Search

• An interval sequence prediction method for power generation is proposed. • The new matrixed grey model can be applied to different interval sequences. • Parameters are estimated by combining Particle swarm optimization algorithm and Ordinary Least Square method. • The new model has higher prediction accuracy compared with four competing models. • Intervals of different types of power generation in China are predicted. Development trends of power generation sequences have nonlinearity and uncertainty. The nonlinearity can be reflected by the nonlinear grey Bernoulli model. The uncertainty can be reflected by the interval sequence. A matrixed nonlinear grey Bernoulli model based on interval sequences is proposed in this paper. In order to make the model directly applicable to interval sequence, the traditional grey Bernoulli model is linearized by power function transformation, then the parameters are matrixized, and finally the recursive prediction formula is obtained by Cramer's law. Particle swarm optimization algorithm is used to estimate the nonlinear parameter. By adjusting the parameter, the new model can be used to predict exponential, saturated and parabolic interval sequences. The new model is used to predict the intervals of China's thermal power, hydro power, nuclear power, wind power, national power generation and power generation in east China. The prediction results show that the new model has higher prediction accuracy than the competing models. Finally, the power generation of four modes and national power generation from 2022 to 2025 are predicted and analyzed. [ABSTRACT FROM AUTHOR]

• Four time-delay weights structural types are designed by the grey incidence model. • The novel multivariable grey prediction model considering the time-delay periods and time-delay weights is proposed. • The accumulating impact of influencing factors on CO 2 emissions of China's transport sector is measured. CO 2 emissions from the transportation sector occupy an increasingly important proportion in China's carbon dioxide emissions. Measuring the accumulative impact of factors on carbon emissions over time is of great significance for formulating corresponding policies. This paper aims to propose a novel time-delay multivariate grey model to measure the CO 2 emissions' accumulating impact of China's transportation sector. Firstly, the grey incidence model is used to identify time lags between the input and output variables, and also analyze the structure type of time-delay weights. Then, an accumulative time-delay multivariate grey prediction model is developed. In this model, a Gaussian formula is used to discretize the convolution integral of the time response function, and the particle swarm optimization algorithm is employed to determine the optimal weight coefficients. Finally, a case for CO 2 emissions prediction is adopted to test the effectiveness and practicality of this model compared with the alternative models. The results show that the proposed model outperforms other six competing models in accordance with two measuring indices, and suggestions on emissions mitigation are proposed based on the prediction results. [ABSTRACT FROM AUTHOR]

In this paper, a tramway's system model is established to evaluate its longitudinal dynamics performance, where the model includes multiple masses point connecting together powered by the catenary and a hybrid energy-storage system which consists of a battery pack and ultracapacitor pack. To explore an optimal or appropriate real-time power distribution among these energy sources, a dual fuzzy logic control-based energy management strategy combined with wavelet transform and multi-objective optimization is proposed. The control parameters of the dual fuzzy logic controller are adjusted using a multi-objective particle swarm optimization algorithm while considering the daily operating cost of the tramway. To verify the effectiveness and validity of the proposed model and energy management strategy, its simulation results are further compared and verified with the field experimental results of a real tramway and a real driving cycle in China. • A improved simulation model is presented for a multi-sources hybrid tramway. • A optimal strategy combined with fuzzy logic, wavelet transform and PSO is proposed. • The dual fuzzy logic controller is optimized by multi-objective PSO algorithm. • The strategy shows lower tramway daily operating cost and better energy efficiency. [ABSTRACT FROM AUTHOR]

Energy consumption is an international issue and plays an important role in the national energy security, especially for China of which the energy market is in transition. Accuracy and trustable forecasting of future energy consumption trends with nonlinear data sequences is very important for the decision makers of governments and energy companies. In this paper, a novel nonlinear grey Bernoulli model with fractional order accumulation, abbreviated as FANGBM(1,1) model, is proposed to forecast short-term renewable energy consumption of China during the 13th Five-Year Plan (2016–2020). The new model is discussed in details with the fractional accumulated generating matrix and the Bernoulli equation. Further, the Particle Swarm Optimization algorithm is used to search optimal system parameters. Based on the updated real-world data sets from 2011 to 2015, the FANGBM(1,1) model is established to forecast the total renewable energy consumption, hydroelectricity consumption, wind consumption, solar consumption, and consumption of other renewable energies, respectively. The FANGBM(1,1) model presents high accuracy in all cases and is also proved to be efficient to deal with nonlinear sequences. [ABSTRACT FROM AUTHOR]

The paper designed an information integration system for prediction of the spontaneous combustion in the coal stockpile, which is characterized by real-time data acquisition, temperature cloud field formation and parameter prediction via ZigBee wireless network and industrial laser scanner, along with the ABC-MGM(1,1) hybrid model of the moving GM(1,1) and Artificial Bee Colony (ABC) algorithm. In order to enhance the short-term prediction accuracy, a moving GM(1,1) (MGM) scheme has been provided by constantly modifying the data input; and the generation coefficient of MGM(1,1) model has been optimized by ABC algorithm. • Direct sensing and prediction method is provided to prevent spontaneous combustion. • Multilayer Zig-bee sensor network is designed to facilitate acquisition of parameters. • A novel grey prediction model is proposed based on GM(1,1) and ABC algorithm. • Better performance for hazard control is achieved in large Power plant in China. Spontaneous combustion of coal can lead to serious environmental pollution, safety hazards and huge economic losses. Hazard prevention and environmental protection requires effective monitoring network and efficient prediction methods. To acquire the real-time data of coal parameters we designed multi-layered Zig-bee based wireless sensor network. The shape of the coal pile is formed by the industrial laser scanner followed by the formation of 3D temperature point cloud field via Kriging interpolation. Afterwards, an integrated evaluation indicator Heat Loss Capacity (HLC) is proposed, which synthesizes various factors inducing spontaneous combustion. The real-time and high accurate HLC prediction is realized by using metabolic method to update the raw sequence of the GM(1,1) grey prediction model in cycle with the latest acquired sensor data. Finally, a hybrid model ABC-MGM(1,1) combining the ABC optimization algorithm with the metabolic GM(1,1) model is proposed. Experiment shows that the ABC-MGM(1,1) model has better performance in parameter optimization as well as HLC prediction, especially for short-term prediction. The proposed method has been utilized to predict spontaneous combustion of the stockpiled coal in Xutang power plant of China. The timely parameter acquisition by 25 Zig-bee nodes and effective prediction by the proposed model shows great practicability in safety and hazard prevention. [ABSTRACT FROM AUTHOR]

With growing attentions paid to ecological protection in recent years, the ecological operation of cascade hydropower reservoirs is becoming an increasingly significant problem in water resource system. Mathematically, the ecological operation of cascade hydropower reservoirs is a high-dimensional, nonlinear and strong spatiotemporal coupling constrained optimization problem. To overcome the premature convergence and stagnation search of traditional methods in resolving this problem, this paper develops a novel algorithm known as simplex quantum-behaved particle swarm optimization, where the probabilistic mutation operator is performed on historical best position of some individuals, and then the simplex neighborhood search strategy based on the dynamic probability identification is used to enhance the local exploration ability of the swarm. The numerical experiments of 17 classical test functions indicate that the presented method can achieve satisfactory results in both convergence speed and global search ability. The application results from China's Wu hydropower system indicate that our method has satisfying performance in reducing the ecological water shortage. Hence, this paper provides a novel effective tool for the complex ecological operation problem of cascade hydropower reservoirs. • An optimization model for hydropower system ecological operation is presented. • Simplex quantum-behaved particle swarm optimization is proposed to address it. • Nelder–Mead simplex search and mutation operator can improve swarm diversity. • SQPSO obtains satisfying results in benchmark functions and engineering cases. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

• Optimize the electricity prices based on the DR characteristics of multi-type users. • Construct a flexible power retail portfolio optimization model. • Propose a flexible electricity retail strategy with integrated DR. Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

In the context of small sample and poor information, the data often change rapidly and interact with multiple factors which make it a challenge to analyse and predict multivariate sequences efficiently. Due to the fact that real systems usually show fractional order characteristics, the aim of this paper is to consider the approach improving the forecast results of multivariate interval grey prediction model via exploiting fractional accumulation. Noting that fractional accumulation could disturb the exponential law, the connotation prediction method is introduced to balance the disturbance. Correspondingly, a fractional connotation prediction method is constructed. In addition, traditional background value coefficient is optimized by using the particle swarm optimization algorithm (PSO). Therefore, a multivariate interval grey fractional accumulative connotation prediction model with optimized background value coefficient is constructed, in which the interval grey number time series are transformed into kernel series and radius series. Finally, the developed model is applied to clean energy prediction in China to verify the feasibility and validity. [Display omitted] • A multivariate interval grey prediction model via fractional accumulation is bulit. • The dynamic background value is optimized by integral mean value theorem and PSO. • The developed model is applied to model clean energy datum in China. [ABSTRACT FROM AUTHOR]

• The primary requirements of multi-objective have been recognized and guaranteed. • MOSRR has been proposed to alleviate the fierce competition among multiple objectives. • MOSRR can achieve the equilibrium of primary and secondary requirements. • MOSRR improve multi-objective at the expense of a reduction in power generation. • MOSRR can combat runoff reduction by minimizing the damage of primary requirements. Multi-objective requirements including water supply, hydropower generation, water–sediment regulation (WSR), and inner-river ecosystem maintenance should be fully valued in the operation of reservoirs located on a sediment-laden river. In order to balance the satisfaction of multiple objectives with intense competition and guarantee the primary requirements preferentially, this paper presents a novel framework that incorporates the recognition and classification of primary and secondary requirements of multiple operation targets, a set of principles for multi-objective synergetic regulation of reservoirs (MOSRR) following the theory of synergetics, and the optimization model construction of MOSRR considering the maximization synergetic degree of multi-objective. The Analytic Hierarchy Process and Particle Swarm Optimization algorithm are employed to determine the weight and solve the MOSRR model, respectively. MOSRR is applied to a system of cascade reservoirs along the Upper Yellow River in China considering climatic and hydrologic variability. The results from MOSRR are compared to those from a traditional multi-objective optimal operation model. It is found that the guarantee of multi-objective requirements from MOSRR have been improved visibly compare with the traditional model at the expense of a slight reduction in the hydropower generation (especially the secondary requirement of the energy output in the dry season). Also, MOSRR can mitigate the effects of runoff reduction through minimizing the damage of primary requirements, which is more appropriate for multi-objective reservoir operation with limited water in sediment-laden river. The proposed MOSRR framework can be applied to other large-scale reservoir systems with stiff competition in water resources. [ABSTRACT FROM AUTHOR]

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide. [ABSTRACT FROM AUTHOR]