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Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

The short-term hydropower generation scheduling (STHGS) is a complicated problem in the utilization of hydropower and water resources. An improved binary-coded whale optimization algorithm (IBWOA) is proposed in this paper to solve the complex nonlinear problem. The STHGS problem is divided into unit combination (UC) subproblem and economic load distribution (ELD) subproblem. For the UC subproblem, we use the sigmoid function (SF) to generate a binary array representing the start/stop state of the unit. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. Each generation solution is optimized by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint. For ELD subproblem, the optimal stable load distribution table (OSLDT) is used to distribute the load quickly. The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. Finally, the proposal is applied to solve the STHGS of the Three Gorges Hydropower Station. When the water head is 75 m,88 m, and 107 m, the minimum water consumption calculated by the IBWOA algorithm is 1,058,323,464 m3, 892,524,696 m3, and 745,272,216 m3, respectively. Compared with the traditional whale optimization algorithm, the water consumption of the IBOWA algorithm corresponding to 75 m, 88 m, and 107 m water heads is reduced by 0.76%, 0.26%, and 0.05%, respectively. The comparison between the IBWOA algorithm and other heuristic algorithms shows that the IBWOA has good feasibility and high optimization accuracy.

Inevitable evaporation and sedimentation will influence reservoir operational performance if the two fluxes have been ignored when sizing reservoir systems. While this is to be expected, very few studies have quantified this influence. This study has analysed the effects of evaporation and sedimentation on reservoirs operational performance. It used seven reservoirs in Yesilirmak Basin, Turkey as case studies. The Dams serve a variety of purposes including irrigation, domestic and industrial, hydropower generation and flood control. Performance with regard to meeting these needs was characterized using reliability (time-based and volume-based), resilience, vulnerability and sustainability. The results showed that while ignoring both the evaporation and sedimentation during planning does affect subsequent operational performance, the influence of evaporation was more significant. Possible reasons are provided for the outcome and ways by which both can be accommodated during planning analyses thus mitigating the operational effects are suggested.

Daily inflow forecasts provide important decision support for the operations and management of reservoirs. Accurate and reliable forecasting plays an important role in the optimal management of water resources. Numerous studies have shown that decomposition integration models have good prediction capacity. Considering the nonlinearity and unsteady state of daily incoming flow data, a hybrid model of adaptive variational mode decomposition (VMD) and bidirectional long- and short-term memory (Bi-LSTM) based on energy entropy was developed for daily inflow forecast. The model was analyzed using the mean absolute error (MAE), the root means square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (r). A historical daily inflow series of the Baozhusi Hydropower Station, China, is investigated by the proposed VMD-BiLSTM with hybrid models. For comparison, BP, GRNN, ELMAN, SVR, LSTM, Bi-LSTM, EMD-LSTM, and VMD-LSTM, were adopted and analyzed for evaluation and analyzed. We found that the proposed model, with MAE = 38.965, RMSE = 64.783, and NSE = 95.7%, was superior to the other models. Therefore, the hybrid model is robust and efficient for forecasting highly nonstationary and nonlinear streamflow. It can be used as the preferred data-driven tool to predict the daily inflow flow, which can ensure the safe operation of hydropower stations in reservoirs. As an interdisciplinary field spanning both machine learning and hydrology, daily inflow forecasting can become an important breakthrough in the application of deep learning to hydrology.

Acquiring sustainable water resources for water-based development of countries is the experts concern in this field, who seek to follow the clean development mechanism (CDM) regulations and overcome water crisis through integrated water resources management (IWRM). The Great Karun River basin is one of the major basins in the Middle East. This basin, containing six of the largest reservoir dams with a cumulative power plant capacity of more than 10,500 MW generates about 93% of hydropower of Iran. The water balance of the aquifer in the study area was simulated using MODFLOW model while water resources and surface water reserves were simulated by the water evaluation and planning (WEAP) model. A separate simulation was performed with each of two models and the results of two models were coupled using a link file. The multi-objective function optimization process including the maximized supply of demands and hydropower and the minimized aquifer drawdown was completed using non-dominated sorting genetic algorithm (NSGA-II). All effective system components, such as inter-basin water transfer, integrated use of water resources, variation of irrigation network efficiencies, and the effect of water shortage were studied and analyzed under the targeted scenarios. Finally, the best scenario, which was capable to supply the future needs until time horizon of 2040 was planned for the basin considering minimization of aquifer drawdown and optimal generation of hydropower resulting in a maximum decrease in emission of greenhouse gases.

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.

One of the greatest challenges in the electricity generation sector is to operate hydrothermal plants in view of the randomness of hydrological events and climate change, that may impact the inflows into the systems. In several Brazilian watersheds, conflicts among water users are already registered, in addition to inflow changes that affect the electricity generation system. The Sao Francisco River Basin (SFRB) is an important source of water for the development of northeastern Brazil. In this context, this work aimed at carrying out a study on the statistical behavior of time series related to the management of the SFRB hydrosystem network, as well as to measure the performance of hydropower plants in supplying multiple users, in different critical periods. Historical records of natural streamflow, natural inflow energy and stored energy were used. Some statisctical tests were applied to detect trends and change-points. The Natural Energy method was applied to different subsamples to define critical periods. Next, a deterministic nonlinear operation optimization model was used to assess the supply to the multiple users for the different critical periods. The main contribution of this study is the impact of nonstationarity in planning and operation of hydrosystems. The results indicated that the natural inflow energy and the stored energy time series are predominantly non-stationary, with a trend change in the 1990s, which modifies the critical period of the basin to 2013–2019, significantly increasing the vulnerability of the system in about 35% when compared to the currently used critical period (1949–1956).

Optimal operation of multi-objective reservoirs is one of the complex and, sometimes nonlinear, issues in multi-objective hydrologic system optimization. Meta-heuristic algorithms are good optimization tools which look for decision space via simulating the behavior of animals and providing the possibility for presenting a set of points as a set of problem solutions. Therefore, in this study, developing multi-objective water cycle algorithm (MOWCA) was investigated for the optimal operation issue of Halilrood basin reservoir system (Baft, Safarood, and Jiroft Dams) in order to hydropower energy generation of Jiroft Dam, downstream demand supply (drinking, agricultural, and environmental requirements), and flood control for a period of 223 months (from October 2000 to April 2019). Also, the results of the algorithm were compared with those of the well-known non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the efficiency of the used multi-objective algorithms, four performance evaluation criteria including generational distance (GD), metric of spacing (S), metric of spread (Δ), and maximum spread (MS) were used. The results of applying multi-objective performance evaluation criteria showed the superiority of the developed MOWCA method in three criteria of distance, spread, and maximum spread criteria, while the NSGA-II algorithm was superior to the MOWCA only in metric of spacing (S) criterion. Moreover, the MOWCA algorithm with total 236.07 objectives performed better than the NSGA-II algorithm with total 268.01 objectives. In Jiroft Dam’s hydropower energy generation, the MOWCA algorithm with 4278.69 MW power generation was considerably superior to the NSGA-II algorithm with 3138.55 MW MW generation during the studied period. Finally, the obtained results showed higher performance of the MOWCA algorithm than the NSGA-II algorithm in the optimal operation of Halilrood basin multi-objective reservoirs systems with different objectives.

This study aimed to apply the Markovian Model (MM) to obtain optimized operating rules for a hydropower reservoir to maximize its efficiency. In this study, a Markovian Control Model with continuous state space (MCM-CSS) is presented to analyze the climatological effects through stochastic integrals. The MCM-CSS compute the optimal policies of hydropower reservoir system over the Grijalva Hydropower System (GHS). The MCM-CSS can be described as follows: a) Historical data review, b) histograms of variables studied, c) state space analysis, d) action space analysis, e) admissible decision rule, f) expected benefit analysis. As a result, the optimal policy graphs were obtained. The result obtained in this research demonstrated the advantage of having applied a continuous space in reservoir inflow and water demand with the MMC-CSS method to avoid the uncertainness and inaccuracies in simulation results with a discrete space. Modified Svanidze’s Method (MSM) is also employed to investigate their capabilities to predict streamflow over the GHS and mean energy generated. The hydrological monthly simulations were calibrated and validated at GHS station for the period 1952–2018. The main objective of the study is to simulate the optimal policies obtained with the MMC-CSS for continuous states using MSM in GHS.

The traditional operation of the cascade hydropower stations system (CHPS) mainly focus on the maximization of power generation benefits, but ignores the interference of CHPS operation to the river ecosystem, therefore, carrying out the multi-objective optimal operation (MOOP) of CHPS considering ecological demands is crucial. In this paper, a MOOP model considering the ecological objective is established. To effectively solve the MOOP problems, a novel multi-objective Jaya algorithm (MOCOM-Jaya) is proposed, where the quality of the initial population is enhanced based on the chaotic sequence, the later disturbance term and Gaussian mutation are incorporated to improve the local search ability, the elite opposition-based learning is adopted to broaden the optimization space. The proposed algorithm is applied to the study of MOOP of CHPS in the Wujiang river, and the results show that compared with MOPSO and NSGA-II, MOCOM-Jaya can gain the solution set with better convergence and distribution for the MOOP. The competition relationship between the power generation objective (PGO) and the ecological objective (ECO) is revealed based on the partial replacement ratio method. The results show that the competitiveness of PGO and ECO experienced a trade lead with the increase of power generation. The mean competitiveness ratios of PGO to ECO ( $\overline {CP{R_{P - E}}} $ ) in three typical years (dry, normal, wet) are 3.22, 3.17 and 3.15, indicating that the PGO is dominant in the competition with the ECO as a whole.

In China, there are numerous single-reservoir and multicascade hydropower plants (SMHPs), which provide high-quality peak-shaving power supply due to their characteristics of rapid load tracking and flexible regulation. However, the short-term peak-shaving operation (SPSO) of SMHPs serving multiple power grids faces difficulties from complicated hydraulic and electrical connections, dimensional disasters and complex solution difficulties. To effectively address the above problems, this paper presents a new method combining a typical peak-shaving output curve and a spillage adjustment strategy for the SPSO of SMHPs. First, the typical peak-shaving output curves of SMHPs are determined by the fuzzy membership function, flat-to-peak ratio and valley-to-peak ratio to reduce the dimension of SPSO. The fuzzy membership function is applied to identify the peak, flat and valley periods of multigrid loads. Then, taking the maximum total final energy storage of the scheduling period as the objective function, an SPSO model of SMHPs serving multiple power grids is established to balance the energy storage and peak-shaving requirements. Energy storage refers to the maximum hydropower generation that a hydropower plant can theoretically produce under a certain storage state. Finally, a spillage adjustment strategy is adopted to modify the power outputs of the SMHPs to minimize water spillage. The results from a real-world hydropower system in China demonstrate that the proposed method can allow SMHPs to play an important role in peak shaving while obtaining relatively high energy storage with negligible water spillage.

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%).

Forecasted inflow is one of the most important input information for reservoir operation planning. However, current inflow prediction accuracy is difficult to meet the needs of long-term operation. Therefore, it is of great significance to consider the uncertainty of inflow forecast and study its influence on reservoir operation decisions. In this paper, a long-term scheduling framework considering inflow forecast uncertainty based on a temporal disaggregation method is proposed. First, the uncertainty of forecast is described from two aspects. Gaussian distribution is used to simulate the annual forecast error, and an Adaptive Nearest Neighbor Gaussian sampling method (A-NGS) is proposed to decompose annual inflow into temporal series. Based on the implicit scheduling model, the sample set of generation scheduling plans, actual dispatching schemes and theoretical optimal results are constructed. On this basis, a series of indexes are presented to evaluate the inflow simulation performance and the scheduling benefits. A case study of the Xiluodu-Xiangjiaba cascade reservoirs is conducted to analyze the effects of forecast uncertainty on operation benefits, and the effectiveness of forecast information is identified. Compared with the deterministic fragment method, the inflow processes simulated by A-NGS achieve better precision and behave more conducive to the scheduling. Although the uncertainty of forecast errors will bring some hydropower generation losses, a certain degree of forecast accuracy is effective to improve scheduling benefits when in the electricity market.

In theory, emergence of robustness concept has pushed decision-makers toward designing alternatives, such as resistant against the potential fluctuations fueled by uncertain surrounding environment. This study promotes an objective-based multi-attributes decision-making framework that takes into account the uncertainties associated with the impacts of the climate change on water resources systems. To capture the uncertainties of climate change, Monte Carlo approach has been used to generate a series of ensembles. These generated ensembles represent the stochastic behavior of the hydro-climatic variables under climate change. This framework represents the inherent uncertainties associated with hydro-climatic simulations. Next, a coupled TOPSIS/Entropy multi-attribute decision-making framework has been formed to prioritize the feasible alternatives using system performance measures. The main objective of this framework is to minimize the risk of deceptive and subjective assessments during decision-making process. Karkheh River basin has been selected as a case study to demonstrate the implication of this framework. Using a set of system performance attributes, the performance of two hydropower systems has been estimated during the baseline period and under the future climate change conditions. According to the conducted frequency analysis, the alternative in which both hydropower projects would go under construction emerged as the robust solution (i.e., there was a 99.9% chance that it outperforms other solutions). The results indicate that the construction of these hydropower systems leads to the increase of Karkheh River basin robustness in the future.

Hydrological models are the main tools for water resources management. The Lavras Simulation of Hydrology (LASH) model was developed for watersheds with scarce data, and great results have been obtained in Brazil. This study aimed to incorporate hydrological response units (HRUs) in the LASH model to assess soil water storage (SWS) across space and time. The LASH model was calibrated and validated for the Grande River basin upstream of the Furnas Hydropower Plant, in southeastern Brazil. The Nash-Sutcliff coefficient (CNS) and its logarithmic version were analyzed in terms of both calibration and validation to appraise the model’s performance in a daily time step. The CNS for calibration and validation was 0.86 and 0.77, respectively, showing that LASH using the HRUs produced improvements in the simulations. The calibrated parameters showed a good relationship with hydrological processes in HRUs, and SWS estimates reflected the soils, topography, and land use of the watershed. LASH could describe the SWS behavior and identify the sub-watersheds with the highest and the lowest values. Therefore, the LASH model is a promising tool for SWS simulation in time and space.

The “curse of dimensionality” is a major problem in dynamic programming (DP) algorithms for large-scale hydropower systems. This study proposes a parallel DP algorithm based on Spark (PDPoS) to alleviate the “curse of dimensionality”. Parallel computing experiments are formulated by varying the number of reservoirs, the number of discrete water levels and the number of CPU cores to analyze the quality and efficiency of PDPoS. The methodologies were applied to a cascade reservoir system made up of eight reservoirs in the Yuanshui River Basin in China. The results are as follows. (1) The number of discrete water levels is the dominant factor in the solution quality, while the number of reservoirs is the dominant factor in the solving efficiency. (2) The runtime of PDPoS is markedly affected by the calculational scale (determined by the number of reservoirs and discrete water levels), and the relationship between the number of CPU cores and the runtime is triphasic with increasing calculational scale. (3) The larger the calculational scale is, the better the parallel performance (i.e., the parallel speedup and parallel efficiency). The proposed PDPoS method has strong generality, high parallel performance, and high practical value.

The determination of optimal operation rules for water storage systems will provide a good perception on the ability of these systems to achieve their objective functions. This study aims to identify optimal operation policies by maximizing the annual hydropower generation of a multi-reservoir system that consists of two reservoirs with different functions in flood risk management. These reservoirs, namely, Mosul and Badush, are located in the Tigris River, Northern Iraq. The Mosul Dam was constructed to protect the cities located downstream, and the Badush Dam is building to absorb flood waves in case the Mosul Dam collapses. The particle swarm optimization model for a single reservoir (PSOS) was formulated to determine optimal operation policies during real operation time to maximize annual hydroelectric generation. PSOS was approved during these operations and developed to specify ideal operation rules for a multi-reservoir system (PSOM), which consists of two reservoirs, through three modes of annual inflows. Results of PSOS indicated its superiority during real-time operation. The annual hydropower generation was achieved by the optimal operation rules of PSOM during the three styles of inflows. These optimal policies will provide good insights into the potential of this multi-reservoir system in supplying the national electricity network with hydropower energy, which is considered environmentally friendly, in addition to achieving the original goals of its construction.

This paper develops a deep reinforcement learning (DRL) framework for intelligence operation of cascaded hydropower reservoirs considering inflow forecasts, in which two key problems of large discrete action spaces and uncertainty of inflow forecasts are addressed. In this study, a DRL framework is first developed based on a newly defined knowledge sample form and a deep Q-network (DQN). Then, an aggregation-disaggregation model is used to reduce the multi-dimension spaces of state and action for cascaded reservoirs. Following, three DRL models are developed respectively to evaluate the performance of the newly defined decision value functions and modified decision action selection approach. In this paper, the DRL methodologies are tested on China’s Hun River cascade hydropower reservoirs system. The results show that the aggregation-disaggregation model can effectively reduce the dimensions of state and action, which also makes the model structure simpler and has higher learning efficiency. The Bayesian theory in the decision action selection approach is useful to address the uncertainty of inflow forecasts, which can improve the performance to reduce spillages during the wet season. The proposed DRL models outperform the comparison models (i.e., stochastic dynamic programming) in terms of annual hydropower generation and system reliability. This study suggests that the DRL has the potential to be implemented in practice to derive optimal operation strategies.

Joint operation of cascade hydropower stations maximizes the utilization rate of water resources of a river basin and the benefit of the entire river system. However, under mixed development environment of cascade hydropower stations, i.e. simultaneous existence of operating and under-construction hydropower stations, the difficulty of the joint operation is increased. Moreover, this difficulty is further enhanced due to the cooperation among multiple stakeholders and uncertain evolutionary characteristic of stakeholder’s strategy. To handle these problems, this paper takes two upstream operating hydropower stations and one downstream hydropower station under construction as research objects, where one of upstream hydropower station locates in a tributary. First, all possible strategy combinations among these three stakeholders are comprehensively analyzed, and the benefit of each stakeholder strategy under each strategy combination is respectively calculated. A tripartite evolutionary game model is then established. It aims at exploring directions and conditions of cooperative and non-cooperative strategies evolving into stable states. Finally, the exploration results find that the strategy evolution of a stakeholder relies on its partners’ behaviors and net benefit of self-behavior; the tripartite cooperation will eventually form four stable states; the conditions for cooperation between upstream and downstream hydropower stations are that the compensation paid by downstream hydropower station is greater than the loss of upstream power generation and downstream project benefit is greater than the sum of compensation expenditure and risk benefit.

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.

Water is fundamental to human well-being, social development and the environment. Water development, particularly hydropower, provides an important source of renewable energy. Water development is strongly affected by poverty, but only few attempts have been made to understand the links between water development and poverty from a global water development point of view. In this work, this linkage was explored using reservoir construction, hydroenergy and water use data along with six derived indicators. We used association rule mining and classification and regression trees (CART) to identify the links. Random forests were employed to search for factors sensitive to poverty. This study shows that the reservoir density is significantly related to poverty, and reservoir densities are lower in countries with higher poverty rates. Countries with a higher use of small hydropower (SHP) systems are generally more prosperous as follows: an SHP utilization rate above 27% corresponds to a poverty rate below 4.9%. The ratio of water utilization, water availability per capita (WAPC) and reservoir density were essential for the prediction of the poverty class. All three ratios could be related to poverty alleviation as they enable the identification of the potential for water resource development and their constraints. This study concludes that water development in poor countries needs to receive more attention.

In many parts of the world, especially in metropolitan areas with dry climates, shortages of freshwater resources have become a significant challenge in water resources management. Rapid population growth and climate change in these areas impose additional burdens on water consumption, especially on surface water resources, which are increasingly becoming scarce. In this study, a change from the Standard Operating Policy (SOP) to the Modified Linear Decision Rule (MLDR) policy is considered as a solution to mitigate the impacts of climate change on dams and reservoir operation. The two policies were compared by using five reservoir system performance indices (reliability, resiliency, vulnerability, sustainability and meeting demands) and three hydropower energy generation indices. This study pertained to the Karaj hydropower dam reservoir in Iran and speculated on 2020–2039. Changing the operation policy from current SOP to the MLDR policy can mitigate the adverse effects brought by climate change. According to the results, the SOP policy works best for reservoir operation if there is enough water (optimistic scenario). In the case of pessimistic scenarios (shortage of water), operating the reservoir system with SOP policy would turn into a crisis in supplying drinking water. On the other hand, the MLDR policy prevents the complete draining of the reservoir. In all water-demand scenarios, the MLDR policy reduces the amount of time (by over 90%) in which the reservoir remains empty, compared to the SOP policy. The final results demonstrate how the MLDR policy can mitigate the undesirable effects of climate change.

Obvious inflow variations resulting from changing environments bring big challenges to the operations of hydropower reservoirs. This study reveals the impacts of average annual inflow volume (AAIV) variations on the long term operation of a multi-hydropower-reservoir (MHR) system, and presents a strategy for determining the adaptable operation rule. The strategy includes two parts. One part is making different inflow scenarios based on the change points of AAIVs. Another part is applying the principle of cross validation to select the adaptable rule from the formulated operation rules in various inflow scenarios. Specifically, the change points of AAIVs are identified by three statistical methods. An optimization operation model of an MHR system is built, and three evolutionary and meta-heuristic algorithms are applied to resolve the model in different inflow scenarios. Based on the optimal operation results, two machine learning algorithms are employed to formulate operation rules in each inflow scenario. The MHR system at the upstream of Yellow River basin is taken as a case study. The results show that (1) the long term operation of an MHR system is sensitive to the AAIV variations; and (2) the presented strategy is feasible in determining the adaptable operation rule for an MHR system under the AAIV variations. The findings of the study are helpful for the long term operation of an MHR system under the AAIV variations.

Bayesian stochastic dynamic programming (BSDP) has been widely used in hydropower generation operation, as natural inflow and forecast uncertainties can be easily determined by transition probabilities. In this study, we propose a theoretical estimation method (TEM) based on copula functions to calculate the transition probability under conditions of limited historical inflow samples. The explicit expression of the conditional probability is derived using copula functions and then used to calculate prior and likelihood probabilities, and the prior probability can be revised to the posterior probability once new forecast information is available by Bayesian formulation. The performance of BSDP models in seven forecast scenarios and two extreme conditions considering no or perfect forecast information is evaluated and compared. The case study in the Ertan hydropower station in China shows that (1) TEM can avoid the shortcomings of empirical estimation method (EMM) in calculating the transition probability, so that the prior and likelihood probability matrices can be distributed more uniformly with less zeros, and the problem that the posterior probability cannot be calculated can be avoided; (2) there is a positive correlation between operating benefit and forecast accuracy; and (3) the operating policy considering reliable forecast information can improve hydropower generation. However, an incorrect decision may be made in the case of low forecast accuracy.

Quantifying excess energy using an energy balance model is the key to designing and operating an energy-efficient water distribution system (WDS). Excess energy, which can be recovered instantly or stored in a water-energy storage is the basis to estimate hydropower potential in the system. For a given WDS with its demand, how the excess energy can be managed efficiently to design a water-energy storage to maximize hydropower generation is the focus of this paper. A single-objective optimization model has been developed to optimize the dimensions for up to six water-energy storages for maximizing hydropower generation while minimizing the pumping energy. While the ratio of total energy recovered to total pumping energy is found to be about 40% for all water-energy configurations, the recovered specific energy ranges from 0.116 kWh/m3 to 0.121 kWh/m3 showing the potential use of WDS as an energy storage. Results show that hydropower generation increases with the increase of number of storages up to a certain number representing the constraints of constant drinking water demand and storage dimensions. In-pipe turbines with pump operation for minimizing pumping energy can offer the optimal solution for WDS energy management. A higher number of storages with in-pipe turbines offers uniformity in pressure distribution resulting increase in system robustness.

Lingering droughts and shortage of water sources signify the importance of optimal utilization of water reservoirs such as multi-reservoir systems. These systems could be employed not only as a storage system to manage the water utilization but also as a power generation system. To rise the generated power besides the management of water utilization, an optimization algorithm should be used. In this study, the kidney algorithm in three different scenarios, namely the wet, normal, and dry years is employed to fulfill such an engineering operation in a four-reservoir system in China. Simulations show well compatibility of the water level inside the reservoir with real statistical indices in terms of RMSE and MAE. Results also reveal that using the kidney algorithm not only reduces the required calculation but also increases the convergence pace with respect to other algorithms that have been used (bat, shark, abundance of particles, and genetic algorithms). Moreover, it increases the amount of the generated energy by a factor of 2.2–3.2 with respect to the aforementioned algorithms. Results indicate the capability of the kidney algorithm in the management of water sources and engineering operations.

In this study, we proposed a new method to derive multi-purpose reservoir operation chart with the objectives of maximizing power generation and ecological conservation and minimizing ecological damage under future climate change. This double-parameter ecological operation chart (DEOC) was obtained by incorporating limited ecological curves into original conventional operation chart (COC) and then establishing and solving the operation optimization model considering both ecological profits and hydropower generation. Unlike COC where reservoirs are operated based on the current water level, reservoir operation under DEOC is based on both current reservoir water level and inflow. A case study was conducted in Jasajiang (JS) and Madushan (MDS) cascade reservoirs on the Yuan River in southwestern China. The monthly runoff data for the period 2021–2050 under RCP4.5 and RCP8.5 was simulated using the Soil and Water Assessment Tool (SWAT) driven by the General Circulation Models (GCMs), and the optimal ecological flow range calculated by PHABSIM for a given river section was used to determine the limited ecological curve. The operation optimization model considering both ecological profits and hydropower generation was established and solved to obtain DEOC. Compared with COC, DEOC results in a significant increase in power generation (>9%) and a reduction in ecological damage frequency without affecting the overall ecological conservation rate, especially at an ecological target of 70% and 80%, which can improve the adaptation of reservoir operation to climate change.

Short-term hydro-unit load dispatching (SHULD) refers to the determination of the power output of each unit within a hydropower station over a planning horizon to minimize the operational cost or maximize the power-generation profit while satisfying hydraulic and electrical constraints. In China, huge hydropower stations, such as the Three Gorges (TG) and Xiluodu (XLD) stations, are composed of a large number of hydro units, which feature a high installed capacity and a high water head. SHULD models of these stations are more complex and difficult to solve compared with those of traditional stations, especially when the stations serve multiple power grids. This study develops a practical method for optimizing SHULD models by considering the XLD hydropower station as a case study. First, a SHULD model for huge hydropower stations with multiple vibration zones and multiple receiving power grids is presented. Second, classical and sophisticated dynamic programming (DP) is applied to the SHULD model, and a practical strategy is proposed to balance the available water in a reservoir’s left and right banks to satisfy their load demands. Finally, the Fork/Join framework is used to parallelize DP to reduce the computation time and fully utilize the computer resources. The wet and dry season results demonstrate that the approach is efficient and suitable for huge hydropower stations with a high water head and multiple receiving power grids, thereby demonstrating its potential practicability and validity for solving the SHULD problem.

As an important input of hydropower stations operation, the forecasted inflow provided by forecaster directly determines the power generation efficiency of hydropower stations. In order to promote the self-improvement of forecasters’ forecasting level through effective reward and punishment, it is necessary to determine a fair and reasonable comprehensive forecasting level evaluation method. In view of this, this paper puts forward the concept of forecasting difficulty, and classifies the external forecasting situations according to different rainfall conditions, different forecasting foresight periods and different inflow levels. Based on the physical meaning of forecasting difficulty, a new calculation method of forecasting difficulty using the error distribution and area moment is proposed in this paper, which can realize the forecasting difficulty calculation under different situations and different forecasting level standards. Coupling the forecasting difficulty coefficient with the forecasting level evaluation, a comprehensive forecasting level evaluation method of forecasters is proposed. The case study results show that, because the forecasting difficulty of different forecasting situations are considered in the proposed method, the higher the forecast accuracy of a forecaster in difficult situations (such as rainy situation), the higher his/her comprehensive forecast level. The result obtained by this method is fairer and more reasonable compared with the traditional method, which is of great significance to promote the forecasting level of forecasters in difficult situations.

The need for achieving efficient, equitable and sustainable use of water resources to meet water demands of different sectors is necessary, particularly in areas where water resources are decreasing. In the basins where water is required for both energy production and irrigation, allocation of water resources must be planned in such a way that both objectives can be achieved. In this research, a simulation-optimization approach has been used to solve the problem of optimal planning at the watershed scale. The water evaluation and planning system (WEAP) simulation model link with the multi-objective particle swarm optimization (MOPSO) model for optimal long term planning at the basin scale. Therefore, the objective functions of the problem are 1) maximize the cultivation area of agricultural development sectors and 2) maximize the energy produced by the hydropower plant. The developed simulation-optimization model was employed in the problem of optimal water resources planning in the Kashkan river basin in the west of Iran. The Pareto front obtained represents the best trade-off between hydropower and agricultural development in the basin and can be used for water-energy-food nexus planning. For example one of the solutions of Pareto front, in addition to an increase of about 8% of the objective function 2 (generated energy), the value of the objective function 1 (cultivation area) is approximately 5 times higher than the results of previous studies. This demonstrates the proper performance of the simulation-optimization model in the optimal allocation and planning of water resources at the basin scale based on the water-energy-food nexus approach.

Reservoir inflow forecasts are important for guiding reservoir operation. This study proposes an integrated framework of incorporating different forms of seasonal inflow forecasts in identifying the optimal releases policy. Gridded precipitation forecasts from climate models have been widely used for forecasting inflow. Both precipitation forecasts and soil moisture estimates are used as predictors to provide one-season-ahead reservoir inflow forecasts by constructing a regression problem. Principal component analysis is used to reduce the dimension of the regression problem, and a Bayesian regression technique is employed to generate various forms of inflow forecasts such as deterministic, probabilistic and ensemble forecasts. Two optimization models are constructed to couple with different forms of inflow forecasts. The first model aims to maximize hydropower generation and the second one aims to minimize end-of-season reservoir storage deviation from the target storage. Both single-value inflow and ensemble forecasts are incorporated to find the optimal water releasing policy considering inflow uncertainty and end-of-season reservoir storage requirement. The proposed methodology is demonstrated for Huangcai Reservoir in southern China. Bayesian regression technique shows good performance of seasonal inflow forecasts with a Pearson correlation of 0.8 and rank probability score of 0.4, which outperforms climatology. The coupling of ensemble inflow forecasts and optimization models provides water managers a set of release policies considering inflow uncertainty.

Several researchers have extensively investigated the flow-induced vibrations caused by flood release because numerous structures have been destroyed by such release. Nevertheless, none of the previous research has considered vibration safety during flood control operation. In this study, a hedging scheduling model considering the near-field vibrations induced by flood release and hydrological uncertainty is proposed to optimize the discharge process. The dispatch model was applied to the Xiangjiaba reservoir due to the reservoir’s classic problem of near-field vibrations induced by flood release. The measured root-mean square (RMS) data for the acceleration in the vertical direction of near-field vibrations were used to summarize safety constraints. The vibration safety discharge ceiling was determined according to field test records and was regarded as an operational safety constraint for the dispatch model. Based on this innovative safety constraint, this paper demonstrates the development of an optimal dispatch model for reservoirs by considering forecast uncertainty. The proposed strategy utilizes storage capacity to optimally allocate the gap between the expected flood volume and vibration safety discharge capacity (GBEV) in the discharge process. Optimal flood control operation considering the near-field vibrations induced by flood release falls into four categories, with each category corresponding to one optimal operation strategy. The solution set to this model can provide decision support for reservoirs with similar flood-induced vibration problems and optimize the power output of hydropower stations.

Short-term hydropower generation scheduling (STHGS), a highly complicated nonlinear optimization problem with various equality and inequality constraints, plays an important role in the utilization of hydropower and water resources. To overcome the complexity and nonlinearity of STHGS problem effectively, an improved cloud adaptive quantum-inspired binary social spider optimization (ICAQBSSO) algorithm is proposed in this paper. Quantum bit (q-bit) and quantum rotation gate are used to improve its code mode and search mode and enable it to optimize discrete problems. The improved cooperative operators of ICAQBSSO overcome the problem of unreasonable parameters and elements in its original cooperative operators. With the heuristic strategies for repairing minimum uptime/downtime constraint and spinning reserve capacity constraint, the ICAQBSSO algorithm is coupled with an optimal stable load distribution table (OSLDT) to optimize the sub-problems of STHGS, unit commitment (UC) and economic load dispatch (ELD). In the case study of the STHGSs for Three Gorges hydropower station, corresponding to 75 m, 88 m and 107 m water heads, the results of the proposed algorithm and other intelligent algorithms show the feasibility and effectiveness of the proposed algorithm for obtaining near-optimal solutions in less time.

Conflict-resolution methods have been applied to water resources management to balance conflicting interests of stakeholders. Due to the climate change impacts on hydrologic processes, the strategy selections of conflict-resolution methods can be influenced, resulting in different selection rules for historical and future periods. This study aims to quantify the impacts of climate change on the strategy-selection rules of the conflict-resolution methods for better long-term strategic decision-making. The methodology of this study consists of climatic, hydrological, environmental and multi-objective optimization models, two fuzzy social choice methods (FSCMs) and four game-theoretical bargaining methods (GTBMs). The hydro-environmental conflict-resolution management in the Yangtze River of China is selected as the case study. The results show that the strategy selection of GTBMs is more stable and results in a better balance between hydropower and environmental objectives, compared to that of FSCMs. Moreover, considering climate change, under the appropriate environmental flow pattern, the stabilities of the strategy selections of FSCMs and GTBMs are slightly influenced, and the average satisfied degrees of both objectives obtained by FSCMs and GTBMs in the future period (2021-2080) are lower than those in the base period (1950-2012). The findings from this study provide guidance for hydro-environmental conflict-resolution management from a sustainable development perspective.