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The capacity configuration of energy storage systems has recently become a widespread research topic, especially within the field of renewable energy system research. An improper capacity configuration may result in an inadequate power supply to the power grid. Therefore, this paper proposes a method for allocating capacity for an energy storage plant based on power load forecasting technology. It integrates the Grey Prediction Model, an improved Back Propagation (BP) neural network prediction model, and a multiple linear regression prediction model to establish a Multiple Algorithm integrated Load prediction model (MAILP) - grey regression neural network prediction model. Furthermore, the Informer model is introduced, and a stacking strategy is employed to enhance the prediction accuracy of the model. Subsequently, a capacity allocation model for photovoltaic storage plants is constructed based on the prediction results. Finally, the multi-objective serpentine optimization algorithm is utilized to solve the model.The capacity allocation model for photovoltaic (PV) energy storage power plants is constructed based on the prediction results, and the model is solved using a multi-objective snake optimization algorithm. The optimal value for PV energy storage plant capacity configuration is determined using a multi-objective snake optimization algorithm. The results indicate that the MAILP proposed in this paper yields reliable predictions with a high accuracy rate, and the correlation coefficient reaches up to 99.7% in the regression analysis. The energy storage power plant can share approximately 150 kW of power loads in the grid during peak electricity consumption periods, effectively alleviating the operational pressure on the grid.

This paper attempts to develop an intelligent plate fin-and-tube heat exchanger (PFTHE) design system, which is entirely self-programming, to achieve quickly design. The proposed design system consists of four modules: (1) formulation, (2) optimization, (3) post-processing, and (4) decision-making. The proposed design system is implemented and validated with the application of shape optimization of ellipse tubes of plate-fin heat exchanger. In the formulation module, the physical problem to be studied is mathematized and the main design variables will be determined. In the optimization module, a famous algorithm, non-dominated sorting genetic algorithm of type II (NSGA-II), is embedded in an in-house Multi-concept Heat Transfer (MHT) code to achieve call CFD simulation during optimization process. To reduce computation time, Open multi-processing (OpenMP) is employed. The optimal solutions (Pareto solutions) obtained by the optimization module will be stored in the database and also taken as the input for the post-processing module and the decision-making module. In the post-processing module, Artificial neural network (ANN) is utilized to establish the correlation between design variables and heat transfer performance indicators assisting engineers to quickly design. As a short cut for heat exchanger design, both forward and backward designs have been implemented. Finally, in the decision-making module, technique for order preference by similarity to an ideal solution (TOPSIS) is applied to determine the best compromise solution from Pareto solutions according to the actual requirements provided by users. Results show that the proposed design system could determine a best compromise solution by reducing the pressure drop (80%) of the tube bundle without sacrificing too much heat transfer performance (5%) and also save much time for designers. For the forward design, the ANNs taken six decision variables as inputs are modelled to forecast two objectives has reached acceptable precisions. This research provides a promising tool for PFTHE optimization to improve heat transfer and comprehensive performance, also for quickly design based on historical simulation or experimental results.

鉴于传统的单一径流预报模型很难描述径流未来变化规律,将自适应变分模态分解(AVMD)与基于组合物理核函数的高斯过程回归(GPR-CK)相结合,构建了AVMD-GPR-CK预报模型,该模型采用AVMD将实测径流分解为多个子序列,对子序列依据其自身特点分别建模,子序列预报结果叠加重构即为最终预报结果.模型应用于金沙江流域向家坝站未来1～12个月的径流预报的结果表明,所有预见期AVMD-GPR-CK模型的确定性系数均大于0.94,平均绝对百分比误差(MMAPE)在±17％以内,预见期在10个月以内时,MMAPE在±10％以内;预报精度明显优于常见的BP、GRNN、RBF、RELM模型.

Accurate and reliable runoff prediction is of great significance to water resources management, disaster moni-toring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective.

The study adopts the Copula function to evaluate index data and historical flood disaster simulation samples to reduce the subjectivity of the evaluation results. A genetic algorithm is used to calculate the model parameters and predict flood hazard levels. The spatial data processing technology of the geographic information system (GIS) is employed to extract and analyze spatial data to acquire indicators. A comprehensive hazard evaluation index system containing a maximum of 1, 6, 24 h heavy rain, relative height difference, average gradient, and drainage density is established to perform detailed analysis. The complex links of the evaluation index values to flood hazard analysis are uncovered by applying this data-focused flood hazard evaluation strategy. By comparing the actual occurrence times and forecast results of flood disasters in 64 research areas of Hubei Province, we find the established model has good prediction effect and can provide data support for flood disaster early warning.

This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000.

The stable operation of the hydropower plant is an important guarantee for power delivery quality and the security of power grids. Nevertheless, the current stability analysis is only focused on simplified models, leading to an inaccuracy result. Therefore, a novel nonlinear model of the grid-connected hydropower plant system with fractional order PI controller considering nonlinear characteristics is taken as the research object. Then, a new universal stability quantification method for complex nonlinear systems is proposed to overcome the application limitations of traditional methods. Finally, the stability and parameter sensitivity of the system are investigated. The results demonstrate that the proposed method is highly accurate and robust, whose errors fall within the range of [-0.0025,0.0005]. The results show that the governor nonlinearity and integral order of fractional order PI controller have an obvious negative influence on the stability of the hydropower plant.

Due to the inherent non-stationary and nonlinear characteristics of original streamflow and the complicated relationship between multi-scale predictors and streamflow, accurate and reliable monthly streamflow forecasting is quite difficult. In this paper, a multi-scale-variables-driven streamflow forecasting (MVDSF) framework was proposed to improve the runoff forecasting accuracy and provide more information for decision-making. This framework was realized by integrating random forest (RF) and Gaussian process regression (GPR) with multi-scale variables (hydrometeorological and climate predictors) as inputs and is referred to as RF-GPR-MV. To validate the effectiveness and superiority of the RF-GPR-MV model, it was implemented for multi-step-ahead monthly streamflow forecasts with horizons of 1 to 12 months for two key hydrological stations in the Jinsha River basin, Southwest China. Other MVDSF models based on the Pearson correlation coefficient (PCC) and GPR with/without multi-scale variables or the PCC and a backpropagation neural network (BP) or general regression neural network (GRNN), with only previous streamflow and precipitation, namely, PCC-GPR-MV, PCC-GPR-QP, PCC-BP-QP, and PCC-GRNN-QP, respectively, were selected as benchmarks. Experimental results indicated that the proposed model was superior to the other benchmark models in terms of the Nash–Sutcliffe efficiency (NSE) for almost all forecasting scenarios, especially for forecasting with longer lead times. Additionally, the results also confirmed that the addition of large-scale climate and circulation factors was beneficial for promoting the streamflow forecasting ability, with an average contribution rate of about 15%. The RF in the MVDSF framework improved the forecasting performance, with an average contribution rate of about 25%. This improvement was more pronounced when the lead time exceeded 3 months. Moreover, the proposed model could also provide prediction intervals (PIs) to characterize forecast uncertainty, as supplementary information to further help decision makers in relevant departments to avoid risks in water resources management.

This paper investigates the nonlinear modeling and stability of a doubly-fed variable speed pumped storage power station (DFVSPSPS). Firstly, the mathematical model of DFVSPSPS with surge tank considering nonlinear pump turbine characteristics was derived and established. Then, Hopf bifurcation analysis of DFVSPSPS was performed. The stable region was identified and verified by example analysis. Moreover, the effect mechanism of nonlinear pump turbine characteristics on the stability of DFVSPSPS was explored. Finally, the influence of factors on the stability and dynamic response of DFVSPSPS was studied. The results indicate that the emerged Hopf bifurcation of DFVSPSPS is supercritical and the region on the low side of the bifurcation line is the stable region. Nonlinear head characteristics have a significant influence on the stability and dynamic response of DFVSPSPS. Nonlinear speed characteristics have an obvious effect on the stability and dynamic response of DFVSPSPS only under positive load disturbance and unstable surge tank. Nonlinear head characteristics are unfavorable for the stability of DFVSPSPS under positive load disturbance and favorable under negative load disturbance. A smaller flow inertia of penstock, a smaller head loss of penstock and a greater unit inertia time constant are favorable for the stability of DFVSPSPS. The stable region under the positive disturbance of active power is larger than that under the negative disturbance of active power. The time constant of the surge tank presents a saturation characteristic on the stability of DFVSPSPS.

This paper aims to investigate the stability and dynamic characteristic of the grid-connected hydropower station with fractional order PI (FOPI) controller considering nonlinear governor characteristic. Firstly, a novel nonlinear hydro-turbine governing system-power grid (HTGS-PG) with FOPI controller considering nonlinear governor characteristic (delay, saturation, backlash and so on) is established. Then, in order to study the stability of nonlinear system, a new stability quantitative evaluation method is proposed to overcome the application limitations of traditional methods. Finally, based on the proposed stability quantification method, the stability and dynamic characteristic of system is investigated under grid-connected modes. The coupling influence mechanism of integral order and time delay on stability and dynamic performance of system are revealed for the first time in this paper. The influence mechanism of system parameters on the stability and dynamic characteristics of system are analysed. The results show that, the integral order and time delay have a negative influence on the stability and the stable region area of nonlinear HTGS-PG changes from 40.4107 to 9.2652 with the increase of integral order and time delay. The stability and dynamic characteristics of nonlinear HTGS–PG coupling system can be significantly improved by the reasonable determination of system parameters.

The precise forecast of solar radiation is exceptionally imperative for the steady operation and logical administration of a photovoltaic control plant. This study proposes a hybrid framework (CBP) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced Gaussian process regression with a newly designed physical-based combined kernel function (PGPR), and the backtracking search optimization algorithm (BSA) for solar radiation forecasting. In the CEEMDAN-BSA-PGPR (CBP) model, (1) the CEEMDAN is executed to divide the raw solar radiation into a few sub-modes; (2) PACF (partial autocorrelation coefficient function) is carried out to pick the appropriate input variables; (3) PGPR is constructed to predict each subcomponent, respectively, with hyperparameters optimized by BSA; (4) the final forecasting result is produced by combining the forecasted sub-modes. Four hourly solar radiation datasets of Australia are introduced for comprehensive analysis and several models available in the literature are established for multi-step ahead prediction to demonstrate the superiority of the CBP model. Comprehensive comparisons with the other nine models reveal the efficacy of the CBP model and the superb impact of CEEMDAN blended with the BSA, respectively. The CBP model can produce more precise results compared with the involved models for all cases using different datasets and prediction horizons. Moreover, the CBP model is less complicated to set up and affords extra decision-making information regarding forecasting uncertainty.

This article conducts a two-dimensional numerical model to simulate the ferrofluid droplet formation from microfluidic T-junction under inhomogeneous magnetic fields with diverse strengths. This external magnetic field is produced by two electric straight wires in a finite computational domain. A coupled volume-of-fluid and level-set interface tracking method (VOSET) is adopted to capture the evolution of two-phase interface. Meanwhile, a two-region computational domain method is designed for situations that the droplets are in close contact with the solid boundaries for the fluid flow. All 2-D numerical simulations are implemented by a self-developed CFD code, named as MHT (Multi-concept Heat Transfer). The numerical results show a significant inhibition effect in droplet formation at the presence of external magnetic field. With the increase of the current intensity, the magnetic force of the ferrofluid droplet increases and decreases periodically, especially when the electric current intensity is less than 60 A. The increasing current intensity enlarges the departure diameter and prolongs the departure period of ferrofluid droplet, especially when the current intensity in the range 12 A similar to 54A. In the cases of electric current within [12A, 54 A], the departure diameter growths monotonically and nearly in a quadratic manner with the increase of the current intensity. However, when the current intensity exceeds 60 A, the departure characteristic of ferrofluid will be changed due to ferrofluid droplet absorbed on the upper wall of the main channel.

Wind speed and streamflow series always are nonlinear and unstable because the effects of chaotic weather systems. These inherent features make them difficult to forecast, especially in a changing environment. To improve forecasting accuracy, an innovation uncertainty forecasting architecture is developed by coupling data decomposition method, feature selection, multiple artificial intelligence (AI) techniques and composite strategy to do unstable time series forecasting. In the designed architecture, the AVMD (adaptive variational mode decomposition) is first applied to excavate implicit information from the original time series. Then, the random forest is utilized to select the suitable inputs for each mode. After that, the GPR (Gaussian Process Regression), a very famous probabilistic AI technique, is driven by various neural networks (ELM (Extreme Learning Machine), BP (Back Propagation Neural Networks), GRNN(Generalized Regression Neural Networks) and RBF (Radial Basis Function Neural Networks)) to produce both deterministic and probabilistic forecasting results in a nonlinear manner to play strengths of each other. The effectiveness and applicability of the proposed approach is verified by unstable wind speed data and streamflow data, and also compared with eleven related models. Results indicate that the proposed model not only improves the forecasting accuracy for deterministic predictions, but also provides more probabilistic information for decision making. The proposed method achieves significantly better performance than the traditional forecasting models both on wind speed forecasting and streamflow forecasting with at least 50% average performance promotion over all the eleven competitors. Comprehensive comparisons demonstrate the superior performance of the proposed method than the involved models as a powerful tool for unstable series forecasting.

Data analysis and mathematical statistics are used to analyze the Interannual variation of runoff and precipitation in the Jinsha River basin from 1961 to 2015. The area average annual precipitation showed no significant upward trend, and the annual runoff showed no significant downward trend. The results of cumulative anomaly test, MK mutation test and sliding t test are consistent, there were three mutation years of 1984, 1997 and 2005 in the time series of annual precipitation and runoff in the Jinsha River basin. This study also provides necessary hydrometeorological basic information for quantitative assessment of the contribution rate of climate change and human activities to runoff change.

The Three Gorges Reservoir (TGR) intervening basin is one of the most important, ecologically fragile and sensitive areas in the upper reaches of the Yangtze River. Since the completion and operation of the TGR, the change of the ecological environment in this region—with vegetation as an indicator—has been a consistent focus of attention. Based on the six phases of land use data and normalized difference vegetation index (NDVI), temperature and precipitation data from 1998 to 2017, the change and trend of land use and vegetation cover in the TGR intervening basin were analyzed quantitatively by using a transition matrix, linear regression and partial correlation analysis. The area of unchanged land use type is 56,565 km2, accounting for 97.27% of the total area of the basin. The vegetation coverage with NDVI as the indicator showed a significant upward trend, with a growth rate of 7.5%/10a. The impact of temperature on vegetation was greater than that of precipitation on vegetation. The non-linear fitting curve of NDVI to temperature and precipitation rose with the time course of TGR impoundment, although the mechanism remains to be studied further. In general, climate change, ecological restoration measures, urbanization and reservoir impoundment did not significantly change the spatial distribution pattern of land use and the climate driving mechanism of vegetation growth in the TGR intervening basin.

Artificial neural network (ANN) models combined with time series decomposition are widely employed to calculate the river flows; however, the influence of the application of diverse decomposing approaches on assessing correctness is inadequately compared and examined. This study investigates the certainty of monthly streamflow by applying ANNs including feed forward back propagation neural network and radial basis function neural network (RBFNN) models integrated with discrete wavelet transform (DWT), at Jinsha River basin in the upper reaches of Yangtze River of China. The effect of the noise factor of the decomposed time series on the prediction correctness has also been argued in this paper. Data have been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors, mean absolute errors, mean absolute percentage error and Nash–Sutcliffe Efficiency. Results show that time series decomposition technique DWT contributes in improving the accuracy of streamflow prediction, as compared to single ANN’s. The detailed comparative analysis showed that the RBFNN integrated with DWT has better forecasting capabilities as compared to other developed models. Moreover, for high-precision streamflow prediction, the high-frequency section of the original time series is very crucial, which is understandable in flood season.

This paper proposes a model of integrated scheduling of hydro, thermal and wind power with spinning reserve and an improved mixed binary and real number differential evolution algorithm based on SHADE. Scenario A with spinning reserve of hydro plants and thermal plants in integrated power system and Scenario B without spinning reserve provided from integrated power system are designed. In addition, the different wind power installed capacity in the integrated power system are considered. The model proposed is illustrated using an example and case study. Some conclusions of the influence of wind power on integrated scheduling of hydro, thermal and wind power system are finally drawn.