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Flood wave superposition (FWS) of upstream and tributary rivers, as a typical compound hydrological event, may lead to considerable downstream flood hazards. In spite of this, the quantitative identification of FWS classification so far remains elusive. In this study, we quantitatively examined the typical matching patterns of FWS based on flood peak magnitude and timing between the upstream and tributary discharge, to evaluate the flood severity for both present-day conditions and future climate projections. The future FWS projection was realized using hydrological modeling coupled with multiple outputs of global climate models (GCMs) under the Representative Concentration Pathway (RCP) 2.6 and 8.5 emission scenarios. A triple point of discharge stations, including upstream, downstream and tributary stations, located at a river confluence of the Poyang Lake Basin, China, was selected as the study area. The results revealed that the frequency of perfect temporal matching (0-day time lag) of projected peaks between upstream and tributary floods increased from 61% in the reference period to >68% and > 66% in the future under RCPs 2.6 and 8.5, respectively. Furthermore, both the future numbers and frequencies of the projected peaks between upstream and tributary floods with both perfect temporal and magnitude matching (the classification associated with the most damage in FWS) would substantially increase across all GCMs under RCPs 2.6 and 8.5. These findings indicate that future FWS is expected to experience increasing severity due to the changing climate under no matter RCP 2.6 or 8.5 emission scenarios. Overall, it is suggested that effective adaptation strategies be developed in order to stagger the timing of upstream and tributary floods in the future.

Electricity transmission in a grid-connected system provides an effective solution to promoting clean energy accommodation. However, with arbitrary determination in current operation models, the clean energy utilization ratio (CEUR) is not satisfactory largely due to the lack of electricity curtailment (the electricity equivalent of clean energy curtailment) cost-dependent optimization. In this study, a curtailment cost-dependent multi-objective operation (CCMO) model was proposed to complementarily operate a grid-connected hybrid energy system, identify optimal CEUR, and thus maximally reduce electricity curtailment. The CCMO model centers on coupling the punishment cost of electricity curtailment with the multi-objective function defined as the total cost of each grid component. The CCMO model was solved to derive the optimal equilibrium solution determined based on multiple non-dominated solutions. A grid-connected hybrid energy system including the Yunnan, Guangdong, and Guangxi Power Grids was used to test the model performance. The results showed that the CCMO model’s CEUR was up to 100% at hourly scale and 96.9% on daily average, which were both significantly higher than those in the current operation models. Furthermore, the CCMO’s optimal equilibrium solution, i.e., respective minimum total cost of each grid component, can also identify optimal transmission schemes of the daily channel utilization to make the peak utilization hours largest.

In hydrological research, flood events can be analyzed by flood hydrograph coincidence. The duration of the flood hydrograph is a key variable to calculate the flood hydrograph coincidence risk probability and determining whether flood hydrograph coincidence occurs, while the actual duration of the flood hydrograph is neglected in most of existing related research. This paper creatively proposes a novel method to analyze the flood hydrograph coincidence risk probability by establishing a five-dimensional joint distribution of flood volumes, durations and interval time for two hydrologic stations. More specifically, taking the annual maximum flood of the upper Yangtze River and input from Dongting Lake as an example, the Pearson Type III and the mixed von Mises distributions were used to establish the marginal distribution of flood volumes, flood duration and interval time. Subsequently, the five-dimensional joint distribution based on vine copula was established to analyze the flood hydrograph coincidence risk probability. The results were verified by comparison with a historical flood sequence, which show that during 1951–2002, the hydrograph coincidence probabilities corresponding to its flood event coincidence volumes of 2.00 × 1011 m3, 4.00 × 1011 m3, and 6.00 × 1011 m3 are 0.213, 0.123, and 0.049, respectively. It has provided theoretical support for flood control safety and risk management in the middle and lower Yangtze River. This study also demonstrates the significant beneficial role of regulation by the Three Gorges Water Conservancy Project in mitigating flood risk of the Yangtze River. The hydrograph coincidence probability corresponding to its flood event coincidence volume of 2.00 × 1011 m3 has decreased by 0.141.

Substantial uncertainty is inherent in reservoir inflow forecasting, which exerts a potential negative impact on reservoir risk. However, the risk propagation from the inflow forecast uncertainty (IFU) to reservoir operations remains elusive. Thus, a new integrated assessment framework was developed in this study to characterize the risk coupling with flood and electricity curtailment risks that propagate from the IFU to the reservoir operations. First, to incorporate the IFU, an improved Gaussian mixture distribution (IGMD) and Markov chain Monte Carlo (MCMC) algorithm were constructed to model the measured forecast errors and generate ensemble inflow forecasts, respectively. Next, to assess the reservoir risk, the flood risk induced by the IFU overestimation and the electricity curtailment risk related to the IFU underestimation were identified according to the reservoir operation rules. The sub-daily inflow forecast at the Jinping First Stage Hydropower Plant Reservoir of Yalong River, China (Jinping I Reservoir) was selected. The results indicated that the IGMD-based MCMC was capable of deriving robust ensemble forecasts. Furthermore, there was no flood risk (risk rate was zero) induced by the IFU when taking designed reservoir floods with a &ge, 10-year return period as the benchmark. In contrast, the electricity curtailment risk rate significantly increased up to 41% when considering the IFU. These findings suggested that compared with the flood prevention pressure, the IFU would more likely result in severe electricity curtailment risk at the Jinping I Reservoir.

Given the threats from rainstorm events that have been intensified due to climate change, the investigation of heavy/extreme precipitation is attracting increasing attention. The investigation of extreme precipitation, with its restricted tools and data, is quite different from that of nonextreme precipitation. Satellite precipitation estimates (SPEs) are promising precipitation measurements due to fine spatial resolution. However, the spatiotemporal patterns of SPE extremes remain unclear. Therefore, this study attempted to systematically analyse the spatiotemporal patterns of extreme SPEs provided by the Tropical Rainfall Measurement Mission (TRMM) research product from statistical characterization to stochastic behaviour modelling. The statistical characterization was depicted using comprehensive extreme precipitation indices (EPIs), while the stochastic behaviour was modelled using the generalized Pareto distribution (GPD) to fit the ‘peak over threshold’ samples and employing the max‐stable process to fit the ‘block maximum’ samples (Max: annual 1‐day maximum, RX1day: monthly 1‐day maximum, and RX5day: monthly 5‐day maximum) of the TRMM data. The TRMM extremes were analysed across the Xijiang River Basin, China. The results of the statistical characterization revealed that compared with gauge precipitation, most TRMM EPIs were discovered to have higher temporal dynamic levels. Moreover, the spatial median of the return level for each return period derived from temporal TRMM extreme modelling with the GPD was lower than that from gauge extremes. The spatial dependence of Max in TRMM was weaker than that of either RX1day or RX5day, primarily due to different precipitation‐generating processes existing between two extreme events at annual scale compared with monthly scale. Each of the Max, RX1day, and RX5day in TRMM exhibited stronger spatial dependence than that in gauge extreme precipitation, maybe related to the area‐average‐based representation of the SPEs. These findings can enhance our insight into the spatiotemporal patterns of extreme satellite precipitation, which is beneficial for operational flood risk management.

A new improved shuffled frog leaping algorithm (SFLA), the chaos catfish effect SFLA (CCESFLA), is proposed by coupling a local “refine search” mechanism and a “global incentive adjustment” mechani...

The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based ...

The power generation plan formulated based on the forecasted runoff is an important basis for the operation of hydropower station. However, due to the unavoidable forecast error, the ideal practical operation process usually does not match with the plan. In view of this, after a detailed analysis on the current processes of making and implementing power generation plan, this paper constructed an early warning mechanism according to different operation states of cascade hydropower stations, and considering the uncertainty of runoff forecast and the security constraints of power grid, a new load adjustment model with characteristics of real-time tracking, early warning and timely adjusting was proposed, and three basic principles of load adjustment were put forward in this model. Taking the cascade hydropower stations in Yalong River of China as an instance, two typical scenarios that threaten the operation safety of hydropower stations are simulated and analyzed. Results show that the proposed model can be used to scientifically adjust the output of hydropower stations through the proposed adjustment principles and strategies, and ensure the safe and stable operation of power grid, and avoid the output shortage and water abandonment of hydropower stations caused by the uncertainty of runoff forecast.

Power generation plan formulated based on the forecasted runoff is an important basis for the actual operation of hydropower station. However, due to the forecast error, the load adjustment is necessary in the actual operation, and how to find out the most satisfactory load adjustment scheme from the scheme set is an important topic. So, in order to comprehensively reflect the risk and benefit state of hydropower station operation, a risk-benefit collaborative evaluating indicator system of load adjustment scheme is established firstly in this paper, and aim at the strong subjectivity of present evaluation methods, an improved grey target decision model based on moment estimation method is proposed, and the combinatorial weight integration technology and the Mahalanobis distance are coupled in this model. Taking the cascade hydropower stations of Yalong River in China as an instance, six schemes are evaluated by the proposed model, and the results are compared and analyzed with another six evaluation models. Results show that the proposed model can effectively coordinate different weighting methods and overcome the shortcomings of traditional grey target decision model about the insufficient consideration on the importance and correlation of evaluating indicators, and it has a good value of popularization and application.

With the rapid development of cascade reservoirs, the joint operation chart of cascade reservoirs and its optimization methods have been widely researched. Aimed at the defects of the conventional two-stage Progressive Optimality Algorithm (POA) in the optimization of energy storage operation chart, this paper proposed a new multi-stage POA optimization model. It took the traditional reverse calculation result as the initial solution, and expanded the two-stage optimization mode of conventional POA to three-stage mode, four-stage mode, or higher stages mode, and implemented the iterative calculation by taking the result of lower stages POA as the input of higher stages POA, until the result converged. In addition, enumeration method was used to iterate over all the possible combinations in local optimization to improve the efficiency of local search. In order to test and verify the rationality and validity of the proposed model, two cascade reservoirs had been taken as the instances of case study, compared with conventional two-stage POA, results showed that the power generation of Li Xianjiang and Three Gorges cascade reservoirs by the proposed multi-stage POA can respectively increase by 0.055% ($32000) and 0.077% ($3900000). So the economic benefits are remarkable, and the proposed model is reasonable and effective.

Reservoir operation charts have been widely researched and applied to reservoir operation. However, these achievements are generally used for a single reservoir and have rarely been applied to cascade reservoirs. Considering the requirements of flood control and water supply, this paper studied the extraction and application of energy storage operation chart (ESOC) for cascade reservoirs. Steps in the methodology mainly include: (1) model building of cascade reservoirs operation optimization (CROO), (2) extracting ESOC based on discriminant coefficient method (DCM) and CROO model, (3) simulation operation of ESOC based on DCM, (4) choosing the optimal ESOC and verifying its efficiency through the results. Cascade reservoirs in the Yangtze River of China were selected for a case study. Compared with the conventional operation method, the simulation results show that the ESOC presents better performance in terms of power generation, guaranteed output and assurance rate. In detail, the annual power generation of ESOC can be increased by 0.9%, the total guaranteed output can be increased by 3.4% and the assurance rate can be increased by 9.6%, which indicates that the proposed ESOC method can greatly improve the hydropower energy efficiency and reliability of cascade reservoirs’ power supply.

Difficulty remains to represent water propagation impact to construct water balance constraints for the Short-Term Cascade Reservoirs Optimal Operation (SCROO), due to the complex water propagation mechanism existing between cascade reservoirs. The imprecise representation of water propagation impact hinders the implementation of hydropower generation schemes and leads to operational risks. Thus, a modified SCROO model considering water propagation impact was proposed in this study to improve the accuracy of cascade operation. Specifically, water propagation is expressed using the Muskingum method in the modified SCROO model, and characterized by two variables, including propagation time related to the magnitude of the upstream reservoir’s outflow and flow attenuation related to the propagation time, which were identified using the successive approximation method under water balance constraints within the modified SCROO model. Subsequently, this research applied the Nested Progressive Optimality Algorithm (NPOA) in the model calculation innovatively, in which the stage benefit function and nest computation could be improved. The modified model and algorithm developed herein were applied and validated on cascade reservoirs along the Yalung River in China by comparing it with traditional methods and algorithms. The results show that: 1) Compared to traditional methods, considering water propagation within water balance constraints as a dynamic parameter can calculate the inflow of downstream reservoir more accurately and mitigate risks in the actual operation of cascade reservoirs. Besides, the reservoir inflow calculated by traditional methods will lead to different magnitudes of benefit loss; and 2) the NPOA can significantly reduce dimensionality problems caused by the water propagation impact and greatly improve the actual hydropower generation than that of a traditional optimal algorithm, Multi-Stage Dynamic Programming (MSDP). Compared to the MSDP, the NOPA is more applicable in a cascade system with a larger number of reservoirs. The modified model and algorithm can significantly improve the effectiveness of cascade reservoir optimal operation schemes and provide more information for decision-makers.

The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal theory represented by the stochastic dynamic programming model (SDPM) is commonly used for SHSOO, which is focus on the uncertainty during a single lead time and lack of an overall consideration of multiple independent lead times of differing length. Thus, this paper proposes a short-term hydropower station optimal operation model based on mean-variance theory (MV-SHSOOM) for reducing operational risk. The proposed multi-step model considers the forecasting uncertainty of different lead times as an integral component to guide a station’s optimal operation. Specifically, the forecast uncertainty was integrated by the joint probability distribution of forecast error using a multivariate hydrologic uncertainty processor (HUP) at first. Then, possible observed streamflow sequences were obtained using the joint probability distribution of forecast error and taken as input to the short-term optimal operation model of the hydropower station. Finally, the mean-variance theory was applied to construct the optimal operation model for balancing the power generation and operational risk. The proposed model was applied to the Jinxi hydropower station, China, to determine its performance. The results show that: 1) the dependent structure among the forecast errors at different lead times is significant in terms of Spearman correlation, and the joint probability distribution of forecast error can be effectively described by a multivariate HUP; 2) the proposed MV-SHSOOM is superior to the traditional deterministic dynamic programming model (DDPM) and SDPM, and can more effectively reduce operational risk while increasing power generation.

In Short-term Cascade Reservoirs Optimal Operation (SCROO), the flow from upstream reservoir to downstream reservoir which propagates in the natural channel can be generalized as not only transposition but also attenuation. In order to get the relative accurate inflow series of downstream reservoir, the paper tries to adopt the Muskingum model to simulate the inflow of the downstream reservoir instead of the easier processing in most papers of SCROO: ignoring the flow attenuation in natural channel. Considering the flow attenuation between the cascade reservoirs, the SCROO problem do not satisfy the “Principe of Optimality” anymore, so the Dynamic Programming(DP) is no longer applicable. The paper proposes a new improved DP named Multi-Stage Dynamic Programming (MSDP) based on DP. In MSDP, multi-stage’s outflow of upstream reservoir is taken into account at the same time and then inflow of downstream reservoir can be calculated by the Muskingum model, which can include much more flow information than the easier processing with DP, and the inflow of downstream reservoir will be closer to the actual one. The paper takes the cascade hydro-power stations consisting of Jindong and Guandi in Yalong river basin as an example to solve the SCROO problem with MSDP. By comparing the operation result of MSDP and DP with the easier processing, the operation strategy of MSDP can gain further benefits than DP’s in actual operation.

Objective: To innovate the teaching contents and teaching methods for the course of Water Resources Planning and Utilization which is one of the Hydrology and Water Resources Engineering professional 433 core courses of NCEPU. Methods: Literature review, modification of the course syllabus, text-books, curriculum design task and construction of the professional teachers’ team. Results: According to the basic requirements of the course construction of Water Resources Planning and Utilization, we have made some enrichment, expansion and necessary complements to the teaching contents. We have also tried to impart professional knowledge to students and to develop their practical abilities in the professional field of water resources. Gradually our under-graduate students are able to independently complete the summary of an academic paper, which includes their research methods, result analysis and conclusion drawing by writing their course papers. Conclusion: The innovation of the course of Water Resources Planning and Utilization is very successful by updating the content and changing the teaching method.

In order to allocate the raw water of the complex water supply system in Shenzhen reasonably, this paper studied the complex network relationship of this large-scale urban water supply system, which consists of 46 reservoirs, 67 waterworks, 2 external diversion water sources, 14 pumping stations and 9 gates, and described each component of the system with the concepts of point, line and plane. Using the topological analysis technology and graph theory, a generalized model of the network topological structure of the urban water allocation system was established. On this basis, combined with the water demand prediction and allocation model of waterworks, a water resources allocation model was established, aiming at satisfying the guaranteed rate of the water supply. The decomposition and coordination principle of the large-scale system and the dynamic simulation technology of the supply-demand balance were adopted to solve the model. The forward calculation mode of controlling waterworks and pumps, and the reverse calculation mode of controlling reservoirs and waterworks were designed in solving the model, and a double-layer feedback mechanism was formed, which took the reverse calculation mode as outer feedback and the reservoir water level constraint or pipeline capacity constraint as inner feedback. Through the verification calculation of the case study, it was found that the proposed model can deal well with the raw water allocation of a large-scale complex water supply system, which had an important application value and a practical significance.

Discriminant coefficient method (DCM) is a traditional method in guiding the cascade reservoirs joint operation, but it cannot be well used directly in the practical application. This paper combined DCM with the cascade reservoirs energy storage operation chart (CRESOC) and proposed a new DCM based drawing model. In addition, three key technologies in the drawing process and one key technology in the practical application were studied emphatically, which can realize the combined utilization of DCM and CRESOC perfectly. In the case study, we took the cascade reservoirs of Li Xianjiang River in southwest China as an instance to study the CRESOC by the proposed drawing model. Compared with the existing single reservoir operation charts, the simulation results show that the CRESOC presents better performance in terms of power generation and assurance rate, especially on the latter, which has a growth of 6.8%. Moreover, in order to eliminate the insufficient of DCM in practical application, the boundary control curves of each reservoir for water storage and water supply were optimized by the proposed optimization technique, and the simulation results show that there is no water abandonment in all the 43 dry seasons after the optimization.

Summary The dynamic control operation of reservoir flood limited water level (FLWL) can solve the contradictions between reservoir flood control and beneficial operation well, and it is an important measure to make sure the security of flood control and realize the flood utilization. The dynamic control bound of FLWL is a fundamental key element for implementing reservoir dynamic control operation. In order to optimize the dynamic control bound of FLWL by considering flood forecasting error, this paper took the forecasting error as a fuzzy variable, and described it with the emerging credibility theory in recent years. By combining the flood forecasting error quantitative model, a credibility-based fuzzy chance constrained model used to optimize the dynamic control bound was proposed in this paper, and fuzzy simulation technology was used to solve the model. The FENGTAN reservoir in China was selected as a case study, and the results show that, compared with the original operation water level, the initial operation water level (IOWL) of FENGTAN reservoir can be raised 4 m, 2 m and 5.5 m respectively in the three division stages of flood season, and without increasing flood control risk. In addition, the rationality and feasibility of the proposed forecasting error quantitative model and credibility-based dynamic control bound optimization model are verified by the calculation results of extreme risk theory.

An efficient reservoir operation technique plays a very important role in improving the water resources and energy efficiency of reservoirs. In order to effectively avoid or alleviate the “curse of dimensionality” of Multi-dimensional Dynamic Programming (MDP) in the application of cascade reservoirs operation optimization (CROO) and keep a global convergence at the same time, two dimension reduction methods are proposed in this paper. One is a hybrid algorithm of MDP and a Progressive Optimality Algorithm (POA), named MDP-POA, which combines the global convergence of MDP and the strong local search ability of POA. MDP-POA first takes the global optimal trajectory of MDP in a low discrete degree as the initial trajectory of the POA, and then implements further optimization to the obtained initial trajectory by the POA with a high discrete degree, so as to avoid the “curse of dimensionality” of MDP in high discrete degree and the dependency of the POA for the initial trajectory. The other is an improved MDP (IMDP), which first constructs a corridor by the optimal trajectory of MDP in a lower discrete degree, and then implements further optimization in the corridor by MDP with a relatively high discrete degree, so as to avoid a large number of unnecessary calculations, and shorten the run-time effectively. In a case study, the results of MDP-POA, IMDP, and MDP are compared and analyzed from the aspects of power generation and run-time. The analysis indicates that the proposed MDP-POA and IMDP both have a good application effect and are worthy of further promotion.

With the rapid development of cascade hydropower stations in recent decades, the cascade system composed of multiple reservoirs needs unified operation and management. However, the output distribution problem has not yet been solved reasonably when the total output of cascade system obtained, which makes the full utilization of hydropower resources in cascade reservoirs very difficult. Discriminant criterion method is a traditional and common method to solve the output distribution problem at present, but some shortcomings cannot be ignored in the practical application. In response to the above concern, this paper proposes a new total output operation chart optimization model and a new optimal output distribution model, the two models constitute to a double nested model with the goal of maximizing power generation. This paper takes the cascade reservoirs of Li Xianjiang River in China as an instance to obtain the optimal total output operation chart by the proposed double nested model and the 43 years historical runoff data, progressive searching method and progressive optimality algorithm are used in solving the model. In order to take the obtained total output operation chart into practical operation, mean value method and stepwise regression method are adopted to extract the output distribution ratios on the basis of the optimal simulation intermediate data. By comparing with discriminant criterion method and conventional method, the combined utilization of total output operation chart and output distribution ratios presents better performance in terms of power generation and assurance rate, which proves it is an effective alternative method to deal with the cascade reservoirs joint operation problem.

This paper presents a multi-objective optimization model for the coordinated regulation of flow and sediment in cascade reservoirs. The model was developed to address two contradicting issues: sediment trapping and flow regulation. The benefits of flood control, hydropower generation and navigation, and sedimentation in cascade reservoirs were considered as the target functions; then the corresponding submodels for reservoir operation and sediment computation were established. The model was implemented by reducing it to a single objective nonlinear model using the constraint method. Non-inferior solutions were obtained by solving the model with catfish effect particle swarm optimization algorithm. The model was applied to the cascade system of Xiluodu and Xiangjiaba reservoirs in the lower Jinsha River in the flood recession period. Under the safety of flood control and navigation, a non-inferior set for impounding time, power generation, and siltation-loss rate of capacity was obtained and optimal solutions with different weights were derived. The results demonstrate that the model is a useful tool in coordinated operations of cascade reservoirs.

The reservoir optimal operation depends on not only specific characteristics of reservoirs and hydropower stations but also stochastic inflows. The key issue of actual hydropower operation is to make an approximate optimal decision triggered by limited inflow forecasts. To implement actual optimal operation of hydropower system with limited inflows forecast, this paper makes use of Support Vector Regression (SVR) to derive optimal operating rules. To improve the performance of SVR, parameters in SVR model are calibrated with grid search and cross validation techniques. The trained SVR model describes the complex nonlinear relationships between reservoir operation decisions and factors by considering both generalization and regression performance, which overcomes local optimization and over fitting deficits. Hybrid programming platform is further developed to implement system simulation. This SVR model along with simulation platform is applied to the largest hydropower base in China – Jinsha system. Three scenarios are developed for comparison: deterministic optimal operation, SVR based simulation with calibrated parameters, SVR based simulation with default parameters. Comprehensive evaluation indicates that, operating rules derived from SVR presents a reliable performance in system power generation and output processes with respect to ideal deterministic results, especially when the parameters are calibrated. Hybrid programming technique provides a feasible and compatible platform for future research.

In making short-term optimal operation schemes of cascade reservoirs, water flow hysteresis between the upper reservoir and the lower reservoir is often considered as constant, which cannot really reflect the hysteresis variation between different water flows and will lead to losses of the optimal operation scheme&rsquo, s benefit. To depict the water flow hysteresis, a Dynamic Water Flow Hysteresis Method (DWFHM) is proposed, based on the space mapping principle. With the mapping operator in the DWFHM, the lower reservoir inflow can be directly obtained. Besides, the DWFHM is used to deal with the hydraulic relation constraint in the short-term optimal operation model of cascade reservoirs. Then, the improved model is applied to the Jinguan cascade reservoirs in the Yalong River basin and solved by an Improved Progressive Optimal Algorithm (IPOA). The results are as follows. (1) Compared with the traditional Fixed Water Flow Hysteresis Method (FWFHM), the inflow processes of the lower reservoir obtained by the DWFHM are more in line with the actual values, due to full consideration of the attenuation effect. (2) The optimal operation with the DWFHM can effectively increase the generated energy (2827 MW&middot, h and 504 MW&middot, h in the non-flood season and the flood season, respectively). Through the analysis of this case, the DWFHM developed in this study can effectively improve the practicability of the optimal operation scheme and reduce the risk in the operation of cascade reservoirs.

A reservoir operation chart is effective in guiding the actual operation of reservoirs. With the development and construction of large-scale mixed reservoirs, operating reservoirs using multiple objective operations has become a hot issue. This study, therefore, improved and expanded the traditional energy storage operation chart (ESOC) model for pure cascade reservoirs based to the special relationships between hydraulics and water volume of the upstream and downstream reservoirs in a mixed reservoir system. Considering the objectives of flood control, energy generation (including power generation, guaranteed output, and assurance rate), and ecological flow, a multi-objective ESOC optimization model for large-scale mixed reservoirs is established, and the corresponding solving method for this model is proposed. Taking the Pearl river basin of China as an example, the multi-objective Pareto fronts of different minimum discharges of upstream reservoirs are obtained. According to the actual operation demands and constraints, the optimal multi-objective schemes under different assurance rates and different ecological flow satisfaction rates are extracted based on the obtained Pareto fronts. For example, if the assurance rate of power generation is required to be equal to or greater than 90% and the ecological flow, the satisfaction rate of downstream control sections is required to be equal to or greater than 0.97, the scheme is the best when the minimum discharge of upstream reservoirs is 300 m3/s, and the corresponding power generation and guaranteed output are 58.989 × 108 kWh and 439 GW, respectively. On the whole, the proposed models and related results have significance for the actual operation of large-scale mixed reservoirs.

Ji, C. and Wu, H., 2019. Medium and long term optimal scheduling of cascade hydropower stations based on topological parallel computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 572–577. Coconut Creek (Florida), ISSN 0749-0208.In order to shorten the calculating time effectively for solving the medium-and-long term optimal scheduling model of cascade hydropower stations, based on the decomposition and coordination method of large-scale system, topological parallel computing is proposed in this paper. Then, taking the cascade hydropower station system located in one watershed of Southwest China as an example, the decomposition and coordination topology parallel computing method is validated in its medium-and-long term optimal scheduling of power generation. It provides a new research direction for improving the calculating efficiency of the optimal scheduling model of giant hydropower stations, the purpose of which is to make full use of water resources.

To narrow the gap between theoretical research and practical application of short-term optimal reservoir power generation operation under uncertain conditions, a comprehensive study is conducted on the formulation, evaluation, and implementation of operation schemes throughout the entire procedure of optimal operation. Firstly, the three implementation modes of the optimal operation scheme are assessed with the post-evaluation method. After the optimal implementation mode is determined, the formulation and implementation of optimal operation schemes are improved by combining the advantages of conventional and optimal operation and using the concept of warning water level in operation rules. Finally, the Xiaoxuan Reservoir is taken as an example for simulation calculation under different operating conditions. The results show that the operation model integrated with operation rules has both the security of conventional operation and the profitability of optimal operation. The accordingly-formulated optimal operation schemes, when implemented with the output control mode, can provide valuable guidance for the actual operation of hydropower stations.

Currently research on joint operation of a large reservoir and its re-regulating reservoir focuses on either water quantity regulation or water head regulation. The accuracy of relevant models is in need of improvement if the influence of factors such as water flow hysteresis and the aftereffect of tail water level variation are taken into consideration. In this paper, given the actual production of Pankou-Xiaoxuan cascade hydropower stations that combines two operation modes (&lsquo, electricity to water&rsquo, and &lsquo, water to electricity&rsquo, ), a coupling model of their short-term optimal operation is developed, which considers Xiaoxuan reservoir&rsquo, s regulating effect on Pankou reservoir&rsquo, s outflow volume and water head. Factors such as water flow hysteresis and the aftereffect of tail water level variation are also considered to enhance the model&rsquo, s accuracy. The Backward Propagation (BP) neural network is employed for precise calculation of the downstream reservoir&rsquo, s inflow and the upstream reservoir&rsquo, s tail water level. Besides, we put forth Accompanying Progressive Optimality Algorithm (APOA) to solve the coupling model with aftereffect. An example is given to verify the scientificity of the proposed model and the advantages of APOA. Through analysis of the model calculation results, the optimal operation rules of the cascade reservoirs are obtained in terms of water quantity regulation and water head regulation, which can provide scientific reference for cascade reservoirs&rsquo, optimal operation.

Regional operation of the electricity market has begun in China. With the development of China’s market economy, hydropower will enter the arena of bidding for connection to the grid in light of “fair, impartial and open” principles. For the electricity market, the solution of an inter-plant economical operation of a cascade hydropower system is extremely important. If a hydropower company is incapable of predicting the generation capability and the amount of generation of hydropower stations as well as the risk-rate corresponding to various generation amounts, then there is no information available to the hydropower company to refer to when it makes future agreements in a certain period. This paper develops mathematical models of (1) total minimum stored energy consumption and (2) total minimum water consumption for a cascade hydropower system. The models provide a solution to the problem of rational distribution of next-day’s generating schedule in a cascade hydropower system awarded a contract in the bidding for connection to the grid. A case study is conducted to verify the effectiveness and applicability of the methodology.

The multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignored. In order to alleviate this problem, this paper proposes a new MDP algorithm named multilayer nested multidimensional dynamic programming (MNDP), which is based on a multilayered, nested structure. MNDP is mainly used to deal with computer memory space and computation complexity problems of MDP in CROO, and its recursive equation of reverse recursion calculation and specific calculation steps are presented in detail. This paper takes the cascade reservoirs of the Li Xianjiang River in China as an example to solve the CROO problem with the proposed MNDP. By comparing with the dynamic programming with successive approximations (DPSA) method, MNDP presents better performance in terms of power generation and the assurance rate in wet, normal, dry, and average years. The gl...

The paper presents an incompressible Smoothed Particle Hydrodynamics (SPH) model to investigate the wave overtopping of coastal structures. The SPH method is a grid-less Lagrangian approach which is capable of tracking the large deformations of the free surface with good accuracy. The incompressible algorithm of the model is implemented by enforcing the constant particle density in the pressure projection. The SPH model is employed to reproduce a transient wave overtopping over a fixed horizontal deck and the regular/irregular waves overtopping of a sloping seawall. The computations are validated against the experimental and numerical data and a good agreement is observed. The SPH modelling is shown to provide a promising tool to predict the overtopping characteristics of different waves. The present model is expected to be of practical purpose if further improvement in the spatial resolution and CPU time can be adequately made.

The paper presents a 2-D large eddy simulation (LES) modelling approach to investigate the properties of the plunging waves. The numerical model is based on the smoothed particle hydrodynamics (SPH) method. SPH is a mesh-free Lagrangian particle approach which is capable of tracking the free surfaces of large deformation in an easy and accurate way. The Smagorinsky model is used as the turbulence model due to its simplicity and effectiveness. The proposed 2-D SPH–LES model is applied to a cnoidal wave breaking and plunging over a mild slope. The computations are in good agreement with the documented data. Especially the computed turbulence quantities under the breaking waves agree better with the experiments as compared with the numerical results obtained by using the k–e model. The sensitivity analyses of the SPH–LES computations indicate that both the turbulence model and the spatial resolution play an important role in the model predictions and the contributions from the sub-particle scale (SPS) turbulence decrease with the particle size refinement. Copyright © 2006 John Wiley & Sons, Ltd.

Risk analysis of reservoir generation dispatching plays an important role in seeking the best scheduling scheme that gives consideration to both risk and benefit. Traditional risk calculating method can't meet the needs of medium and longterm electricity generation dispatching very well, so this paper proposed probability optimization method of risk analysis, and constructed a risk evaluation index system of long-term generation scheduling to quantify the risk. Based on the probability optimization method of risk analysis, a random expectation model of generation dispatching risk analysis is established. The case study shows that this model can get the relative maximize benefit of power generation under the condition of taking certain risks and have some theoretical significance and reference value for scheduling program development and decision-making.

Traditional leveling optimization algorithms are confined to such simple situations as single-mode activities and start-to-finish precedence relations with zero time-lag between a pair of activities. However, they are not correspondent with the practical working mode. In this paper, according to the analysis on practical working mode, types of generalized precedence relations between a pair of activities and characteristics of multi-mode activities are studied, and constraints between a pair of activities with generalized precedence relations are established, and an leveling optimization model for multi-mode activities with generalized precedence relations is also constructed. It is proved by examples and comparative analysis that this optimization model is good in effectiveness and practicality.

The relative turbulence intensity within the natural reed population is studied in this paper. We analyze the changes of turbulence intensity under three conditions corresponding to the different densities of 54, 108, 202 stems per square meter. Results reveal that the flow state in sparse reed population approximates to that in control experiment (no reeds), and the relative turbulence intensity increases with the reed density. Furthermore, the relative turbulence intensity of a measure point is affected by both the longitudinal location of its cross-section and the horizontal distance to the channel wall. Therefore, the relative turbulence intensity becomes more stable when the flow passes through the reed population.Copyright © 2010 by ASME

A series of experiments were carried out to study the hydraulic resistance of rigid unsubmerged vegetation in open constant gradual channel flow. Flow depth and velocity were measured in the density of 54, 108 and 202 reed stems per square meter within a range of discharge values. The results demonstrated that the back-up water height increases with the reed density, while the velocities show an opposite trend. The phenomenon can be explained by the relationship between Manning coefficient and the vegetation density parameters.Copyright © 2010 by ASME