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The changing energy mix under the integration of wind and solar power widens the load peak-valley difference of the power grid and poses great challenges in power grid operation, especially for peak shaving. Hydropower has a crucial role to play in enabling the integration of more variable renewables. To explore a reasonable approach for the changing roles of hydropower and the development of wind and solar power in response to energy transition, a long-term typical daily aggregate model is developed for hydro-wind-solar power with peak shaving operation. Scenarios of integrated wind and solar power proportions under increasing penetrations are simulated by incrementally increasing the installed wind and solar power capacity. The model is converted to a mixed integer linear programming formulation. Groups of numerical experiments divided by influencing factors are used to analyze the complementary influence and optimization mechanisms. The results demonstrate that (1) the proposed model decouples hydro-wind-solar sources at temporal scales and conducts peak shaving effectively. (2) Different scales of wind and solar power have notable impacts on both long-term and short-term hydropower operation. (3) Developing wind and solar power in reasonable proportions could effectively utilize the complementarity and reduce hydropower output fluctuations on different time scales.

Extreme weather lasting for 5–15 days not only brings large inflows in the medium-term but also causes sharp fluctuations in the daytime load of the power grid which imposes considerable challenges to the modeling and solving of medium-term power grid scheduling. The complex nonlinear constraints and water delay times of cascade hydropower plants significantly increase the difficulty of finding solutions. Hence, this study first advocates an accurate optimization model for determining the medium-term peak shaving operation of cascade hydropower plants considering the water delay time. The objective is to minimize the variance of the remaining load and maximize energy production that aims to regulate the load fluctuation and enhance hydropower efficiency. Then, to describe the water delay time of cascade hydropower plants, a piecewise delay time method (PDTM) is proposed based on the water balance equation. Finally, cascade comprehensive benefits (CCB) are established to alleviate the deviations from the scheduling plan and actual plan affected by the lag time. The developed model is implemented on the eleven cascade hydropower plants of the Lancang River in China. Simulation results demonstrate that the model is able to provide more realistic and executable generation scheduling while obtaining larger hydropower generation benefits and good peak shaving performance.

The increasing peak-valley differences pose a major threat to safe operation of the thermal-dominant power grid in China. Cascade hydropower stations, especially for one-reservoir and multi-cascade hydropower stations (OMHS), as the second largest power supply in China form an important part of peak shaving operation. However, nonconvex and nonlinear constraints have made it deeply difficult to perform the daily peak shaving operation (DPSO) of OMHS. In this paper, an improved DPSO model of OMHS considering water delay time is formulated and solved. Firstly, the confluence coefficient method (CCM) is developed to accurately describe the water delay time of OMHS. Secondly, to reduce the errors and water spillage, aggregation of nonlinear constraints, selection of the linearized area and periodic hypothesis are performed. Finally, an MIQP model integrated CCM is established by considering one-reservoir and six-cascade hydropower stations from the Hongshui River of China as a case study. The optimization results demonstrate that the proposed model can accurately simulate real-world system, and the optimized peak-valley differences on typical days in dry season and wet season reduce by 25% and 33%, respectively. Moreover, the CCM can produce more realistic and executable generation scheduling than the state-of-the-art description method of water delay time.

The irregular forbidden zone (FZ) is a common phenomenon with giant hydropower plants developed in the past two decades in China. Irregular shapes of FZs significantly challenge hydro unit commitment (HUC). This paper proposes a novel MILP based framework for HUC considering irregular FZ related constraints including the FZ constraint, effects of linearization errors in both the net head and the output, and the FZ crossing constraint. In the framework, the FZ constraint is handled by the optimal convex partitioning algorithm and the common structure-based formulation method. Inspired by the planar translating robot placement problem, linearization errors are considered by the Minkowski sum method. To handle the FZ crossing constraint, we then propose a graph theory-based approximate formulation method. The framework is integrated into a HUC model with an objective of peak shaving. The model is then tested with a batch of real-world instances of a cascade hydropower system formed by ten giant units with highly irregular FZs. The results show our framework can effectively consider the irregular FZ related constraints. The major advantage of our framework is its ability to handle the irregular shapes of FZs without any tedious and error-prone manual processing.

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.

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.

To meet the rapid growth of electricity demand and reduce carbon intensity, China is developing renewable energies rapidly including hydropower, wind and solar power. Due to the geographical mismatch of energy sources and demands in China, many long-distance and large-scale UHVDC and HVDC transmission projects have been built to transmit electric power from the western renewable bases to eastern coastal load centers. Some provincial power sources serve both local demands and deliver power to multiple regional power grids via HVDC transmission lines. As large capacity HVDC power transmission projects have great impacts on receiving-end power grids. Thus, the local exporting power grid should consider both local demands and energy importing area demands. A mixed-integer linear programming day-ahead peak shaving model to minimize the peak-valley difference in residual load after renewable generation of multiple power grids is developed. The model uses chance constraints to compensate for forecast errors of wind and solar power with hydropower, and introduces maximum daily power regulation times and stair-like power curve constraints of HVDC tie lines to avoid frequent HVDC power change and ensure power grid safety. The case studies in Yunnan province, which has large scale hydro, wind and solar power sources and delivers power to multiple regional power grids via HVDC transmission lines, shows the proposed model can shave peaks from multiple power grids effectively, hydropower can compensate for wind and solar forecast error and obtain satisfying results for multiple power grids, and that HVDC constraints can avoid their frequent power change and ensure the power grid safety.

For low construction cost, the hydropower plant with multiple units sharing a common tunnel (HPMUST) has been widely used, which plays an essential role in the short-term scheduling of powe...

The basic objective of long-term optimization of large-scale hydropower system operations (LOLHSO) is to increase water resource use efficiency and construct a clean and low-carbon-intensit...

With structural changes in power consumption, peak load demands and peak-valley differences are growing, which motivates peak shaving operations of hydropower systems. A multiobjective mixe...

Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change.

In China, cascade hydropower stations are widely used in the ultra-short-term scheduling of power grids to eliminate power fluctuations caused by large-scale wind power grid integration due to their quick load tracking characteristics. They are usually operated jointly by multilevel dispatch centers (MLDCs). However, the ultra-short-term hydropower scheduling (USHS) of cascade hydropower stations controlled by MLDCs faces difficulties in coordinating power output regulation (POR) among stations with different dispatch levels, frequent fluctuations in the station power output, and complex solution difficulties. In this paper, a USHS model for cascade hydropower regulated by MLDCs suppressing wind power volatility is developed to address the above problems based on mixed-integer linear programming. First, a novel objective function that minimizes the POR of cascade hydropower considering the planed load rate and operating state is combined with the coordination strategy to cooperate the POR among MLDC stations. Then, the minimum continuous generating period constraint containing the output ramping limit is proposed to prevent the hydropower station power output from fluctuating frequently. Finally, the power output function of the hydropower station, obtained by aggregation technology from multiple nonlinear characteristic curves, is linearized by improved special ordered sets of type two to achieve an efficient solution. The scheduling results from the Beipan River in southwest China demonstrate the feasibility and validity of the model, and the model can obtain reasonable POR results in an acceptable computational time.

With continuing development of hydropower in China, cascade hydropower system will account for more in the power grid, and may increase power grid operation risk under global climate change. This paper presents a parallel chance-constrained dynamic programming model to derive optimal operating policies for a cascade hydropower system in China. The innovation work of this paper is mainly embodied in two aspects. First, the reliabilities of meeting the firm power requirements of the cascade hydropower system and avoiding extreme system failure under extreme events are explicitly embedded in the model using Lagrangian duality theory and a penalty function. Multiple operating policies are generated by updating the values of Lagrangian multiplier and penalty coefficient for system disruption, then best operating rules are selected based on system performance and evaluated according to simulated reliability, extreme system failure, and maximum benefit. Second, the Fork/Join parallel framework is deployed to parallelize the chance-constrained dynamic programming in a multi-core environment for improving computational efficiency. Two computing platforms with contrasting configurations are employed to illustrate the parallelization performance. Results from a cascade hydropower system operation demonstrate that the proposed method is computationally efficient and can obtain satisfying operating policies, especially for extreme drought events.

China has the richest hydropower resources in the world. Yunnan's exploitable hydropower resources rank third in China, second only to Tibet and Sichuan Province. Yunnan has experienced soaring growth in installed hydropower capacity in the past several decades. The fast development of hydropower makes the province one of the main producers and exporters of hydroelectricity. However, with inconsistencies between hydropower development and various related resources, more and more hydropower generation has been curtailed in recent years. The increasing curtailment of hydropower generation not only caused large-scale waste of sustainable energy, but also impaired the potential higher contribution of hydropower for GHG emission reduction. This paper provides an overview of the hydropower resources and their development in Yunnan and systematically analyzes the constraints causing the curtailment of hydropower generation. We argue that there are several constraining factors such as slowdown of economic growth, inadequate transmission capacity due to a weak grid structure, inconsistency between power grid companies and local government, and uneven distribution of precipitation seasonally and annually. Recommendations are made to alleviate or eliminate hydropower curtailment in Yunnan and other hydropower rich areas.

Power flow transfer (PFT) analysis under various anticipated faults in advance is important for securing power system operations. In China, PSD-BPA software is the most widely used tool for power system analysis, but its input/output interface is easily adapted for PFT analysis, which is also difficult due to its computationally intensity. To solve this issue, and achieve a fast and accurate PFT analysis, a modular parallelization framework is developed in this paper. Two major contributions are included. One is several integrated PFT analysis modules, including parameter initialization, fault setting, network integrity detection, reasonableness identification and result analysis. The other is a parallelization technique for enhancing computation efficiency using a Fork/Join framework. The proposed framework has been tested and validated by the IEEE 39 bus reference power system. Furthermore, it has been applied to a practical power network with 11052 buses and 12487 branches in the Yunnan Power Grid of China, providing decision support for large-scale power system analysis.

With the fast development of hydropower in China, a group of hydropower stations has been put into operation in the past few decades and the hydropower system scale is experiencing a booming period. Hence, the “curse of dimensionality” is posing a great challenge to the optimal operation of hydropower system (OOHS) because the computational cost grows exponentially with the increasing number of plants. Discrete differential dynamic programming (DDDP) is a classical method to alleviate the dimensionality problem of dynamic programming for the OOHS, but its memory requirement and computational time still grows exponentially with the increasing number of plants. In order to improve the DDDP performance, a novel method called orthogonal discrete differential dynamic programming (ODDDP) is introduced to solve the OOHS problem. In ODDDP, orthogonal experimental design is employed to select some small but representative state combinations when constructing the corridor around the current trajectory, and then dynamic programming recursion equation is used to find an improved trajectory for the next iteration. The proposed method is applied to the optimal operation of a large-scale hydropower system in China. The results indicate that compared to the standard DDDP, ODDDP only needs about 0.37% of computing time to obtain the results with about 99.75% of generation in the hydropower system with 7 plants and 3 states per plant, providing a new effective tool for large-scale OOHS problem.

A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.

Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities make accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework with ERA-Interim reanalysis data as input, adopting gradient boosting regression trees (GBRT) and the maximum information coefficient (MIC) was developed for multi-step ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis dataset provides enough information for the framework to discover inflow for longer lead times. Secondly, MIC can identify effective feature subset from massive features that significantly affects inflow so that the framework can avoid over-fitting, distinguish key attributes with unimportant ones and provide a concise understanding of inflow. Lastly, the GBRT is a prediction model in the form of an ensemble of decision trees and has a strong ability to capture nonlinear relationships between input and output in long lead times more fully. The Xiaowan hydropower station located in Yunnan Province, China is selected as the study area. Four evaluation criteria, the mean absolute error (MAE), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE) and the Pearson correlation coefficient (CORR), were used to evaluate the established models using historical daily inflow data (1/1/2017–31/12/2018). Performance of the presented framework was compared to that of artificial neural networks (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The experimental results indicate that the developed method generally performs better than other models and significantly improves the accuracy of inflow forecasting at lead times of 5–10 days. The reanalysis data also enhances the accuracy of inflow forecasting except for forecasts that are one-day ahead.

Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS poses great challenges in modeling for operators. This paper presents an improved proximal bundle method (IPBM) within the framework of Lagrangian relaxation for STHS, which incorporates the expert system (ES) technique into the proximal bundle method (PBM). In IPBM, initial values of Lagrange multipliers are firstly determined using the linear combination of optimal solutions in the ES. Then, each time PBM declares a null step in the iterations, the solution space is inferred from the ES, and an orthogonal design is performed in the solution space to derive new updates of the Lagrange multipliers. A case study in a large-scale hydrothermal system in China is implemented to demonstrate the effectiveness of the proposed method. Results in different cases indicate that IPBM is superior to standard PBM in global search ability and computational efficiency, providing an alternative for STHS.

The Xinanjiang model has been successfully and widely applied in humid and semi-humid regions of China for rainfall–runoff simulation and flood forecasting. However, its forecasting precision is seriously affected by antecedent precipitation (Pa). Commonly applied methods relying on the experience of individual modelers are not standardized and difficult to transfer. In particular, the Xinanjiang daily model may result in obvious errors in the determination of Pa. Thus, a practical method for estimating Pa is proposed in this paper, which is based on a genetic algorithm (GA) and is estimated during a rising flood period. In the optimization process of a GA, Pa values form a chromosome, the root-mean-squared error between the observed and simulated streamflow is chosen as the fitness function. Simultaneously, the best individual reserved strategy is adopted between correction periods to avoid complete independence between each optimization process as well as to ensure the stability of the algorithm. Twenty-seven historical floods observed at the gauge station of the Shuangpai reservoir in Hunan Province of China are used to test the presented algorithm for estimation of Pa, and the results demonstrate that the proposed method significantly improves the quality of flood forecasting in the Xinanjiang model.

There are many cascaded hydropower plants with poor regulation performance and sensitive water heads accompanied by water spillage during the wet season. Faced with the increasing load peak&ndash, valley differences, it is necessary to tap the peak-shaving potential of such head-sensitive cascaded hydropower plants (HSCHPs) because relying solely on hydropower plants with better regulation performance for peak shaving is inadequate. To address the modeling, solving, and water spillage treatment difficulties posed by HSCHPs, a new short-term peak-shaving method based on spillage adjustment is introduced. First, fuzzy cluster analysis is used to determine when to release more water spillage by automatically identifying valley periods of the daily load curve. Furthermore, a spillage adjustment strategy, implemented through an easy gate operation, is adopted to readjust the water release during each period of the load curve. The ratio of the water spillage released in advance in a certain period to its total water spillage is defined as the water spillage ratio (WSR) of the period. Finally, a mixed-integer linear programming model linearized by special ordered sets of type two is solved to determine the optimal WSRs, which achieves the optimal peak-shaving effect. HSCHPs in the Hongshui River Basin during the wet season were selected as case studies. The results demonstrate that the proposed method can achieve a good peak-shaving effect without significantly reducing the power generation and adding additional water spillage.

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

Booming renewable energy development, such as wind and solar power, with their intermittency and uncertainty characteristics, pose challenges for power grid dispatching, especially for power grid peak shaving. In this paper, coordinated operation of hydropower and renewable energy in a provincial power grid is explored to alleviate fluctuation and aid peak shaving. Considering their aggregate effect, this study aggregates wind power plants and solar power plants into a virtual wind power plant and a virtual solar power plant, respectively, and the forecasted error distribution of wind and solar power is analyzed with kernel density estimation. Then, based on the principles of using hydropower to compensate for fluctuating wind and solar power, a day ahead peak shaving model with the objective of minimizing residual load peak-valley difference is built, which introduces chance constraints for forecast errors and coordinate hydropower operation with wind and solar power. To simplify the solution, the proposed model is recast as a successive linear programming problem. Day-ahead scheduling case studies of a provincial power grid system indicate that the proposed model can conduct peak shaving effectively, hydropower can compensate wind and solar power fluctuation, improve the stability of combined output, and make better use of renewable energy. Therefore, this study provides an alternative approach for peak shaving operation of power system with hydropower and increasing integration of wind and solar power in China and other places worldwide.

Forecasting long-term power production of small hydropower (SHP) plants is of great significance for coordinating with large-medium hydropower (LHP) plants. Accurate forecasting can solve the problems of waste-water and abandoned electricity and ensure the safe operation of the power system. However, it faces a series of challenges, such as lack of sufficient data, uncertainty of power generation, no regularity of a single station and poor forecasting models. It is difficult to establish a forecasting model based on classical and mature prediction models. Therefore, this paper introduces a correlation analysis method for forecasting power production of SHP plants. By analyzing the correlation between SHP and LHP plants, a safe conclusion can be drawn that the power production of SHP plants show similar interval inflow to LHP plants in the same region. So a regression model is developed to forecast power production of SHP plants by using the forecasting inflow values of LHP plants. Taking the SHP plants in Yunnan province as an example, the correlation between SHP and LHP plants in a district or county are analyzed respectively. The results show that this correlation method is feasible. The proposed forecasting method has been successfully applied to forecast long-term power production of SHP plants in the 13 districts of the Yunnan Power Grid. From the results, the rationality, accuracy and generality of this method have been verified.

Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir.

The short-term scheduling problem of a hydropower system in China Southern Power Grid (CSG) is studied. As one of the largest in China, the system consists of 92 hydro plants with total installed capacity of 41GW occupying 14.7% of the national hydropower capacity at the end of 2013. Abundant hydroelectricity of the system is transmitted from the western provinces to the eastern load centers in CSG. Obvious difficulties of the hydropower scheduling of CSG are large-scale system, complex constraints and multiple power receiving grids of single plants and cascaded systems due to huge capacity. A short-term hydropower scheduling model for peak shaving of multiple power grids is developed for the operations of the hydropower system of CSG. The model is composed of multi-objective optimal peak shaving (MOPS) model, inter grid power distribution (IGPD) model and load fluctuation balance (LFB) model. The MOPS model minimizes the maximum residual loads of each power grid in which the IGPD model is embedded to distribute power of a plant among several power grids. To solve the model, an aggregate function and a multi-objective fuzzy optimization model are combined to establish an alternative objective function, and a proposed constraint successively satisfying (CSS) algorithm is used to address the period coupling constraints in local search. A case study shows that the proposed approach is practicable, adaptable and robust to obtain near optimal results efficiently, and is applicable for large-scale hydropower systems with both multiple and single power receiving grids.

With a huge energy demand, the small hydropower (SHP) has undertaken a rapid development in the past six decades in China. Especially in the recent three decades, the increasing environmental pressure has promoted its development rate and the significance of its management and operation. The number of SHP stations in China has exceeded 45,000 with a total installed capacity of more than 68 GW, ranked the first in the world. SHP provides approximately 5% of China׳s gross electricity generation and is the major backbone for rural electrification. However, how to manage and operate SHP effectively is still a difficult problem in China. In this paper, the current status of SHP in China is investigated. A special emphasis is given to their dispatching management. The southwestern province of Yunnan, ranked second in exploitable potential and first in established capacity of SHP in China, is taken as a typical example for the management and operation of SHP. The difficulties and challenges of management and operation of SHP in Yunnan province are depicted. A case system for the SHP management in Yunnan is introduced in details. It is believed that the system should be beneficial for regions with rich SHP resources in China and elsewhere as well.

Conceptual rainfall-runoff models (CRRMs) are widely used for flood forecasting and hydrologic simulations. However, parameter calibration poses a major challenge for using CRRMs, especiall...

Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700 MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250 MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations.