Hydropower Reservoir Operation Research Articles

Midpoint characterization factors to assess impacts of turbine water use from hydropower production

Abstract Purpose Life cycle assessment (LCA) distinguishes three types of water use: (1) consumptive water use, (2) degradative water use, and (3) in-stream water use. When it comes to assessing the impact of turbine water use (TWU, major source of in-stream water use) in LCA, so far, no method exists to quantify the related environmental impacts. Here, we developed the first midpoint characterization factors (CFs) with global coverage for turbine water use of storage and pumped storage hydropower power plants. Methods The midpoint CF at the basin scale describes the hydropower regulation potential (HRP) [HDOR·y] per TWU [m3]. The HRP indicates the probability of how strongly the natural flow regime of a river is potentially affected by all upstream reservoir operation, calculated as the quotient between reservoir volume [m3] and the annual river discharge [m3/y]. The hydropower degree of regulation (HDOR) thereby equals the unitless m3/m3 fraction. The TWU depends on the electricity production [kWh] and the turbine efficiency [m3/kWh]. We tested the sensitivity of the input data on the calculated CFs for four parameters (discharge, turbine efficiency, multipurpose allocation, and plant type). Furthermore, we performed a case study to analyze if consumptive and TWU impacts of producing 1 kWh are correlated or not. Results and discussion The calculated CFs for the 342 basins vary from 1.13E-13 HDOR·y/m3 to 3.28E10-7 HDOR·y/m3. The HDOR values range from 0.0015 to 16.66, and the TWU varies between 0.0030 km3 and 2824 km3. A HDOR ≥ 0.02 can be interpreted as affected basin, and only 23 out of 342 basins have a HDOR below this threshold. This confirms that TWU of hydropower production can have important environmental impacts. The sensitivity analyses revealed that discharge and turbine efficiency are the most sensitive parameters because they are influencing almost all basins. The results of the case study showed that a high consumptive water-use impact does not automatically lead to a high TWU impact and vice versa (R2 values of 0.0081 and 0.003). Conclusion Our study highlights that it is important to account for the environmental impacts of in-stream water use in LCA, as otherwise, the environmental impact can be underestimated, which could lead to wrong conclusions. However, the CFs are not meant to replace a local risk assessment of hydropower reservoir operation and should only be used for relative comparison between basins. The CF application in LCA will represent a step forward towards more sustainable hydropower development.

Effects of Cascading Dams on Streamflow within the Downstream Areas of the Rufiji River Basin in Tanzania

Despite their popularity, the construction and operation of hydropower reservoirs pose challenges to water resources. This study investigated the impacts of cascading dams on streamflow in Tanzania’s Rufiji River Basin. The SWAT model was developed to represent the entire Rufiji River Basin. The model simulated the streamflow for 41 years, from 1982 to 2022, and developed two main scenarios: with-dam and without-dam. To capture the influence of all dams, the results were emphasized from 2000 to 2022, when all three dams were operating. Calibration and validation were applied at the Rufiji-Stiegler and Kilombero-Swero stations with good performance. The results show that cascading dams annually decrease the streamflow by 1% at Rufiji-Stiegler station. In contrast, individually, the Mtera Dam displayed a 5% decrease while the Kidatu and Kihansi Dams exerted a 1% increase on the annual streamflow downstream at Rufiji-Stiegler. During 2000–2022, the Rufiji River Basin showed an annual reduction in streamflow contribution of 104.97 m3/s. Therefore, the reservoir’s operation significantly impacts the downstream streamflow. The findings are expected to guide policymakers, water resource managers, and environmentalists in mitigating potential adverse effects while optimizing the benefits of hydropower generation and water regulation within the region.

Hydropower Reservoir Optimization with Solar Generation-Changed Energy Prices in California

AbstractGrowing solar photovoltaic supply has significantly reshaped energy prices, lowering them during solar generating hours. Large-scale hydropower reservoir operations need to adapt to changes in energy prices to maximize hydropower revenue. This paper evaluates effects of solar generation-changed energy prices on hydropower generation for five multipurpose reservoirs in California using a hydroeconomic optimization model. In California, major solar generation began in 2013, so years 2010–2012 are a pre-solar period, and years 2013–2018 are post-solar. Reservoir operations, hydropower generation and revenue between these periods are compared. Operations in the wet season (January to June), and the dry season (July to December) are evaluated. Results show that releases are more profitable when hydropower is generated twice a day during on-peak hours in the morning and evening in the wet season. When water is scarce, energy is generated only during the higher-price evening peak. Hydropower generation is mostly curtailed between 10am and 6pm due to large solar supplies, and increase during morning and evening peaks when solar generation is unavailable. However, by optimizing hydropower scheduling hours, the new energy price pattern can be more profitable. With increased energy price variability and adaptation, overall daily revenue can increase by about 14% in the wet season and 30% in the dry season.

A LSTM-based approximate dynamic programming method for hydropower reservoir operation optimization

Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP’s recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP’s computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem.

Deriving hydropower reservoir operation policy using data-driven artificial intelligence model based on pattern recognition and metaheuristic optimizer

Robust reservoir operation policies are crucial in ensuring the effective utilization of water resources. However, owing to multiple complicated and changeable factors in practice, it is difficult for standalone approaches to derive reasonable reservoir operation policy. To address the practical requirement, this research proposes a novel artificial intelligence method for deriving reservoir operation policy. The proposed method uses the fuzzy clustering iteration method to identify multiple typical operation patterns from the influencing factors; secondary, for all the samples within each pattern, the novel twin support vector regression (TSVR) is utilized to model the nonlinear mapping relationship between the influence inputs and the target outputs, while the emerging equilibrium optimizer is chosen to determine suitable computation parameters for the TSVR model. The feasibility of the proposed method is fully evaluated on two real-world huge hydropower reservoirs in China. The simulations demonstrate that the developed method can yield better comprehensive benefits than several control methods in deriving reservoir operation policy under uncertain environments. Hence, the experiments confirm that metaheuristic algorithms and pattern recognition techniques can enhance the performance of a standalone artificial intelligence methods in deriving reservoir operation policy.

Optimal reservoir operation using the improved multi-step-ahead time-varying hedging rule under climate and land-use changes

ABSTRACT This study aims to optimize Yeywa Hydropower Reservoir (YHR) operation using the multi-step-ahead time-varying hedging (TVH) rule under climate change (CC) and land-use change (LUC) to improve summer power generation. The performance of three multi-objective algorithms – the Multi-objective Salp Swarm Algorithm (MOSSA), the Multi-objective Antlion Optimizer (MOALO) and the Non-dominated Sorting Whale Optimization Algorithm (NSOWA) – are compared. MOSSA provides the best solutions with a higher mean hypervolume in a shorter computation time, and it is utilized to optimize the TVH rules for four periods: monthly (TVH-1), quarterly (TVH-3), half-yearly (TVH-6) and yearly (TVH-12). The six-month-ahead TVH-6 rule and the five-month-ahead TVH-6 rule generate the highest summer power for the historical period (2011–2020) and the future period (2020–2059), respectively. The future decadal power generation is expected to be higher than the historical power generation. The future TVH-6 rule is more reliable and it has lower water deficits than the historical YHR operation rule.

A stochastic linear programming model for maximizing generation and firm output at a reliability in long-term hydropower reservoir operation

The firm output, usually determined in the design stage of a hydroplant to serve as a threshold to measure system reliability, can be regarded as an unknown parameter to be explored to its maximum in long-term hydropower reservoir operation (LHRO). An unknown firm output to be ensured at certain reliability, however, will make the problem much more nonlinear and then complicate the modeling. This work presents a stochastic linear programming (SLP-1) model that can explicitly incorporate reliability in ensuring an unknown firm output to be maximized by using a probability variable to represent a decision at a state and introducing binary variables to decide whether the decision will ensure the unknown firm output. The present SLP-1 is improved on 1) another previous SLP model (SLP-0) that must have a firm output prespecified at certain reliability, and compared with 2) the stochastic dynamic programming (SDP) model that can only manage to estimate the firm output at a reliability with trial and error. Case studies show the superiority of the present SLP to the SDP that can hardly make the reliability any closer to what the SLP can achieve, especially in ensuring a high firm output, with gaps to desired reliability ranging up to 42.33% for Xiaowan and 31.8% for Nuozhadu. Indeed, the SLP-1 will encounter the dimensional difficulty that needs further efforts to overcome when applied to cascaded reservoirs.

Evaluation of Nominal Energy Storage at Existing Hydropower Reservoirs in the US

AbstractLong‐term planning and operation of hydropower reservoirs require an understanding of both water and energy storage. As energy storage needs of the evolving grid increase, we must account for the water and energy storage potential of these reservoirs. Given the limitations of current data on existing hydropower, we compile statistics related to storage volume and hydraulic head from publicly available data sets and examine differences in descriptions of US hydropower storage. Assembled characteristics are used to calculate nominal energy storage capacity, a simple measure of potential to generate power from a given volume of water, not factoring in detailed constraints. Inventory‐based estimates of energy storage are calculated at 2,075 dams, which helps put the potential for US hydropower to support energy storage in context with similar evaluations in other regions and with other energy storage technologies. The national energy storage capacity ranges between 34.5 and 45.1 TWh depending on the information used, with 52% of energy storage located at the 10 largest reservoirs in the US. Energy storage capacities are also calculated at 236 dams with historical volume and elevation data. Finally, reservoir inflows provide context for the storage volumes and sensitivities to hydrologic variability. Larger reservoirs with greater storage volume to inflow ratios are concentrated in the Western US, but the majority of hydropower reservoirs store less than the annual inflow. We address several infrastructure and water resource informatics challenges and highlight remaining issues, including representing seasonal or shorter variability in water volumes and representing connected hydropower facilities.

Optimal operation of hydropower reservoirs under climate change

Optimal operation of hydropower reservoirs under climate change

Multi-Level Dependent-Chance Model for Hydropower Reservoir Operations

Some hydropower reservoirs are operated under different constraint levels. For these reservoirs, a multi-level (ML) dependent-chance (DC) model is established. In the model, only when the higher-level constraints are satisfied are the lower-level constraints or system benefits considered. The multi-level dependent-chance (MLDC) model is specified by two models. One is based on existing reliability-constrained (RC) dynamic programming (DP), in which the soft constraints are addressed using reliability constraints of 1, and the priorities are reflected using the order of magnitudes of Lagrange multipliers. The other is the explicit dependent-chance reasoning in the DP recursive function, in which each soft constraint is represented as an objective function of negative expected failure time and the optimum is the solution with a larger value for all higher-level objective functions. The proposed models are applied to derive long-term operation rules for the hydropower system on the middle-lower Lancang River. The results show the feasibility and performances of the explicit graded constraint control of the proposed model and the solution methods.

Evaluation of Hydropower Generation and Reservoir Operation under Climate Change from Kesem Reservoir, Ethiopia

Climate changes significantly cause the precipitation deficiency and in turn reduce the inflow amount in reservoir affecting hydroelectric power generation. The primary objective of this study was to evaluate hydropower generation and reservoir operation under climate change from Kesem reservoir. Recent Representative Pathway (RCP) scenarios were used to evaluate the impact of climate change on power generation. Power transformation equation and variance scaling approach were amalgamated to adjust the bias correction of precipitation and temperature, respectively. Bias, root mean square error, and coefficient of variation were used to check the accuracy of projected rainfall. The base and future precipitation, temperature, and evaporation trend was analysed using the Mann–Kendall test. The flow calibration and validation were carried out by the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and hydropower generation was evaluated with reservoir simulation model (MODSIM 8.1) under climate scenarios. The performance of the model was found good with Nash–Sutcliffe coefficient (NSE) of 0.72 and coefficient of determination (R2) of 0.73 for calibration and NSE of 0.74 and R2 of 0.75 for validation. Projected future climate scenarios predicted increasing and decreasing trend of temperature and precipitation, respectively. For RCP4.5 climate scenario, the average energy generation is likely to decrease by 0.64% and 0.82% in both short-term (2021–2050) and long-term (2051–2080), respectively. In case of RCP8.5 climate scenario, the average energy generation will be decreased by 1.06% and 1.35% for short-term and long-term, respectively. Remarkable reduction of energy generation was revealed in RCP8.5 with relation to RCP4.5 scenario. This indicates that there will be high energy fluctuation and decreasing trend in the future energy generation. The research finding is crucial for decision-makers, power authorities, governmental and nongovernmental organizations, and watershed management agencies to take care for sustainability in the future hydropower generation in the Kesem reservoir.

Optimal Operation of Nashe Hydropower Reservoir under Land Use Land Cover Change in Blue Nile River Basin

Changes in LULC (land use land cover), which significantly influence the spatial and temporal distribution of hydrological processes and water resources in general, have a substantial impact on hydropower generation. The utilization of an optimization approach in order to analyze the operation of reservoirs is an important concern in the planning and management of water resources. The SWAT (Soil and Water Assessment Tool) and the HEC-ResPRM (Hydrologic Engineering Center reservoir evaluation system Prescriptive Reservoir Model) were combined to model and optimize the Nashe hydropower reservoir operation in the Blue Nile River Basin (BNRB). The stream flow into the reservoir was determined using the SWAT model, considering the current and future impacts of LULC changes. The HEC-ResPRM model has been utilized in order to generate the optimal hydropower reservoir operation by using the results of the SWAT calibrated and validated stream flow as input data. This study proposes a method for integrating the HEC-ResPRM and SWAT models to examine the effects of historical and future land use land cover change on the watershed’s hydrological processes and reservoir operation. Therefore, the study aimed to investigate the current and future optimal reservoir operation scenarios for water resources management concerning hydropower generation under the effect of LULC changes. The results reveal that both the 2035 and 2050 LULC change scenarios show the increased operation of hydropower reservoirs with increasing reservoir inflows, releases, storage, and reservoir elevation in the future. The effects of LULC change on the study area’s hydrological components reveal an increase in surface runoff until 2035, and its decrease from 2035 to 2050. The average annual reservoir storage and elevation in the 2050 LULC scenario increased by 7.25% and 2.27%, respectively, when compared to the current optimized scenario. Therefore, changes in LULC have a significant effect on hydropower development by changing the total annual and monthly reservoir inflow volumes and their seasonal distribution. Reservoir operating rule curves have been commonly implemented in the operation of hydropower reservoirs, since they help operators to make essential, optimal decisions with available stream flow. Moreover, the generated future reservoir rule curves can be utilized as a reference for the long-term prediction of hydropower generation capacity, and assist concerned authorities in the successful operation of the reservoir under the impact of LULC changes.

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

The use of evolutionary algorithms (EAs) for solving complex engineering problems has been very promising, so the application of EAs for optimal operation of hydropower reservoirs can be of great help. Accordingly, this study investigates the capability of 14 recently-introduced robust EAs in optimization of energy generation from Karun-4 hydropower reservoir. The best algorithm is the one that produces the largest objective function (energy generation) and has the minimum standard deviation (SD), the minimum coefficient of variations (CV), and the shortest time of CPU usage. It was found that the best solution was achieved by the moth swarm algorithm (MSA), with the optimized energy generation of 19,311,535 MW which was 65.088% more than the actual energy generation (11,697,757). The values of objective function, SD and CV for MSA were 0.147, 0.0029 and 0.0192, respectively. The next ranks were devoted to search group algorithm (SGA), water cycle algorithm (WCA), symbiotic organism search algorithm (SOS), and coyote optimization algorithm (COA), respectively, which have increased the energy generation by more than 65%. Some of the utilized EAs, including grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), antlion optimization algorithm (ALO), and whale optimization algorithm (WOA), failed to produce reasonable results. The overall results indicate the promising capability of some EAs for optimal operation of hydropower reservoirs.

Optimal management of hydropower production: Case of Memve’ele hydropower reservoir policy

Optimizing the production of electrical energy through the efficient management of reservoirs is one of the major concerns in power supply. Then, optimization methods have been employed to solve the problem of service continuity in a generation. Many reservoir management issues are indeed addressed without achieving a desired solution in line with demand. The main motivation for state-of-the-art technology is to develop robust and intelligent methods that enable strict compliance with production restrictions while meeting energy demand. This paper follows this perspective by proposing the water supply and hydroelectric production through a hybrid method combining the differential evolution and two constraints processing treatments (TCPT). The interest of the constraint method using here, including its superiority of feasible solutions (SF) and auto-adaptative penalty (AP) is its ability to overcome premature convergence and to obtain very accuracy near optimal solutions. AP technique improve by its ability process through tedious trial and error methods to select the best penalty coefficient during implementation. SF, for its part, is a technique for comparing unrealistic solutions on the basis of the violation of global constraint aimed at pushing unrealistic solutions towards the feasible region, while comparing two workable solutions on objective value improves the overall solution. To compare the performance of the proposed hybrid method of optimization with the SF and AP techniques as a constraint method, a series of experiments was firstly carried out using 25 well know benchmark functions. The obtained results as opposed to the current hybrid method such as TCPT-PSO, TCPT-ABC and TCPT-GA have exhibited the method’s superiority. Based on these performances, we employed the proposed hybrid method to manage the water supply and hydroelectric production of the Memve’ele reservoir. The application on the management of Memve’ele Reservoir demonstrates its accuracy in providing a near-optimal solution on the order of 99.7% for 1945s, 1985s and 1988s of demand during the seven hydrological years of the Ntem Valley with real mean square error (RMSE) in order of 10−7 for water supply and hundredth for hydropower reservoir operation.

Multiple Strategies Based Salp Swarm Algorithm for Optimal Operation of Multiple Hydropower Reservoirs

Reasonable optimal operation policy for complex multiple reservoir systems is very important for the safe and efficient utilization of water resources. The operation policy of multiple hydropower reservoirs should be optimized to maximize total hydropower generation, while ensuring flood control safety by effective and efficient storage and release policy of multiple reservoirs. To achieve this goal, a new meta-heuristic algorithm, salp swarm algorithm (SSA), is used to optimize the joint operation of multiple hydropower reservoirs for the first time. SSA is a competitive bio-inspired optimizer, which has received substantial attention from researchers in a wide variety of applications in finance, engineering, and science because of its little controlling parameters and adaptive exploratory behavior. However, it still faces few drawbacks such as lack of exploitation and local optima stagnation, leading to a slow convergence rate. In order to tackle these problems, multiple strategies combining sine cosine operator, opposition-based learning mechanism, and elitism strategy are applied to the original SSA. The sine cosine operator is applied to balance the exploration and exploitation over the course of iteration; the opposition-based learning mechanism is used to enhance the diversity of the swarm; and the elitism strategy is adopted to find global optima. Then, the improved SSA (ISSA) is compared with six well-known meta-heuristic algorithms on 23 classical benchmark functions. The results obtained demonstrate that ISSA outperforms most of the well-known algorithms. Then, ISSA is applied to optimal operation of multiple hydropower reservoirs in the real world. A multiple reservoir system, namely Xiluodu Reservoir and Xiangjiaba Rservoir, in the upper Yangtze River of China are selected as a case study. The results obtained show that the ISSA is able to solve a real-world optimization problem with complex constraints. In addition, for the typical flood with a 100 return period in 1954, the maximum hydropower generation of multiple hydropower reservoirs is about 6671 GWh in the case of completing the flood control task, increasing by 1.18% and 1.77% than SSA and Particle Swarm Optimization (PSO), respectively. Thus, ISSA can be used as an alternative effective and efficient tool for the complex optimization of multiple hydropower reservoirs. The water resources in the river basin can be further utilized by the proposed method to cope with the increasingly serious climate change.

Future operation of hydropower in Europe under high renewable penetration and climate change

SummaryAs large renewable capacities penetrate the European energy system and the climate faces significant alterations, the future operation of hydropower reservoirs might deviate from today. In this work, we first analyze the changes in hydropower operation required to balance a wind- and solar-dominated European energy system. Second, we apply runoff data obtained from combining five different global circulation models and two regional climate models to estimate future reservoir inflow at three CO2 emissions scenarios (RCP2.6, RCP4.5, and RCP8.5). This enables us to address the climate model uncertainty reported in previous literature. Despite large interannual and intermodel variability, significant changes are measured in the climate model signal between today and future climate. Annual inflow decreases by 31% (20%) in Southern countries and increases by 21% (14%) in Northern countries for high (mid)-emission scenarios. Projections also show impacts on seasonal profiles and more frequent and prolonged droughts in Mediterranean countries.

Future precipitation, hydrology and hydropower generation in the Yalong River Basin: Projections and analysis

In recent years, the impact of global warming has raised public concern over the precipitation-induced geological hazards, water security and energy security in the Yalong River Basin (YRB). Therefore, it is advisable to identify the variations of future precipitation, hydrology and hydropower generation under the joint impact of global warming and the operation of local hydropower reservoirs. To this end, the ensemble mean of the bias corrected NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) climate projections downscaled from 10 global climate models (GCMs) was applied to seven precipitation indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) and the ensemble of two distributed hydrologic models (VIC and CREST) under the global warming of 1.5 °C and 2 °C. Reservoir operation based on actual operation rules and the hydropower optimization model were explicitly included in the routing module to represent the impact of the local hydropower reservoirs, respectively. The future natural runoff is likely to increase under RCP8.5, decrease under RCP4.5, and see a larger daily variability under both. The local hydropower reservoirs can effectively mitigate the negative climate impact on the basin hydrology, and the basin is expected to see increased dry-season runoff, decreased wet-season runoff, and lower daily variability of runoff under all scenarios as compared to the baseline period. The future hydropower generation of the YRB is consistent with the variation of future runoff, and reservoir operation with hydropower optimized rules can produce about 5% electricity more than the current actual operation rules, highlighting the potential need to adapt the current operation rules to future climate conditions. Our framework and findings are expected to provide instructive information for water resources management not only in the YRB but also in different regions worldwide.

Using Cellular Automata Approach to Optimize the Hydropower Reservoir Operation of Folsom Dam

While hydropower in California is one the main sources of renewable energy, population growth has continuously increased demand for energy. In addition, recent droughts reduced the amount of available water behind the hydropower dams to provide the water head needed to run the turbines in hydropower plants. A more sustainable alternative, instead of developing new infrastructure, is to enhance the daily operation of reservoirs to support hydropower generation. This study suggests a new optimal operation policy for Folsom Reservoir in California and hydropower plants, which maximizes hydropower generation and reduces flood risk. This study demonstrates the application of the cellular automata (CeA) approach to optimize the daily hydropower operation of Folsom Reservoir. The reservoir operation is a nonlinear problem, where the hydropower generation and elevation-area-storage functions are the main nonlinearity to accurately represent the daily operation of the system. Moreover, the performance of the CeA approach under two extreme climate conditions, wet and dry, was evaluated and compared to the operation during normal conditions. Results showed that the CeA approach provides more efficient solutions in comparison to the commonly used evolutionary optimization algorithms. For the size of the non-linear optimization problem designed in this study, CeA outperformed genetic algorithm for finding optimal solutions for different climate conditions. Results of CeA showed that although the annual average inflow to the reservoir during the dry period was about 30% less than the normal condition, CeA offered about a 20% reduction in average hydropower generation. The new operation policy offered by CeA can partly compensate for the loss of the snowpack in California’s Sierra Nevada under a warming climate. The approach and its outcomes support an informed decision-making process and provide practical reservoir operational guideline to remediate the adverse effects of hydroclimatic changes in the future.

Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation

AbstractOptimal operation of hydropower reservoir systems is a classical optimization problem of high dimensionality and stochastic nature. A key challenge lies in improving the interpretability of...