Additionally, our modeling results show that operating rules that trigger withdrawal decreases sooner tend to increase the probability of an adaptive response; the findings from this model are bolstered by the three case studies. While there are many factors influencing the response to water stress, our results demonstrate the importance of: i) evaluating design and operational choices in concert, and ii) examining the role of information salience in adapting water supply systems to changing conditions.

Two-Phase Risk Hedging Rules for Informing Conservation of Flood Resources in Reservoir Operation Considering Inflow Forecast Uncertainty

Water shortages during dry periods can be successfully mitigated by managing reservoirs in real-time to conserve water as floods recede. The inherent uncertainty in inflow forecasts however means that it remains a challenge to balance the risks of flooding against those of water shortages. Few studies have examined how the risks of floods and water shortages can be managed using reservoir operation rules. In this study, a two-phase stochastic optimization model was built to determine the optimal conservation level for flood water by minimizing the risks from both floods and water shortages. For the optimal condition, hedging rules were analytically derived as a quasi-linear function of the storage capability and the expected water shortage. The rules indicate that the flood water conservation was achieved when the marginal upstream flood risk was equal to the marginal water shortage risk, and that the limits of three operation zones divided by the expected water availability should be used when determining the water release. The results from testing the model with data from the Xianghongdian Reservoir (China) showed that the hedging rules outperformed the capacity-constrained pre-release rules for conserving flood water without increasing the flood risk. This proposed methodology will inform the process for making decisions about how to operate reservoirs to ensure optimal real-time flood water conservation.

Surrogate-Based Multiperiod, Multiobjective Reservoir Operation Optimization for Quality and Quantity Management

AbstractDeriving optimal reservoir operation rules in a selective withdrawal framework (SWF) considering quality and quantity objectives is a challenging problem due to its computational burdens. T...

A Reservoir Operation Policy Using Inter-Basin Water Transfer for Maximizing Hydroelectric Benefits in Brazil

The Hydrothermal Coordination problem consists of determining an operation policy for hydroelectric and thermoelectric plants within a given planning horizon. In systems with a predominance of hydraulic generation, the operation policy to be adopted should specify the operation of hydroelectric plants, so that hydroelectric resources are used economically and reliably. This work proposes the implementation of reservoir operation rules, using inter-basin water transfer through an optimization model based on Network Flow and Particle Swarm Optimization (PSO). The proposed algorithm aims to obtain an optimized operation policy of power generation reservoirs and consequently to maximize the hydroelectric benefits of the hydrothermal generation system, to reduce the use of thermoelectric plants, the importation and/or energy deficit and to reduce the cost associated with meeting the demand and reduce CO2 emissions from combustion of fossil fuels used by thermoelectric plants. In order to illustrate the efficiency and effectiveness of the proposed approach, it was evaluated by optimizing two case studies using a system with four hydroelectric plants. The first case study does not consider transfer and water and the second case study uses water transfer between rivers. The obtained results illustrate that the proposed model allowed to maximize the hydroelectric resources of a hydrothermal generation system with economy and reliability.

Socio-Hydrological Modelling: The Influence of Reservoir Management and Societal Responses on Flood Impacts

Over the last few years, several socio-hydrological studies have investigated the risk dynamics generated by the complex interactions between floods and societies, with a focus on either changing reservoir operation rules or raising levees. In this study, we propose a new socio-hydrological model of human–flood interactions that represents both changes in the reservoir management strategies and updating of the levee system. Our model is applied to simulate three prototypes of floodplain management strategies to cope with flood risk: green systems, in which societies resettle outside the flood-prone area; technological systems, in which societies implement structural measures, such as levees; and green-to-techno systems, in which societies shift from green to technological approaches. Floodplain dynamics are explored simulating possible future scenarios in the city of Brisbane, Australia. Results show that flood risk is strongly influenced by changes in flood and drought memory of reservoir operators, while risk-awareness levels shape the urbanisation of floodplains. Furthermore, scenarios of more frequent and higher magnitude events prove to enhance social flood memory in green systems, while technological systems experience much higher losses. Interestingly, green-to-techno systems may also evolve toward green floodplain management systems in response to large losses and technical/economical unfeasibility of larger structural measures.

Identifying Adaptive Reservoir Operation for Future Climate Change Scenarios: A Case Study in Central Vietnam

The assessment of flood risk under climate change impacts is necessary for sustainable flood management strategies at national level. Referring to the aforesaid statement, this research aims to evaluate the potential impacts of climate change on reservoir operations in the Huong River Basin, Vietnam. To enable further representation of climate change impacts, the HadGEM3-RA Regional Climate Model (RCM) under Representative Concentration Pathways (RCPs) 8.5 climate change scenario was used in this study. For assessing the level of flood risk posed to the study area, a coupled HEC-HMS hydrologic model and HEC-RAS hydrodynamic model was used to represent the behaviour of flow regimes under climate change impacts in the Huong River Basin. The key results demonstrated that the mean temperature and mean annual rainfall would be increased in the future from 0.2–0.8°C, and 4.8–6.0%, respectively. Consequently, the mean annual runoff and mean water level would also be increased from 10–30%, and 0.1–0.3 m above mean sea level, respectively. Moreover, the proposed reservoir operation rules corresponding to flood control warning stages was also derived to reduce peak flows downstream during the rainy season. Finally, the main findings of this study can be a good example for future planning of flood control reservoir systems in Vietnam.

Multi-reservoirs with inter-reservoir water transfer operation rules

Reservoir operation is strongly influenced by water balance conditions in the catchment area. The amount of inflow, reservoir capacity, and water requirements are major factors in creating reservoir operation rules. The purpose of this research is to obtain the extent of the reliability of parallel reservoir operation with simultaneous execution by utilizing water transfer from wet to dry watersheds. Reservoir operation rules were derived by utilizing the storage capacity of the two reservoirs with two spillways and two separate water demands in the downstream parts. The various parameters applied for the operation rules are expected to optimize the operation of the reservoirs.

Heuristic Input Variable Selection in Multi-Objective Reservoir Operation

Deriving operating rules for multi-objective cascade reservoir systems is an important challenge in water resources management. To address, this study combines a radial basis function network with an evolutionary algorithm to propose a heuristic input variable selection (HIS) method that extracts reservoir operating rules based on feature selection. For a case study of the Hanjiang cascade reservoirs in China, we initially describe the operating rules with radial basis functions and subsequently refine them based on the HIS method. We select the most suitable input variables for each reservoir conditioned on water supply and power generation targets to derive and optimize the rules with a Pareto-archived dynamically dimensioned search algorithm. From this we can analyze input variable selection and the corresponding impact on multi-objective cascade reservoir operations. The results demonstrate that the HIS method selects the input variables accurately and the reservoir operating rules refined by the method could increase water supply by up to 6.6% and power generation by up to 1.2%. The most suitable input variables for reservoir operation vary depending on reservoir objective, however the HIS method appears effective at selecting the appropriate input variables for individual reservoirs in a cascade system.

Effects of an outflow regime adoption of the São Francisco River reservoir system to meet water demands for multiple uses

ABSTRACT The establishment of reservoir operation rules is a strategy used to increase storage volumes and satisfy water demands. However, these rules are not always compatible with the flow regime required to meet environmental needs. This paper aims to evaluate the effects of an outflow regime adopted by the São Francisco River reservoir system, which includes environmental water requirements, in the current laws for meeting water demands for multiple uses, according to Resolution 2081/2017 of Agência Nacional de Águas - ANA. The methodology adopted was the construction and simulation of alternative outflow operation scenarios, for a regular and a dry hydrological period, which were the following: 1) Outflow scenario according to Resolution 2081/17 and (2) Outflow scenario that considers a proposed environmental flow hydrogram for the low course of the São Francisco river. The operation effects in the meeting of multiple water uses were quantified in each scenario and compared with each other. The results suggest that when the maintenance of the aquatic ecosystems is a priority, the system demonstrated low water security in meeting the reservoirs target volumes and satisfying water demands, including the environmental flows.

Optimizing Operation Rules of Cascade Reservoirs for Adapting Climate Change

Climate change leads to great impact on hydrological cycle and consequently affects water resources management. Historical strategies are no longer applicable under a changing environment. Therefore, adaptive management, especially adaptive operation rules for reservoirs, has been developed to mitigate the potential adverse impacts of climate change. However, previous studies generally provide a similar framework for adaptation strategies of individual reservoir without consideration of cascade reservoirs in the future scenario. This study derives adapting operation rules for cascade reservoir system based on future projections (2021–2100) of two global climate change models (GCMs). By using Pareto archived dynamically dimensioned search (PA-DDS) algorithm with maximization of water supply and power generation, the performance of the adaptive operation rule curves is compared with the designed operation rule. The results demonstrate that Pareto solutions of the PA-DDS algorithm provide a wider, more optimal range of annual power generation and water supply, and the projection pursuit method can select the best. The adaptive operation rules focusing on power generation can significantly increase the cascade reservoir annual power generation (by 3.7% in GCM-BCC or 4.8% in GCM-BNU), which shows that the proposed method can adapt future climate change.

Identifying Efficient Operating Rules for Hydropower Reservoirs Using System Dynamics Approach—A Case Study of Three Gorges Reservoir, China

In the long-term operation of hydropower reservoirs, operating rules have been used widely to decide reservoir operation because they can help operators make an approximately optimal decision with limited runoff information. However, the problems faced by reservoir managers is how to make and select an efficient operating rule properly. This study focuses on identifying efficient and reliable operating rules for the long-term operation of hydropower reservoirs using system dynamics (SD) approach. A stochastic hydrological model of reservoir inflow time series was established and used to generate a large number of inflow scenarios. A deterministic optimization operation model of hydropower reservoirs was constructed and then resolved using dynamic programming (DP) algorithm. Simultaneously, within implicit stochastic optimization (ISO) framework, different operating rules were derived using linear fitting methods. Finally, the most efficient one of the existing operating rules was identified based on SD simulation coupled with the operating rules. The Three Gorges Reservoir (TGR) in central China was used as a case study. The results show that the SD simulation is an efficient way to simulate a complicated reservoir system using feedback and causal loops. Moreover, it can directly and efficiently guide reservoir managers to make and identify efficient operating rules for the long-term operation of hydropower reservoirs.

Adapting water resources systems to climate change in tropical areas: Ecuadorian coast

Climate change is expected to increase rainfall and temperature in the tropical areas of the Ecuadorian coast. The increase in temperature will also increase evapotranspiration therefore, future water balance on Ecuadorian coast will have a slight variation. Changes in precipitation patterns and evapotranspiration will produce an increase in the water requirements for current crops, so an imbalance in the water resources systems between natural resources and water demands is expected. This study presents water resources management as an adaptation measure to climate change for reducing vulnerability in tropical areas.Twelve bias-corrected climate projections are used, from: two AR5 General Circulation Models (GCMs), two Representative Concentration Pathways, 4.5–8.5 scenarios, and three time periods, short-term (2010–2039), medium-term (2040–2069) and long-term (2070–2099). These data were incorporated into the Lumped Témez Hydrological Model. Climate change scenarios predict for the long-term period both a mean rainfall and temperature increases up to 22%-2.8 °C, respectively. Besides, the potential evapotranspiration will increase until 12% by Penman-Monteith method and 60% by Thornthwaite method. Therefore, natural water resources will finally have an increase of 19% [8–30%]. Additionally, water requirements for crops will increase around 4% and 45%.As this research shows, in tropical regions, currently viable water resources systems could become unsustainable under climate change scenarios. To guarantee the water supply in the future additional measures are required as reservoir operation rules and irrigation efficiency improvement of system from 0.43 to 0.65, which it involves improving the distribution and application system. In study area future irrigation areas have been estimated for 13,268 ha, which under climate change scenarios is unsustainable, only 11,500 ha could be expanded with a very high irrigation efficiency of 0.73. Therefore, in tropical areas the effect of climate change on expansion projects for irrigated areas should be considered to ensure the functioning systems.

Tradeoff analysis between economic and ecological benefits of the inter basin water transfer project under changing environment and its operation rules

Inter basin water transfer projects inevitably affect the ecosystem of the river in the water source area, especially under the changing environment. The tradeoff analysis between economic and ecological benefits has become a focus problem in actual multi-objective reservoirs operation. Therefore, this paper aims to research the tradeoff relationship by the multi-objective reservoirs optimal operation model considering economic and ecological objectives. Specifically, mutated runoff series are corrected by seasonal and trend decomposition using Loess (STL), and three ecological water demand processes are gained from the corrected runoff. Then, an improved evolutionary algorithm is proposed to solve this model, named Non-dominated Shorting Particle Swarm Optimization with Feasible Search Space (FSS–NSPSO). Furthermore, contour maps and functional formulae are fitted to describe the tradeoff analysis among three objectives. Finally, operation rules are generated considering ecological water demand. The paper applies the model to the Hanjiang to Wei River Water Diversion Project. The results indicate that: (1) the ecological water quantity in the river has decreased further under the influence of changing environment. (2) The FSS-NSPSO has advantages in terms of convergence, uniformity, and diversity compared to the Fast Elitist Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Particle Swarm Optimization. Real number coding, less evolutionary operator parameters and a feasible search space in the FSS-NSPSO are benefit for solving complex multi-objective reservoir models. (3) The contradiction between ecological and economic benefits is obvious. Ecological water demand process affects reservoir operation benefit. (4) The reservoir operation rules can be generated based on the modified ecological water demand process to balance the interests of multiple parties. This study is expected to be of merit to quantitative tradeoff analysis between ecological and economic benefits in inter basin water transfer projects, and as a reference method for multi-objective reservoir operation models.