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• 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]

The application of a photovoltaic (PV) system for zero energy buildings is crucial to balance energy consumption. This paper investigated the potential of PV systems for buildings in different climate zones of China. The tilt angle, orientation, plot ratio, PV conversion efficiency and location were selected to study their influences on electricity production. The performance of the PV system was analyzed based on a residential building, and employed RETScreen in combination with TRNSYS. The results indicate that southwest China is the best place to develop zero energy buildings. Low-rise residential buildings can realize zero energy in China when the PV conversion efficiency is higher than 20%. However, an energy balance only occurs in the southwest when the PV conversion efficiency is 10%. For different building heights, with a high conversion efficiency (20%), the energy consumptions rates are less than 15 kW h/m2a all over China. In the southeast, the effect of the building shape on energy generation can be ignored due to the high solar elevation angle in the summer. Although the optimal tilt angle varies for different cities, the energy deviations are less than 10% when tilt angle is within 10° of the optimal angle. This study proposed a formula to evaluate the potential of a building PV system. The maximum difference between the simulated and calculated results is approximately 10%, which is reasonable and acceptable. This paper provides some references for the application of PV systems in zero energy buildings. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

High-speed rail (HSR) networks in China profoundly altered China's urban tourism spatial patterns. This paper examines the characteristics and evolution of spatial patterns of the urban hinterland before and after HSR network. Economic relation model and spatial analysis in ArcGIS were utilized on 338 national prefecture-level administrative units. The results show: (1) HSR strengthens tourism-based economic relationships between cities, and demonstrates a “corridor” effect of the spatial distribution of the change rates of tourism external economic relationships; (2) Center cities with larger tourism comprehensive scale are enhanced with expanding trans-province hinterland; the hinterland of central cities are enlarged, of which spatial linkages between the hinterlands are increased; (3) the competition and difference for the hinterland of central cities are intensified. Based on these, this paper proposes a tourism spatial structure with 19 urban agglomerations, 6 1st-class and 21 2nd -class tourist economic zones, and strategies for optimizing China's tourist space. [ABSTRACT FROM AUTHOR]

Emergency supplies pre-positioning is crucial to improve the speed of response and mitigate the effects of disasters. According to the requirements of emergency preparedness for both civilian and military operations in China, the paper presents a problem of integrated civilian–military emergency supplies pre-positioning, the aim of which is to maximize both the expected military operational efficiency and the expected civilian operational efficiency while minimizing the overall cost of pre-positioning. To solve the problem, the paper proposes a solution method that uses a multiobjective algorithm to evolve a population of main location solutions and uses another single-objective algorithm to search optimal allocation sub-solutions for each main solution. Among different heuristics, we select water wave optimization (WWO) to design a multiobjective algorithm for the main problem and select biogeography-based optimization (BBO) for the subproblem, and demonstrate that they have competitive performance advantage in comparison with a set of well-known algorithms. [ABSTRACT FROM AUTHOR]

• An improved box-plot is used to modify the outliers in the original carbon price data. • The optimal parameter structure of the individual model is determined by a novel comprehensive evaluation criterion. • The adaptability between the optimum forecasting model and the predicted subseries is ensured by an original matching strategy. • A new hybrid optimization algorithm is employed to optimize the ensemble models parameters. Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. [ABSTRACT FROM AUTHOR]

Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1, r , ξ , Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed. • The highlights of this paper are as follows: • A new grey model is proposed to forecast the hydropower generation in China. • This paper studies the optimization algorithm and implementation process of the new model. • It solves the problem of low accuracy of traditional model in China's hydropower prediction. • Finds can help government of China formulate reasonable power policies. [ABSTRACT FROM AUTHOR]

This paper construct a CCHP and renewable energy based hybrid energy system driven by distributed energy resources (DERs CCHP). Then, the paper constructs performance indexes from energy, economic and environment. Thirdly, the paper proposes a multi-objective optimization model for DERs CCHP system under four optimization of energy rate (ER), total operation cost (TOC), carbon dioxide emission reductions (CER) and joint optimization. Finally, Guangzhou Higher Education Mega Center (GHEMC) in China is taken as the object for comparatively analyzing the operation performance of DERs CCHP system and CCHP system driven by natural gas (NG CCHP system). Results show: Joint optimization mode could balance the results of different optimization modes. DERs CCHP system shows better operation performance. ER of ER optimization, CER optimization and joint optimization are higher than that of NG CCHP system. NPV of all optimization modes is positive, IRR are bigger than the expected yield rate. DERs CCHP system could reduce CO 2 emission by utilizing wind energy and solar energy to replace NG. The sensitivity analysis indicates the performance of the DERs CCHP system will become better with the increase of chiller COP, decrease of NG price and wind-photovoltaic equipment cost. [ABSTRACT FROM AUTHOR]

This paper searches for a deployment method with minimum sensor nodes to achieve agricultural intelligent monitoring. Because of the various features, topographies and complicated weather conditions, the selection of the location for deploying sensor nodes is difficult, especially in hilly areas. This paper puts forward a sensor node deployment algorithm for agricultural intelligent monitoring based on an optimized theory. It first develops a mathematical model for the node location selection. During the solution process, the features of the locations are of primary concern, as a location with more features is easier to choose than other locations. Next, a theoretical analysis and experimental simulation are carried out. Finally, OASNDFA is used in 3000 mu orange orchards in China. The result demonstrates that OASNDFA could perform relevant monitoring tasks with fewer monitoring nodes. [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]

Discharge for wastewater treatment plays a key role in improving the water quality, thereby guaranteeing living quality of citizens. With high-speed economics growth and economics reforming, total amount of China's discharge of wastewater treatment is sharing high uncertainty, leading to many difficulties in accurate forecasts of discharge of wastewater treatment. Based on grey system theory, the hyperbolic time-delayed term is introduced in this paper to develop a novel forecasting model in order to deal with uncertainties of China's sewage discharge forecasting. The key nonlinear parameter of the proposed model is determined by the Grasshopper Optimization Algorithm. A series of practical numerical cases prove that the proposed model is reliable in comparison with six existing models are used for comparison. Then we apply it to predict the behavior of sewage discharge in China, those results against demonstrating the model our proposed has more satisfactory prediction precision. [ABSTRACT FROM AUTHOR]

Reliability is a prerequisite for community integrated energy systems to provide consumers with uninterrupted high-quality energy supply. However, it is still challenging to consider reliability in the planning of community integrated energy systems due to the complexity of energy conversion relationships. Conventional reliability-constrained planning methods usually form a bi-level optimization problem in which the planning scheme optimization and reliability index calculation are separated into two subproblems. In this paper, a novel reliability-constrained planning method for community integrated energy systems is proposed. First, the fault incidence matrices are employed to construct the analytic reliability evaluation model of community integrated energy system. Then, the reliability evaluation model is linearized and explicitly integrated into the planning model, forming a mixed-integer linear programming model. This model can be efficiently solved by mathematical optimization algorithms while effectively balancing the operation cost and reliability at the planning stage. Case studies are conducted based on a practical community integrated energy system in North China to verify the effectiveness of the proposed method. • The reliability calculation is transformed into inequality constraints for planning. • The reliability-constrained planning is a mixed-integer linear programming model. • Challenge posed by complex energy conversion relationships in planning is addressed. • Optimal configuration is efficiently obtained considering economy and reliability. [ABSTRACT FROM AUTHOR]

Energy planning for sustainable development plays an important role in guiding the future of local, regional and national energy systems. This paper presents a multi-objective energy planning methodology for regional distributed energy site selection based on natural gas (NG) CCHP systems in a high-tech industrial park in China. The energy planning developed in this paper includes four hierarchies: the planning objective; pretreatment, including load calculation and available resource estimation; site selection and configuration; and implementation. The treatment procedures in each hierarchy are described. Particularly for site selection and the configuration of CCHP schemes, multi-objective optimization is used to configure the capacities of the CCHP systems, and the multi-criteria decision making (MCDM) method is employed to evaluate their integrated performance, including energy generation, and impact on the environment, economy and society. The analysis results indicate that this methodology is at supporting the decision maker with regards to energy planning and site selection. [ABSTRACT FROM AUTHOR]

Boiler combustion optimization is an important method to improve the flexibility of thermal power units and ensures the stability of unit operation. However, time-variability of boiler combustion systems and time-consuming optimization methods pose great challenges for the use of boiler combustion optimization techniques because many optimization methods cannot be used online in practical engineering due to time constraints. In this paper, we propose a case-based reasoning optimization method based on grey-relational theory (GR-CBR) for online optimization of a boiler combustion system. After the introduction of the proposed algorithm, we discuss the potential of applying the proposed GR-CBR optimization method to a boiler combustion system; a case study of an existing fossil fuel power plant is conducted to demonstrate the feasibility of the proposed method. A least-squares support vector machine (LS-SVM) model of the boiler combustion process is established by using the real-time operation data of a 350-MW coal-based power plant. Based on the model, a non-linear global optimization algorithm is proposed to obtain the optimal case base and real-time data mining and online optimization are used to achieve efficient and stable boiler combustion optimization. The results of combining offline optimization with online querying show that this approach is suitable for online real-time combustion optimization, and provides support for power plant operators for optimization and condition monitoring to improve boiler efficiency, reduce NOx emissions, and ensure stable and efficient operation of the power system. • Novel approach for online real-time combustion optimization of coal-fired boiler. • Subjective and objective factors based adaptive allocation of cases attribute weights. • Data source and application of the method are based on Dandong Power Plant, China. • Boiler efficiency increase 0.68% without NOx excessive emissions. [ABSTRACT FROM AUTHOR]

• An optimization method for coal-fired power plant desulfurization systems is proposed. • The optimization method can effectively reduce the desulfurization cost. • An enhanced FCM clustering method is developed. Desulfurization systems in coal-fired power stations often suffer the problem of high operating costs caused by a rule-of-thumb control strategy, which implies great potential for optimization of the operation. Due to the complex desulfurization mechanism, frequently fluctuating unit load, and severe disturbance, it is challenging to determine the optimal operating parameters based on the traditional mechanistic models, and the operating parameters are closely related to the operational efficiency of the flue gas desulfurization system. In this paper, an operation strategy optimization method for the desulfurization process is proposed based on a data mining framework, which is able to determine online the optimal operating parameter settings from a large amount of historical data. First, Principal Component Analysis (PCA) is used to reduce data redundancy by mapping the data into a new vector space. Based on the new vector space, an enhanced fuzzy C-means clustering (Enhanced-FCM) is developed to cluster the historical data into groups sharing similar characteristics. Taking sulfur dioxide emission concentration as a constraint condition, the system is optimized with economic benefits and desulfurization efficiency as the objective function. When performing optimization, the group that current operating conditions belong to is determined first, then the operating parameters of the best performance are searched within the group and provided as the optimization results. The method is validated and tested based on the data from a wet flue gas desulfurization (WFGD) system of a 1000 MWe supercritical coal-fired power plant in China. The results indicate that the proposed operation strategy can appropriately obtain operating parameter settings at different conditions, and effectively reduce the desulfurization cost under the constraint of meeting emission requirements. [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]

Abstract: Recent advances in artificial intelligence, particularly in the field of multi-agent system theory and techniques, offer great promises in the development of decision support systems. This paper designs an agent-based regional agricultural economy decision support system (RAEDSS) to deal with complex decision problems. It introduces the architecture of the system, including interface agents, management agents, functional agents, model agents, information agents and knowledge agents and their interactions. Since dynamic analysis, evaluation, forecast, optimization and decision of regional agricultural economy are the central task of RAEDSS, this paper gives a detailed discussion on the decision processes and internal mechanisms in the system. Meanwhile, agent-based modeling is introduced to simulate and evaluate policy impact on rural development in different scenarios as an important part in RAEDSS. The simulation result shows that this agent-based agricultural development model is able to perform regeneration and is able to produce likely-to-occur projections of reality. The related issue such as building an agent based on the theory of classifier systems is also surveyed. [Copyright &y& Elsevier]

As the ecological climate deteriorates, common disasters including typhoons, earthquakes and ice disasters occur frequently in southern China. In particular, ice disasters can make severe ice coating on power line, resulting in power outage accidents. Not only do power supply disruptions cause major economic losses, but also they threaten people's lives in extremely cold conditions. Therefore, studying on the security early warning of ice coating damage to power line is significant and urgent. This paper adopts grey correlation analysis (GRA) to obtain 16 influence factors, of which the correlation degrees are over 0.74. In order to reduce the internal relevance among the impact factors, 5 common factors are extracted as the predictive model input values through factor analysis. With the input weight and hidden layer threshold optimized, a new extreme learning machine based on the adaptive whale optimization algorithm improved by chaotic sine cosine operator (CSCWOA-ELM) is established to forecast the ice coating damage to power line in southern China. To verify the accuracy and advancement of the proposed model, real data from the power repair projects under ice disaster are selected for experiments. The simulation results prove that the extraction of common factors can highly improve the prediction accuracy by approximately 27.80%. Compared with the 3 benchmark models, the CSCWOA-ELM model, of which the root mean square error (RMSE) is 0.02341 and the mean absolute percentage error (MAPE) is 1.82175%, shows a better performance in predicting the ice coating damage to power line. [ABSTRACT FROM AUTHOR]

Tight oil and shale oil are the important unconventional resources in shale systems, in which the tight oil is the most realistic and profitable resources. Due to special geological environments, lacustrine tight oil is different from marine tight oil in North America. Sweet-spotting is critical to develop these abundant resources, in which three aspects including geology, engineering and economy have been taken into consideration. In this paper, we proposed the definition of sweet spot zone and sweet spot interval in shale system, and discuss the geological significance & related technologies. Tight oil and shale oil should be assessed as a whole system during the future geological evaluation and technology development. The productivity of tight oil and shale oil will be increased by 30–50 million tons, making the economic development of oil in continental shale systems in China. • The world-level lacustrine tight oil accumulations in China. • Geological aspects of tight oil in shale systems. • Critical evaluation and development technologies for sweet spotting. Crude oil in unconventional shale systems, present as tight oil and shale oil, accumulates inside an oil kitchen in formations with coexistent sources and reservoirs. Organic matter present in oil shale is not yet mature and requires heating to convert it into crude oil. Oil exploration in shale systems involves the exploration of shale oil retained in source rocks and tight oil rich zones located near source rocks. Tight oil is a type of realistic unconventional oil resources in China. The marked increase in potential shale oil reserves, and exploration of these reserves, will result in a shale oil revolution similar to that experienced for shale gas. Based on a systematic comparison of geologic features of shale systems in the US and China, the geologic significance of the sweet spots in shale systems is proposed. This zone contains an abundance of unconventional oil in shale systems that can be explored and developed under current economic and technical conditions. The sweet spot zone refers to the zone in the tight oil rich zone which has industrial value within the scope of matured high-quality source rocks on the plane. The sweet spot interval refers to the high-productivity interval of tight oil which has industrial value through artificial stimulation. The main aim of oil exploration in shale formations is to identify the sweet spots. The distribution of the economic sweet spots in shale systems is evaluated by overlapping the geologic, engineering and economic sweet spots. Resource assessment techniques, the identification of logging data properties, high-resolution 3D seismic surveys, horizontal well production from well pads, and artificial reservoir development of sweet spots in oil-bearing shale formations can assist efficient development of oil. Globally, shale formations contain a significant volume of oil reserves. Currently, stimulated reservoir volume (SRV) techniques in horizontal wells in marine shale gas formations in the United States have average peak-productivity cycles of 10–15 years. To achieve commecial oil production in lacustrine shale systems in China, it is important to utilize large formation thicknesses and the high abundance of organic matter. In addition, the development of practical and economic techniques will result in an increase of productivity of tight oil and shale oil by 30–50 million tons, as well as the economic development of oil in lacustrine shale systems in China. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an intermodal network optimization model to examine the competitiveness of 36 alternative routings for freight moving from China to and beyond Indian Ocean. The proposed model, which is built upon the principles of goal programming, is able to handle multiple and conflicting objective functions such as minimizing transportation cost, transit time and transit time variability while ensuring flow continuity and transit nodes compatibility among the rail, road, ocean vessel, airplane and inland waterway transports. Transportation time and cost obtained from comprehensive industry sources are then fed into the intermodal transport network connecting two important Chinese origins and four Indian destinations, from which the most attractive routes are identified. In addition, the paper investigates several non-dominated transportation cases through sensitivity analysis tests, and analyzes the potential competitiveness and possible influences of future route developments to current transportation patterns in Asia. [Copyright &y& Elsevier]

In this paper, the premature failure of the integral droppers in high speed railway catenary in China was investigated. Series of microscopic characterization and analysis were conducted to identify the root causes of the unexpected failure. By numerically simulating the existing crimping behavior, distribution of the contact stress in the dropper was obtained. It was found that there was stress concentration at the dropper sliced end, which became the primary reason for the permanent distortion and damage on the droppers, and consequently resulted in the untimely final fracture with bending fretting fatigue acting. Through the failure analysis and numerical simulation, a scheme of the improvements in measuring the existing crimping was brought up. Meanwhile, a plan was also put forward to change the current crimping model and reduce the depth of indentation of clamp tube. Static tensile tests and bending fretting fatigue tests were carried out to evaluate the optional plans, which would help extend the service life of integral dropper in practice. [ABSTRACT FROM AUTHOR]

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

Abstract: With the start up of West–East Natural Gas Transmission Project, the construction of natural gas-pipeline will enter on a new era in China. The development tendency will be towards large-diameter, high-pressure and long-distance for natural gas-pipelines. Correspondingly, the life cycle cost of natural gas-pipeline networks is increasing gradually. The mainline system is a vital part of natural gas network systems. The investment required for the mainline system is enormous, usually accounting for 80% of the total investment for this system. In general, the investment required for a gas-pipeline depends on its operating parameters. Therefore, based on the characteristics of gas networks, optimization for investment becomes indispensable to gas networks design. A comprehensive and optimal mathematic model of a gas networks system is established in this paper which considers all the factors influencing the total investment of a gas networks system (e.g. pipe diameter, thickness, pressure, length, compression ratio, etc). From the standpoint of the characteristics of a model comprising both continuous and discrete variables, a new methodology, rank-optimization, is presented. On the basis of this model, a simple and visual high-pressure networks optimization program has been compiled. Furthermore, the developed optimization program has been applied to a practical project and the effects of operating parameters on the total investment have been analyzed. The simulation model in this paper is shown to be an effective method to solve optimization on mainline system in high-pressure gas networks. [Copyright &y& Elsevier]

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 rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]

In China, industry is responsible for 88.15% of the sulfur dioxide emissions, of which coal-fired power plants account for around 35.62%. Moreover, over 90% of power plant had installed FGD implementers. On the road to an optimal economic emission control, this article develops an ILP algorithm to optimize the sorting SO 2 emission control technology costs, based on a detailed database that contains large amounts of information for 1,966 thermal power plants, such as unit installed capacity and annual generating capacity, coal consumption, sulfur content, emission control technologies, operation duration and geographical location. The results demonstrate that the total operating costs will increase along with emission abatement. When the average desulfurization efficiency reaches 95%, 98% and 99%, the total operating cost in China is 54.4 billion CNY, 64.8 billion CNY and 78.6 billion CNY, respectively. Under the scenario that every power plant reaches the SO 2 emission limitation, the total operating cost of following the least-cost retrofitting laws could reduce by 5.05% to 15.31% nationwide. Finally, the study suggests that desulfurization equipment with high removal efficiency should be installed primarily in larger units, and policymakers could develop cost-effectiveness control strategies for coal-fired power plants using the results of this paper. [ABSTRACT FROM AUTHOR]

To improve projection efficiency of future energy demand, this paper proposes a hybrid optimization method, which is Bat Algorithm, Gaussian Perturbations and Simulated Annealing Energy Demand Estimation (BAG-SA EDE) model. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also enhances local search ability and speeds up the global convergence rate. The causality of energy demand and the selected factors is further investigated via the Stationarity, Cointegration and Granger causality tests. Then, BAG-SA algorithm is employed to optimize the coefficients of multiple linear and quadratic equations of energy demand estimation models. Results indicate that the proposed algorithm has higher precision and reliability than other single optimization methods, such as Genetic Algorithm, Particle Swarm Optimization or Bat Algorithm, and the quadratic form of BAG-SA EDE model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate energy demand of China from 2016 to 2030 in dissimilar scenarios. The forecasting findings show that energy demand in 2030 will be 4.6, 6.1 and 7.9 billion tce in three scenarios. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of “decomposition and ensemble” and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China ( i.e. , Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems. [ABSTRACT FROM AUTHOR]

Understanding the uncertainty of satellite aerosol products for different surface types is very important for algorithm optimization and related applications. Ground-based aerosol observations provided by AERONET are most often used to validate satellite aerosol products. However, AERONET ground-based sites are unevenly distributed in China, mainly concentrated in urban areas, with few sites in other surface types, leading to limitations in evaluating the accuracy of products for different surface types. To address this issue, the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is constructed to complement ground-based aerosol observations for multiple surface types in China and support the validation research of satellite aerosol products. The main purpose of this paper is to introduce SIAVNET and analyze the supplementary role it plays in the validation of satellite aerosol products for different surface types in China. Firstly, this study introduces the supplementation of SIAVNET to ground-based observations in different surface types. SIAVNET supplements 21 sites with multiple surface types, mainly supplementing vegetated areas, with 17 sites. In particular, SIAVNET places a site in bare land, which fills the gap of ground-based observation for bare land. Then, to further demonstrate the supplemental value of SIAVNET observation in the validation of satellite aerosol products, the accuracy of MODIS aerosol products with four different resolutions (i.e., 1 km, 3 km, 10 km, and 1°) for different surface types in China is evaluated using all ground-based observations. For bare land, the accuracy of different resolution products is obtained based on SIAVNET observations, with 50.91%–69.23% of the retrievals falling within the expected error, which fills the validation gap for bare land. For vegetated areas, compared to the AERONET-only based products evaluation, the use of SIAVNET significantly affects the validation results in grassland for the 3 km and 10 km products, with the proportion of retrievals falling within the expected error decreasing from 78.61%, 74.30% to 64.16% and 63.17%, failing to reach satisfactory levels (66%). For urban areas, supplemental SIAVNET observations have almost no effect on the validation results for the different aerosol products obtained based on AERONET. These results are expected to provide guidance for detailed satellite aerosol product validation over China using SIAVNET data. • SIAVNET complements the 21 sites with multiple surface types, especially filling the gap of observations in bare land. • By using the SIAVNET data, the accuracy of aerosol products over China is obtained. • The supplement of SAVNET-China data significantly influences the validation results of 3 km and 10 km products in grassland. [ABSTRACT FROM AUTHOR]

An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development. [Display omitted] • A new prediction mechanism is developed by complex effects. • A grey multivariate coupled time-delay model is proposed. • The model parameters are optimized by the whale optimization algorithm. • Comparative study shows that new model is superior to other various models. [ABSTRACT FROM AUTHOR]

To devise low-carbon cement clinker product, in-depth knowledge about the variation mechanism of cement clinker CO 2 emissions is the prerequisite. The determinants of cement clinker CO 2 emissions in Chinese cement industry have been inquired by several researches recently. However, the impacts and abatement effect of clinker quality on cement clinker CO 2 emissions have not been identified yet. In technological sense, there lacks an integrated quantitative model that can elucidate the dynamic mechanism between cement clinker CO 2 emissions and clinker quality. To fill this gap, this paper devises a CO 2 emission accounting framework integrated with optimization model to explore the dynamic mechanism between cement clinker CO 2 emissions and clinker quality. Based on survey data of 2 typical cement clinker production lines in China, the accounting framework is validated with measured data. Through numerical simulation within this framework, a set of sensitivity analysis is conducted. The results indicate that if the target KH (lime saturation ratio) of both production lines climb from −5% to +5%, the process CO 2 emission factor will give rise to an increase of about 16.4 and 14.8 kg CO 2 /t clinker, respectively. With the same variation, the fuel CO 2 emission factors of two production lines will gain 17.2 and 13.7 kg CO 2 /t clinker, respectively. Obviously, controlled decrease of clinker quality can reduce CO 2 emission of clinker manufacturing. The results of sensitivity analysis are robust and highlight the remarkable role of clinker quality in both process and fuel emissions. Besides, the integrated framework can provide in-depth investigation into the dynamic mechanism between CO 2 emissions and the other determinants. Using this framework, cement plants or industry can develop optimal clinker quality standards on a quantitative basis to achieve low-carbon clinker. [ABSTRACT FROM AUTHOR]

• A novel fractional grey Chebyshev polynomial Bernoulli model is proposed. • The structure of the NGBM(1,1,Tn) model is variable. • Comparative study shows that the new model is superior to other five grey models. Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted. [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]

Support vector regression (SVR) is widely used in the field of wind speed forecasting because of its excellent nonlinear learning ability. However, the drawback of SVR is the model selection problem, which has the high complexity O (K × m 3) including kernel function selection and parameter selection. To solve this problem, this paper proposes a multi-kernel SVR ensemble (MKSVRE) model based on unified optimization and whale optimization algorithm (WOA), where the MKSVRE model is used to solve the kernel function selection problem, and the unified optimization and the WOA are used to solve the parameter selection problem. The proposed model provides an alternative without the need to specifically select a kernel function and thus enhances the adaptability of SVR to diverse data. In addition, the unified optimization takes into account the interactions between models and achieves a global parameter selection. The proposed model is tested by simulations on wind speed data from Shandong Province, China. By comparing the prediction results of the proposed model, the single kernel SVR models, the models before and after optimization, and six other models, the effectiveness of the proposed model is confirmed. • A multi-kernel SVR ensemble model is proposed for wind speed forecasting. • A unified optimization is proposed to find the global parameters of the ensemble model. • Using a whale optimization algorithm for parameter optimization. • The validity of the proposed model is verified using the case of Shandong Province, China. [ABSTRACT FROM AUTHOR]

• A doublet system was selected to carry out reinjection tracer test with sodium naphthalenesulfonate as the tracer. • 1D and 3D models were established to simulate and optimize the multiple exploitation-reinjection schemes. • A dominant fissure channel with a length of 513.7 m was shown between the exploitation well and reinjection well. The imbalance between large-scale exploitation and reinjection year by year has led to the decline of the water level of the geothermal reservoirs in the North of China. The reinjection tracer test was carry out based on the doublet system in the Xianxian geothermal field in China with sodium naphthalene sulfonate as the tracer in this paper. The 1D and 3D models were modeled on the software COMSOL in this study to simulate the predictive analysis of multiple exploitation-reinjection schemes and optimize the design. The monitoring data of the tracer concentration in the water samples collected at the wellhead of the exploitation well showed that the tracer was firstly detected on 30th days after the tracer test started. Afterwards, the tracer concentration reached a peak on the 100th day and then gradually decreased until ∼ 140th day, with a tracer recovery rate of 0.0225%. And one dominant fissure channel between the exploitation well and reinjection well with a length of ∼ 513.7 m was shown in the test. The thermal breakthrough time of the exploitation well was defined as the time when the temperature decreases by 2 °C after 100 years of geothermal exploitation. Based on this, the optimal exploitation solution of the geothermal resources was proposed as the spacing between the exploitation well and reinjection well should be set as ∼ 375 m and the exploitation and reinjection volume is 75 m3/h. [ABSTRACT FROM AUTHOR]

Biomass, such as agricultural straw, can be converted into briquette fuel using technology to expand the possible applications of biomass and improve biomass utilization efficiency. The major machines required in a briquette fuel system, such as those used for drying, chopping, briquetting, and cooling, have become more efficient. However, a biomass briquetting fuel system requires special machines to reach a high character index, and all units in the system must match and be combined to produce an optimum system that satisfies multiple objectives, such as economy, environmental protection, stability, and large-scale operation. In this paper, a mathematical model for a synthesized evaluation was established according to theories of grey relational analysis (GRA) and the analytic hierarchy process (AHP). This model was used to select a biomass briquette fuel (BBF) system scheme considering hierarchies of economy, cleanliness and environmental protection, production capacity, product quality, and production stability, along with 20 other indices, including capital investment, dust content, drying capability, briquette rate, and the machine repair cycle. The most significant factors influencing each hierarchy were analyzed using a sensitivity analysis. Based on the GRA and AHP theories, an optimal scheme was selected for a fully operational 2 × 10 4 t/a cornstalk briquette fuel plant in China. The optimum scheme included six sets of briquetting machines with a capacity of 2 t/h + three sets of chopping machines with a capacity of 5 t/h + six sets of drying machines with a capability of 2 t/h + 1 set of cooling machines with a capacity of 12 t/h. The evaluated indices and weight coefficients were chosen objectively, and the comprehensive and technical performances of the selected BBF system scheme improved. These results provide a reference for the scheme selection and operation of large-scale BBF systems. [ABSTRACT FROM AUTHOR]

Due to the fact that escape panics are unexpected and dangerous, which excludes real-life experiments because of technical difficulties and ethical reasons, the evacuation simulations in real-life disasters are still rare. In this paper, the escape panics of classroom evacuation in real-life 2013 Ya’an earthquake in China are simulated and reproduced using the social force model. Firstly, a robust differential evolution (DE) is employed to calibrate and optimize the parameters of social force model to achieve a desired nonlinear evacuation speed which is consistent with real-life video data. It is demonstrated the DE-calibrated social force model can reproduce the characteristics of pedestrian flow in the real-life earthquake evacuation including the nonlinear evacuation speed curve and the pedestrian position distributions at different time. Moreover, the trained evacuation leader has remarkable impact on the evacuation process which can be beneficial to maintain the calm and order of crowd, decrease the desired velocity of crowd, and thus avoid fatal accident to the most degree. Finally, different layouts of classroom have similar evacuation efficiency because different internal layouts can only change partial flow of pedestrians in the left and right corridors, and total flow aggregating in the exit of classroom is still unchanged. However, total evacuation time can be sharply reduced to 17 s from above 36 s, and evacuation efficiency has almost doubled when the back door is open, which means we should try to keep all the emergency exits open in order to provide the crowd with more chances to escape when disasters occur. [ABSTRACT FROM AUTHOR]

Blind application and extensive copy of building energy saving technologies have been found very common through investigation in China. Emphases should be put on the suitability assessment when selecting and optimizing building energy saving technologies. This paper created an assessment method, namely an assessment framework to assess the suitability level of building energy saving technologies from a holistic point of view. Fuzzy analytic hierarchy process was adopted. 3 factors and 8 sub-factors were included in the framework. The office buildings were classified into 3 types to calculate weights of factors and sub-factors. The assessment framework was established for each type of office buildings. 20 energy saving technologies from surveyed cases was selected as case study. Ranks of suitability level of the assessment objects were obtained for each type of office buildings. The assessment results could be referred when selecting building energy saving technologies in the design stage. [ABSTRACT FROM AUTHOR]

Oil production and consumption is of great importance for the sustainable development and management of energy and environment. The forecasting of oil imports dependence caused by the gap between production and consumption is particularly crucial in the strategic deployment of oil development. However, researches on oil import dependence forecasting are often limited by the small size of data samples and assumptions, and the previous single-objective optimization algorithms only focus on the improvement of forecasting accuracy but ignore the stability. Therefore, in order to overcome the shortcomings of researches, in this paper, a hybrid forecasting system for oil imports dependence forecasting based on fuzzy time series and multi-objective optimization algorithm is proposed considering the accuracy and stability simultaneously to achieve the balance and optimality and bridge the limitations of small sample forecasting. The proposed forecasting system is compared with other small sample forecasting models and the fuzzy times series model with traditional interval partition methods. The results show that the proposed system is superior to the traditional methods in all indicators for oil import dependence forecasting. Otherwise, the out of sample forecasting results provided in our research indicate that the oil import dependence will maintain an upward trend, but the rate of increase will slow down. The research results can not only provide the basis for the planning and control of oil import and safety, but also benefit the perceptions of oil price trend in the world energy market and the adjustment of energy market structure. • Develop a novel hybrid forecasting model for oil imports dependence forecasting. • Improve fuzzy time series model for the small sample forecasting. • Consider the forecasting accuracy and stability simultaneously in forecasting. • Search the optimal interval length and weight coefficient for fuzzy time series. [ABSTRACT FROM AUTHOR]

Demand for electricity in China kept accelerating in recent years; moreover, there exist serious mismatches among the distribution of power demand, energy resources, and power generation infrastructure across different regions in China, both of which indicate a necessity of a holistic and integrated approach to the strategic planning and development of its power industry. Material benefits could be realized by ensuring that the long term development of the power system is optimized by taking into consideration the different regional dynamics and characteristics. This paper proposes a multi-region optimization model that can deliver insights into how planning of the long term development of China’s power sector could minimize the total cost of China’s power sector by considering regional variations in availabilities of resources and inter-region power transmission line capacity. A case study considered how investment decisions to expand and alter the existing generation mix could be optimized across a timeframe from 2011 to 2050. By comparing results between single and multi-region optimizations, it was possible to show the likely impact on how investment decisions would differ when regional differences were taken into account. The multi-region optimization arguably better reflects and considers conditions and challenges in the real world. [ABSTRACT FROM AUTHOR]

Coal blending is becoming increasingly common as more and more off-specification coals are received in coal-fired power plants, given the present coal market in China. This situation requires optimization of the operating parameters for matching the varying coal properties. The motivation for such optimization includes confirming good performance of the units regarding the security, the economy and environmental protection. However, the current adjustments to operation of the plant rely mostly on human experience because of the imperfections of existing theoretical models for coal-blend combustion. In this paper, a Case-Based Reasoning (CBR) method providing online decision-making for optimization of coal-blend combustion was investigated using cases representing successful operation of the unit for specific coal blends and loads. A case base containing a wealth of knowledge about optimal operation modes was constructed from a large number of cases. The development process for the CBR system includes case design, case evaluation, case generation, case retrieval and case reasoning. Case evaluation focused mainly on heating surface security, output capability, slagging tendency, comprehensive fuel consumption and pollutant emissions. Five indexes were introduced to quantify the above characteristics based on actual combustion parameters. A case-generating algorithm employing an evolutionary strategy was proposed in which the case base evolves while retaining new cases. Two methods for measuring case similarity - termed entirely similarity and eigenvalue similarity - were used for case retrieval. Run-time optimization strategies were recommended by the case-reasoning model based on the current operating status. The CBR system using Browser/Server framework were successfully applied to a 600-MW power plant, which provided an opportunity for coal-blend combustion optimization. [ABSTRACT FROM AUTHOR]

More than 257 sizable landslide dams were induced by the 2008 Wenchuan earthquake, which required timely risk mitigation measures. This paper aims to summarize the engineering measures taken to mitigate the landslide-dam risks; provide a method for optimal design of spillways to minimize the risks; and quantitatively evaluate practical options for spillway design with a physically-based dam breach model. The engineering measures taken to mitigate the risks of 37 highly hazardous landslide dams induced by the Wenchuan earthquake are summarized. An optimization method is proposed and applied to the spillway design for the Tangjiashan landslide dam, which is the largest one caused by the Wenchuan earthquake. It is found that the peak outflow rate could be reduced from the recorded 6500 m 3 /s to 2900 m 3 /s using the optimal design. A spillway with a shallow and broad cross section is suitable for low-erodibility dams, while a spillway with a deep and narrow cross section is suitable for high-erodibility dams. A spillway with a large longitudinal gradient is beneficial for high-erodibility dams with large dam widths and gentle downstream dam slopes. A spillway close to one bank can reduce the peak outflow rate if the bank is erosion resistant. [ABSTRACT FROM AUTHOR]

The effective distribution of critical relief in post disaster plays a crucial role in post-earthquake rescue operations. The location of distribution centers and vehicle routing in the available transportation network are two of the most challenging issues in emergency logistics. This paper constructs a nonlinear integer open location-routing model for relief distribution problem considering travel time, the total cost, and reliability with split delivery. It proposes the non-dominated sorting genetic algorithm and non-dominated sorting differential evolution algorithm to solve the proposed model. A case study on the Great Sichuan Earthquake in China expounds the application of the proposed models and algorithms in practice. [ABSTRACT FROM AUTHOR]

Natural gas, an efficient, eco-friendly and clean green energy, has become one of the important energy structures of various countries in the world, accurately predicting the production of natural gas can help the national energy agency solve "gas shortage" problem. To accurately predict natural gas production in China, this paper establishes an optimized grey system model with weighted fractional accumulation generation operation (abbreviated as WFNGBM(1,1,N)). The proposed model has all the advantages of the GMP(1,1,N) model, NGBM(1,1) model and weighted fractional accumulation generation operation, which makes it have excellent prediction performance. Moreover, five outstanding intelligent optimization algorithms (whale optimization algorithm, marine predators algorithm, grasshopper optimization algorithm, equilibrium optimization algorithm and arithmetic optimization algorithm) are used to solve the hyperparameters of the WFNGBM(1,1,N) model. It is found that the WFNGBM(1,1,N) model has the characteristics of convertibility and small sample modeling, which indicates that it is a small sample prediction model with strong compatibility. After confirming the feasibility of the proposed model compared with its competing models by using natural gas production in Germany, Italy and Canada as examples, the proposed model is used to study China's natural gas production. The results show that this model is very suitable for predicting and analyzing natural gas production in China. Based on this, the WFNGBM(1,1,N) model is used to estimate China's natural gas production in the next three years, and some reasonable suggestions are given according to the development trend of natural gas production. • The concept of weighted fractional accumulation generation operation is given. • An optimized nonlinear grey Bernoulli prediction model is established. • Five algorithms are used to solve the parameters of the proposed model. • The proposed model is used to study natural gas production in China. [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]