Operation Of Reservoir Systems Research Articles

Deep reinforcement learning for multiple reservoir operation planning in the Chao Phraya River Basin

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based algorithm to provide comprehensive and flexible plans for reservoir operation planning of the multiple reservoir system in the Chao Phraya River Basin (CPYRB), Thailand aiming to mitigate flood and drought risks in the region. The multi-agent-based Deep Reinforcement Learning (DRL) model is accordingly constructed considering 7-D predicted inflow, reservoir water released from adjacent reservoir, downstream flow condition, and changes in reservoir water storage, as state variables. The desired goal is to increase water storage levels in all reservoirs by 10–15% to ensure higher potential in supplying water for crop cultivation over the dry seasons and preventing flood occurrences during wet season. Simulation results from 2009 to 2022 indicate that DRL–DDPG-based algorithm can perform well in solving sequential decision problems for optimal operation of multiple reservoir system to achieve the desired water storage goal. It can offer realistic simulation results of seasonal and annual release schemes and reservoir release ratios among reservoirs in the system compared to actual operation and Fmincon and ANFIS optimizations. Importantly, DRL model demonstrates a significant advantage in view of increasing the long-term water storage levels in all reservoirs as targeted in the modelling process while maintaining the similar and consistent release schemes in the reservoir system. For the multipurpose multiple reservoir system operation, adjusting the dynamic desired goals within multi-agent-based RL model is advisable to attain the specific desired outcomes and address various water scenarios.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)

ABSTRACT Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.

Multiobjective multihydropower reservoir operation optimization with transformer-based deep reinforcement learning

The paper introduces a transformer-based deep reinforcement learning (T-DRL) approach designed to address the multiobjective multihydropower reservoir operation optimization (MMROO) problem. Unlike existing literature that primarily focuses on maximizing power generation from individual reservoirs, the MMROO model in this study considers the broader context of multiple reservoirs, encompassing total power generation, ecological protection, and residential area water supply. The computational challenges posed by the numerous constraints and nonlinearities of multiple reservoirs render conventional multiobjective evolutionary algorithms both expensive and lacking in generalization capabilities for solving the MMROO problem. To overcome these challenges, the paper proposes a T-DRL approach that leverages the multihead attention mechanism within the encoder module to adeptly extract complex information from reservoirs and residential areas. The two-stage encoder effectively processes diverse information separately. The multireservoir network of the decoder then generates optimal decisions based on contextual information. The case study focusing on Lake Mead and Lake Powell in the Colorado River Basin demonstrates the efficacy of the T-DRL approach, producing operation strategies that outperform a state-of-the-art method. Specifically, the proposed approach yields a 10.11% increase in electricity generation, a 39.69% reduction in amended annual proportional flow deviation, and a 4.10% rise in water supply revenue. Overall, the T-DRL approach emerges as an effective method for the multiobjective operation of multihydropower reservoir systems.

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Δ, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms’ criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability compared to the other two. The final results demonstrated the MOAHA algorithm’s excellent performance, particularly in difficult and significant problems such as multi-reservoir systems' optimal operation under various objectives.

The results of the study on the technical condition of hydraulic structures in reservoirs

The article discusses the results of a study on the technical condition of operated hydraulic structures in reservoirs. During operation, various effects of long-term use, shortcomings in design, construction and operation of reservoirs as well as factors affecting the reliability of their hydraulic systems are manifested. The article also provides main criteria for ensuring reliable operation of reservoir systems.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

A Comprehensive Model for Assessing Synergistic Revenue–Cost for the Joint Operation of a Complex Multistakeholder Reservoir System

The joint operation of a multiobjective multistakeholder reservoir system enhances the revenues of downstream-compensated reservoirs at the expense of increasing the operation cost of upstream-compensating reservoirs. Challenges in quantifying the synergistic revenue–cost tradeoffs with incomplete information arise from difficulties in multistakeholder, high-dimensional, and combinational joint optimal operation modeling. This study proposed an equivalent aggregated reservoir multiobjective operation and synergistic revenue–cost assessment model. The proposed methodology includes three parts. Module I constructs revenue indexes covering energy production, water supply, ecological protection, and shipping objectives and uses the maximum outflow change degree as a surrogate “cost” index. Module II defines “aggregated reservoirs” that aggregate upstream reservoirs within the same river system as a single reservoir, reducing model complexity with the least information. Module III evaluates the revenue–cost tradeoffs under various operation scenarios. The following conclusions were derived from a 27-reservoir system: (1) The model complexity was reduced by 67.18% with precision preserved. (2) Key compensating reservoirs are identified via tradeoff curves, which are reservoirs controlling high streamflow with large storage. (3) Upstream compensating reservoirs homogenize the inflows of downstream-compensated reservoirs to increase the downstream synergistic revenue by sacrificing upstream benefit. The proposed method provides a new approach for revenue–cost estimation via the joint optimal operation of a multistakeholder-reservoir system.

Development of an Optimal Water Allocation Model for Reservoir System Operation

Allocating adequate water supplies under the increasing frequency and severity of droughts is a challenge. This study develops an optimal reservoir system operation method to allocate water supplies from upstream reservoirs to meet the downstream water requirements; validates the proposed optimization model through the system operation of upstream reservoirs; and proposes new water supply policies that incorporate a transformed hydropower reservoir with an add-on water supply function and two multipurpose reservoirs. We use linear programming to develop an optimal water allocation model. This model provides an operational strategy for managing upstream reservoirs with different storage capacities. By integrating the effective storage ratio of each reservoir into the allocation estimation, the model ensures an optimal distribution of downstream water requirements. The results indicated well-balanced, effective storage ratios among the Chungju, Soyanggang, and Hwacheon Reservoirs across varying hydrological conditions. Specifically, during drought years, the average effective storage rates were 20.5%, 20.6%, and 19.07%, respectively. In normal years, these figures, respectively, were 59.3%, 68.6%, and 52.4%, while in wet years, the rates stood at 64.08%, 62.90%, and 54.61%. This study enriches the reservoir operation literature by offering adaptable solutions for collaborative reservoir management and presents efficient strategies for reservoir operations.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Joint probability analysis of storage reservoir system characteristics

A comprehensive evaluation of the frequency distribution analysis of reservoir storage capacity plays an important role in reducing the uncertainty in the design and operation of reservoir systems. In most studies, a simplified reservoir system (such as over-year system and using annual data, without incorporating evaporation losses and performance measures, i.e. reliability, vulnerability, and constant monthly demand) was investigated. On the other hand, the required reservoir capacity variable is not independent of other characteristics of the reservoir system such as performance measures, critical period, and evaporation losses, and has a high correlation with these variables. Therefore, in this study, the joint probabilities of different characteristics of the reservoir system were comprehensively developed using copulas. Hence, an extensive Monte Carlo reservoir simulation procedure incorporating monthly streamflow data, performance measures, evaporation losses, and demand was applied to extract the population of dependent variables of an actual reservoir. The results showed that a) the required storage capacity in the reservoir system is highly dependent on other reservoir characteristics such as performance measures, critical period, and evaporation losses; b) the joint probability of the reservoir system characteristics are well modeled by a Gaussian copula and c) the joint probability of the reservoir characteristics has a systematic behavior for given reliability and vulnerability values.

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization.

Scenario-based model predictive control of water reservoir systems

The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy.

MUWOS - Multiple use water optimization system for the power generation and navigation trade-offs analysis

This article aimed to develop a model for managing multiple uses of water to minimize conflicts of use related to the operation of reservoir systems in hydroelectric watersheds. The MUWOS model – Multiple Uses Water Optimization System, which consists of an optimization model based on non-linear programming, developed and structured in GAMS (General Algebraic Modeling System) using the MINOS solver, for conflict mitigation and minimization from the modeling and operational optimization for different navigation and hydrological scenarios. The MUWOS was applied to Tapajós watershed for the future Hydropower Project – HPP São Luiz do Tapajós, Itaituba, state of Pará, Brazil. For power generation and navigation water depth, considering inflows for dry, medium and wet hydrological scenarios, MUWOS showed, in relation to reference levels for the low, medium and high navigation scenarios, water depths dropped below the minimum for average generations of 2411 MW; 2939 MW and 3586 MW, respectively. For energy generation and transported cargo capacity, considering the same hydrological scenarios, MUWOS demonstrated that, in relation to the reference levels of the low, medium and high navigation scenarios, average generations above 2869 MW; 3508 MW and 4740 MW, respectively, result in no gains in transported cargo capacity, and average generations below 1344 MW; 1622 MW, and 2056 MW, respectively, make cargo transport unfeasible. The developed model represents a tool of fundamental importance for the operational optimization of reservoir systems with multiple uses, allowing the optimization of generation and outflow in HPPs, with the maintenance of navigability conditions downstream of dams.

Spatial distribution characteristics and pollution levels of heavy metals in surface water and sediments of the Heihe cascade reservoir system, China

Abstract The operation of cascade reservoir systems has altered river hydrology and sediment distribution patterns. In this study, 31 surface water and sediment samples were collected from the Heihe River from July to August in 2019 and 2020 to investigate the spatial distribution and sources of heavy metals and assess their ecological risks. The results revealed that the concentrations of heavy metals in surface water were much lower than the quality standards for surface water in China, and there were no significant differences in the natural reaches, center and tail of the reservoir. Cd in surface sediments was at a heavy contamination and high risk level, and the heavy metal pollution levels in the main streams and tributaries differed greatly, especially in the graded reservoirs with a gradual accumulation trend. This may be related to the fact that there were many fine-grained sediments in the reservoir center near the dam. Factor analysis-multiple linear regression (FA-MLR) revealed that heavy metals mainly come from natural factors and anthropogenic input, with anthropogenic inputs mainly coming from mining activities in the tributaries and industrial and agricultural activities in the main stream.

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Development of an optimal operating policy of multi-reservoir systems in Mahanadi Reservoir Project Complex, Chhattisgarh

Real-world optimization problems are usually non-linear and solving nonlinear problems requires time. Hence heuristic techniques are frequently used. Simulated annealing (SA) is one of the most commonly used heuristic algorithms. SA is useful in the narrow search area. In the present study, simulated annealing has been developed to derive the optimal for multi-reservoir systems. Problem statement: In respect of water release, multi-reservoir system in Mahanadi Reservoir Project is an example of such a complex system, therefore the optimum operation of such a reservoir is one of the complicated and uncertainxs issues water resource management. Approach: Traditional models to optimize water resource systems have proven to be ineffective due to increased decision variables and time constraints. In recent decades, the adaptive random research SA model has shown acceptable performance in solving major problems. Results: The model applies to derive the optimal operating policies of existing multi-reservoirs system in Chhattisgarh, India. The formulated objective function of the present study is to minimize the sum of squared deviation on downstream release and demand and compare the performance of the SA model to the existing reservoir systems operation policy. The overall performance of existing multi-reservoir system has increased on average by, 28.09%, 10.12%, 36.49% in case of reliability, resilience, sustainability respectively, and vulnerability reduced up to 11.83%. Conclusions/Recommendations: The results of these algorithms show that SA can deal with complex problems. SA is a novel promising reservoir optimization search technique. It merits further research for other applications in this field.

Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm

Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.

Multi-objective Optimal Operation of Combined Cascade Reservoir and Hydrogen System

As a kind of zero-carbon and high-efficiency energy, hydrogen has attracted more attention with clean energy technology development. However, large-scale green hydrogen application is difficult to promote because of its high production cost. China is rich in water resources, but the utilization rate is low, which causes serious water abandonment problems. This article studies the optimal operation of combined cascade reservoir and hydrogen system to improve the utilization of water resources and the system's economic benefits. In this article, a multi-objective nonlinear model is established. An expanded progressive optimization algorithm is adopted to solve the problem more efficiently and accurately. The weights of objective functions are discussed with data from the Hongru River Basin in China. The rationality and effectiveness of combining cascade reservoir and hydrogen system are verified through the optimal operation of water resources and the optimal allocation of power and hydrogen. In addition, the overall economic benefits and reservoir operation efficiency are improved.

Considering ecological flow in multi-objective operation of cascade reservoir systems under climate variability with different hydrological periods

The trade-off between ecological and socioeconomic benefits in the reservoir operation has become a focus issue in the watershed water resource management. However, finding a suitable reservoir ecological operation scheme in the multi-objective cascade reservoir systems remains unclear. At present, most ecological operation models are designed on the basis of water quantity balance, neglecting the dynamic variability of the hydrological process. This study proposed a multi-objective ecological operation system, which coupled a two-dimensional hydrodynamic model with a rainfall–runoff model, and integrated the ecological operation scheme into the hydrodynamic simulation system considering ecological flow. Moreover, the applicability of the operation scheme under climate variability with different hydrological periods was evaluated. Results indicated that multi-reservoir joint operation had the largest effect in normal years; the variation in the monthly hydrological magnitude, extreme events and their duration, temporal change and frequency of streamflow were significantly reduced after reservoir ecological operation. The SAM0-UNICON model performed better than the two other climate models, the ecological deficit (ED) under the Representative Concentration Pathway (RCP) 8.5 climate change scenario was larger than other scenarios with different operation schemes. Future climate change will have a larger impact on discharge change in the wet season than in other hydrological periods. This study emphasises the comprehensive application of the hydrological and hydrodynamic methods, which is of considerable importance for decision-making in basin water resource management and reservoir regulation.