Upstream Power Research Articles

Investigation of wind turbine unsteady aerodynamics and trailing-edge noise characteristics under wake steering

In wake steering strategy, the shaft of an upstream wind turbine is yawed to deflect the wake and decrease the wake-swept area of the downstream wind turbine rotor. This intentional yaw misalignment increases the output power of the downstream wind turbine at the expense of some loss of upstream wind turbine power, consequently increasing the total wind farm power. However, the asymmetric non-uniform inflow into the downstream wind turbine due to the deflected wake causes unsteady aerodynamic loads and affects the wind turbine noise characteristics. In this study, the unsteady vortex lattice method coupled with the curled wake model is used to predict the unsteady aerodynamics of a downstream wind turbine in wake steering. Based on the unsteady aerodynamic results, the trailing-edge noise characteristics are predicted by a semi-empirical method. The impact of wake steering on trailing-edge noise level and amplitude modulation is evaluated. The results show that the asymmetric non-uniform inflow due to wake steering increases the amplitude modulation of the downstream wind turbine.

Research on sedimentation characteristics of the end bend water extraction project of cascade reservoir backwater

Abstract The operation mode of upstream and downstream cascade power stations significantly impacts the river bed in the water intake section, leading to complex sediment deposition characteristics in the curved river section at the reservoir’s end. In this research, a standard physical model for bed load and suspended load at a scale of λl=λh=100 was conducted, combined with the operation mode of upstream and downstream power stations, to investigate the sediment deposition patterns in the intake section and to study the sediment deposition characteristics in the intake area. The experimental results reveal that bed load sediment predominantly accumulates on the right bank (convex bank) of the river, with no bed load sediment accumulation at the mushroom head layout of the water intake project on the left bank (concave bank). When the operation of upstream and downstream power stations is uncoordinated, suspended sediment settles in the water intake section, with no sediment accumulation in the local area of the mushroom head of the intake due to the disturbance effect. Safety sensitivity analysis indicates that the safety of the water intake project can be ensured.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Convex relaxation of two-stage network-constrained stochastic programming for CHP microgrid optimal scheduling

Different from most studies only incorporating economic aspect into operation scheduling, this paper develops a novel network-constrained framework that can address both economic and secure issues for combined heating and power (CHP) microgrids integrated with wind power considering uncertainties. According to quadratically constrained programs, a two-stage network-constrained stochastic programming (TSNCSP) model is formulated to improve economic benefits and meanwhile capture active/reactive power and off-nominal bus voltage constraints in the full decision variable domain. In the first stage, namely day-ahead, the schedule of transacted electricity with the upstream power grid and the generation cost of heat is determined according to the forecast information. In the second stage, namely real-time, a recourse function of adjustable resources is defined to reduce the expected cost incurred by the perturbation of random wind power outputs. Moreover, the original non-convex problem is innovatively transformed into a semidefinite programming through incorporation of demand response program (DRP), duality, complementary slackness, and relaxation techniques to improve the solving efficiency. Finally, a proposition is presented and proved that provides a sufficient condition for the exactness of the proposed convex relaxation. Numerical simulations on the 33-bus test system verify the effectiveness of the proposed framework in multi-period scenario-based scheduling problems.

Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

(Invited) Pilot Power Platform for Integrated Coupled Electrolysis of Furfural to Maleic Acid and Levulinic Acid to Valeric Acid

In recent years it has become evident that electrification of the chemical industry is essential to shift from a fossil based society to a renewable and sustainable one. In this talk, we will address development of a pilot scale setup to upconvert bioderived feedstocks to relevant building blocks for the chemical industry. Specifically, we focused our attention to the concept of paired electrolysis: oxidation of furfural to maleic acid coupled with reduction of levulinic acid to valeric acid within the same electrochemical cell. Within the framework of this mentioned reaction, we will show different steps of the reactions optimization, scale up, process intensification that eventually led to the construction and testing of pilot power platform installation with electrochemical surface area of 1m2 (10 cell stack 1000cm2) andproductivities of about 6kg/day (Figure 1). We will also focus on downstream process needed to go from crude electrolyte to pure maleic acid and pure valeric acid. Finally, we will present insights from technoeconomic analysis.Biography:PERFORM: Highly selective electrochemical conversions (performproject.eu)Figure 1. Developed infrastructure for electrochemical conversion of bioderived feedstocks to maleic acid and valeric acid. Left: upstream electrochemical Power Platform. Right: electrodialysis pilot, first downstream process of maleic acid purification Figure 1

A day‐ahead energy management strategy for electric vehicles in parking lots considering multi‐scenario simulations and hydrogen storage system

AbstractThis article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.

Simulation studies of tungsten impurity behaviors in helium plasma in comparison with deuterium plasma via SOLPS-ITER

The sputtering and transport of tungsten (W) impurities in helium (He) and deuterium (D) plasma discharges are compared using the SOLPS-ITER code. To reduce the computational resources of modeling, W ions are treated using the bundled charge state model. The results show that the W erosion flux of He plasma is almost a factor of two higher than that of D plasma under the same upstream electron density and heating power due to the higher W sputtering yield in He plasma. Moreover, the W self-sputtering flux is significantly higher than the W flux sputtered by the main ions. The leakage and retention of W impurities in the divertor region is also analyzed. W ions mainly escape from the near scrape-off layer (SOL) region through the divertor entrance as the stagnation point of the average W impurity poloidal velocity is considerably closer to the target plates in the near SOL region. Furthermore, the leakage flux of W ions in He plasma is higher than that in D plasma, mainly because of the higher W sputtering level in He plasma, which results in a larger W density. W ions with low-lying charge states, mostly comprising the charge state of W10–12+, easily escape from the divertor through the near SOL flux tubes in both D and He plasmas. In addition, the effects of upstream electron density on W sputtering and retention in He and D plasma discharges are presented.

Charting a path to low-carbon leadership: unlocking up to 90% emissions reductions in greenfield LNG

The liquefied natural gas (LNG) industry stands at the cusp of a transformation driven by the climate imperative. Focussing on the Middle Arm Industrial Precinct, this paper shows how carbon dioxide (CO2) emissions are being reduced in a proposed world-class LNG project by assessing, screening and selecting known technologies using a Marginal Abatement philosophy linked to key emissions drivers. (1) We propose a new breed of electrified LNG facilities that could deliver an up to 90% reduction in CO2 intensity. (2) We show that through developing strategic partnerships in upstream renewable power, hydrogen supply, and enabled downstream carbon capture and storage (CCS), the significant low carbon energy requirements of an LNG facility can be delivered. (3) We discuss a Marginal Abatement assessment strategy which ensures a pathway to viable lowest cost opportunities selected in an ‘Avoid & Mitigate’ first approach. This proposed strategy ensures that CO2 emission reduction opportunities are assessed and selected consistently to determine both a lifecycle cost ($) and volume (tons CO2e) of emission reduction. These innovative strategies collectively articulate a roadmap for a low carbon future for LNG and other heavy industrial industries, underlining Australia’s potential to become a leading provider of low-carbon natural resources.

Effect of Daily Regulation on Navigation Conditions in Deep Reservoirs

The daily regulation and anti-regulation of upstream and downstream power stations, respectively, frequently alter the river flow regime, velocity, and surface gradient, thus resulting in unsteady flow characteristics of the river and hindering shipping, waterway maintenance, and wharf operations. This study investigated the influence of daily regulation on the navigation conditions in the deep reservoir by taking the rivers between the Three Gorges Dam and the Gezhouba Dam as the research object. Prototype observations and a depth-averaged 2-D model were used to determine the main factors affecting the propagation law of unsteady flow. The propagation pattern of unsteady flow and channel navigational conditions and measures of the power station were analyzed systematically. The results showed that the water level amplitude was affected primarily by the peak amplitude and duration of the peak shaving. Additionally, the base flow significantly influenced time spatial distributions of the water level amplitude. Impacted by the reservoir storage capacity, a threshold for the duration of peak shaving was noted; this may result in maximum water level variation. As the peak shaving duration increased, the amplitude of the water level decreased. The research results can provide theoretical support for the optimization of hub shipping.

Spatialized carbon-energy-water footprint of emerging coal chemical industry in China

Concerns about energy security have driven the launch of the emerging coal chemical industry (ECCI) across China. However, the resulting spatially explicit carbon–energy–water footprint remains unclear. This study aims to comprehensively identify the spatial heterogeneity, distribution, and transfer characteristics of the carbon–energy–water footprint of the ECCI in China. An original spatialized accounting method based on a multi-flow multi-node model is developed to address the difficulty that spatially integrating large amounts of spatial data for ECCI projects and their upstream coal mines and power plants. The results indicate that the provincial intensities of the carbon footprint, energy footprint, and water footprint varied by −13.4–43.6 %, −7.2–1.8 %, and −22.6–59.5 % relative to the national averages, respectively. The three intensity types have quite different spatial heterogeneities, always showing an impossible trinity of carbon reduction, energy saving, and water conservation at the provincial level. The total national carbon footprint, energy footprint, and water footprint reached 280.20 Mt CO2-eq, 5.35 EJ, and 1155.18 Mt, respectively, of which more than 90 % were centrally distributed in North China, Northwest China, and East China. Interprovincial coal transport and electricity transmission driven by the spatial mismatch between the upstream and downstream processes together led to the transfer of 10.35 Mt CO2-eq of carbon footprint, 68.72 PJ of energy footprint, and 100.92 Mt of water footprint, whereas their respective contributions significantly varied by footprint type. These results provide spatial-specific knowledge and insights for layout optimization and sustainability enhancement for the ECCI across the country.

Climate change energy futures in business, industry and mining in Saskatchewan, Canada

PurposeThis study aims to report business preferences for achieving net-zero power production emissions in Saskatchewan, Canada as well as business perceptions of the most preferable power production sources, barriers to change and suggestions for improvement. Mixed methods included focus groups and a survey with experimental design. This research demonstrates that this method of advancing academic and business knowledge systems can engender a paradigmatic shift to decarbonization.Design/methodology/approachThe study is a mixed-methods study using five focus groups and a survey which included a 15-min information video providing more information on power production sources (small modular reactors and biomass). Participants requested more information on these topics in the initial three focus groups.FindingsThere is a significant gap in Canadian Government targets for net-zero emissions by 2050 and businesses’ plans. Communications, knowledge and capacity gaps identified include lack of regulatory requirements, institutional barriers (including a capacity charge in the event a business chooses to self-generate with a cleaner source) and multi-level governance dissonance. More cooperation between provincial governments and the federal government was identified by participants as a requirement for achieving targets. Providing information to survey respondents increased support for clean and renewable sources, but gender and knowledge are still important characteristics contributing to support for different power production sources. Scientists and teachers were the most trusted sources of information. Power generated from small modular nuclear reactors was identified as the primary future source of power production followed by solar, wind and natural gas. Research results also confirmed the high level of support for hydropower generated in Saskatchewan versus import from Manitoba based on high values of energy solidarity and security within the province.Originality/valueThis study is original, as it concerns upstream system power production portfolios and not failed projects; the mixed-method research design including a focus group and an experimental survey is novel. This research partially addresses a gap in knowledge surrounding which knowledge systems advance paradigmatic shifts and how and whether involving business people in upstream power production decisions can inform decarbonization.

Moving upstream: healthcare partnerships addressing social determinants of health through community wealth building

BackgroundHealthcare-based interventions addressing social needs such as food and housing generally fail to impact the upstream wealth and power inequities underlying those needs. However, a small number of US healthcare organizations have begun addressing these upstream inequities by partnering with community wealth building initiatives. These initiatives include community land trusts, resident-owned communities, and worker cooperatives, which provide local residents ownership and control over their housing and workplaces. While these partnerships represent a novel, upstream approach to the social determinants of health, no research has yet evaluated them.MethodsTo assess the current state and key aspects of healthcare-community wealth building partnerships, we conducted a multiple case study analysis using semi-structured interviews with thirty-eight key informants across ten partnerships identified through the Healthcare Anchor Network. To analyze the interviews, we used a two-stage coding process. First, we coded responses based on the phase of the intervention to which they corresponded: motivation, initiation, implementation, or evaluation. Then we assessed responses within each aspect for common themes and variation on salient topics.ResultsPartnerships were generally motivated by a combination of community needs, such as affordable housing and living wage jobs, and health system interests, such as workforce housing and supply chain resilience. Initiating projects required identifying external partners, educating leadership, and utilizing risk mitigation strategies to obtain health system buy-in. Implementation took various forms, with healthcare organizations providing financial capital in the form of grants and loans, social capital in the form of convening funders and other stakeholders, and/or capacity building support in the form of strategic planning or technical assistance resources. To evaluate projects, healthcare organizations used more process and community-level metrics rather than metrics based on individual health outcomes or returns on investment. Based on best practices from each partnership phase, we provide a roadmap for healthcare organizations to develop effective community wealth building partnerships.ConclusionsAssessing healthcare partnerships with community wealth building organizations yields key strategies healthcare organizations can use to develop more effective partnerships to address the upstream causes of poor health.

PQ-Based Local Voltage Regulation for Highly Resistive Radial Distribution Networks

Modern distributed energy resources (DER) consisting of energy storage coupled with a voltage source converter (VSC) provides flexible and dynamic operations in active and reactive power quadrants. These technologies present significant opportunities to provide distribution network support instead of traditional approaches if effective controls are in place. Although voltage regulation using reactive power is typical, active power is more effective at manipulating voltage in highly resistive distribution networks. This paper proposes a local controller that regulates VSC terminal voltage and allows fast switching of the VSC power factor output, with minimized disturbance on the terminal voltage. This improves the effectiveness of the VSC in managing voltage in highly resistive distribution networks using both active and reactive power and provides a strategy to manage upstream power flow. The approach uses gain equations derived from the voltage sensitivity to power injection and the point on wave switching in the stationary reference frame. The control strategy is verified through detailed simulation in MATLAB/Simulink using a modified IEEE 4-node test feeder, including performance during a switched load, DER disturbance and controller interaction via voltage droop. The small-signal model and stability analysis of the proposed approach is also presented.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

Method for quantitatively assessing the impact of an inter-basin water transfer project on ecological environment-power generation in a water supply region

To tackle ever-changing global water problems, inter-basin water transfer (IBWT) projects provide water supply alternatives for water-scarce regions and create potential risks for water-supply regions. To analyze the multiple interrelated impacts of IBWT projects on supply regions, in this study, we establish a cascade multi-objective complementary optimal scheduling model and apply the model to the water supply regions of the west route of China’s South-North Water Transfer Project and the Dadu-Minjiang Water Transfer Project. With the goal of fully utilizing water resources to gain comprehensive benefits, we objectively and quantitatively evaluate the impacts of IBWT projects on the ecology and hydropower generation of water supply regions. To reduce the complexity of solving the model, the model is decomposed into a two-stage optimization model and is solved with particle swarm optimization and fuzzy optimization. The results show that: (1) The multi-objective model that simultaneously considers minimal ecological water shortage degree, maximal power generation, and the maximization of the annual minimum output is more conducive to the full utilization of water resources. (2) The factors affecting the ecological flow, the power generation and its structure in the water supply region, and the annual minimum output are different. (3) To deal with risks, the upstream power station in the water supply region is controlled to meet the ecological needs as the primary goal, and the downstream power station is mainly responsible for the task of compensating and adjusting the output gap during the dry season. This study can provide technical support for the demonstration and decision-making of IBWT projects, and it explores the ideas of water resource planning and management.

Optimizing the aluminum supply chain network subject to the uncertainty of carbon emissions trading market

Aluminum production is characterized by high electricity consumption; hence its decarbonization hinges on upstream low-carbon electricity generation. As a result, the supply chain of aluminum product manufacturing can be strongly affected by the uncertainty of carbon emissions price via electricity generation. This paper investigates these impacts on the strategic design and the tactic operations of the aluminum supply chain by constructing a stochastic mixed-integer linear programming (SMILP) model, in which carbon price uncertainty and demand uncertainty are both accounted for. The supply chain case in the study covers a full process from upstream power generation to end-use consumption. A variety of stochastic carbon price scenarios are designed and assessed. Sensitivity analysis with respect to demand fluctuation is also performed. The results show that carbon price uncertainty significantly affects the configuration of the whole system. The main driving forces are the supplier selection and configuration alteration triggered by the carbon price variation, whereas other metrics such as carbon emissions costs and the responses to demand fluctuations display complicated patterns of change. This study provides a useful analytical framework that can be applied to the planning problem for other industries facing the same challenge.

Modeling of the impact of neon seeding on the detachment in EAST by SOLPS-ITER

In the EAST radiative divertor experiments, stable detachment and divertor protection have been achieved with mixed impurity gas seeding. Experiments indicate that neon (Ne) is an appropriate external impurity for plasma detachment in EAST. In this work, the physical mechanism of plasma detachment with Ne seeding in EAST is studied by using the SOLPS-ITER code package. The electron temperature Tet on the upper outer divertor target decreases to below 10 eV with Ne seeding, which is mainly due to the power loss and upstream power drop caused by the impurity radiation. For Tet < 10 eV, the significant drop of the poloidal saturation ion current js,p occurs around the upper outer strike point. The simulation results show that the js,p drop is mainly driven by the rise of the momentum loss and the upstream pressure loss. The increase in the momentum loss is mainly caused by the plasma–neutral interactions in the region from the X-point to the upper outer divertor target. The upstream pressure loss around the X-point is related to the decrease in the upstream electron temperature Teu, which originates from the increased radiation loss with Ne seeding.

Simulation study of the influence of upstream density and power in the scrape-off layer on the double-peaked density profile at the divertor target

The double-peaked distribution of particle deposition at divertor targets has been observed in various tokamaks, and is considered a potential approach for mitigating divertor particles and heat load in future fusion reactors. Recently, the systematical analysis of the double-peaked distribution behavior during EAST experiments shows that the appearance of the double-peaked profile is related to the line-average density and heating power. In order to understand general trends and related mechanisms, the influences of the upstream density (n e,sep) and power in the scrape-off layer (P SOL) on the double-peaked density profile are investigated by SOLPS-ITER simulations with full drifts and currents. It is found that the n e peak near the strike point is mainly contributed by the strong ionization source close to the target, and the n e peak in the far-SOL region is caused by the synergetic effects of poloidal and radial E × B drifts along the SOL. The double-peaked distribution is affected by the P SOL and impurity seeding by increasing or decreasing the whole profile of the electron temperature at the target (T et). When the peak value of T et (T et,peak) is fixed, the density peak in the far-SOL is increased for higher n e,sep by reducing the T et in the far-SOL region on the lower-field side under unfavorable B T and by the upstream-extended ionization source due to the geometry effect on the high-field side under favorable B T. Statistical analysis of the simulated results shows that the scaling expression of the peak ratio is ∼. In addition to the upper boundary found in the analysis of EAST experiments, a lower boundary of the region where the double-peaked feature appears on the P SOL-n e,sep plane is identified by simulations and preliminarily confirmed according to the measurements in several EAST discharges.

Life Cycle Assessment of Energy Consumption and CO2 Emission from HEV, PHEV and BEV for China in the Past, Present and Future

In order to fulfill the commitment of China to achieve carbon peak by 2030 and carbon neutrality by 2060, all industries have been taking their respective carbon reduction actions. The transportation industry accounts for 11% of CO2 emission of the whole society, and its energy conservation and carbon reduction benefit is of great significance to the national carbon reduction process. New energy vehicles are undoubtedly one of the most important means of carbon emission reduction in the transportation sector. However, electric vehicles still have CO2 emissions, as the fossil fuel use comes from upstream power. To systematically and comprehensively evaluate the CO2 emissions of HEV, PHEV and BEV in the whole process, this study introduces the life-cycle method to research on the past and current situations, and predict future scenarios for ICEV and EV light-duty vehicles at the national and regional levels, by deeply analyzing the generation mix and generating efficiency from the WTT stage, and fuel economy from the TTW stage. The study shows that compared with ICEV, HEV and PHEV could reduce around 30% of CO2 emissions. Currently, BEV could reduce 37% of CO2 emission in the region where the proportion of coal-fired power is high, and 90% of CO2 emission in the region where the proportion of hydro power is high. This study discusses the impact of the proportion of renewable energy application on the carbon emissions from electric vehicles, analyzes the environmental benefits of promoting electric vehicles in different regions, and lays a foundation for the promotion strategy of electric vehicles for different regions in the future.