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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.

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...

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.

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.

Short-term load dispatching (STLD) for a hydropower plant with multiple turbines in one tunnel (HPMTT) refers to determining when to startup or shutdown the units of different tunnels and scheduling the online units of each tunnel to obtain optimal load dispatch while simultaneously meeting the hydraulic and electric system constraints. Modeling and solving the STLD for a HPMTT is extremely difficult due to mutual interference between units and complications of the hydraulic head calculation. Considering the complexity of the hydraulic connections between multiple power units in one tunnel, a two-phase decomposition approach for subproblems of unit-commit (UC) and optimal load dispatch (OLD) is described and a two-stage model (TSM) is adopted in this paper. In the first stage, an on/off model for the units considering duration constraints is established, and the on/off status of the units and tunnels is determined using a heuristic searching method and a progressive optimal algorithm. In the second stage, a load distribution model is established and solved using dynamic programming for optimal load distribution under the premise of the on/off status of the tunnel and units in the first stage. The proposed method is verified using the load distribution problem for the Tianshengqiao-II reservoir (TSQII) in dry season under different typical load rates. The results meet the practical operation requirements and demonstrate the practicability of the proposed method.

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.

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...