Hydropower Reservoir Operation Research Articles

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...

Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs

Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of dimensionality poses huge challenges for operators because the computation cost often grows exponentially with the expansion of hydropower system. With strong search performance and high execution efficiency, metaheuristic search algorithms become the research hotspot in the reservoir operation field. Here, cooperation search algorithm (CSA) inspired by team cooperation behaviors in modern enterprise is introduced for power generation operation of cascade hydropower reservoirs. In CSA, the team communication operator is designed to determine promising search areas, and then the reflective learning operator is adopted to enhance the local search, while the internal competition operator is used to retain the elite individuals. The feasibility of the CSA method in numerical optimization problems is fully proved by the simulation results of several test functions. Then, the CSA method is used to solve the power generation operation of cascade hydropower reservoirs. The results show that the CSA method outperforms several traditional methods in both convergence rate and search precision. Thus, an effective tool with strong reliability and robustness is provided for the complex reservoir operation problem.

Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS’s search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.

Multi-strategy gravitational search algorithm for constrained global optimization in coordinative operation of multiple hydropower reservoirs and solar photovoltaic power plants

Recently, the solar photovoltaic power, a promising renewable energy, is witnessing a rapid development period. However, it is often difficult to perfectly capture the generation of solar photovoltaic plants because of various factors (like weather condition, solar radiation and human activities), increasing the operational risk and cost of power system. Hybrid energy system proves to be an effective measure to address this problem. Motivated by this practical necessity, this paper develops a novel hybrid gravitational search algorithm to solve the coordinative operation model of multiple hydropower reservoirs and solar photovoltaic power plants. In the proposed method, the gravitational search algorithm is set as the unified framework; the neighborhood search strategy is used to improve the convergence rate by considering the social information and individual experience; the adaptive mutation strategy is used to improve the population diversity by elite conservation and mutation operator; the modified elastic-ball strategy and constraint handling technique are used to enhance the solution feasibility. The simulation results of numerical functions demonstrate the superiority of the developed method in convergence rate and global search ability. The hydro–solar operation results in different cases show that compared with the traditional methods, the proposed method can yield high-quality scheduling schemes to alleviate the peak shaving pressure of power system. Thus, the novelty of this paper is to provide an effective HGSA method for solving the complex engineering optimization problem.

Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm

The production and use of energy from hydropower generation play a vital role in the economy. Besides, the presence of uncertainty further increases the complexity in optimizing the reservoir operation. A synthetic streamflow generation based on historical inflow records was employed using the Thomas–Fiering model for handling the uncertainty and variability of reservoir inflows. However, under the circumstances of water deficiency, the hydropower output is significantly reduced. In this study, an investigation of a parameter free Jaya algorithm as an optimization method for reservoir operation was carried out. When deriving the optimal operational rule a hedging strategy is introduced to attenuate the impact of reduced water supply. This strategy can effectively counterbalance the lack of water supply with reservoir storage requirements. The higher amount of hydropower generated by the proposed algorithm than the other algorithms used in this study, such as genetic algorithm (GA), the ant colony algorithm (ACO), the bat algorithm (BA), the particle swarm optimization (PSO) algorithm, chicken swarm optimization (CSO) algorithm, grasshopper optimization algorithm (GOA), equilibrium optimizer (EO) and firefly algorithm (FA), has shown its efficiency in the reservoir system. Several reservoir performance indices, such as total hydropower generation, reliability, and resilience, were used to access the proposed algorithm and other algorithms efficiency

An efficient and accurate Mixed‐integer linear programming model for long‐term operations of large‐scale hydropower systems

Mixed-integer linear programming is widely used in hydropower reservoirs operation optimisation because of its modelling flexibility, solution stability, and global search capability. A major obstacle to applying mixed-integer linear programming to hydropower reservoirs operation optimisation, especially for long-term operations of large-scale hydropower systems, is the non-linear phenomenon while converting water energy into electricity, which is normally expressed as a non-linear bivariate function, that is, hydropower production function. To cross this technological barrier, a novel linearisation method for hydropower production function is proposed. The method uses rectangular meshing techniques to approximate hydropower production function. Then Special Ordered Sets of type 2 (SOS2)constraints are adopted for sub-rectangle selection, which are formulated based on the binary branching schemes generated by binary reflected grey code. The proposed method can linearise the hydropower production function with only logarithmic-sized binary variables of the traditional methods and maintain high accuracy. The method is then applied to optimising the operation of the Lancang River Basin hydropower system, which has 13 head-dependent reservoirs with a deterministic model and a two-stage stochastic model. The results show that the authors' method significantly outperforms conventional linearisation methods and can obtain accurate solutions within an acceptable time for deterministic and stochastic large-scale hydropower reservoirs operation optimisation problems.

Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation

Optimal operation of cascade hydropower reservoirs is a complex high-dimensional engineering problem. Developing an appropriate model to solve such problems requires an efficient search method proportional to the dimensions of the problem. Accordingly, this research employed the new fitness-distance-balance (FDB) selection method in the moth swarm algorithm (MSA) to achieve promoted FDB-MSA with a high performance in solving complex large-scale problems. To ensure the efficiency of the developed algorithm, five benchmark functions of Shekel, Six-Hump Camel, McCormick, Goldstein-Price and Rosenbrock were used. Then, the FDB-MSA was used for optimization of hydropower generation of a real five-reservoir system along Karun River at Iran. This is the largest cascade reservoir system in Iran, which supplies more than 90% of the country’s hydropower demand. The results of the developed algorithm were compared with those of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. It was found that the FDB-MSA could successfully increase the hydropower generation by 59.5% (6724 GW) compared to the actual generation of energy over a 180-months operational period. The corresponding values for PSO and GA algorithms were 54.3% and 9.2% respectively. In addition, the results revealed the superiority of FDB-MSA to GA and PSO, so that, it demonstrated the smallest difference (3.41%) between nominal and optimal power generation compared to the PSO (6.58%) and GA (33.89%).

Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation

Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

Controlling biodiversity impacts of future global hydropower reservoirs by strategic site selection

Further reservoir-based hydropower development can contribute to the United Nations’ sustainable development goals (SDGs) on affordable and clean energy, and climate action. However, hydropower reservoir operation can lead to biodiversity impacts, thus interfering with the SDGs on clean water and life on land. We combine a high-resolution, location-specific, technical assessment with newly developed life cycle impact assessment models, to assess potential biodiversity impacts of possible future hydropower reservoirs, resulting from land occupation, water consumption and methane emissions. We show that careful selection of hydropower reservoirs has a large potential to limit biodiversity impacts, as for example, 0.3% of the global hydropower potential accounts for 25% of the terrestrial biodiversity impact. Local variations, e.g. species richness, are the dominant explanatory factors of the variance in the quantified biodiversity impact and not the mere amount of water consumed, or land occupied per kWh. The biodiversity impacts are mainly caused by land occupation and water consumption, with methane emissions being much less important. Further, we indicate a trade-off risk between terrestrial and aquatic biodiversity impacts, as due to the weak correlation between terrestrial and aquatic impacts, reservoirs with small aquatic biodiversity impacts tend to have larger terrestrial impacts and vice versa.

A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation

Sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation; the random weighting agent produced by multiple leader solutions is integrated into the agent’s evolution equation to improve the convergence rate; the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem.

Development of a SWAT Hydropower Operation Routine and Its Application to Assessing Hydrological Alterations in the Mekong

Reservoir operations and climate change can alter natural river flow regimes. To assess impacts of climate and hydropower operations on downstream flows and energy generation, an integrated hydropower operations and catchment hydrological model is needed. The widely used hydrological model Soil and Water Assessment Tool (SWAT) is ideal for catchment hydrology, but provides only limited reservoir operation functions. A hydropower reservoir operation routine (HydROR) was thus developed for SWAT to analyze complex reservoir systems under different policies. The Hydrologic Engineering Center’s Reservoir System Simulation (HEC-ResSim) model, a well-established reservoir simulation model, was used to indirectly evaluate functionality of the HydROR. A comparison between HydROR and HEC-ResSim under a range of operation rule curves resulted in R2 values exceeding 0.99. The HydROR was then applied to assess hydrological alterations due to combined impacts of climate change and reservoir operations of 38 hydropower dams in the 3S basin of the Mekong River. Hydropower production under climate change varied from −1.6% to 2.3%, depending on the general circulation model chosen. Changing the hydropower operation policy from maximizing energy production to maintaining ecological flows resulted in a production change of 13%. The calculation of hydrological alteration indices at the outlet of the 3S basin revealed that over 113% alteration in the natural river outflow regime occurred from the combined impacts of climate change and reservoir operations. Furthermore, seasonal flows and extreme water conditions changed by 154% and 104%, respectively. Alterations were also significant within the basin, and, as expected, were larger for high-head and small-river reservoirs. These alterations will adversely affect ecological dynamics, in particular, habitat availability. HydROR proved to be a valuable addition to SWAT for the analyses of complex reservoir systems under different policies and climate change scenarios.

Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies

Recently, the multiple hydropower reservoirs operation optimization is attracting rising concerns from researchers and engineers since it can not only improve the utilization efficiency of water resource but also increase the generation benefit of hydropower enterprises. Mathematically, the reservoir operation problem is a typical multistage constrained optimization problem coupled with numerous decision variables and physical constraints. Sine cosine algorithm (SCA), a new swarm-based method, has the merits of clear principle and easy implementation but suffers from the premature convergence and falling into local optima. To improve the SCA performance, this paper proposes an adaptive sine cosine algorithm (ASCA) where the elite mutation strategy is used to increase the population diversity, the simplex dynamic search strategy is used to enhance the solution quality, while the neighborhood search strategy is used to improve the convergence rate. The simulations of 25 test functions show that ASCA outperforms several existing methods in both convergence rate and solution quality. The results of a real-world hydropower system in China demonstrate that ASCA betters the SCA method with obvious increase in power generation. Thus, the main contribution of this study is to provide an effective optimizer for multiple hydropower reservoirs operation.

Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction

The dimensionality problem is posing an enormous challenge for cascade hydropower reservoirs operation because the memory usage and execution time grow exponentially with the expansion of system scale. To effectively address this problem, this paper develops a novel Latin dynamic programming algorithm for dimensionality reduction in hydropower reservoir operation problem, where the Latin hypercube sampling method is firstly adopted to produce a subset of discrete state variables at each stage, and then the standard dynamic programming recursive equation is used to search for a modified trajectory around the newly-generated solutions, while the iterative search strategy is used to gradually enhance the solution quality. The results in a real-world hydropower system of China demonstrate that compared with the standard dynamic programming method, the execution efficiency of the presented method is significantly improved while the power generation is well maintained in different scenarios. Hence, the novelty of the paper is to provide an effective dimensionality reduction tool for solving the complex hydropower operation problem.

Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization

Accurate monthly runoff prediction plays an important role in the planning and management of water resources. However, owing to climate changes and human activities, natural runoff often contains a variety of frequency components, and existing monthly runoff estimation techniques may fail to capture potential change processes effectively. To overcome this problem, we have developed a hybrid model for monthly runoff prediction. First, observed runoff is decomposed into several subcomponents via variational mode decomposition. Second, support vector machine models based on quantum-behaved particle swarm optimization are adopted to identify the input-output relationships hidden in each subcomponent. Finally, the total output of all submodules is treated as the final forecasting result for the original runoff. Three quantitative indexes are considered to test the performance of the developed models. The monthly streamflow of two reservoirs in China’s Yangtze Valley is considered as the survey target. This area contains the world’s largest hydropower project (Three Gorges Reservoir) and the waterhead of the middle line of Asia's largest inter-basin water transfer project (Danjiangkou Reservoir). Test results indicate that the hybrid model provides better forecasting accuracy compared to several traditional methods (artificial neural networks and extreme learning machines), making it an effective tool for the scientific operation of hydropower reservoirs.

Balancing competing interests in the Mekong River Basin via the operation of cascade hydropower reservoirs in China: Insights from system modeling

As one of the most important international rivers in Asia, the Mekong River is famous for its abundant water and hydropower resources, fascinating biodiversity, and geopolitical importance. However, the rapid hydropower development in the Mekong River Basin has induced ecological risks and triggered water conflicts among six riparian nations. Balancing the competing interests of water, energy and ecosystems in the Mekong River Basin is a great challenge for riparian nations and is of global concern. This study quantitatively investigates what role the operation of the cascade reservoirs in China may play in mitigating existing conflicts and achieving cobenefits in the entire Mekong River Basin. An integrated modeling approach is adopted, and simulation-optimization analyses are performed to explore the tradeoff and/or synergy effects among the competing interests under varying reservoir operation strategies. The study reveals the disparate interactions among water, energy and ecosystem sectors and identifies the transboundary cobenefits of hydropower production and water supply. The major study findings are as follows. First, hydropower development in the upper Mekong River (also known as the Lancang River) can be beneficial for downstream agricultural water supply in certain circumstances if the cascade reservoirs in China are operated wisely. Second, the natural flow index adequately reflects ecological concerns in general, but additional objectives need to be included in the optimization if specific interests (e.g., phosphorous export from the Lancang River) are prioritized. Third, the tradeoffs between the agricultural water supply and ecosystem conservation, the two critical concerns of downstream countries, are intrinsic in the lower Mekong River Basin and are not induced by hydropower production in China. Fourth, the agricultural water supply in the downstream countries calls for a larger shift in the flow regime than hydropower production in China. Overall, the results of this study provide new insights into holistic management and effective transboundary cooperation in the Mekong River Basin, as well as in other international river basins with similar water-energy-ecosystem nexuses.

Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions

Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.

Numerical analysis of landslide-generated impulse waves affected by the reservoir geometry

Impulse waves generated by landslides are a potential major hazard for the operation of hydropower reservoirs, as they pose a serious threat to the dam, to residents and to properties along the shoreline. A number of experimental studies have been conducted based on generalized models to predict the wave characteristics, but using the results from these models to predict landslide-generated impulse waves may result in significant errors due to wave reflection caused by changes in the reservoir width. To investigate the effects of reservoir geometry on the characteristics of landslide-generated waves during propagation, 14 numerical experiments were performed using Tsunami Squares. The real-sized numerical models were based on statistical data from the Yangtze River and a generalized cross section of the Gongjiafang landslide located in the Three Gorges Reservoir, China. Two types of generalized reservoir geometries were investigated – namely with converging and diverging reservoir widths. It is shown that the wave amplitudes decrease with increasing reservoir width, while wave heights and troughs have a complicated dependence on changes in reservoir width. Compared to the amplitudes of the leading waves, the wavelengths and start-up time of wave amplitudes were more affected in the converging reservoir models by variations in the reservoir width.

Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation

Gravitational search algorithm (GSA) is an evolutionary algorithm developed to solve the global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. In order to improve the GSA performance, this paper develops a novel multi-strategy gravitational search algorithm (MGSA) where the Lévy flight strategy is adopted to increase the local search ability of the global best-known agent; and then the mutation strategy is used to improve the swarm diversity in the evolutionary process; finally, the elitism selection strategy is used to enhance the exploration ability and convergence speed of the swarm. The MGSA method is compared with several methods in 24 famous benchmark functions, and the results demonstrate the superiority of the MGSA method in both search ability and convergence rate. Next, the MGSA method is used to solve the ecological operation problem of the Wu hydropower system. The results indicate that compared with several existing methods, MGSA can obtain better scheduling schemes to make obvious reductions in the inappropriate ecological water volume in different scenarios. Thus, this paper provides a new effective tool for the complex engineering optimization problems.

The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter?

AbstractThe quality of the forecasts, that is, the accuracy in predicting the observed streamflow, affects the decisions that can be taken thus determining the success or failure of hydropower operations, that is, the so‐called forecast value. Although preprocessing techniques can be employed to improve forecast quality, the corresponding improvement in forecast value to hydropower is not straightforward to anticipate because of the complex relationship between quality and value, which depends on the hydrometeorological regime and water system features. The objective of this paper is to demonstrate the value to hydropower reservoir operations of preprocessed (i.e., bias corrected and downscaled) subseasonal forecasts and to compare the forecast quality and value across and within seasons. We use the forecast ensembles provided by the European Center for Medium‐Range Weather Forecasts (ECMWF). We assess forecast quality in terms of bias, Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS), and forecast value in terms of mean revenue and avoided unproductive spill. We consider daily subseasonal hydrometeorological forecasts covering lead times up to 1 month and a quantile mapping technique to preprocess the hydrometeorological forecasts. Forecasts are then used in a rolling horizon set up to optimize hydropower operations of the Verzasca hydropower system in the Alps (CH). Results show that preprocessing is essential to improve both forecast quality and value. Although hydropower reservoir operations benefit from considering forecasts all the yearlong, the relationship between forecast quality and value is complex and strongly depends on the metrics used to assess forecast quality and value and on the season.