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Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of dimensionality poses huge challenges for operators because the computation cost often grows exponentially with the expansion of hydropower system. With strong search performance and high execution efficiency, metaheuristic search algorithms become the research hotspot in the reservoir operation field. Here, cooperation search algorithm (CSA) inspired by team cooperation behaviors in modern enterprise is introduced for power generation operation of cascade hydropower reservoirs. In CSA, the team communication operator is designed to determine promising search areas, and then the reflective learning operator is adopted to enhance the local search, while the internal competition operator is used to retain the elite individuals. The feasibility of the CSA method in numerical optimization problems is fully proved by the simulation results of several test functions. Then, the CSA method is used to solve the power generation operation of cascade hydropower reservoirs. The results show that the CSA method outperforms several traditional methods in both convergence rate and search precision. Thus, an effective tool with strong reliability and robustness is provided for the complex reservoir operation problem.

Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

Objective To investigate the effect and mechanism of total paeony glycosides (TPG) on spinal cord apoptosis in rats with spinal cord injury (SCI). Methods A total of 18 SD rats were randomly divided into control group, SCI group and TPG group (6 each). The SCI rat model was established using Allen method. For rats in control group, the spinal cord was sutured directly after laminectomy without spinal cord injury; those in TPG group were intraperitoneally injected with 25 mg/kg TPG every 24 hours after the establishment of SCI model, while those in SCI group and control group were intraperitoneally injected with the same volume of normal saline for 28 days. The rats' behavioral score were evaluated using Basso-Beattie-Bresnahan (BBB) method. The apoptosis of spinal cord cells was measured by flow cytometry. Western blotting was performed to detect the expression levels of cell apoptotic protein cleaved caspase-3 and cleaved caspase-9. The contents of MDA, SOD and LDH in the supernatant of spinal fluid were quantified with corresponding kits. Results Compared to those in control group, the BBB score decreased significantly, while the apoptosis rate of spinal cord cells and the expression levels of cleaved caspase-3 and cleaved caspase-9 were obviously elevated, and the contents of MDA and LDH increased, SOD decreased in the supernatant of spinal fluid in the rats of SCI group (P

Accurate streamflow prediction information is of great importance for water resource planning and management. The goal of this research is to develop a hybrid model for forecasting short-term runof...

The dimensionality problem is posing an enormous challenge for cascade hydropower reservoirs operation because the memory usage and execution time grow exponentially with the expansion of system scale. To effectively address this problem, this paper develops a novel Latin dynamic programming algorithm for dimensionality reduction in hydropower reservoir operation problem, where the Latin hypercube sampling method is firstly adopted to produce a subset of discrete state variables at each stage, and then the standard dynamic programming recursive equation is used to search for a modified trajectory around the newly-generated solutions, while the iterative search strategy is used to gradually enhance the solution quality. The results in a real-world hydropower system of China demonstrate that compared with the standard dynamic programming method, the execution efficiency of the presented method is significantly improved while the power generation is well maintained in different scenarios. Hence, the novelty of the paper is to provide an effective dimensionality reduction tool for solving the complex hydropower operation problem.

Accurate calculation of power generation output is crucial to the operation and management of reservoir. The calculation of power generation output is related to the downstream water level,...

Accurate hydrologic forecasting plays a significant role in water resource planning and management. To improve the prediction accuracy, this study develops a hybrid hydrological forecasting...

Generally, accurate hydrological forecasting information plays an increasingly important role in promoting the comprehensive benefit of hydropower reservoirs. With satisfying generalization ability and search rate, the extreme learning machine (ELM), a famous single-layer feedforward neural network, has been widely used to address regression and classification problem. However, the standard ELM method often falls into second-best solutions with a high probability due to the random assignments of network parameters. In order to overcome this problem, this article aims at developing a hybrid model for monthly runoff time series forecasting. In the hybrid method, an effective swarm intelligence method, grey wolf optimizer (GWO), is adopted to optimize the input-hidden weights and hidden biases of the ELM method; and then the Moore-Penrose generalized inverse method is adopted to determine the hidden-output weights. The world's largest hydropower reservoir, Three Gorges, is chosen to compare the performances of various forecasting methods. Based on the simulation results, the presented method outperforms several traditional forecasting methods (like artificial neural network and support vector machine) in several quantitative indexes. Thus, a novel alternative is presented to predict the nonlinear hydrological time series in China.

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

As a renewable and clean energy, wind energy plays an important role in easing the increasingly serious energy crisis. However, due to the strong volatility and randomness of wind speed, large-scale integration of wind energy is limited. Therefore, obtaining reliable high-quality wind speed prediction is of great importance for the planning and application of wind energy. The purpose of this study is to develop a hybrid model for short-term wind speed forecasting and quantifying its uncertainty. In this study, Minimal Gated Memory Network is proposed to reduce the training time without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression and Minimal Gated Memory Network is proposed to predict conditional quantile of wind speed. Afterwards, Kernel Density Estimation method is used to estimate wind speed probabilistic density function according to these conditional quantiles of wind speed. In order to make the model show better performance, Maximal Information Coefficient is used to select the feature variables while Genetic Algorithm is used to obtain optimal feature combinations. Finally, the performance of the proposed model is verified by seven state-of-the-art models through four cases in Inner Mongolia, China from five aspects: point prediction accuracy, interval prediction suitability, probability prediction comprehensive performance, forecast reliability and training time. The experimental results show that the proposed model is able to obtain point prediction results with high accuracy, suitable prediction interval and probability distribution function with strong reliability in a relatively short time on the prediction problems of wind speed.

In practice, the rational operation rule derived from historical information and real-time working condition can help the operators make the quasi-optimal scheduling plan of hydropower reservoirs, leading to significant improvements in the generation benefit. As an emerging artificial intelligence method, the extreme learning machine (ELM) provides a new effective tool to derivate the reservoir operation rule. However, it is difficult for the standard ELM method to avoid falling into local optima due to the random determination of both input-hidden weights and hidden bias. To enhance the ELM performance, this research develops a novel class-based evolutionary extreme learning machine (CEELM) to determine the appropriate operation rule of hydropower reservoir. In CEELM, the k-means clustering method is firstly adopted to divide all the influential factors into several disjointed sub-regions with simpler patterns; and then ELM optimized by particle swarm intelligence is applied to identify the complex input-output relationship in each cluster. The results from two reservoirs of China show that our method can obtain satisfying performance in deriving operation rules of hydropower reservoir. Thus, it can be concluded that the model’s generalization capability can be improved by isolating each subclass composed of similar dataset.

To satisfy the huge energy demand of China, a rapid development rate of hydropower system was undertaken over the past decades. Currently, thousands of hydropower reservoirs with a total installed capacity of more than 330 GW have provided approximately 20% of China’s gross electricity generation, ranked number 1 in the world. Besides, it is foreseeable that in the coming decades, the same development speed of hydropower will be maintained in order to effectively handle the increasing environment and energy problem. The dimensionality problem caused by booming system scale, unprecedented development speed and operational complexity are posing a giant challenge to the operation of large-scale hydropower system in China. Thus, after giving an overview of China’s large-scale hydropower system, this paper deeply analyses the key operation characteristics leading to the dimensionality problem and highlights the possible dimensionality reduction strategies to sharply alleviate the optimization difficulty, providing necessary technical support to achieve the goal of energy conservation and emission reduction of China. To sum up, this paper could be a good and inspiring example, contributing to the sustainable development of worldwide countries with a high proportion of hydropower integration.

Recently, the booming electricity demand and intermittent energy has sharply increased the peak shaving pressure in China. However, for a majority of regional power grids in China, the installed capacity of flexible energy (like hydropower and pumped-storage) is small and the thermal plants are asked to respond the sudden load change at peak periods. The existing method based on specialist experience and historical information may generate inferior solutions and fail to reduce the peak pressure. Thus, a practical mixed integer linear programming (MILP) model is developed for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy, where the goal is chosen to minimize the peak-valley difference of the residual load series obtained by subtracting all the thermal generation from the original load curve while satisfying the necessary physical constraints. The MILP model is used to the thermal system in the China’s largest regional power grid, East China Power Grid. The simulations show that the MILP model can effectively smooth the residual load curve by gathering power generation of thermal plants at peak periods. Therefore, an alternative tool is provided to alleviate the peak pressure of thermal-dominant regional power grid in China.

Short-term load forecasting plays an essential role in the safe and stable operation of power systems and has always been a vital research issue of energy management. In this research, a hybrid short-load forecasting method with Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks considering relevant factors which optimized by the Bayesian Optimization Algorithm (BOA) is studied. This method firstly decomposition with VMD which is a non-recursive signal processing technology that can decompose a signal into a discrete number of modes, then, consider the relevant factors and extend to the sequence according to the coefficient of association. Specifically, for the day type and higher or lower temperature, the nonlinear mapping is used and optimized by the BOA. Finally, the subsequences are predicted by LSTM which is a special Recurrent Neural Network with memory cells and reconstructed. To validate the performance of the proposed method, two categories of contrast methods including individual methods and decomposition-based methods are demonstrated in this study. The individual methods which without decomposition processes including LSTM, Support Vector Regression, Multi-Layered Perceptron Regressor, Linear Regression, and Random Forest Regressor, and the decomposition based methods including Empirical Mode Decomposition-Long Short-Term Memory, and Ensemble Empirical Mode Decomposition-Long Short-Term Memory. The simulation results, which developed in four periods of Hubei Province, China, show that the prediction accuracy of the proposed model is significantly improved compared with the contrast methods.

To satisfy growing energy demand, the hydropower industry of China is experiencing unprecedented development, and the total power generation and installed capacity of hydropower in China ra...

In China, due to the unbalanced distribution of energy center and load center, numerous transmission projects are built to improve the national energy allocation efficiency and the external electricity is playing an increasing important role in the daily operation of receiving-end grids. The current fixed-proportion method for energy allocation is relatively simple and easy to implement, but may produce unreasonable energy injection with the features of a straight line or “anti-peak regulation” in some cases, increasing the peak operation pressure of power systems. Thus, it is of great necessity to further improve the allocation scheme of inter-regional transmitted electricity among receiving-end power grids. As a new contribution to the research field, this paper develops a mixed integer linear programming model with the goal of minimizing the weighted peak-valley difference of multiple remaining load curves to address this problem. The presented model is applied to the East China Power Grid, and the results show that the presented model can obtain satisfying results in reducing the peak loads of multiple power grids. For instance, in the long-term simulations, the presented model can make about 23.7%, 5.9%, 5.7% and 8.3% reductions in the maximum loads of Shanghai, Jiangsu, Zhejiang and Anhui, respectively. Then, it can be concluded that optimizing the allocation of inter-regional transmitted electricity will be a viable way to reduce the peak operation pressure of multiple receiving-end power grids in China.

Due to the expanding system scale and increasing operational complexity, the cascade hydropower reservoir operation balancing benefit and firm output is becoming one of the most important problems in China’s hydropower system. To handle this problem, this paper presents a parallel multi-objective particle swarm optimization where the swarm with large population size is divided into several smaller subswarms to be simultaneously optimized by different worker threads. In each subtask, the multi-objective particle swarm optimization is adopted to finish the entire evolutionary process, where the leader particles, external archive set and computational parameters are all dynamically updated. Besides, a novel constraint handling strategy is used to identify the feasible search space while the domination strategy based on constraint violation is used to enhance the convergence speed of swarm. The presented method is applied to Lancang cascade hydropower system in southwest China. The results show that PMOPSO can provide satisfying scheduling results in different cases. For the variation coefficient of generation in 30 independent runs, the presented method can bettered the serial algorithm with about 66.67% and 61.29% reductions in normal and dry years, respectively. Hence, this paper provides an effective tool for multi-objective operation of cascade hydropower system.

The power industry is playing an increasingly important role in the world economy, and insufficient power supply may lead to huge enormous economic loss throughout the world. Then, the problem of power shortage is receiving a great deal of attention from operators and managers in electrical power system. With the merits of fast startup-shutdown, hydropower is regarded as one of the most reliable renewable energy sources to smooth the electricity shortage of power grid. Thus, this paper focuses on the operation of head-sensitive hydropower system considering power shortage aspect. To effectively address this problem, a novel method based on linear programming and successive approximation is proposed, where the initial hydraulic heads of each hydroplants is generated based on the actual working condition, and then the linear programming method is used to solve the fixed-head hydropower optimization problem involving a carefully-designed min-max optimization objective, while the successive approximation strategy is employed to incrementally improve the solution's quality by dynamically updating water heads of all the hydropower plants. The simulations demonstrate that compared with the original load demand, our method can make an average of approximate 20% reduction in electricity shortage of power system, demonstrating its effectiveness and practicability.

As one of the important renewable energy, hydropower is experiencing a booming development period throughout the world in recent years. By the end of 2016, hydropower has occupied 20.1% installed capacity and 19.5% generation in China. Thus, it is of great importance to develop some effective methods to guarantee the overall generation benefit of hydropower system. As a famous optimization tool to solve this problem, the progressive optimality algorithm cannot effectively handle with large-scale hydropower system because its computational burden grows exponentially with the increasing number of hydroplants. Thus, in order to effectively alleviate the dimensionality problem, a novel method called uniform progressive optimality algorithm is introduced here. In the presented method, the complex multistage problem is firstly divided into several two-stage optimization subproblems, and then the uniform design is adopted to sample a small subset from all the possible state vectors at each subproblem, while the successive approximation strategy is adopted to gradually improve the quality of solution. The results from a real-world hydropower system of China indicate that compared with progressive optimality algorithm, the proposed method has superior performance in execution efficiency and convergence speed, which is an effective alternative method for the complex hydropower system operation problem.

Recently, with increasing attention paid to energy production and ecological protection, the hydropower reservoirs operation balancing generation benefit and ecological requirement is playing an important role in water resource and power systems. Thus, the parallel multi-objective genetic algorithm is introduced to effectively resolve this multi-objective constrained optimization problem with two competing objectives and numerous physical constraints. In the proposed method, the original large-sized swarm is decomposed into several smaller subpopulations that will be simultaneously evolved on several computing units, effectively enhancing the execution efficiency and population diversity. During the evolutionary process, the chaotic initialization method is used to enhance the quality of initial population, while the feasible space identification method and the modified domination strategy are designed to improve the feasibility of solution and convergence rate of individuals. The results from the Wu hydropower system of China show that the presented method can make full use of computationally expensive resources to improve the performance of population. For instance, compared with the traditional method, the presented method can make 69.23% and 27.44% improvements in the standard deviation of power generation and water deficit in normal year, respectively. Thus, this paper provides an effective tool to support the multi-objective operation optimization of hydropower system.

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.

With the rapid economic growth in recent years, the peak operation of hydropower system (POHS) is becoming one of the most important optimization problems in power system. However, the rapid expansion of system scale, refined management and operational constraints has greatly increased the optimization difficult of POHS. As a result, it is of great importance to develop effective methods that can ensure the computational efficiency of POHS. The progressive optimality algorithm (POA) is a commonly used technique for solving hydropower operation problem, but its execution time still grows sharply with the increasing number of hydropower plants, making it difficult to satisfy the efficiency requirement of POHS. To address this problem, a novel efficient method called parallel progressive optimality algorithm (PPOA) is presented in this paper. In PPOA, the complex problem is firstly divided into several two-stage optimization subproblems, and then the classical Fork/Join framework is used to realize parallel computation of subproblems, making a significant improvement on execution efficiency. The simulations in a real-world hydropower system demonstrate that as compared with the standard POA, PPOA can use abundant multi-core resources to reduce execution time while keeping the quality of solution, providing a new alternative to solve the complex hydropower peak operation problem.