Forecast of natural gas supply and demand in China under the background of “Dual Carbon Targets”

As a kind of clean energy which creates little carbon dioxide, natural gas will play a key role in the process of achieving “Peak Carbon Dioxide Emission” and “Carbon Neutrality”. The Long-range Energy Alternatives Planning System(LEAP) model was improved by using new parameters including comprehensive energy efficiency and terminal effective energy consumption. The Back Propagation(BP) Neural Network–LEAP model was proposed to predict key data such as total primary energy consumption, energy mix, carbon emissions from energy consumption, and natural gas consumption in China. Moreover, natural gas production in China was forecasted by the production composition method. Finally, based on the forecast results of natural gas supply and demand, suggestions were put forward on the development of China’s natural gas industry under the background of “Dual Carbon Targets”. The research results indicate that under the background of carbon peak and carbon neutrality, China’s primary energy consumption will peak(59.4×10^(8)tce) around 2035, carbon emissions from energy consumption will peak(103.4×10^(8)t) by 2025, and natural gas consumption will peak(6100×10^(8)m^(3)) around 2040, of which the largest increase will be contributed by the power sector and industrial sector. China’s peak natural gas production is about(2800–3400)×10^(8)m^(3), including(2100–2300)×10^(8)m^(3)conventional gas(including tight gas),(600–1050)×10^(8)m^(3)shale gas, and(150–220)×10^(8)m^(3)coalbed methane. Under the background of carbon peak and carbon neutrality, the natural gas consumption and production of China will further increase, showing a great potential of the natural gas industry.

Analysis of faulting destruction and water supply pipeline damage from the first mainshock of the February 6,2023 Türkiye earthquake doublet

In 2023,two consecutive earthquakes exceeding a magnitude of 7 occurred in Türkiye,causing severe casualties and economic losses.The damage to critical urban infrastructure and building structures,including highways,railroads,and water supply pipelines,was particularly severe in areas where these structures intersected the seismogenic fault.Critical infrastructure projects that traverse active faults are susceptible to the influence of fault movement,pulse velocity,and ground motions.In this study,we used a unique approach to analyze the acceleration records obtained from the seismic station array(9 strong ground motion stations)located along the East Anatolian Fault(the seismogenic fault of the MW7.8 mainshock of the 2023 Türkiye earthquake doublet).The acceleration records were filtered and integrated to obtain the velocity and displacement time histories.We used the results of an on-site investigation,jointly conducted by China Earthquake Administration and Türkiye’s AFAD,to analyze the distribution of PGA,PGV,and PGD recorded by the strong motion array of the East Anatolian Fault.We found that the maximum horizontal PGA in this earthquake was 3.0 g,and the maximum co-seismic surface displacement caused by the East Anatolian Fault rupture was 6.50 m.As the fault rupture propagated southwest,the velocity pulse caused by the directional effect of the rupture increased gradually,with the maximum PGA reaching 162.3 cm/s.We also discussed the seismic safety of critical infrastructure projects traversing active faults,using two case studies of water supply pipelines in Türkiye that were damaged by earthquakes.We used a three-dimensional finite element model of the PE(polyethylene)water pipeline at the Islahiye State Hospital and fault displacement observations obtained through on-site investigation to analyze pipeline failure mechanisms.We further investigated the effect of the fault-crossing angle on seismic safety of a pipeline,based on our analysis and the failure performance of the large-diameter Thames Water pipeline during the 1999 Kocaeli earthquake.The seismic method of buried pipelines crossing the fault was summarized.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

Low-frequency oscillation of train-network system considering traction power supply mode

The low-frequency oscillation(LFO)has occurred in the train-network system due to the introduction of the power electronics of the trains.The modeling and analyzing method in current researches based on electrified railway unilateral power supply system are not suitable for the LFO analysis in a bilateral power supply system,where the trains are supplied by two traction substations.In this work,based on the single-input and single-output impedance model of China CRH5 trains,the node admittance matrices of the train-network system both in unilateral and bilateral power supply modes are established,including three-phase power grid,traction transformers and traction network.Then the modal analysis is used to study the oscillation modes and propagation characteristics of the unilateral and bilateral power supply systems.Moreover,the influence of the equivalent inductance of the power grid,the length of the transmission line,and the length of the traction network are analyzed on the critical oscillation mode of the bilateral power supply system.Finally,the theoretical analysis results are verified by the time-domain simulation model in MATLAB/Simulink.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

Wireless Power Supply Based on MNG-MNZ Metamaterial for Cardiac Pacemakers

To solve the low power transfer efficiency and magnetic field leakage problems of cardiac pacemaker wireless powering, we proposed a wireless power supply system suitable for implanted cardiac pacemaker based on mu-negative(MNG) and mu-nearzero(MNZ) metamaterials. First, a hybrid metamaterial consisted of central MNG unit for magnetic field concentration and surrounding MNZ units for magnetic leakage shielding was established by theoretical calculation. Afterwards, the magnetic field distribution of wireless power supply system with MNG-MNZ metamaterial slab was acquired via finite element simulation and verified to be better than the distribution with conventional MNG slab deployed. Finally, an experimental platform of wireless power supply system was established with which power transfer experiment and system temperature rise experiment were conducted.Simulation and experimental results showed that the power transfer efficiency was improved from 44.44%,19.42%, 8.63% and 6.19% to 55.77%, 62.39%, 20.81%and 14.52% at 9.6 mm, 20 mm, 30 mm and 50 mm,respectively. The maximum SAR acquired by SAR simulation under human body environment was-7.14 dbm and maximum reduction of the magnetic field strength around the receiving coil was 2.82 A/m. The maximum temperature rise during 30min charging test was 3.85℃,and the safety requirements of human bodies were met.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Seismic performance evaluation of water supply pipes installed in a full-scale RC frame structure based on a shaking table test

As an important part of nonstructural components,the seismic response of indoor water supply pipes deserves much attention.This paper presents shaking table test research on water supply pipes installed in a full-scale reinforced concrete(RC)frame structure.Different material pipes and different methods for penetrating the reinforced concrete floors are combined to evaluate the difference in seismic performance.Floor response spectra and pipe acceleration amplification factors based on test data are discussed and compared with code provisions.A seismic fragility study of displacement demand is conducted based on numerical simulation.The acceleration response and displacement response of different combinations are compared.The results show that the combination of different pipe materials and different passing-through methods can cause obvious differences in the seismic response of indoor riser pipes.

CFD investigation of structural effects of internal gas intake on powder conveying performance in fuel supply systems for aerospace engines

Optimal design of gas intake in powder fuel supply systems is crucial for performance of aerospace engines.There is little research on the impact of intake structure on powder conveying performance.Three novel internal intakes were proposed,which are spherical,cube-shaped,and dome-shaped.After validation,CFD simulations demonstrate that fluctuation of mass flow rate of powders in the dome-shaped intake is reduced by about 73.3%compared with the annular external one.Variation trends of phase velocities are similar for the spherical and cube-shaped intakes,while those are similar for the annular external and dome-shaped internal intakes.Fluctuation of area of gas zone for the annular external and spherical internal intakes is larger than that for the cube-shaped and dome-shaped internal intakes.Pressure and relative pressure drop in the fluidization chamber have a stable stage,and fluctuation of relative pressure drop is small when dome-shaped internal intake is used.

International Supply Chain Cooperation and China’s Role

In recent years,the challenges facing the development of international supply chains have been increasing.Major country competition,COVID pandemic and geopolitical conflicts have impacted the development of international supply chains and continue to have negative effects.Against this background,the international community should cooperate to deal with the risks and challenges.

Study on green power supply modes for heavy load in Remote Areas

In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the applicability of related technologies,such as grid-forming storage and power load management,is studied,including grid-connection technologies,such as grid-forming converters and power load management.On this basis,three power-supply modes were proposed.The application scenarios and advantages of the three modes were compared and analyzed.Based on the local development situation,the temporal sequences of the three schemes are described,and a case study was conducted.The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.

Connecting the World for a Shared Future:Firmly Safeguarding Stable and Smooth Global Industrial and Supply Chains

In order to follow through on the important instructions of General Secretary Xi Jinping to safeguard stable and smooth global industrial and supply chains,the China Council for the Promotion of International Trade held in Beijing from November 28 to December 2,2023,the first China International Supply China Expo(CISCE).Premier Li Qiang attended and delivered a keynote speech at the opening ceremony of the CISCE&International Supply China Innovation and Development Forum.As the world’s first national-level exhibition themed on the supply chain,the CISCE,themed“Connecting the World for a Shared Future”,aims to create a new window for promoting high-level opening up,a new platform for fostering a new development paradigm,a new vehicle for building an open world economy,and a new practice for building a community with a shared future for mankind.It was a pragmatic,effective,and fruitful global trade event,a positive contribution to global economic recovery,development and prosperity.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

Global Supply Chain Governance and the Future of Globalization

Over the past 30 years or so,globalization has been underpinned by ever-evolving supply chains.The heightened attention to supply chain issues in recent years has been due to a series of global events in the world that have had a significant and profound impact.Factors such as the COVID pandemic,the Ukraine Crisis and increased geopolitical tensions have led to supply chain crises globally or regionally.

Rural Resilience: A Comprehensive Study on Water Supply, Sanitation, Disease Patterns, and Hygiene Practices in Munshiganj, Bangladesh

This research project investigates the current status of water supply, sanitation, and hygiene practices in Munshiganj District, Bangladesh. Data collection involved a structured questionnaire and a reconnaissance survey. Findings reveal that 30% of individuals rely on surface water (hand-tube wells, rivers, and ponds), prioritized as canal > river > pond, while 70% depend on groundwater (subterranean electric motor, deep tube-well). Drinking water is generally sufficient, with 95% reporting adequacy throughout the year. About 45% use hand tube-well water, 28% use deep tube-well water, and 11% use supply tap water for various purposes. Bathing trends include underground water through electric motor > pond > hand tube-well water > river, while for cooking, the order is underground water through electric motor > pond > hand tube-well water > river. Toilet water supply ranks as supply tap water > hand tube-well water > deep tube-well water. Although sanitation awareness is high, some lack knowledge of good hygiene practices. After defecating, handwashing methods include soap, ash, soil, or water. Children’s waste disposal varies, with some discarding it in open areas. Approximately 40% suffer from diseases like Diarrhoea due to unsafe water, primarily affecting children and elders. Training exists, but a significant portion lacks sanitation education. Dry skin or exposure to cold water may cause temporary irritation. Local government involvement in sanitation efforts is less active compared to non-governmental organizations. Results emphasize the need to enhance community awareness of safe water supplies and sanitation practices. .