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The multi-reservoir operation rules require accuracy in developing its technical parameters. This is done to prevent operational failure in one of the reservoirs. The water transfer concept is to manage the water resources distribution between the receiving watershed and the donor watershed. The availability of transferable water must be prioritized, meeting the water demand of the donor reservoir. Storage capacity in both reservoirs aims to meet water demand, especially in the recipient. The elevation of the interconnecting tunnel is the minimum limit for water use in simulation. The interconnected tunnels' location and capacity will determine the multi-reservoir's operation rule. The interconnected tunnel in the Rukoh Tiro reservoir transfers water in the operation of the two reservoirs. The simulation is carried out in three seasons, considering the inflow of each watershed, the reservoir's downstream water demand, and the reservoir's technical conditions. The simulation results of the Rukoh Tiro reservoir operated simultaneously in all three seasons show that the fulfillment of irrigation water demand can reach 100% as needed. The water transfer process through interconnected tunnels occurs throughout the year. The reservoir operating rule is expected to be a reference in the multi-reservoir operation to obtain an optimal reservoir operating rule. [ABSTRACT FROM AUTHOR]

The subtropical monsoon climate zone features abundant water resources but with uneven temporal and spatial distribution, so seasonal water shortages are frequent. In order to reduce the water shortage and water spill in this region, a nonlinear optimization model for the joint operation of a system of a reservoir and two pumping stations is developed in this paper. In this model, the water supply of the reservoir and pumping volume of the pumping stations in each period are two types of decision variables, which are subjected to the annual available water in the reservoir, water rights of the two pumping stations and the operation rule of the reservoir. However, modern intelligent algorithms may fail in dealing with constraints of if-statements like the operation rule of the reservoir in this model. In light of the shortcoming of the classical genetic algorithm, a modified genetic algorithm is proposed by comparing the different methods for dealing with constraints. The modified algorithm shows a better adaptability to the operation rule. The modified genetic algorithm may provide a reference for similar modern intelligent algorithms to solve optimal water resources allocation for systems of multiple reservoirs and multiple pumping stations. HIGHLIGHTS A modified method for the genetic algorithm is proposed to solve the optimal water allocation model for a system of reservoir and pumping stations.; The method can deal with the constraints of if-statements.; The method shows a better adaptability to the operation rule of a reservoir, compared with other methods.;

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes. [ABSTRACT FROM AUTHOR]

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

The reservoir operation is a notable source of uncertainty in the natural streamflow and it should be represented in hydrological modelling to quantify the reservoir impact for more effective hydrological forecasting. While many researches focused on the effect of large reservoirs only, this study developed an online reservoir module where the small reservoirs were aggregated into one representative reservoir by employing a statistical approach. The module was then integrated into the coupled Noah Land Surface Model and Hydrologic Model System (Noah LSM-HMS) for a quantitative assessment of the impact of both large and small reservoirs on the streamflow in the upper Gan river basin, China. The Noah LSM-HMS was driven by the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) with a very good performance and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.89, which proved to be more effective than the reanalysis data from the National Centers for Environmental Prediction (NCEP) over China. The simulation results of the integrated model indicate that the proposed reservoir module can acceptably depict the temporal variation in the water storage of both large and small reservoirs. Simulation results indicate that streamflow is increased in dry seasons and decreased in wet seasons, and large and small reservoirs can have equally large effects on the streamflow. With the integration of the reservoir module, the performance of the original model is improved at a significant level of 5%.

An interval-valued intuitionistic linguistic set (IVILS) is introduced as an extension of both a linguistic term set and an interval-valued intuitionistic fuzzy set. However, the arithmetic operations and comparison methods of IVILS exist some limitations, which may result in information loss and distortion. To avoid this problem, the arithmetic operations are improved and a new comparison method based on a possibility degree is developed. Then, several arithmetic aggregation operators are proposed to aggregate interval-valued intuitionistic linguistic numbers. Moreover, a multi-criteria group decision-making (MCDM) approach based on the newaggregation operators is presented. Finally, the validity and effectiveness of the proposed method are confirmed by a comparable example. [ABSTRACT FROM AUTHOR]

In this paper, we promote an effective approach to develop short-term operation solutions for Three-gorge and Gezhouba cascade hydropower stations in flood season. After a rule discovery step, three operation rules are proposed and verified by a case study with a numerical model using genetic algorithm. Operation solutions with rules applied can be obtained with larger objective values and higher optimization efficiency. When average water discharge of Three-gorge reservoir exceeds 30,000 m 3 /s, water level of Gezhouba reservoir should rise or fall down to a key value firstly and stay at the key value in the rest time of an operation period. The corresponding water discharge processes can maximize total allowable output capacity and total amount of electricity. When average water discharge ranges from 20,000 m 3 /s to 30,000 m 3 /s, hydraulic head of Gezhouba hydropower station is the decision factor. Solutions are optimal in which average hydraulic head is more near 17.0 m. When average water discharge is less than 20,000 m 3 /s, several factors should be considered for operation decision, such as maximum water discharge capacity, initial water level of Gezhouba reservoir. Optimal operation solutions can be obtained in which water level of Gezhouba reservoir in an operation period follows as an expected process. [ABSTRACT FROM AUTHOR]

A water flows network modeling system was constructed to quantify water flows in the Balan Watershed, South Korea, which has two serial irrigation reservoirs. A modified version of the Synthetic Streamflow and Reservoir Regulation (SSARR) model was used to simulate the water flows, taking into account irrigation return flows and reservoir operations. The model parameters were calibrated using a heuristic search method, Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). The simulated streamflows and reservoir water levels were in good agreement with the observed. The irrigation return flows from paddy fields considerably affected the flow regimes of the streams. Specifically, the return flow rates of the irrigation water ranged from 28.0% to 35.0%. In addition, the water supply capacities of the irrigation reservoirs were evaluated using the modeling system, and joint operation rules were assessed. The results showed that these could have negative influences on the water supply of existing irrigation districts, and the potential effects of these rules on water management should be investigated. The modeling system is useful for identifying the states of water flows in multiple irrigation reservoir systems and for providing information about reservoir operations to water managers and stakeholders. [ABSTRACT FROM AUTHOR]

Abstract: Information hidden in the characteristics and relationship data of a cascade hydropower stations can be extracted by data-mining approaches to be operation rules and optimization support information. In this paper, with Three-gorge and Gezhouba cascade hydropower stations as an example, two operation rules are proposed due to different operation efficiency of water turbines and tight water volume and hydraulic relationship between two hydropower stations. The rules are applied to improve optimization model with more exact decision and state variables and constraints. They are also used in the population initiation step to develop better individuals with culture algorithm with differential evolution as an optimization method. In the case study, total feasible population and the best solution based on an initial population with an operation rule can be obtained with a shorter computation time than that of a pure random initiated population. Amount of electricity generation in a dispatch period with an operation rule also increases with an average increase rate of 0.025%. For a fixed water discharge process of Three-gorge hydropower station, there is a better rule to decide an operation plan of Gezhouba hydropower station in which total hydraulic head for electricity generation is optimized and distributed with inner-plant economic operation considered. [Copyright &y& Elsevier]

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time. [ABSTRACT FROM AUTHOR]

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

Operation rules are commonly used to make decisions for cascaded hydropower reservoirs for profit maximization, given the energy price of current and future periods. For large-scale cascaded hydropower reservoirs whose decisions can affect market price, the operation optimization models are transformed into game models to get Nash equilibriums. For market competition of multiple cascaded hydropower reservoirs, a multiplayer game model is established in which the actions are storage energy based operation rules and the payoff function values are simulated profits of cascades using the rules. The game model is solved using a successive low dimensional search method in which only one parameter of the rules is optimized at each step. The proposed method is tested using data of three cascaded reservoirs in Southwestern China in a hypothetical pure hydropower market. Results show the effect of different kind of models on rule curves and the potential impact of market reformation to the operation of the cascaded hydropower reservoirs. For the studied cascaded reservoirs, the profit increasing percentages can be 2.6%–3.9% with 0.5%–2.0% energy losing, comparing the game model to the energy maximization model. [ABSTRACT FROM AUTHOR]

The reservoir operation is a notable source of uncertainty in the natural streamflow and it should be represented in hydrological modelling to quantify the reservoir impact for more effective hydrological forecasting. While many researches focused on the effect of large reservoirs only, this study developed an online reservoir module where the small reservoirs were aggregated into one representative reservoir by employing a statistical approach. The module was then integrated into the coupled Noah Land Surface Model and Hydrologic Model System (Noah LSM-HMS) for a quantitative assessment of the impact of both large and small reservoirs on the streamflow in the upper Gan river basin, China. The Noah LSM-HMS was driven by the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) with a very good performance and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.89, which proved to be more effective than the reanalysis data from the National Centers for Environmental Prediction (NCEP) over China. The simulation results of the integrated model indicate that the proposed reservoir module can acceptably depict the temporal variation in the water storage of both large and small reservoirs. Simulation results indicate that streamflow is increased in dry seasons and decreased in wet seasons, and large and small reservoirs can have equally large effects on the streamflow. With the integration of the reservoir module, the performance of the original model is improved at a significant level of 5%. [ABSTRACT FROM AUTHOR]