Real-time Reservoir Operation Research Articles

Real-time reservoir operation using inflow and irrigation demand forecasts in a reservoir-regulated river basin

Real-time reservoir operation using inflow and irrigation demand forecasts can help reservoir system managers make effective water management decisions. Forecasting of inflow and irrigation demands is challenging, owing to the variability of the weather variables that affect inflows and irrigation demands. In this context, bias-corrected Global Forecasting System (GFS) forecasts are used here in a hybrid approach (reservoir module with Long Short Term Memory (LSTM)) to forecast the reservoir inflows. Concurrently, the bias-corrected GFS forecasts are used in irrigation demand module to forecast the irrigation demands. The ‘Scaled Distribution Mapping’ method is used to bias-correct the GFS data of 1–5 days lead. The study area is the Damodar river basin, India, consisting of five major reservoirs: Tenughat and Konar located upstream of Panchet, and Tilaya situated upstream of Maithon. With the upstream reservoir outflow forecasts, the inflows are forecasted in Panchet and Maithon reservoirs with NSE values of 0.88–0.96 and 0.78–0.88, respectively, up to a 5-day lead. The irrigation demand module with bias-corrected GFS forecasts forecasted the irrigation demands close to the irrigation demands with the observed weather data. The percentage errors in irrigation demand forecasts of the Kharif (June–October) season at 1–5 days lead are 9.45 %, −15.45 %, −20.52 %, −26.36 %, −27.31 %, respectively. On the contrary, percentage errors in irrigation demand forecasts of Rabi (November–February) and Boro (January–May) are in the range of 8.17–8.79 % and 3.48–8.06 %, respectively. With the inflows and irrigation demand forecasts, the Panchet and Maithon reservoirs satisfied the downstream demands and reduced the floods. The inflow and irrigation demand forecasts, based on the GFS forecasts, have substantial potential for real-time reservoir operation, leading to efficient water management downstream.

Physical and Economic Determinants on Forecast Horizon for Long-Term Reservoir Operation

The forecast horizon (FH) reflects the effective length of future inflow inputs for real-time reservoir operation. Its determination currently still relies on numerical experiments. Using a conceptual model of multistage water supply operation, this paper presents an analytical basis for determining FH, in which physical and economic determinants are explicitly incorporated. The criteria for FH determination are first derived employing the comparative relationship between the marginal benefits among stages. The effects of discount rate, inflow forecast variance, and reservoir storage capacity on FH are then analyzed both theoretically and numerically. The results show that FH responds nonlinearly and sometimes nonmonotonically to these determinants. A higher discount rate tends to consume more water in advance and shortens FH in most cases; however, a prolonged FH can also be observed when an extreme drought is expected and the resulting potential economic loss outweighs the immediate release benefit brought by high discount rate. In contrast, a larger forecast variance results in more water stored for future use and a generally prolonged FH; but a shortened FH also exists. A larger storage capacity enables higher flexibility in flow regulation, and therefore consistently prolongs FH if it changes. The presented analytical framework provides a promising access to analyzing FH in flood control, hydropower generation, and other analogy operations.

Carbon emissions affected by real-time reservoir operation: a hydrodynamic modeling approach coupled with air-water mass transfer

Air-water diffusive carbon fluxes (e.g., CO2 and CH4) in reservoirs, particularly those dammed in river valleys, are the major pathway of reservoir carbon emissions. Hydrodynamic conditions caused by real-time reservoir operation could potentially affect air-water transfer of these greenhouse gases (GHGs), yet still under explored. Here, we proposed an estimation method of gross carbon emissions based on a computational fluid dynamic (CFD) modelling approach. The model assumed that air-water mass transfer was primarily regulated by surface turbulence, and disregarded contributions from biogeochemical processes as well as seasonality of meteorological parameters (i.e., wind speed and direction; air temperature). Through the hydrodynamic modeling, reservoir water level, flow velocity, surface turbulence, and air-water transfer velocity of carbon fluxes were elaborated. Gross carbon emissions were integrated by the carbon fluxes in each discrete cell and time under real-time reservoir operation. The Xiangjiaba Reservoir (XJB), located in the upper Yangtze Basin, was selected as the case of the study. Based on daily hydrological data in 2018, such as reservoir inflow, outflow and water level, the gross CO2 and CH4 emissions in the reservoir were approximately 6.7 Gg and 5.6 Mg. Variations of daily water level and discharge induced by reservoir operation could evidently affect carbon emissions. In particular, when reservoir initiated its impoundment, the discharge could be the probably critical factor that affected mass transfer velocity and carbon emissions in the reservoir. Our model could provide a new vision for evaluating the effect of real-time reservoir operation on carbon emissions.

Exploiting multiple hydrologic forecasts to inform real-time reservoir operation for drought mitigation

To timely prompt anticipatory operations and relieve prolonged droughts effectively, multiple hydrological forecasts that provide thorough information about future streamflow need to be employed. However, it is challenging to integrate multiple forecasts in reservoir optimization and obtain a proper trading-off between current and future water supply benefits. This study proposes a novel Model Predictive Control (MPC) that can not only utilize the streamflow forecast but also utilize other two categorical forecasts, including the regime state forecast to capture the long-term persistence of the streamflow process and the annual streamflow volume state forecast. In the novel MPC, a discount factor depending on the annual streamflow volume state forecast weighs up the current deficit cost decided by the streamflow forecast and the future cost function determined by the regime state forecast. Taking the Biliuhe Reservoir in China as a case study, the value of each forecast in drought mitigation is evaluated. Results show that with an appropriate discount factor, using seasonal streamflow forecast can relieve intense deficits for extreme droughts and avoid unnecessary intense limitations for slight droughts. Utilizing the annual streamflow volume state forecast contributes to guaranteeing a reliable water supply. And employing the regime state forecast relieves the intensity of severe water deficit. By incorporating these hydrological forecasts, a 31.86% performance gain can be obtained with only a 5.03% reliability decrease compared to the baseline Stochastic Dynamic Programming informed by no forecast information. Nevertheless, forecast value is beset by forecast uncertainty. For streamflow forecast, forecast value decreases with increasing forecast uncertainty. But for the other two categorical forecasts, their forecast value depends on not only the forecast accuracy but also the hydrologic conditions and forecast bias.

Reservoir Inflow Forecasting in Hydropower Industry: A Generative Flow-Based Approach

Forecasting the inflow of reservoirs plays an essential role in the hydropower industry. Existing studies are either limited to point estimates or inefficient in capturing higher-order dynamic correlations across data. It is nevertheless necessary to estimate data uncertainty in actual dam operation. This article presents a novel inflow prediction method that exploits generative flows to model complex multivariate hydrological time series. Our flow-to-flow method (F2F) augments the deterministic models with the normalizing flow-based generative networks to explicitly capture the multivariate correlations and approximate the predictive inflow distribution. Besides, F2F can quantify the prediction uncertainty to help interpret model behavior and predicted results while facilitating safety-critical decision-making on real-time reservoir operation. We conduct extensive experiments on real-world datasets collected from large-scale hydropower stations. The experimental results show that our method consistently outperforms existing methods while accounting for uncertain observations and providing tractable multistep ahead inflow forecasts.

Risk-aversion optimal hedging scenarios during droughts

The primary purpose of a water supply reservoir is to regular highly fluctuating streamflow for providing reliable water supplies. Reducing water shortage risk for impending droughts is a challenge task in real-time reservoir operation due to future inflow uncertainty. The main aim of this study is to propose risk-aversion optimal hedging scenarios during droughts, which is achieved by a two-stage approach. The water shortage probabilities of future lead times are analytically estimated first, then follows an optimization framework that simultaneously minimizing water shortage probabilities of future lead times and sustaining long-term water supply reliability. With an illustration application of the Nanhua Reservoir located in southern Taiwan, the results indicate that the proposed optimization framework provides an efficient hedging to reduce future water shortage probabilities and mitigate severe water shortages in real-time operation. The proposed optimal hedging scenarios outperforms the rule-curve-based current operation. Further improvements are noted for the time-varying rationing coefficient scenarios than the constant-coefficient scenario and the most favorable scenario is the scenario with the highest time-varying frequency. Using the storage to trigger hedging associated with estimated future lead-time water shortage probabilities as the objective functions, the proposed optimal hedging scenarios are not only risk aversion but also executable in real-time operation during droughts.

Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm

Current conventional and optimal reservoir flood control operation methods insufficiently utilize historical reservoir operation data, which include rainfall, runoff generation, and inflow from the watershed, as well as the operational experience of decision makers over many years. Therefore, this study proposed and evaluated a new method for extracting reservoir flood control operation rules from historical operation data using the C4.5 algorithm. Thus, in this paper, the C4.5 algorithm is first introduced; then, the generation of the flood control operation dataset, the construction of decision tree-based (DT-based) rules, and the subsequent design of a real-time operating scheme are detailed. A case study of the Rizhao Reservoir is then employed to demonstrate the feasibility and even superiority of the operating scheme formulated using DT-based rules. Compared with previously proposed conventional and optimal reservoir operation methods, the DT-based method has the advantages of strong and convenient adaptability, enabling decision makers to effectively guide real-time reservoir operation.

Reducing Predictive Uncertainty in Real-Time Reservoir Operations by Coupling LARS-WG with ARNO Continuous Rainfall–Runoff Model

AbstractIn reservoirs with relatively low capacity-inflow (C/I) ratios, real-time daily operations provide the possibility of minimizing the risk of failure and meeting downstream demands sustainab...

Real-time reservoir flood control operation enhanced by data assimilation

Real world reservoir operations are usually not fully automatic based on computer models; instead, reservoir operators conduct the operations based on their experiences, professional justification, as well as modeling support. In this paper, we propose a human-machine interactive method, namely Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) tested with simple but reasonable rules for direct interaction between operators and a computer model for reservoirs which have complex storage and stage relations (e.g. long and narrow reservoirs). ROMEDA couples 1) an optimization model to search for optimal releases, 2) a reservoir storage-stage simulation and data assimilation schedule to update the storage based on real-time reservoir stage observations, and 3) reservoir operators’ choices based on the optimization model solutions, as well as their experiences, knowledge, and behaviors. For every time period and based on the updated storage, ROMEDA provides optimal releases as recommendations and adopts actual releases made by operators. ROMEDA does not assume that operators strictly accept the recommendations, and the storage will be updated based on the actual release at each time period via a data assimilation procedure, which plays a key linkage between human and machine in ROMEDA. Via a case study on-channel reservoir, it is found that for both small and large flood events, ROMEDA, which integrates the advantages of both machine and human, shows better performance on flood risk mitigation and water use (hydropower) benefit than the case with historical operation records (HOR) or optimization with single/multi-objective. ROMEDA is one of the first attempts of a human-machine interactive method for online use of an optimization model for real-time reservoir operation based on integrated modeling, observation, and operators’ choice.

The Value of Long-Term Streamflow Forecasts in Adaptive Reservoir Operation: The Case of the High Aswan Dam in the Transboundary Nile River Basin

AbstractTransboundary river basins are experiencing extensive dam development that challenges future water management, especially for downstream nations. Thus, adapting the operation of existing reservoirs is indispensable to cope with alterations in flow regime. We proposed a Forecast-Based Adaptive Reservoir Operation (FARO) framework to evaluate the use of long-term climate forecasts in improving real-time reservoir operations. The FARO approach was applied to the High Aswan Dam (HAD) in the Nile River basin. Monthly precipitation and temperature forecasts at up to 12 months of lead time are used from a suite of eight North American Multi-Model Ensemble (NMME) models. The value of NMME-based forecasts to reservoir operations was compared with perfect and climatology-based forecasts over an optimization horizon of 10 years from 1993 to 2002. Our results indicated that the forecast horizon for HAD operation ranges between 5 and 12 months lead time at low- and high-demand scenarios, respectively, beyond which the forecast information no longer improves the release decision. The forecast value to HAD operation is more pronounced in the months following the flooding season (October–December). During these months, the skill of streamflow forecasts using NMME forcings outperforms the climatology-based forecasts. When considering the operation of upstream Grand Ethiopian Renaissance Dam (GERD), using streamflow forecasts minimally helps to maintain current target objectives of HAD operation and therefore result in higher operation costs as opposed to current conditions without GERD. Our study underlined the importance of deriving a new adaptive operating policy for HAD to improve the value of available forecasts while considering GERD filling and operation phases.

Post-processing of reservoir releases to support real-time reservoir operation and its effects on downstream hydrological alterations

Reservoir operation relies on two sets of flow data to conduct error analysis: inflow data and release data. Previous studies applied only the inflow data to reduce errors and improve the reservoir’s operational effectiveness. Little attention has been paid to the accuracy of the release data. However, release data provides more accurate information than inflow data for the protection of downstream river ecosystems. In this paper, we aim to illustrate the necessity of fully using the release data to improve a reservoir’s operational performance in a real-time reservoir operation system. Firstly, we designed a hypothetical reservoir system, performed five numerical experiments with different ranges of model parameters, and found that the error variances of reservoir releases were always larger than those of inflows. This indicates that the post-processing of reservoir releases is required to improve the accuracy of release data and support real-time reservoir operation. Then, we used a Bayesian joint probability (BJP) model for post-processing release data to reduce errors and quantify the uncertainty. We also applied the ecodeficit and ecosurplus based on flow duration curves to evaluate downstream hydrological alterations and further demonstrate the necessity of post-processing of release data. Results showed that the BJP model led to considerable improvement in the root-mean-square error and the continuous ranked-probability score. This demonstrates that the BJP model was effective in reducing errors. Our results highlight the importance of applying release data and improving their accuracy to support reservoir operation. If the release data is neglected, operational performance will degrade.

Improving real-time reservoir operation during flood season by making the most of streamflow forecasts

Streamflow forecasts play an important role in real-time reservoir operation, especially during the flood season. However, nowcast, short-term, and mid-term forecasts have significant differences in their uncertainties, leading to a gap between these forecasts and their combined use. To make the most of these forecasts, this paper defines a reliable forecast horizon (RFH) and proposes a model based on the three-stage-based operation rule (Three-OR), in which the streamflow forecasts are jointly used with different levels of uncertainty. To evaluate the effectiveness and efficiency of the proposed model, it is applied to the Wan’an Reservoir located at the midstream of the Ganjiang River in China with the objectives of flood control and water utilization (i.e., hydropower generation and water supply). The results reveal the following: 1) both flood control and water utilization can be improved when compared with the current operation rule; 2) floods with a higher peak and larger volume gain more improvements using Three-OR; 3) a smaller difference in the forecast error produces better operation performance in water utilization; and 4) the extension of the RFH is beneficial for reservoir operation, as it can help to improve flood control if the flood is large; otherwise, it can help improve water utilization. In summary, the proposed model based on Three-OR can make the best use of streamflow information, thus improving the real-time reservoir operation during the flood season.

Stochastic multi-criteria decision making based on stepwise weight information for real-time reservoir operation

Reservoir operation is a typical combination of engineering and non-engineering measures, and plays an important role in sustainable water resources management. However, reservoir operators always work in uncertain and changing environments, and face various uncertainties arising from criteria weights (CWs) and performance values (PVs), which hinder the application of multi-criteria decision making (MCDM) methods in real-time reservoir operations. To address this issue, this paper proposes a stochastic MCDM framework based on stepwise weight information. The framework consists of five sub-modules/models: (1) formulate a stochastic MCDM problem and determine the stochastic decision matrix; (2) a weight aggregation method based on the game theory allied with feasible weight space is proposed to resolve conflict among multi-stakeholders and estimate the uncertainty of CWs; (3) a novel SMAA-FOM model, i.e., a combination of stochastic multi-criteria acceptability analysis (SMAA) and fuzzy optimization model (FOM), is developed to simulate uncertainties existed in PVs and CWs simultaneously and produces several descriptive measures for robust decision-making; (4) a risk assessment model is used to quantitatively evaluate the robustness and risk of decision-making; (5) a multi-stage decision-making procedure is proposed that enables a stochastic MCDM problem to be solved step by step within acceptable decision-making risks. The effectiveness and superiority of the proposed framework are verified via a multi-objective reservoir operation case study. The results indicate that the framework helps decision makers deal with stochastic MCDM problems with greater confidence and less psychological pressure, and enables a risk-informed decision can be made with higher reliabilities. The stochastic MCDM framework can be extended to other sustainability areas to solve decision-making issues with uncertainties.

Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model

Large-scale reservoirs play an essential role in water resources management for agriculture irrigation, water supply and flood controls. However, we need robust reservoir operation systems under both normal flow and extreme flow conditions. In this study, we applied recurrent neural networks (RNN) to simulate the operation of three multi-purpose reservoirs located in the upper Chao Phraya River basin. Two reservoirs have the function of multiannual flow regulation and one has the function of incomplete annual regulation. The goal of this study is to explore the applicability of RNN models for operation of reservoirs with multiannual flow regulation under different flow regimes, especially under extreme floods and droughts. We used three RNNs, namely nonlinear autoregressive models with exogenous input (NARX), long short-term memory (LSTM) and genetic algorithm based NAXR (GA-NAXR) for reservoir operation based on historical data. For real-time water resources management, an accurate inflow forecast is required to provide a real-time reservoir outflow, and thus we also carried out a real-time reservoir operation using the RNN and the inflow forecast by a distributed hydrological model. Results show that (1) GA-NARX has the highest accuracy among three RNNs and is more stable than the original NARX by optimizing the initial conditions, although it takes longer training time than NARX and LSTM; (2) GA-NARX-based operation model is effective under extreme floods and droughts; and (3) the real-time operation system combining the GA-NARX and the distributed hydrological model has reasonable accuracy in both wet season and dry season. RNN-based operation model developed in this study has potential applicability in practical water management, and the model combining the hydrological prediction is specially useful for real-time reservoir operation.

Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties

Reservoir operation rules play a key role in real-time reservoir operation; the main factors affecting operation decisions are the current reservoir status and the future inflows. However, the future reservoir inflows are stochastic and always contain uncertainty. To study the influence of inflow uncertainty on reservoir operation rules, this paper proposes a Bayesian Deep learning method that considers both model parameter uncertainty and inflow uncertainty. In the model, the Monte Carlo integration is used to convert the complex integrals of inflow probability into a summation form. Variational inference is employed to obtain the posterior distribution of model parameters. The proposed method is applied to a real-world application at Three Gorges Project on the Yangtze River. The uncertainty estimation results show that the influence of inflow uncertainty on reservoir operation rule is greater than model parameter uncertainty, and the decision of reservoir operation is more sensitive to the reservoir inflow during dry season than other seasons. The experimental results demonstrate that the proposed Bayesian deep learning performs better than the comparison method in term of hydropower generation and the root mean square errors. Moreover, the proposed method is more robust than the comparison method when considering the inflow uncertainty.

Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model

As a non-structural river basin management option, development of a reliable inflow forecasting system using the hydrological models is essential to aid in real-time reservoir operation. For real-time streamflow forecasting, this study evaluates the capability of the Variable Infiltration Capacity (VIC) in integration with the linear AutoRegressive (ARu), AutoRegressive Moving Average with eXogenous inputs (ARMAXu), nonlinear static wavelet-based neural network (WNNu), and dynamic wavelet-based Non-linear AutoregRessive with eXogenous inputs (WNARXu) variants of error-correction sub-models in comparison with the predictions by the standalone VIC model forced with European Centre for Medium Range Weather Forecast (ECMWF) and ERA-Interim reanalysis products. These VIC-standalone, VIC-ARu, VIC-ARMAXu, VIC-WNNu, and VIC-WNARXu frameworks are field-tested for 1- to 5-days lead-time streamflow forecasting in the Hirakud reservoir catchment of the Mahanadi River basin in eastern India. The performance evaluation measures and quantile regression (QR)-based predictive uncertainty (PU) analysis reveal that the VIC-WNARXu model is the best approach for short to medium range flood forecasting with up to 7 days lead-time having the potential to be used in other world-river basins as a component of the early flood warning system.

Fuzzy-set-based real-time reservoir operation using genetic algorithm

ABSTRACT A short-term reservoir operation model is developed for irrigation wherein the soil moisture of crops at grids and the reservoir storage are considered as fuzzy sets. The distinguished main features of the model as compared to the former fuzzy short-term reservoir operation model are the consideration of real-coded genetic algorithm and the implementation of the satellite-based gridded soil moisture of crops. The water is released from the reservoir at 10-day time periods by maintaining storage balance and the available water for the irrigation is distributed between the crops at grids by considering ponding depth for paddy, soil moisture continuity and root depth of the crops. The irrigation area is discretized into a number of grids (1º × 1º) to consider the spatial variations of rainfall and soil moisture of the crops. The fuzzy short-term reservoir operation model is optimized by the real-coded genetic algorithm by minimizing soil moisture deficits using forecasted inflow and rainfalls at grids from the present to last periods. The fuzzy short-term reservoir operation model using genetic algorithm performs better as compared to the conceptual reservoir operation policy and is faster than the short-term reservoir operation model using dynamic programming.

Real-time reservoir operation using data mining techniques.

The optimal operation of hydropower reservoirs is essential for the planning and efficient management of water resources and the production of hydroelectric energy. Various techniques such as the genetic algorithm (GA), artificial neural networks (ANN), support vector machine (SVM), and dynamic programming (DP) have been employed to calculate reservoir operation rules. This paper implements the data mining techniques SVM and ANN to calculate the optimal release rule of hydropower reservoirs under "forecasting" and "non-forecasting" scenarios. The employment of data mining techniques accounting for data uncertainty to calculate optimal hydropower reservoir operation is novel in the field of water resource systems analysis. The optimal operation of the Karoon 3 reservoir, Iran, serves as a test of the proposed methodology. The upstream streamflow, storage records, and several lagged variables are model inputs. Data obtained from solving the reservoir optimization problem with nonlinear programming (NLP) are applied to train (calibrate) the SVM, and ANN, SVM, and ANN are executed in the "non-forecasting" scenario based on all inputs along with their time-lagged variables. In contrast, current parameters are removed from the set of inputs in the "forecasting" scenario. The results of the SVM model are compared with those of the ANN model with the correlation coefficient (R), the mean error (ME), and the root mean square error (RMSE). This paper's results indicate performance of the SVM is better than that of the ANN model by 1.5%, 400%, and 10% with respect to the R, ME, and RMSE diagnostic statistics, respectively. In addition, SVM and ANN overcome data uncertainty ("forecasting" scenario) to produce optimal reservoir operation.

Reservoir Operation for Water Supply Considering Operational Ensemble Hydrological Predictions

This paper presents approaches and case studies for the introduction of ensemble hydrological predictions to reservoir operation for water supply. Medium-term operational ensemble forecasts of precipitation are employed to improve the real-time reservoir operation for drought management considering longer prospects with respect to future hydrological conditions in the target river basin. Real-time optimization of the water release strategy is conducted using dynamic programming approaches considering ensemble hydrological predictions. A case study on the application of ensemble hydrological predictions to reservoir operation for water use is reported as an example, with a hypothetical target river basin whose hydrological characteristics are derived from an actual reservoir and river basin.

A conceptual framework for real time estimation of WFP for small hydroelectric power plant

Abstract It is believed that the power sector, particularly hydroelectric power, contributes to a very high consumption of fresh water in terms of evaporation from storage reservoirs. Robust methods of water footprint (WFP) assessment will eventually help in the pricing of water and energy. Conventionally, WFP for hydropower plants is estimated by dividing the gross/net evaporation losses by power generation. This approach is highly biased when it comes to a small hydropower plant connected to a large reservoir. In this study, a two-pronged subjective–objective method is suggested for a reservoir with primary functions of power generation and downstream water release. The objective part is concerned with giving weight to the evaporation loss proportional to the water use while the subjective part is concerned with refining this by considering the real time reservoir operation conditions and taking into account the recommendations of the decision making authorities. The effect of timescale in WFP analysis is also studied and conclusions are drawn. Thumb rules for WFP estimation are evolved which will aid in such analysis. The implementation of the proposed methodology for any reservoir system is recommended using LabVIEW platform.