Virtual Energy Research Articles

Sustainable Charging Stations for Electric Vehicles

In this work, we develop a detailed analysis of the current outlook for electric vehicle charging technology, focusing on the various levels and types of charging protocols and connectors used. We propose a charging station for electric cars powered by solar photovoltaic energy, performing the analysis of the solar resource in the selected location, sizing the photovoltaic power plant to cover the demand completely, and exploring different configurations such as grid connection or physical and virtual electric energy storage. Despite the current development applying for specific working conditions, operating voltage, charging rate, power demand, etc., the proposed configuration is modular, adaptable, and resilient. The simulated system operates within the 360 V to 800 V range of direct current for charging the electric vehicles, with a selectable power range between 20 and 180 kW. The basic layout includes four charging poles, each servicing all working voltages. An oversized PV plant powers the charging station at any time of the year, saving money compared to the alternative of the electric storage unit. In addition, we build simulation tools and algorithms that optimize the design of future projects, providing a solid basis for sustainable energy infrastructure planning and design.

Aggregation Modeling for Integrated Energy Systems Based on Chance-Constrained Optimization

Integrated energy systems (IESs) strengthen electricity–gas–heat multi-energy coupling and reduce wind and light abandonment. For grids with superior distribution, IESs are similar to virtual energy storage systems and are able to realize efficient interaction with the grid by synergizing the operating status of the internal equipment and improving the security, economy, and flexibility of the grid’s operation. However, the internal equipment coupling of an IES is complex, and determining how to evaluate its adjustable capacity range (that is, the upper and lower boundaries of its external energy demand) considering the uncertainty and volatility of wind power and photovoltaic output is a problem to be solved. To solve this problem, this paper presents a chance-constrained evaluation method for the adjustable capacity of IESs. Firstly, mathematical models and operational constraints of each device within the IES are established. Secondly, based on the mathematical model of chance-constrained planning, an adjustable capacity range assessment model considering the uncertainty of wind and photovoltaic output is established. Finally, the MATLAB/Yalmip/Gurobi solver is used for the optimization solution, and the adjustable capacity range interval of the constructed IES model is solved using an arithmetic example to analyze and verify the correctness and validity of the method and to study the influencing factors of its adjustable range.

Modeling and mechanism of the mechanical interlocking for the carbon fiber/epoxy interphase

The intricate details governing the carbon fiber/epoxy interfacial characteristics at the molecular level have yet to be comprehensively unraveled. One of the reasons is that the effects of carbon fiber (CF) surface structures are oversimplified, leading to misconceptions regarding adhesion mechanisms. To advance knowledge of structure-property relationships, we employed molecular dynamics simulations to provide insights into the complexity of the CF surface and CF/epoxy interphase. A series of turbostratic surface models with different protrusion heights were built via crosslinking basic structural units inside hexagonal prism virtual energy walls to better model the physical and chemical features of a CF surface. Epoxy precursor and curing agent molecules were added and crosslinked to generate the CF/epoxy interphase models before loading the systems in tension and shear. It was found that the spatial variation of composition determines the longitudinal and transversal moduli distribution in the interphase. Moreover, higher protrusion increases the tensile strength and toughness in the transverse direction by transferring part of the tensile loading into the shear component. The mechanical interlocking between the polymer and the CF surface with microvoids and protrusions enhances both the interfacial shear strength and the effectiveness of load transmission.

Two-stage distributionally robust optimization operation of virtual power plant considering the virtual energy storage of electric vehicles

Virtual Power Plant (VPP) is a key to aggregate various distributed energy sources. With the vigorous rise of various distributed energy sources, the direct access of large-scale electric vehicle load will increase the complexity of VPP coordinated operation. Hence, this paper proposes a VPP optimization method for Electric Vehicle Virtual Energy Storage (EV-VES). Firstly, the travel characteristics of electric vehicles are analyzed, and EV-VES model is established to coordinate and manage the charge-discharge behavior of EV. Secondly, the “carbon charge rate” model of energy storage (ES) is introduced to establish the relationship between carbon emission and electricity price, and the VPP operation model considering the “carbon charge rate” of energy storage is established. Finally, the two-stage robust optimization operation model of VPP is constructed, Wasserstein distance is used to describe the confidence set of uncertain probability distribution of wind power generation and load, and the uncertainty of system source and load are described by the confidence set. The Column and Constraint Generation (C&CG) algorithm was used to determine the optimal operational benefit solution. The effectiveness of the proposed VPP optimal operation model was validated through case study.

A low-carbon driven price approach for energy transactions of multi-microgrids based on non-cooperative game model considering uncertainties

Under the requirement of improving energy utilization of the grid and the dual-carbon background, balancing the economic and environmental benefits of microgrids (MG) in the competition market has great research significance. To provide reasonable price signals for energy sharing among stakeholders, we propose a new low-carbon driven energy-sharing pricing mechanism based on supply and demand information. The mechanism can incentivize MG to actively participate in non-cooperative games for energy sharing. Specifically, on the one hand, the virtual energy sharing centre (VESC) generates price signals by analysing and integrating the supply-demand information of MG, and then releases them to each MG. On the other hand, each MG receives the latest price signals and accordingly optimizes its own operation. For each MG, a two-stage robust optimization (TSRO) model that considers the uncertainties of the source-load and aims at the economy and environmental friendliness of the MG is established. For the multi-microgrid system, a low-carbon driven energy sharing mechanism is proposed by introducing a low-carbon indicator. Finally, the alternating optimization procedure-looped CCG algorithm (AOP-Looped CCG) is adopted to solve the model effectively. The numerical examples validate that the energy complementarity is promoted and comprehensive benefits are enhanced of the proposed mechanism.

A Coverage Hole Recovery Method for 3D UWSNs Based on Virtual Force and Energy Balance

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs.

Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements.

Optimizing embedded AI systems for autonomous driving: Challenges and solutions using bayesian networks

Abstract. Embedded Artificial Intelligence (AI) systems are important components of autonomous vehicles. However, incorporating AI into autonomous vehicles is technically complex, due to the constraints of computation, real-time processing of data, uncertainty handling, and hardware limitations. Bayesian Networks (BNs) are a promising method that allows probabilistic modelling in adaptive learning and environment perception. Here we report on an overview of the application of BNs on autonomous driving, with an emphasis on how BNs can be optimized for embedded system resource constraints, including both computational and energy. Various optimization techniques are discussed, such as model pruning, approximation, acceleration using hardware accelerators, such as Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuit (ASICs), and advanced cooling and power management to ensure AI reliability under high computational load. By reviewing these approaches, we aim to contribute to the development of more robust and green AI systems for autonomous driving.

Emergy-based evaluation of production efficiency and sustainability of diversified multi-cropping systems in the Yangtze River Basin.

Excessive agricultural investment brought about by increased multiple-cropping index may compromise environmental sustainability. There are few studies on the sustainability of diversified multi-cropping systems in the Yangtze River Basin (YRB). Therefore, this study selected five representative locations in the YRB. According to the local climate characteristics and food demand, diversified multi-cropping systems were designed, and the main local winter crops were selected as the previous crops of the corn-soybean strip compound cropping system, with the local traditional double-cropping model as the control (CK). The emergy evaluation method was introduced to quantitatively compare the efficiency and sustainability of diversified multi-cropping systems in the YRB. The results showed that by incorporating soybean by intercropping with corn, compared with the CK, the total energy input, annual energy output, and annual economic output increased by 15.80%, 9.78%, and 33.12% on average, respectively. The unit emergy value (UEV) and unit non-renewable value (UNV) increased by 6.03% and 5.98%, respectively; the emergy yield ratio (EYR) and environmental loading ratio (ELR) decreased by 0.91% and 0.44%, respectively; the emergy sustainability index (ESI) was the same. In the third mature crop selection, compared with that of corn, the ELR of soybean decreased by 14.32%, and the ESI increased by 18.55%. In addition, the choice of winter crops plays a vital role in the system's efficiency and sustainability. Compared with those of other winter crops, the annual economic outputs of potato (upper reaches of the YRB), potato or forage rape (middle reaches of the YRB), and wheat (lower reaches of the YRB) increased by 51.02%, 32.27%, and 0.94%, respectively; their ESI increased by 71.21%, 47.72%, and 12.07%, respectively. Potato-corn/soybean or potato/corn/soybean (upper reaches of the YRB), forage rape-corn/soybean or potato/corn/soybean (middle reaches of the YRB), and wheat-corn/soybean (lower reaches of the YRB) were chosen to facilitate the coexistence of high economic benefits and environmental sustainability. Additionally, promoting mechanization and reducing labor input were essential to improve the efficiency and sustainability of multi-cropping systems. This study would provide a scientific basis and theoretical support for the development of efficient and sustainable multiple-cropping systems in the dryland of the YRB.

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

Free vibration analysis of damaged laminated piezoelectric plates and composite plates with piezoelectric patch based on the Extended Layerwise Method

The dynamic analytical models for a laminated piezoelectric plate and a laminated composite plate with piezoelectric patch containing the delamination, transverse crack and debonding damages are established by the Extended Layerwise Method (XLWM). The virtual kinetic energy is introduced into Hamilton’s principle, so resulting in mass matrices and displacement-dependent second mode derivatives in the finite element (FE) governing equations. Then, the FE governing equations for the laminated piezoelectric plate, and the characteristic equations of natural frequencies for the laminated piezoelectric plate with delamination and transverse crack are derived. The coupling model of the laminated composite plate and the piezoelectric patch is established by the displacement continuity and internal force equilibrium conditions at the nodes of the contact area. Based on the degrees of freedom (DoFs) in contact, the final governing equations is deduced, followed by the analysis of free vibration responses for both plates with various damages. The accuracy of natural frequencies for each plate is verified by comparing the results with those of FE simulation by ANSYS.

NextGen Infrastructures: Enhancing Cyber-Physical Resilience/Sustainability by Virtual Energy Storage

Abstract This systematic review investigates the pivotal role of virtual energy storage (VES) in enhancing the resilience of energy systems. By systematically selecting and analyzing 158 articles, we address four key research questions about specific features of VES that enhance energy system resilience, how these features influence the overall resilience of energy systems, the enabler technologies associated with VES that impact the resilience of cyber-physical systems, and lastly, we discuss the challenges, and future research directions pertaining to the utilization of VES for bolstering resilience. Highlighting the importance of VES, the findings provide insights for policymakers, energy practitioners, and researchers aiming to enhance the resilience of energy systems in the face of increasing uncertainties and disruptions. Furthermore, this study provides valuable insights for the resilience engineering and energy engineering communities by identifying main features of energy resilience and key enabler technologies of VES for enhancing energy storage and system resilience.

Nonorthogonal basis adapted parameter-free complex absorbing potential and its application to open-boundary cluster model

Abstract This study extends the method of determining the complex absorbing potential (CAP) required for the open-boundary cluster model (OCM) based on the surface Green's function theory [Imamura K, Yasuike T, Sato H. Open-boundary cluster model with a parameter-free complex absorbing potential. 2024. J. Chem. Phys. 160:034103] to allow calculations using nonorthogonal basis sets. We devised a novel scheme that avoids the need to find adiabatically connected solutions for a large number of virtual energy levels, caused by the basis set expansion, and succeeded in constructing an optimal CAP. We generated basis sets with plane wave-like characteristics solely from localized functions, thereby eliminating the numerical instability in calculations of the surface Green's function. Application of the obtained CAP to the OCM calculation of a model 1D system confirms that the density of states of solid and localized states is well reproduced.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Hybrid solar and hydrogen energy system 0-D model for off-grid sustainable power system: A case in Italy

Off-grid solar systems are one of the most promising solutions for achieving complete grid independence. However, the storage of large amounts of energy produced in the summer through solar panels becomes crucial to reach this goal and hydrogen, as a zero-CO2 energy carrier, could play a pivotal role. This paper presents a case study on the integration and simulation of solar energy and hydrogen technologies in an off-grid energy plant for a teaching buildings complex in Italy. A 0-D virtual energy plant model has been developed aimed at estimating the net energy production and hydrogen consumption/production rates using different inputs of irradiance (monthly average, daily) and energy demand (constant and variable daily consumption levels) in the buildings. The outcome of the analysis identifies the most convenient configuration of the plant in terms of sizing and device interactions for achieving complete grid independence, and the impact of different inputs on the plant performance.

Sustainability Assessment of Agricultural Waste Biogas Production System in China Based on Emergy and Carbon Evaluation Methods

Biogas production is widely recognized as an effective solution for addressing agricultural waste treatment in rural areas. However, its development is often hindered by economic and environmental constraints. This study combined emergy evaluation and carbon footprint analysis methods to establish a new environmental radius assessment model for evaluating the ecological performance and optimization direction of an agricultural waste biogas production system, using a biogas production company in China as a case study. Compared with the straw return model and straw power generation model, the results of emergy indicators and carbon accounting showed that the biogas production model had a lower environmental load and higher economic output and level of emergy sustainability. Additionally, the biogas production system was found to reduce 0.47 kg of carbon emissions per 1 kg of agricultural waste utilized. The application of the biogas production model in rural areas had high ecological sustainability and carbon emission reduction benefits. Environmental radius assessment results confirmed that the reasonable changes in resource collection distance could further enhance the ecological sustainability, carbon mitigation ability, and economic benefits of the biogas production system. The environmental radius assessment method offers a new approach to the location planning of agricultural waste biogas utilization companies in rural areas.

Distributed Neural Adaptive Impedance Control for Cooperative Manipulation With Unknown Objects.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.

Building shape optimization based on interconnected embodied and operational energy and carbon impacts

Strategic building design decisions informed by a comprehensive life cycle impact assessment hold substantial potential in addressing global energy consumption and carbon emissions, with buildings accounting for 40 % of energy consumption and 37 % of emissions. While building operational energy (OE) has traditionally received greater attention, the dynamic trade-offs between OE and embodied energy (EE) in building design are often overlooked. Design decisions, such material selection and building shape, can present conflicting considerations between OE and EE. Addressing these impacts in isolation may fail to achieve comprehensive reductions in energy consumption and carbon emissions. This study highlights the necessity of a holistic approach to design decisions such as building shape and material selection, emphasizing the importance of considering both OE and EE simultaneously. Our literature review confirms a gap in studies that consider these factors together in the context of building shape optimization. To analyze the interconnectivity of building shape design, material choice, and the trade-offs between OE and EE, we devised two parametric test cases with identical features but using two different insulation materials: hemp-based and glass wool. The analysis allows us to (1) evaluate the impact of integrating total lifecycle impact on building shape design optimization, in comparison to designs that focus solely on OE; (2) assess how the selection of materials, specifically hemp-based versus glass wool insulation, influences optimized building shape and carbon emissions reduction; and explore the benefits of bio-based materials by analyzing performance variations in the two insulations case studies. Our findings reveal that although OE typically has a larger impact over a building’s lifetime, the optimal building shape can still vary significantly based on the EE contribution. The benefit of bio-based material is context-dependent, varying with factors such as the building shape discussed in this paper, as well as other factors such as climate. In our test cases, we observed that shape variations resulted in up to a 17.9 % change in OE due to different building shapes, and a 60.7 % variation in the envelope’s surface area, while the building area remained unchanged. This expanded understanding will facilitate more informed and sustainable decision-making in the construction and design industries, addressing the often-overlooked interconnectivity of OE and EE.

Emergy-Based Evaluation of the Sustainability of Agricultural Ecosystem in Dazhou, China, from 2002 to 2022

Our aim is to analyze the emergy evaluation indicators of the agricultural ecosystem in Dazhou, northeastern Sichuan, and provide practical and effective recommendations for sustainable agricultural development. Using emergy analysis, the emergy inputs and outputs of an agricultural ecosystem from 2002 to 2022 were calculated. Five emergy indicators were selected for evaluation: emergy yield ratio (EYR), emergy self-sufficiency ratio (ESR), emergy input ratio (EIR), environmental load ratio (ELR), and emergy sustainable indices (ESI). The total emergy input of the agricultural ecosystem showed an upward trend from 2002 to 2017, thus the industrial auxiliary emergy input decreased, somewhat curbing its continued rise from 2017 to 2022. The structure of emergy inputs, in descending order, is as follows: industrial auxiliary &gt; organic emergy &gt; renewable environmental resources &gt; non-renewable environmental resources. The total emergy output of the agricultural ecosystem was highest in 2007, reaching 2.31 × 1022 Sej, and lowest in 2012, at 1.83 × 1022 Sej. The structure of emergy outputs, in descending order, is as follows: livestock &gt; planting &gt; fishery &gt; forestry. The emergy yield ratio fluctuated down from 3.12 to 2.51, with an average of 2.88, below the provincial average of 3.07. The emergy self-sufficiency ratio fluctuated down from 0.30 to 0.26, with an average of 0.27, above the provincial average of 0.13. The emergy input ratio fluctuated up from 2.31 to 2.91, with an average of 2.66, above the provincial average of 1.86. The environmental load ratio fluctuated from 3.8 to 4.75, with an average of 4.40, which is higher than the provincial average of 1.68. The emergy sustainable indices fluctuated down from 0.81 to 0.53, with an average of 0.67, below the provincial average of 1.17. The efficiency of resource utilization in the agricultural ecosystem of Dazhou has decreased, economic inputs have increased, and it is in a consumptive production process. The pressure on the local natural environment is increasing, and the capacity for sustainable development remains at a low level over the long term.

Fighting Alone or Mutual Benefit and Win? A Study on Forestry Embodied Energy Competition Network Based on MRIO

Abstract Measuring energy usage in the forestry trade is a topic of great significance. In this article, we use the World Input–Output Database and the multiregional input–output model to establish an embodied energy competition network and assess the influence of global value chain (GVC) integration on competition. The results show that (1) the intensity of competition for embodied energy in forestry is higher for exports compared with imports. Additionally, the import competition network exhibits lower connectivity and overall efficiency. (2) The core regions of the competitive network are primarily found in Europe, Asia, and the Americas. The United States consistently maintains a competitive advantage. However, forestry policies have led to a decline in the status of the United States, (3) The hierarchical structure of export competition is more pronounced. When the weighting degree exceeds 200, this phenomenon becomes more pronounced. (4) From the perspective of GVC, positive embedding in the GVC is negatively correlated with import competition. Backward GVC embedding is positively correlated with import and export competition. On the basis of the above results, this article proposes that countries where backward embedding is the main method should actively participate in building appropriate export international associations, participating in international organizations, and building a relatively harmonious and stable export competition market, thereby helping to maintain economic development and energy environment management.