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With respect to a traditional point-to-point (P-P) network, a hub-and-spoke (H-S) network not only uses a smaller number of links/paths but also utilizes the scale economy advantage on consolidated flows on hub–hub links and at hubs. However, the inevitable delays through hubs have always been a critical concern. Therefore, this paper develops an H-S model considering flow delay costs and applies the model to a logistics case in Eastern China. The integer quadratic term in the model's objective function is linearized using the algebraic method. Our model is applied to develop an H-S network for its 13-node express package delivery operation, using the particle swarm optimization (PSO) algorithm. The results show using the H-S can save more than 14.1% of the total cost annually. The model also provides an applied case to the H-S configuration, especially for urban express delivery logistics in China. [ABSTRACT FROM AUTHOR]

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China's power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid. [ABSTRACT FROM AUTHOR]

Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it is necessary to execute a train rescheduling procedure to rearrange the train paths of the out-of-gauge trains and the affected trains based on the fundamental timetable. For rescheduling the timetable, considering the blockades and the speed restrictions caused by the out-of-gauge trains, this paper proposed a time-space-state network representation for describing the out-of-gauge train rescheduling problem. A novel concept, speed allowance, is introduced to describe the train speed restriction due to the out-of-gauge trains. An integer programming model based on the time-space network is proposed to minimize the total train delay when running the out-of-gauge trains. The model can be solved by the rolling-time horizon approach for reducing computational time. A numerical example is conducted based on the conventional railway in China, demonstrating the solution performance of the model and the practical use of the methodology. Gurobi solver cannot obtain an optimal solution within 1 h when the planning-time horizon is greater than 120 min. With the rolling-time horizon approach, the rescheduled timetable can be obtained within 124 s for the 300 min planning-time horizon using 180 min rolling-time window. [ABSTRACT FROM AUTHOR]

As agricultural labor is decreasing, famous tea production is nearly hard to sustain and is getting more and more expensive in China. To improve the mechanization of tea plucking, this paper designs a self-propelled track-type tea plucking machine that integrates mechanic, hydraulic and electronics. The main parameters of chassis were optimized based on conditions of tea garden. And, three different kinds of cutter blades were designed and analyzed about vibration performance with finite element software. Also, a rigid-flex mixed motion model was established. Based on the model and plots, it is explicit that: 1) the blades move in a sine law; 2) the blades take a symmetrical deform from middle to end while moving; 3) the deformation increases along the blade from the center to sides. The single factor experiment revealed that both travel speed and speed ratio of cutting and travelling had a significant effect on quality indexes (integrity rate, unpicking rate, stubble unevenness) of plucked tea leaf. By universal rotary combination experiment of quadratic regression, the regression equations of three indexes about two experimental factors were established. And, the comprehensive optimization of three indexes was conducted with software Design expert. The optimal operating parameters of machine are as follow: travel speed is 0.41 m/s, speed ratio of cutting and travelling is 1.06; prediction of three indexes are that integrity rate could be reach to 78.26%, unpicking rate reduced to 0.82%, stubble unevenness reduced to 1.30 mm. The results of verification experiment are that: integrity rate could be reach to 77.41%, unpicking rate reduced to 0.87%, stubble unevenness reduced to 1.23 mm. These test indexes that are very close to the predicted values, all surpass the requests specified in the standard of tea plucking with machine. In all, the optimized parameter set is indeed synthetically optimal one that enables the wide use of this tea plucking machine, which can be used in the tea garden that has a lengthways slope less than 18°, a crosswise slope less than 20°, and a height of tea trees between 50-120 cm. From above, the tea plucking machine investigated in this paper provides an effective solution to present tea picking problem that is the bottleneck of tea industry. Further studies need to do for making this machine more automated and intelligent. [ABSTRACT FROM AUTHOR]

Geothermal heating is considered to be one of the low-carbon-energy technologies for building heating. Aiming at the problem that the operating cost and investment cost of geothermal heating systems are still high, the conventional geothermal heating system coupled with energy storage for office building heating is studied in this paper. Four operational strategy models of the coupled system are established based on time-of-use electricity prices. A genetic algorithm is used to find the optimal value of each decision variable using minimization of levelized cost of heat (LCOH) as the objective function. The influences of electricity and equipment prices on the optimal values of the decision variables are discussed. Four operation strategies are investigated. If only operating cost is considered in the optimization, comparison shows that the best operation strategy is the one giving high priority to use the energy storage tank for heating during the peak electricity period. However, if the investment cost is further considered in the optimization, the best operation strategy is the one using the energy storage tank for heat load peak-regulating. In addition, based on the minimization of LCOH, an optimal energy storage ratio is found for each scenario, and suitable conditions of using energy storage tank are discussed. The geothermal heating system coupled with energy storage can have a good performance when the peak-valley electricity price difference is higher than CNY 0.566/kW·h (USD 0.0847/kW·h)+ or the energy storage tank price is lower than CNY 900/m3 (USD 134.64/m3). The results obtained from this study prove that the cost of geothermal heating systems can be effectively reduced by choosing an optimal operation strategy and using an energy storage device with an optimal energy storage ratio. [ABSTRACT FROM AUTHOR]

Joint distribution means multiple clients were provided distribution services together by only one third-party logistics company. It is a unified plan and implementation used in distribution centres and a distribution activity implemented by multiple consortia. Many problems in distribution can be solved through the joint use of distribution warehouse, vehicles and reasonable logistics business, so as to optimize the overall logistics node and route arrangement. This paper mainly discusses the model of joint distribution of fast moving consumer goods, proposes three types of the optimization model of joint distribution system with Chaopi as an example. We draw the conclusion that Chaopi Trading Co., Ltd. is a joint distribution system optimization business model. This paper puts forward several basic distribution models and analyzes them in combination with practical applications, which has strong practical significance. Although the development of public distribution in China is not very fast, it is an inevitable trend. Through the efforts and explorations of the governments of various countries, there will be more and more choices of public distribution models. [ABSTRACT FROM AUTHOR]

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has. [ABSTRACT FROM AUTHOR]

In traditional opportunistic group replacement maintenance (OGRM) strategy for wind turbines, the reliability thresholds of different subassemblies are set the same and its two‐step optimization for maintenance cost is diseconomy. In this paper, an improved OGRM strategy for wind turbines is proposed. A uniform formulation on maintenance cost concerning different maintenance activities is presented. On the basis of this, an integrated objective function minimizing the maintenance cost is established, and the reliability thresholds of each subassembly of wind turbines are individually severed as the optimization variables of the objective function. The coordinate rotation method is utilized for solving this optimization problem. A maintenance case based on the proposed strategy from a wind farm in North China is implemented, which verifies its superior performance than the traditional opportunistic group maintenance strategy. [ABSTRACT FROM AUTHOR]

Longwall face fall in the open face area is one of the most difficult ground control problems in mining thick coal seams in China especially under difficult geological conditions. This paper has proposed a new flexible bolting and grouting technology for face support. The flexible support method includes face support using flexible coir ropes and face grouting with cement and accelerator mixtures. Numerical and laboratory studies of pullout tests are performed to further improve the flexible support performance including optimization of boreholes and flexible bolts diameters, and selection of grouting materials. The results show that (a) The optimized rope‐borehole diameter ratio is around 0.5 to ensure the best face reinforcement; (b) The grouting material (a mixture of ultrafine cement, mineral powder and sodium silicate) shows advantages of low cost, effective waste disposal, reduced setting time, and good pullout limit force and is recommended for industrial application with sodium silicate at a proportion of 10% by weight and mineral powder at 30%. The application of the new flexible bolting and grouting method in Chenmanzhuang mine shows improved face stability and reduced capital cost. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-layer coordinating optimization and control strategy involving fuzzy neural networks (FNNs) is proposed for industrial plant-wide control. This strategy includes a coordinator (top layer), local optimizers (middle layer), and local controllers (bottom layer). The coordinator determines and coordinates set-points of production indices for each process in the plant, aiming to achieve plant-wide optimal control. The local optimizers optimize individual unit and deliver the information to the coordinator. The controllers calculate optimal control inputs for the processes according to the set-points. Model predictive control (MPC) is used in this layer. Also used are FNNs with a gradient-based learning algorithm, which are trained to represent the models of the processes that are controlled by MPC. By constructing a Lyapunov function, convergence of the FNNs is established. Experiments are carried out in the largest zinc hydrometallurgy plant in China. The results demonstrate performance of our strategy in tracking the set-points and saving processing costs. Moreover, the strategy achieves a more optimal overall plant operation when compared to the same FNN-based MPC scheme that does not use the coordinator. [ABSTRACT FROM AUTHOR]

As the largest developing country in the world, China is currently facing the contradiction between power supply shortage and demand growth. Almost three quarters of the electricity supply of China is derived from thermal power (mainly coal combustion). A proper projection of electricity consumption is helpful to deduce China's dependence on coal as a source of energy and thus will be helpful for the health of the environment. Therefore, it is necessary for the government to accurately predict the electricity demand. This paper employs both rolling mechanism and differential evolution algorithm to improve the prediction accuracy of the original grey model. Then, data from the China Federation of Electric Power Industry Development and Environmental Resources Department was adopted as database to test both the efficiency and accuracy of the improved prediction model. Experimental results show that the proposed model clearly outperforms the original grey model with regard to prediction accuracy. In addition, the future electricity consumption and power generation of China have been forecasted until 2025. The results will be useful to guide the electricity supply planning of the power department to promote the balance of power supply and demand. [ABSTRACT FROM AUTHOR]

Natural gas has been widely promoted by Chinese government as a clean energy source due to its odorless and smokeless combustion process. As an important part of urban natural gas supply, coal bed methane (CBM) accounted for 3.4% of the China's total natural gas production in 2017 and had an increase of 9.2% year-on-year compared with last year. In response to government promotion and so as to maintain the steady production of CBM, some enhancing-production approaches have been taken in CBM fields, and one of the effective approaches is the installation of compressors in gathering networks. However, due to the complexity of CBM gathering networks, the pressurization scheme lacks guidance in practice and is mainly decided by experience. To guide the practical production process, this paper proposes a mixed-integer nonlinear programming (MINLP) model to optimize the pressurization scheme. The MINLP model considers the cost of facilities and electricity and maximizes the daily net profit of CBM field companies. Then, a two-stage improved genetic algorithm-particle swarm optimization algorithm is proposed to solve this problem. Finally, the model is applied to a real CBM field with 132 production wells, 10 manifolds and 1 central processing facility (CPF) in China. The result shows that the optimal scheme can increase the total production from 3.39 × 105 m3/d to 3.73 × 105 m3/d with a growth rate of 10.0%, and the daily net profit increases 2.49 × 104CNY/d (about 3.70 × 103USD/d). • An MINLP model for optimizing the pressurization scheme in CBM fields is established. • A tailored two-stage iGA-PSO algorithm for solving the MINLP model is proposed. • A real CBM field in China is studied to validate the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

• A method for calculating the fatigue life of threaded joint with multi-axis fatigue. • A method for optimizing threaded joint. • Simulated the fatigue life analysis of a threaded joint subjected to 140MPa ultra-high pressure. • Vibration fatigue is the key factor affecting fatigue fracture of the union joint thread. Ultra-high pressure fracturing technology has become more and more important in China's deep Shale Gas extraction. In recent years, fracturing manifold failure accidents have happened frequently and service life has shortened drastically. There are more than 94% of failure accidents that are caused by vibration fatigue of the union joint. In this paper, it was established as a 3d finite element model of the union joint based on finite element theory and fatigue theory. The author proposed a safety evaluation method based on fatigue life, and compared it with that based on strength. The results show that vibration is the key factor affecting fatigue life, and the fatigue life-based safety evaluation method is more suitable to describe the union joint safety. Therefore, set fatigue life as optimization goal and the key parameters of the union joint thread are optimized by orthogonal optimization method. The optimal results are obtained from the study range, namely that the thread turns 8, the pitch is 10.47 mm, the bearing surface angle is 24.5°, and the guide surface angle is 24.5°. In comparison to the initial union joint, the fatigue life of the optimized union joint is increased by two orders of magnitude. These studies improve the fatigue performance of the union joint and have important guiding significance for improving the safety evaluation level of the fracturing manifold system in this paper. [ABSTRACT FROM AUTHOR]

Accidents, bad weathers, traffic congestions, etc. led to the uncertainties of travel times in real-life road networks, which greatly affected the quality of individual’s life and the reliability of transportation system. This paper addressed the school bus routing problem in such a stochastic and time-dependent road environment. Firstly, the problem was set based on a single-school configuration, and the students were picked up at their homes, which was in line with the current situation of school bus systems in China. Thus, it could be regarded as an independent problem of school bus route generation in random dynamic networks, which could be solved as a variant of extended Vehicle Routing Problem. However, due to the fluctuation and uncertainty of link travel times, the arrival time at each stop including the destination was varying. Therefore, the selection of optimal path connecting the current service node with the next one was treated as a sub-problem in this study, where the reliability of travel times in the stochastic and time-varying network was highly concerned by such time-rigid commuters. To this end, a Robust Optimal Schedule Times model with a hard time windows constraint was built to generate a most cost-reliable route for school buses. By the use of Robust Optimization method, it was intended to minimize the worst-case total cost which combined the cost of earlier schedule delays with the disutility of travel times. It was also proved that the proposed model could be converted into solving a conventional problem in deterministic dynamic networks for a reduction of computation complexity, which provided the potential of applying to the practical problems. Finally, the validity of the proposed model and its performance evaluation was analyzed through a small-scale computational instance, where all the link travel times in the simulated network were attributed to both time-varying and stochastic. Then, a mathematical programming solver was used to find the exact optimal solution. The results indicated that the model was valid, and the necessity of considering the stochastic and time-dependent nature of transportation networks was also confirmed in the case study. [ABSTRACT FROM AUTHOR]

The ?1100 kV UHVDC wall bushing is the important power equipment in the ?1100 kV DC power transmission project of China. However, the reported literatures rarely mention its design method and the optimization of its insulation structure. In this paper, two specific designing schemes including the resin impregnated paper (RIP) condenser and the metalshielded core have been both adopted in the design of the wall bushing. Moreover, the actual operating environment and the rated parameters of the ?1100 kV UHVDC wall bushing have been analyzed in detail. Then, the nonlinear finite element method (FEM) analysis has been applied in the three-dimensional electric stress simulation of the UHVDC wall bushing. The electric field and temperature dependencies of the composite insulation used in the wall bushing have been taken into account. The E-field simulation results indicate that with the RIP condenser, the potential and electric field distribution can be much more uniform along the composite insulator of the bushing. Therefore, the compact design of the wall bushing can be obtained by this designing scheme. On the other hand, with the type of the metal-shielded core, the high E-field strength areas are mainly concentrated on the metal shield and the outer grading ring of the wall bushing. Therefore, the nonlinear FEM was applied in the structure optimization of the internal metal-shielded system used in the wall bushing. Finally, the maximum E-field strength of the wall bushing can meet the control requirement. Based on the simulation results and the optimization structure, the ?660 kV scaled prototype of the UHVDC wall bushing has been produced and passed through all the type tests according to the IEC 62199. The feasibility of the design of the ?1100 kV UHVDC wall bushing has been verified. This paper can provide the theoretical guidelines for the design, manufacture and operation of the UHVDC wall bushing. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on investigating the influence of weather conditions on the sensitivity analysis and optimization of a typical passively designed high-rise residential building. A holistic passive design approach combining a variance-based factor prioritizing and surrogate model based multi-objective optimization was previously proposed to explore the green building solution in the hot and humid climate of Hong Kong. The design approach is further extended for application into a broader spectrum of climates across the mainland of China, including the severe cold zone, cold zone, hot summer cold winter zone, temperate zone as well as hot summer warm winter zone. The relative weight analysis is first compared with the Fourier Amplitude Transformation Analysis (FAST) in prioritizing the weighting of design inputs for different climatic zones. The relative weight analysis is then proved a feasible alternative sensitivity analysis method when its corresponding multiple linear regression (MLR) model can achieve good prediction performance. Furthermore, a tuning program in R is developed to improve the prediction performance of surrogate models with the Support Vector Machine (SVM) algorithm under above climatic zones. The model fitting performance with SVM is proved to be greatly improved by modifying the Sigma and C parameters. Finally, optimum design options under the five climatic zones are discussed in relation to the outdoor thermal, ventilation and solar radiation conditions. This research explored the applicability of the proposed passive design optimization approach in diverse climates, and can therefore prompt decision-makers’ endorsement as a national green building design tool in the early planning stage. [ABSTRACT FROM AUTHOR]

With the largest installed capacity in the world, wind power in China is experiencing a ∼20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet unclear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and nonconvex energy generation constraints. The model is applied to simulate a regional energy system (Jing–Jin–Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data, and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly. [ABSTRACT FROM AUTHOR]

CO 2 enhanced shale gas recovery (CO 2 -ESGR) is a promising technology for addressing the global energy and environmental concerns by promoting the shale gas production and simultaneously reducing the CO 2 emissions. To provide a greater understanding of the environmental benefits of CO 2 -ESGR, a three-dimensional numerical model corresponding to the typical shale reservoir parameters for the Ordos basin in China is established. Results show that the shale gas production can be enhanced by 6.74% at a constant CO 2 injection rate of 0.03 kg/s for 50 years, while 9.467 × 10 7 kg CO 2 can be stably stored. By analyzing the impact of CO 2 injection rate and well distance, it is found that the shale gas recovery has non-monotonous variation with CO 2 injection rate and decreases with increase in the well distance before the occurrence of CO 2 -breakthrough. An optimization framework is also proposed to optimize the shale gas production as well as the CO 2 storage for different well distances for the constant rate injection (CRI) mode and the constant pressure injection (CPI) mode. When CO 2 -breakthorugh is the only constraint on the CRI mode, larger well distance is better and the optimal injection rate at well distance of 390 m is 0.0609 kg/s with a total CH 4 production of 4.252 × 10 6 kg and CO 2 storage of 96.08 × 10 6 kg. When adding the constraint on injection pressure, the optimal scenario restricts the CO 2 injection rate at 0.031796 kg/s and the well distance at 360 m. On the other hand, with the same maximum injection pressure constraint, CPI mode shows superior performance compared to the CRI mode both in CO 2 storage and shale gas production. The results in this paper could serve as a reference for the selection of optimal operation parameters and strategies for industrial scale development of CO 2 -ESGR technology. [ABSTRACT FROM AUTHOR]

• LNG point supply has the advantages of safety, economy, and environmental protection. • Incomplete construction regulations are the challenges of LNG point supply method. • LNG supply chain optimization model aims to minimize economic & environmental costs. • This paper employs the actual data of towns in the optimization model for research. • The sensitivity of construction cost is relatively high. Liquefied natural gas (LNG) point supply is one of the most flexible and convenient methods advocated by China for distributing energy to end-users such as towns and villages where it is difficult or uneconomical to construct gas pipelines. This study sorts out the advantages and challenges and designs the optimization model for LNG point supply in China based on a mixed-integer linear programming (MILP) model. In this model, the goal of optimization is to minimize economic and environmental costs in the construction and operation stages. By optimizing location-allocation, gas storage capacity, vaporization capacity, equipment selection, and pipeline distribution, the model meets the fluctuating seasonal demand for natural gas. This model design uses the actual construction and operation-related parameters of the vaporization station and pipeline, and applies the model to four different types of towns. Through sensitivity analysis, this study finds that the sensitivity of construction cost is relatively high. [ABSTRACT FROM AUTHOR]

Efficient irrigation scheduling is crucial for both improving crop production and saving irrigation water use in arid/semi-arid agricultural regions threatened by water shortage and soil salinity. However, irrigation scheduling optimization is hindered by the uncertainties of data and optimization model, and adopting the optimal irrigation scheduling is subject to farmers' acceptance. To effectively tackle these challenges, this paper presents a novel human-machine interactive framework for real-time irrigation scheduling (RIS). The developed modeling framework couples a simulation-optimization model, irrigation farmers, and a data assimilation procedure within the human-machine interactive framework for RIS. The proposed approach is capable of: 1) searching optimal irrigation scheduling through the simulation-optimization model; 2) making actual irrigation decisions based on farmers' experiences, knowledge, behaviors, or optimal solutions; and 3) updating soil water content based on the model simulations and real-time observations at each time period. The RIS is applied to a real-world case in a typical arid agricultural region of China. Based on the comparisons with historical irrigation records and a tradition simulation-optimization model, the proposed RIS can not only achieve higher economic benefit with less irrigation water allocation quotas, but also improve irrigation efficiency. This study contributes to the methodology by integrating computer model, real-time observations and farmers' experiences to optimization modeling framework for supporting sustainable irrigation water management. • A human-machine interactive method is proposed for real-time irrigation scheduling. • Computer model, observation, and farmers' justification are integrated via data assimilation. • The proposed method results in higher profit with less water application than either a conventional optimization model or the history records. [ABSTRACT FROM AUTHOR]

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. [ABSTRACT FROM AUTHOR]

Grassland plays a key role for the food security of China because of the large number of livestock raised in those areas. Thus, grassland degradation due to climate change and overgrazing is considered as one of the most severe environmental and economic threat for the future sustainable development of China. Photovoltaic water pumping systems for irrigation can play a fundamental role for the conservation of grassland areas. This paper investigates the geospatial distribution of the technically suitable grassland locations for the implementation of photovoltaic water pumping systems. The technically suitable grassland areas were taken as starting point to assess the optimal locations. The assessment of the optimal locations was conducted using a spatially explicit optimization model of renewable energy systems based on the cost minimization of the whole forage supply chain. The results indicate that the photovoltaic water pumping systems provide high potential for improving forage productivity, contributing to meet the local demand. The optimal areas are highly sensitive to several environmental and economic parameters such as increased forage potential yield, forage management costs, forage water requirements, ground water depth, forage price and CO 2 price. Most of the optimal areas are selected when the market forage price ranges from 300 to 500 $/tonne DM, indicating that the forage produced using PVWP technology for irrigation is already competitive compared to the imported forage. [ABSTRACT FROM AUTHOR]

A monitoring network that resolves the spatial and temporal variations of the water quality is essential in the sustainable management of water resources and pollution control. Due to cost concerns, it is important to optimize the monitoring locations so to use the least number of stations required to obtain the most comprehensive monitoring. The optimal design of monitoring networks is commonly based on the limited data available from existing measuring stations. The main contribution of this paper is the use of a numerical water quality model, calibrated with the available data. This model yields information on the water quality in any cross-section along the river, including the river reaches that are not monitored. Another contribution of the paper is the use of a matter-element analysis that allows for an objective division of the river in reaches that are homogeneous with respect to the water quality as assessed from multiple water quality parameters. The optimal monitoring network consists of one measuring station in each of these homogeneous reaches. The method has been applied to optimize the water quality monitoring network on the 1890 km long upper and middle reaches of the Heilongjiang River in Northeast China. The results suggest that the monitoring network improves considerably by relocating three stations, and not by adding extra stations. [Copyright &y& Elsevier]

With the proposal of carbon neutralization, countries pay much more attention to environmental protection. As waste electric vehicle battery (WEVB) has an important impact on the environment, its reverse logistics process has been a vital issue, in which an excellent reverse logistics network (RLN) becomes a prerequisite for waste recycling, cost reduction, profit increasement and efficiency improvement. However, reverse logistics network design (RLND) for WEVBs is still a significant challenge. In general, the amount and quality of the returned product in a RLN are highly uncertain, and the WEVB market in China is no exception to this. From a circular economic and environmental perspective, this paper is devoted to designing a multi-participants-based RLN for WEVBs, considering recycling and remanufacturing processes. Then, a fuzzy optimization model is proposed to determine the number and location of facilities in this RLN, promote sustainable development, reduce environmental pollution, and consider carbon emissions and two types of WEVBs. Finally, the results from a case study in Chongqing show good performance in cost reduction, profit increasement and efficiency improvement, which could significantly support the decision-making of WEVBs recycling. [Display omitted] • Build a multi-participants based collaborative optimization model for RLND. • Consider different recovery options and carbon emissions simultaneously. • Introduce the bill of material in recycling process of WEVBs. • Establish an appropriate number and location of different functional facilities. • Determine the flow and route of different items in RLN. [ABSTRACT FROM AUTHOR]

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]

As an effective technology to reduce traffic pollution emissions, the new energy vehicle industry has developed rapidly in recent years, and the sales of new energy vehicles have doubled in 2021. Accurately forecasting methods provide important references for industrial policy deployment, infrastructure construction and energy demand estimation. In order to predict the development trend of China's new energy vehicle industry under the limited data samples, this paper proposes an optimized discrete grey power model to fit the nonlinear relationship between grey information factor and temporal factor. Firstly, the grey differential equation and its discrete form are directly constructed by using power accumulated data, which simplifies the integral solution process of the traditional grey power model. Secondly, fractional accumulation operator is introduced into the discrete model to ensure the new information priority of the original data. Then, the heuristic algorithm is used to accurately estimate the new parameters of the proposed model. Thirdly, the proposed discrete model is the unified form of two existing discrete grey power models, which expands the scope of modeling and has higher adaptability. Simulation experiments and two numerical cases are used to verify the effectiveness of the proposed method. Finally, the proposed method is used to predict the annual sales and ownership of new energy vehicles in China. The predicted results show that by 2025, China's new energy vehicle sales will reach 8.84 million, accounting for about 24% of the total vehicle sales, surpassing the industry development target (20%) set by the Chinese government. [ABSTRACT FROM AUTHOR]

Many countries like China are in a dilemma between economic growth and environmental deterioration (e.g., smog weather). Simultaneously, a growing people concern the product's environmental performance along with their improving environment awareness. This paper focuses on the impacts of carbon footprint and low-carbon preference on the production decision of emission-dependent firms in the cap-and-trade system. The cap-and-trade system uses the total “cap” to attain environmental goals and allows “trade” to achieve the effective scheduling through market regulation. Under this mechanism, through analyzing the influence of carbon footprint and low-carbon preference on the market supply and demand, we build a production optimization model and develop the optimal decisions. Several interesting management insights on environmental preference and policy are concluded. [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]

Wang, Y.C. and Zhai, X.D., 2020. Research on B2C E-commerce and logistics optimization in coastal areas. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 461-464. Coconut Creek (Florida), ISSN 0749-0208. This paper studies the optimization of logistics in coastal areas business processes for B2C E-commerce companies. First, it analyzes the main problems faced by B2C E-commerce companies in China. On the basis, it fully expounds the necessity of optimization, and finally focuses on three B2C enterprise logistics Corresponding optimization strategies and key points are presented for key business processes. This paper proposes that B2C enterprises should continue to optimize logistics business processes to reduce the distance of logistics links and achieve seamless connection between links, further improving the efficiency and efficiency of B2C enterprises. The level of customer service reduces unnecessary operating costs, thereby enhancing the core competitiveness of B2C companies and lying the foundation for the implementation of corporate strategies. [ABSTRACT FROM AUTHOR]

Wang, Y.C. and Zhai, X.D., 2020. Research on B2C E-commerce and logistics optimization in coastal areas. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 461-464. Coconut Creek (Florida), ISSN 0749-0208. This paper studies the optimization of logistics in coastal areas business processes for B2C E-commerce companies. First, it analyzes the main problems faced by B2C E-commerce companies in China. On the basis, it fully expounds the necessity of optimization, and finally focuses on three B2C enterprise logistics Corresponding optimization strategies and key points are presented for key business processes. This paper proposes that B2C enterprises should continue to optimize logistics business processes to reduce the distance of logistics links and achieve seamless connection between links, further improving the efficiency and efficiency of B2C enterprises. The level of customer service reduces unnecessary operating costs, thereby enhancing the core competitiveness of B2C companies and lying the foundation for the implementation of corporate strategies. [ABSTRACT FROM AUTHOR]

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity. • An integrated optimization modeling framework is developed to achieve optimal labor productivity restoration in a warming climate. • Various uncertainties are addressed through ensemble climate projections and interval programming models. • Intensification of heat stress leads to a large decrease in labor capacity over China except for the Tibetan Plateau. • More losses in working hours are projected for moderate labor activities compared to light and heavy labor activities. • Less developed regions tend to have more working hours recovered by working overtime than air conditioning. [ABSTRACT FROM AUTHOR]

Abstract: A distributed energy system refers to an energy system where energy production is close to end use, typically relying on various small-scale energy generation, conversion and storage technologies. The Chinese government has recently expressed interest in promoting this type of energy system. The paper develops an optimization model to evaluate the economic feasibility of adopting a distributed energy system in a new residential community in Beijing, where grid coverage is already well developed and accessible. The economic implications of adopting different grid connection regimes are also assessed. Results show that compared to the more conventional approach of relying entirely on the grid for electricity supplies, a distributed energy system is cheaper when a connection to the power grid can still be used to draw some electricity during periods of peak demand. Additionally, the economic benefits of electricity buy-back provisions for the distributed energy system are found to be minimal. [Copyright &y& Elsevier]

Abstract: Current energy designs for a building in China focus on the energy efficiency of the whole building and thus often neglect the energy difference between different housing units in apartment buildings. The best design technique in terms of energy savings is not only to decrease whole building energy demand, but also to reduce the energy difference between different housing units to a relatively low level. This paper introduces an important index “energy performance difference between housing units” (EDH) to evaluate the drawbacks of conventional designs on a typical residential building in hot summer and cold winter zone. Then nine improved design options were considered as the possible strategies to diminish the EDH, based on a number of building simulations with the program DeST-h. Results show that the option 9 (add movable shading for the east facing windows and west facing windows, and reduce the U-values of the east walls and west facing windows), an improved design strategy according to the EDH of the current design, reduces the EDH to less than 4% that is much lower than other options and meanwhile ensures a decrease in whole building energy demands. A case study was carried out to validate the effectiveness of this index in optimization. As a conclusion, EDH is a very useful index for optimizing energy designs of apartment buildings and can be used in China to improve conventional building designs. [Copyright &y& Elsevier]

Abstract: Electricity is a special energy which is hard to store, so the electricity demand forecasting in China remains an important problem. This paper aims at developing an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. It is designed for making trend and seasonal adjustments which simultaneously presents the electricity demand forecasts. Four actual electricity demand time series in China power grids are selected to illustrate the proposed MA-C-WH model, and one existing seasonal autoregressive integrated moving average model (SARIMA) is selected to compare with the proposed model using the same data series. The results of popular forecasting precision indexes show that our proposed model is an effective forecasting technique for seasonal time series with nonlinear trend. [Copyright &y& Elsevier]

Abstract: The increasing dependence on imported oil and tremendous greenhouse gases (GHG) emission is making the diversification of primary fuel such as petroleum a critical vital energy and environmental issue in China. China is promoting bioethanol by mandatory use in nine provinces and the expansion is on agenda. This paper first reviews China’s bioethanol development. Next, suitable feedstock crops for expanded ethanol production are discussed. Particularly, bioethanol expansion by national application of E10 is investigated from perspectives of potential in bioethanol supply, projected ethanol demand, and the possible cost-effective bioethanol distribution system. It is calculated that by making use of un-used land for feedstock planting and introduction of improved feedstock varieties, potential bioethanol production capacity in China will be up to 25.33 million tons per year. Ethanol demand for national application of E10 is projected to be around 7 million tons per year. A linear optimization model is used to consider the economic costs of distributing bioethanol in China. The optimization result suggests that development of bioethanol industry may focus on Henan, Jilin, Anhui, Jiangxi and Sichuan basin. It also estimates 53.79 RMB per ton of bioethanol for downstream rail or truck transportation remain a relatively small fraction of total fuel cost. Thanks to the well developed railway network in China, more bioethanol can be distributed at a relatively modest premium distribution costs and with low environmental influences. [Copyright &y& Elsevier]

This paper considers the complexity in resolving the conflicts between mine drainage, water supply, and environmental protection for the coal basin of North China, and presents a management optimization framework that addresses these multiple conflicting issues simultaneously in the most cost effective manner. Due to the various unpredictable accidents which may occur in the coal mining process, such as water bursts, gas leaks, fire and collapse of coal beds, the beneficial use of drainage water from the coal mines is generally low. This case study attempts to address the problem of low beneficial usage for drainage water using the Jiaozuo coal mining district in Henan Province, China. By combining a finite-element groundwater simulation model with an optimization code, the economic benefits of using the drainage water as a stable water supply is maximized, while the adverse impact of mine drainage on the environment is controlled. The results indicate that the management model developed in this study achieves an excellent economic outcome and can serve as a potentially powerful tool for solving mining-related water management problems in the coal basin of North China. Citation Wu, Q., Hu, B. X., Wan, L. & Zheng, C. (2010) Coal mine water management: optimization models and field application in North China. Hydrol. Sci. J. 55(4), 609-623. [ABSTRACT FROM AUTHOR]

Abstract: The technical, economical and environmental performances of combined cooling, heating and power (CCHP) system are closely dependent on its design and operation strategy. This paper analyzes the energy flow of CCHP system and deduces the primary energy consumption following the thermal demand of building. Three criteria, primary energy saving (PES), annual total cost saving (ATCS), and carbon dioxide emission reduction (CDER) are selected to evaluate the performance of CCHP system. Based on the energy flow of CCHP system, the capacity and operation of CCHP system are optimized by genetic algorithm (GA) so as to maximize the technical, economical and environmental benefits achieved by CCHP system in comparison to separation production system. A numerical example of gas CCHP system for a hotel building in Beijing is given to ascertain the effectiveness of the optimal method. Furthermore, a sensitivity analysis is presented in order to show how the optimal operation strategy would vary due to the changes of electricity price and gas price. [Copyright &y& Elsevier]

Abstract: Lightweight envelopes are widely used in modern buildings but they lack sufficient thermal capacity for passive solar utilization. An attractive solution to increase the building thermal capacity is to incorporate phase change material (PCM) into the building envelope. In this paper, a simplified theoretical model is established to optimize an interior PCM for energy storage in a lightweight passive solar room. Analytical equations are presented to calculate the optimal phase change temperature and the total amount of latent heat capacity and to estimate the benefit of the interior PCM for energy storage. Further, as an example, the analytical optimization is applied to the interior PCM panels in a direct-gain room with realistic outdoor climatic conditions of Beijing. The analytical results agree well with the numerical results. The analytical results show that: (1) the optimal phase change temperature depends on the average indoor air temperature and the radiation absorbed by the PCM panels; (2) the interior PCM has little effect on average indoor air temperature; and (3) the amplitude of the indoor air temperature fluctuation depends on the product of surface heat transfer coefficient h in and area A of the PCM panels in a lightweight passive solar room. [Copyright &y& Elsevier]

Abstract: An ideal multi-objective financial decision support (MFDS) model for contractors should have ability of handling both the quantitative and qualitative inputs. This paper aims to develop a new MFDS model, which attempts to provide a more accurate way to support financial decision-makers in Chinese state-owned construction firms. Owing to the slow progress of passing the construction regulations, coupled with the inherited ownership problems in the Chinese booming construction industry, it is logical for Chinese project managers to make financial decisions with the consideration of the four objective functions (profit margin, risk factors, government relationship and market share), which are realistic representations of the present situation of Chinese construction industry. The proposed MFDS model is a practical method to support financial decision-making. For the model''s computational performance, Module 3 (stochastic universal sampling selection, adaptive crossover and adaptive mutation of Genetic Algorithm) is more superior. [Copyright &y& Elsevier]

By integrating the vehicle tracking system, discrete-event simulation algorithm, and evolutionary optimization algorithm, we developed HKCONSIM-Realtime, a decision-support platform created specifically for handling ready-mixed concrete operations. This platform is capable of (1) tracking the positions of concrete trucks and monitoring the motion and status of concrete deliveries in real time, (2) transforming the tracking records into data that provide updated input to simulation, and (3) optimizing the operations and logistics of concrete production based on simulation of the production system using the most current data. This paper presents an overview of the design and development of (1) the hardware and software modules, (2) the data flow and processing throughout the system, and (3) the role of the system in providing interactive, effective support for the human operator to attain cost efficiency. Case studies are given to demonstrate the functionality and application of the prototype system. [ABSTRACT FROM AUTHOR]

System simulation has proved to be an effective tool for planning and improving the performance of a construction process in many successful case studies. However, with the aid of a three dimensional (3D) visualization system, simulation technology will be engaged to its farthest-reaching potential. This paper presents a geographic information system (GIS)-based visual simulation method, in which system simulation techniques are integrated with visualization techniques. The GIS-based visual simulation system (GVSS) was developed by the authors. The GVSS is a simulation tool offering powerful planning, visualizing, and querying capabilities that facilitate the detection of logic errors in simulation models. The software also helps to understand the comprehensively complex modeled construction process, and is capable of organizing vast amounts of spatial and nonspatial data involved in simulation. A hydroelectric project, which will take place on the Yellow River in the northwest of China, is used as an example. An optimum equipment set scheme is determined by simulating a variety of scenarios taking place under different construction conditions. Likewise, other parameters, such as the construction sequence of dam blocks, the monthly intensity of the concrete process, and the construction appearance at the middle and end of each year, are obtained. Meanwhile, the complex processes of dam construction are demonstrated dynamically using 3D animation, which provides a powerful tool for quickly and comprehensively understanding the whole construction process. The GVSS has proven to be a helpful and useful tool for the design and management of concrete dams. [ABSTRACT FROM AUTHOR]

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations. [ABSTRACT FROM AUTHOR]

The creation of an efficient and environmentally friendly irrigation canal schedule strongly depends on the accuracy of the prespecified water demand (PSWD) process in existing scheduling practices and modeling studies. Overestimating water demand would result in spillage losses from over-irrigation, while underestimating water demand would lead to crop yield losses due to deficit water supply. However, prespecifying an accurate water demand often poses various challenges for both farmers and canal administrators, as it involves the temporal variability in agrometeorological conditions, the spatial diversity of soil properties, and the sensitivity of crops to water stress at different growth stages. Therefore, to reduce the uncertainty of prespecifying water demand and avoid the adverse effects of an inaccurate water demand on canal water allocation, this paper proposes an alternative canal scheduling model that maximizes canal scheduling service quality by targeting the ultimate objectives of the irrigation system (UOIS) without considering the prespecified water demand as the model input. The UOIS canal scheduling (UOIS-CS) model aims to optimize the canal water delivery schedule via simulation of soil water and salt migration using the Simultaneous Heat and Water (SHAW) model, to provide the most suitable soil moisture content and soil salinity for crops. The UOIS-CS model is used for the Hetao Irrigation District, Inner Mongolia, a typical agricultural watershed in China, where supplementary irrigation is performed after the autumn harvest (i.e., autumn irrigation) to leach excessive soil salt and preserve a moderate amount of soil moisture for the beginning of the next spring sowing period. To evaluate the irrigation performance of the UOIS-CS model further, a comparative analysis between the UOIS-CS model and the widely used PSWD-based canal scheduling (PSWD-CS) model is conducted. Compared with the PSWD-CS model, the proposed UOIS-CS model exhibits a superior irrigation performance in terms of leaching soil salt and preserving soil water, and a lower risk of canal overload operation during a higher water demand period. The improved performance of the UOIS-CS model is attributed to its greater adaptability to the spatial diversity of soil properties and the temporal variability in agrometeorological conditions, which originates from the full use of the equifinality of model performance metrics and model inputs. The UOIS-CS model provides managers with an effective approach to formulate a canal water delivery schedule with improved irrigation efficiency and lower overload risk. The proposed framework is generic and can be applied to non-crop growing seasons. • The canal scheduling method based on the ultimate objectives of irrigation system is well adapted to the environment. • Scheduling canals with prespecified water demand processes has a higher risk of canal overload operation. • Adopting the SHAW model to simulate soil water and salt migration in canal water allocation optimization. [ABSTRACT FROM AUTHOR]

Sustainable building retrofitting has attracted serious research attention in recent years as it can significantly reduce energy consumption and thus help achieve energy reduction targets set by governments around the world. In this paper, a sustainable retrofit decision-making model is developed to uncover the optimal set of retrofit solutions according to local climatic conditions, building features, and retrofit costs. Net present value (NPV), a common method for analyzing the feasibility of a building retrofit solution, is used to determine the optimal retrofit solution of existing high-rise residential buildings in a temperate zone and hot summer-cold winter zone, involving prototypical 1990s apartments of 116 and 53 m2 floor areas respectively. This reveals that the lighting system, wall insulation, and upgraded window glazing are essential optimal retrofit measures for the temperate zone, whereas the heating system and shading devices are also essential for the hot summer-cold winter zone. The results indicate that the optimal NPV can be obtained by pursuing a 40% energy saving, as it can result in energy saving of up to 50 kWh/m2/year and 95 kWh/m2/year at an average retrofit cost of approximately USD 1.30 and 3.20 m2/year in the temperate and hot summer-cold winter zones respectively. The sensitivities associated with the most influential optimal NPV and energy savings input parameters are critically analyzed; these can be used by decision-makers to determine the risks and uncertainties related to various cost-effective retrofit measures. The model can help in devising the most suitable sustainable retrofit measures for existing buildings commensurate with the energy reduction targets of policy and decision-makers in the two climate zones. It is also amenable to being adapted for other climatic zones to identify the most appropriate sustainable building retrofit for particular local climatic conditions and building characteristics. [ABSTRACT FROM AUTHOR]

Distributed wind power (DWP) needs to be consumed locally under a 110 kV network without reverse power flow in China. To maximize the use of DWP, this paper proposes a novel method for capacity planning of DWP with participation of the energy storage system (ESS) in multiple scenarios by means of a variable-structure copula and optimization theory. First, wind power and local load are predicted at the planning stage by an autoregressive moving average (ARMA) model, then, variable-structure copula models are established based on different time segment strategies to depict the correlation of DWP and load, and the joint typical scenarios of DWP and load are generated by clustering, and a capacity planning model of DWP is proposed considering investment and operation cost, and environmental benefit and line loss cost under typical scenario conditions. Moreover, a collaborative capacity planning model for DWP and ESS is prospectively proposed. Based on the modified IEEE-33 bus system, the results of the case study show that the DWP capacity result is more reasonable after considering the correlation of wind and load by using a variable-structure copula. With consideration of the collaborative planning of DWP and load, the consumption of DWP is further improved, the annual cost of the system is more economical, and the quality of voltage is effectively improved. The study results validate the proposed method and provide effective reference for the planning strategy of DWP. [ABSTRACT FROM AUTHOR]

In the context of the energy transition, energy system planning is becoming increasingly relevant for decentralized systems. Motivated by the strong increase of publications on municipal energy system planning (MESP), these studies are subjected to a bibliometric analysis in this paper. The 1235 articles, which are based on the Web of Science database, are examined using the R-tool bibliometrix. The study shows that China is the most important contributor with 225 articles, followed by the USA (205) and Germany (120). Most cross-country collaborations were conducted between China and Canada, focusing mainly on uncertainties in MESP. Among the institutions, the North China Electric Power University is responsible for most articles (42). The core journals on MESP are Energy, Applied Energy, Energy Policy, Energies and Renewable Energy, which published 37% of the 1235 articles. Publications on MESP focus on renewable energies, optimization and hybrid energy systems. Furthermore, district heating seems to be a core topic in MESP and is addressed in three of the top five most cited articles. The demonstration of global research trends in MESP can support researchers in identifying the relevant issues regarding this expanding and transforming research area. [ABSTRACT FROM AUTHOR]