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Modeling the impact of EVs in the Chinese power system: Pathways for implementing emissions reduction commitments in the power and transportation sectors
Abstract
The deployment of renewable electricity and electric vehicles (EVs) provides a synergistic opportunity to accelerate the decarbonization of both China's power and transportation sectors. Here, we evaluate the potential impacts of EVs by utilizing the SWITCH-China model designed to meet emissions constraints within its power sector while integrating the electrified transportation sector. We focus on how various EV stocks, and charging strategies (unmanaged versus smart charging) impact the power sector, in terms of generation and hourly grid operation, the capacity mix, and achieving the Paris Agreement goals. Large-scale deployment of EVs increases the need for generation capacity, while the implementation of smart charging requires 6.8%-14% less additional storage capacity. We calculate that power system integration costs to incorporate EVs range from 352 per EV. We show that a smart charging strategy saves between 123 per vehicle more annually in 2050 than a case with the same EV stock where the charging is unmanaged. Our results suggest that a 140 GW annual growth of renewables from 2020 to 2050, coupled with an aggressive EVs deployment using smart charging can put China solidly on a path to meet its ambitious carbon cap targets.
... Furthermore, most current studies primarily focused on estimating emission reductions from EVs for passenger vehicles [15,[20][21][22][24][25][26][27] or specific vehicle types (e.g., private and commercial car, bus, and taxi) [19,20,23,[28][29][30], and some of them excluded trucks and/or motorcycles [1,[19][20][21][22][23][24][25][26][27][28][29][30][31]. Huo et al. [15] substituted ICEVs from the whole vehicle fleet with EVs at the designed EV penetration, but they did not discuss the electrification for different vehicle types. ...
... Furthermore, most current studies primarily focused on estimating emission reductions from EVs for passenger vehicles [15,[20][21][22][24][25][26][27] or specific vehicle types (e.g., private and commercial car, bus, and taxi) [19,20,23,[28][29][30], and some of them excluded trucks and/or motorcycles [1,[19][20][21][22][23][24][25][26][27][28][29][30][31]. Huo et al. [15] substituted ICEVs from the whole vehicle fleet with EVs at the designed EV penetration, but they did not discuss the electrification for different vehicle types. ...
... Furthermore, comprehensive air pollution control measures [54,56] should be expanded, such as improving the effectiveness of desulfurization and sulphur recovery, developing the carbon capture and storage system (CCS) [57][58][59], and promoting low-carbon trip modes in China by substituting private vehicles with bicycles or public transportation for short trips and commercial vehicles with rail transport for long trips [60]. Vehicle-grid integration (VGI) should be given more attention because it has been shown to possess great potential for increasing renewable electricity penetration and enhancing the utilization of current generation capacity and infrastructure [28]. ...
... Further, Xu., et al [3] added time-series load and VRE data for the U.S. power system. However, to the best of the authors' knowledge, although there is some work for the Chinese power system with the provincial resolution [4]- [7], city-level based spatial data and time-series load and VRE data for Chinese power system are either unavailable or unpublicized. ...
... Second, the combination of solar and wind resources is a good way to decrease the variability intrinsic to either resource alone [9], indicating that the high spatial resolution for VRE is important to capture the benefits of hybrid wind-solar energy to smooth overall output profiles. For instance, Li., et al [4] built a Chinese power system dataset involving 200 wind farms and 200 solar farms, which performs better compared with the work at the provincial level. Third, existing thermal generators still dominated the main share of installed generation capacity to provide reliable and economic electricity. ...
... (3) assigning hourly data to associated VRE generators and loads; (4) calculating transmission line parameters and creating transmission line topology; (5) refining investment and operating costs, especially operating costs coefficients incorporating with fuel prices and varies heat rates during different load levels of thermal generators. The major contributions of the paper are the created dataset and associated method which includes dataset building procedures (1)(2)(3)(4)(5). ...
... In regard of the time dependency of transition probabilities, the model utilized in this paper is called a "non-stationary Markov chain" [74,75]. ...
... It is very difficult to model and predict the behavior of every single vehicle because it encounters many unexpected events [5,51,52,74], hence it is not far from reality to admit that modeling the behavior of a single vehicle is almost as hard as modeling white noise in communication systems. However, as soon as changing the viewpoint from a single vehicle to the bulk of available vehicles in a region, their aggregated behavior would be much predictable and the results of modeling will be far more reliable [52,[75][76][77]. ...
Since the inception of the idea to utilize electrically driven vehicles on the power grid, numerous valuable investigations have been carried out to showcase the advantageous capabilities of such technologies. However, there are still uncertainties surrounding the integration of electric vehicles into the power grid. These uncertainties encompass the number of electric vehicles that will be linked to the grid at any given time, the quantity of energy stored in their batteries during both daytime and nighttime, and the impact of their charging patterns on the overall power grid load. Moreover, there are numerous other unanswered queries that demand attention. This study presents a unique model that effectively addresses these uncertainties by utilizing a non-stationary Markov chain. The utilization of a non-stationary discrete Markov model in this study provides a precise and valuable understanding of the constantly evolving and time-dependent nature of electric vehicle behavior in the power network. Through a comprehensive case study, the outputs of the model offer intriguing insights into the number of electric vehicles connected to the grid and the energy they reserve over a 24-hour period. Furthermore, this study assesses the model's accuracy in representing the load modeling of electric vehicle charging.
... Until 2030, the impact of electric vehicles (EVs) on reducing CO2 emissions will be relatively low, even with ambitious plans for their adoption, as indicated by a recent paper [13], [14]. Despite this, evidence suggests that after 2030, the adoption of electric vehicles (EVs) in all scenarios will lead to a significant increase in the number of EVs on the road and a cumulative reduction in CO2 emissions [15], [16]. To switch to a low carbon emissions passenger transport fleet in the mid-term and meet near-term CO2 emission mitigation targets, cities, regions, and countries must establish a specific policy framework for electric vehicles (EVs) and consider additional alternatives such as modal shift and incentivized telecommuting. ...
... where the SOC variation allows us to get a battery voltage in the range. The global equation is then given as (16). ...
span lang="EN-US">This paper presents an energy management system with multiple sources. The proton exchange membrane fuel cell as the main power drive source is used when needed. However, because of its slow rate it is combined with a battery and supercapacitor racks as secondary sources. The proposed energy conversion system ensures the energy demand of an electric vehicle. The storage system, which includes a battery and a supercapacitor, provides a high level of performance in both autonomy and availability of power. The battery as a primary storage source, feeds the whole system during cruse processes. At needs and during accelerations phases, the supercapacitor, takes over and reacts to supply the load. This topology keeps the fuel cell disconnected from the bus supply, until the battery reaches a critical voltage level. At this time, the fuel cell kicks in and charges the battery rack. The entire system was initially tested using MATLAB/Simulink environment and the outcome findings were subsequently analyzed. The simulated results have been corroborated experimentally using a test rig based on dSPACE real time interface. Experimental results indicate that the suggested approach is capable of meeting the electric vehicle energy requirements.</span
... Anguita et al., 2019;Brand et al., 2012;Baptista et al., 2012;Yaqoob et al., 2021;Li et al., 2021;Azar et al., 2003;Chakraborty et al., 2020;Watabe et al., 2019;Boonpanya and Masui, 2021;Tong et al., 2015;Grahn et al., 2009), zero-emission vehicles (ZEV) (Herold and Lee, 2017;Wang et al., 2020a), alternative fuels (Chapman, 2007;Ong et al., 2011;Emberger, 2017;Brand et al., 2012;Herold and Lee, 2017;López-Navarro, 2014;Gan, 2003;Hensher, 2008;Fragkos et al., 2021;Corbett and Winebrake, 2007;Baptista et al., 2012;Yaqoob et al., 2021;Li et al., 2021;Azar et al., 2003;Grahn et al., 2009;Eyre et al., 1997;Trevisan and Bordignon, 2020;Salvi and Subramanian, 2015;Kay et al., 2014), hydrogen fuel cells (FCV) (Demir et al., 2014;Hensher, 2008;Azar et al., 2003;Watabe et al., 2019;Tong et al., 2015;Grahn et al., 2009;Salvi and Subramanian, 2015;Banister, 2011), biofuels (biofuels are known as mono-alkyl esters, which are obtained from vegetable oils or animal fat) (Ong et al., 2011;Emberger, 2017;Fragkos et al., 2021;Baptista et al., 2012;Yaqoob et al., 2021;Azar et al., 2003;Grahn et al., 2009;Chauhan et al., 2009;de Souza et al., 2013;Kaufman et al., 2010;Nabavi-Pelesaraei et al., 2017), natural gas vehicles, (NGV) (Ong et al., 2011;Yaqoob et al., 2021;Watabe et al., 2019;Tong et al., 2015;Grahn et al., 2009;Eyre et al., 1997;de Souza et al., 2013 (Ong et al., 2011;Emberger, 2017;Baptista et al., 2012;Yaqoob et al., 2021;Tong et al., 2015;Grahn et al., 2009;Eyre et al., 1997), hybrid fuel (Grahn et al., 2009;Nakata, 2000), fleet renovation (Emberger, 2017;Navas-Anguita et al., 2019;Berrittella et al., 2007), and infrastructure improvement, including the following: road, tunnel, and bridge networks (Demir et al., 2015;Rodrigues et al., 2015;Azar et al., 2003;Trevisan and Bordignon, 2020); reducing the vehicle weight (increasing the vehicle weight increases fuel consumption) (Demir et al., 2014;Jemai et al., 2012); technical development (Bouman et al., 2017;Akbari et al., 2020;Herold and Lee, 2017;Hensher and Ton, 2002;Berrittella et al., 2007;Seo et al., 2021;Brand et al., 2013;Anable and Bristow, 2007;Mohamed, 2016;Pavlovic et al., 2016), advanced public transportation systems (Emberger, 2017;Nakamura and Hayashi, 2013;Anas and Lindsey, 2011;Qin et al., 2019); using environmentfriendly trucks in cargo transportation (Kabadurmus and Erdogan, ...
... , zero-emission vehicles (ZEV) (Herold and Lee, 2017;Wang et al., 2020a), alternative fuels (Chapman, 2007;Ong et al., 2011;Emberger, 2017;Brand et al., 2012;Herold and Lee, 2017;López-Navarro, 2014;Gan, 2003;Hensher, 2008;Fragkos et al., 2021;Corbett and Winebrake, 2007;Baptista et al., 2012;Yaqoob et al., 2021;Li et al., 2021;Azar et al., 2003;Grahn et al., 2009;Eyre et al., 1997;Trevisan and Bordignon, 2020;Salvi and Subramanian, 2015;Kay et al., 2014), hydrogen fuel cells (FCV) (Demir et al., 2014;Hensher, 2008;Azar et al., 2003;Watabe et al., 2019;Tong et al., 2015;Grahn et al., 2009;Salvi and Subramanian, 2015;Banister, 2011), biofuels (biofuels are known as mono-alkyl esters, which are obtained from vegetable oils or animal fat) (Ong et al., 2011;Emberger, 2017;Fragkos et al., 2021;Baptista et al., 2012;Yaqoob et al., 2021;Azar et al., 2003;Grahn et al., 2009;Chauhan et al., 2009;de Souza et al., 2013;Kaufman et al., 2010;Nabavi-Pelesaraei et al., 2017), natural gas vehicles, (NGV) (Ong et al., 2011;Yaqoob et al., 2021;Watabe et al., 2019;Tong et al., 2015;Grahn et al., 2009;Eyre et al., 1997;de Souza et al., 2013 (Ong et al., 2011;Emberger, 2017;Baptista et al., 2012;Yaqoob et al., 2021;Tong et al., 2015;Grahn et al., 2009;Eyre et al., 1997), hybrid fuel (Grahn et al., 2009;Nakata, 2000), fleet renovation (Emberger, 2017;Navas-Anguita et al., 2019;Berrittella et al., 2007), and infrastructure improvement, including the following: road, tunnel, and bridge networks (Demir et al., 2015;Rodrigues et al., 2015;Azar et al., 2003;Trevisan and Bordignon, 2020); reducing the vehicle weight (increasing the vehicle weight increases fuel consumption) (Demir et al., 2014;Jemai et al., 2012); technical development (Bouman et al., 2017;Akbari et al., 2020;Herold and Lee, 2017;Hensher and Ton, 2002;Berrittella et al., 2007;Seo et al., 2021;Brand et al., 2013;Anable and Bristow, 2007;Mohamed, 2016;Pavlovic et al., 2016), advanced public transportation systems (Emberger, 2017;Nakamura and Hayashi, 2013;Anas and Lindsey, 2011;Qin et al., 2019); using environmentfriendly trucks in cargo transportation (Kabadurmus and Erdogan, ...
Given the importance of the environment around the world and its accelerating destruction brought on by the increase in emissions resulting from the growing use of various forms of transportation, this paper shall aim to eliminate this research gap via a thorough investigation of the literature. These goals include the effect of greenhouse gases emitted by the transportation industry on the environment, the impact of pollutants on transportation mode choice, a study of the obstacles to reducing pollution in transportation, and the presentation of solutions and suggestions. In the research, papers related to this topic in various transportation industries, including road, rail, marine, air, and multimodal transportation, and the variables affecting the control of greenhouse gas emissions in any mode of transportation were collected. Afterward, fundamental analysis was carried out, conclusions drawn, and the presentation of suggestions and solutions in this area addressed. Reducing greenhouse gases in transportation is a challenge that requires examining numerous influential variables and factors. The studies presented in this research are expected to be useful, especially for the energy activists, researchers, and policymakers who would like to conduct long-term studies of pollutants in the transportation industry and the variables influencing the control of greenhouse gas emissions.
... Due to dramatic cost reduction in wind and solar photovoltaic (PV), renewable energy is being increasingly incorporated into power grids [1], [2]. The uncertainty and intermittent of renewable energy generation considerably affect the security operation of power grids [3]. ...
... where Erate is the capacity of the battery (MWh); n is the cycle number; h is the depth of the cycle. When the battery capacity is fixed, Ah is affected by the two factors at the same time; μ can be substituted from (2). Thus, the following cycle aging model of the BESS is proposed: ...
Battery energy storage systems (BESSs) have been widely used in power grids to improve their flexibility and reliability. However, the inevitable battery life degradation is the main cost in BESS operations. Thus, an accurate estimation of battery aging cost is strongly needed to cover the actual cost of BESSs. The existing models of battery life degradation either are not fully accurate to estimate the actual cost or are not solved easily because of their computation nonlinearity. In this paper, a piece-wise linear battery aging cost model with an accurate estimate of battery life degradation for BESSs is proposed to extend battery life and improve battery profits. In our method, the widely-used Arrhenius law is modified to quantify the battery life degradation affected by the depth of cycle. Further, a nonlinear battery cycle aging cost model is developed by finding the derivative of battery life degradation with respect to discharging power, which indicates the battery life degradation rate due to depth of cycle. To reduce the complexity of computation, a piece-wise linearization method is proposed to simplify the battery cycle aging cost model. Finally, the cycle aging cost model with an accurate estimation of battery life degradation is applied to the optimization dispatch in the day-ahead energy and auxiliary service market. The results show that the error of estimating the battery cycle aging cost of BESSs is less than 5% under proper piece-wise segment numbers. The profits are increased by 27% and the battery life is extended by 11% than the fixed cost method.
... Modeling EV charging load has been studied intensively in recent year using mobility survey datasets [9][10][11], such as household travel survey [12] and GPS data [13]. Most studies focus on home charging behavior of EVs and ignore the charging behavior differences caused by socio-economy factors. ...
... We design an aggressive EV deployment scenarios from 2020 to 2050 based on existing reports and studies [11,[22][23][24], in which the market share of EV population in the total vehicle population is used to represent the degree of EV development. The aggressive EV deployment target in 2050 is similar to the assumptions in [25]. ...
This paper presents a stochastic model to quantify the impact of the electric vehicle (EV) on China’s electricity load profiles. Most of the existing literature utilized travel data to model EV charging behavior and ignored the influence of people’s social attributes that are significant for the accuracy of EV charging behavior. Based on the dataset of the national household travel survey, the most significant influencing factors, e.g. age, location, and weekday/weekend, are identified. Markov-chain is used to construct a sequence of destinations of each vehicle trip, depending on EV’s driver, day of the week, and time of day. Vehicle-driven distance, driving time, and parking duration are used to model electricity demand and potential EV charging flexibility. The charging infrastructure accessibility in a certain parking location has an influence on EV charging decisions. The model’s outputs are used to assess the impacts of various EV charging strategies on electricity load profiles on a national scale. It is found that at 60% gasoline vehicle replacement with EVs by 2050, the electricity demand of EV will be 510 TWh, accounting for 4.5% of the national demand in 2050. The national peak loads will further increase by 8.2% under the unmanaged charging strategy of EV. In contrast, implying last-minute charging strategy only increases peak demand by 2.6% relative to the unmanaged charging strategy.
... Currently, one of the most important initiatives for direct CO2 reduction is the development of electric cars, or electric vehicles (EVs). The energy crisis [3], environmental issues [4], including local air pollution, particularly in urban areas, and global warming [5], are major driving forces behind the development of EVs. One of the decarbonization strategies described in this study is the use of EVs to energize active buildings [6]. ...
The role of electric vehicles (EVs) is very important in the coming years because of their environmental friendliness and ability to absorb excess electricity from renewable energy sources (RES). Charging EVs will have an immediate negative impact on the power grid. EV smart charging provides a solution to these problems. For sustainable energy management, smart charging technology offers significant advantages in terms of faster charging times and optimized grid usage. By leveraging advanced algorithms and real-time communication capabilities, smart chargers enhance the efficiency, convenience, and environmental sustainability of EV charging infrastructure. Constant-current (CC) and constant-voltage (CV) technologies are essential components of smart charging systems, contributing to improved charging efficiency, battery safety, and grid stability. By regulating the charging process and optimizing power flow, these technologies play a crucial role in advancing the adoption of EVs and promoting sustainable energy management. When advanced CC-CV technologies are added to smart charging systems, the whole paradigm changes. Charging efficiency goes up by 40%, charging time goes down by 50%, and the grid's impact is reduced by 50% through better energy distribution.
... Many research groups have further developed different versions of the SWITCH model to analyze decarbonization pathways in different regions 1,[37][38][39][59][60][61][62][63][64] . We use the SWITCH WECC 65 model which represents the Western Interconnection by dividing it into 50 geographical zones. ...
As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050. In this work, we identify cost targets for offshore wind and wave energy to become cost effective, calculate a 17% reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and show how curtailment, generation, and transmission change as offshore wind and wave energy deployment increase.
... New asset net-zero projects are extensively discussed in the decarbonization of the energy sociotechnical system (Kim & Bae, 2022;Marchi et al., 2018;Murele et al., 2020;Oshiro et al., 2017;Zarazua de Rubens & Noel, 2019) and the transport sociotechnical system (B. Li et al., 2021;Mckinnon, 2016;Rose & Neumann, 2020;Zhang & Zhang, 2021). ...
Projects are essential for the net-zero transition, yet the project studies literature largely ignores net-zero transition and net-zero projects. We argue that projects are vectors of change enabling the transition toward net-zero sociotechnical systems. Leveraging a systematic literature review, we identify four types of net-zero projects: (1) new assets; (2) upgrade assets; (3) behavioral intervention; and (4) Research, Development, and Demonstration (RDD). We present how “net-zero projects” can enable the transition of sociotechnical systems toward net-zero, reducing emission intensity or quantity. Finally, we underline the heterogeneity of net-zero projects in terms of complexity, barriers, benefits realization time span, and complementarities.
... Ke et al (2017) considered passenger vehicle electrification in China's Yangtze River Delta region and found that EVs could improve air quality at both regional and urban levels. Li et al (2021) employed a wellto-wheel model that considers regional differences in the electricity mix to reveal the potential to reduce SO 2 and NO X emissions associated with EV adoption. However, the varying electricity mix for EVs' adoption and energy efficiency of existing ICEVs among provinces also suggests the importance of balancing the energy efficiency improvement of existing ICEVs against the further adoption of EVs in differing provinces for maximizing the total emission reduction there. ...
Private passenger vehicles, with its high emissions of CO2 and air pollutants, poses a severe threat to global climate and human health, particularly for a large developing country like China. Although both energy efficiency improvement of internal combustion engine vehicles (ICEVs) and the wide adoption of electric vehicles (EVs) could contribute to reducing emissions, how they should be jointly implemented in provinces with a heterogeneous context to maximize their net benefits remains insufficiently explored. Here, based on an integrated modeling framework associated with one factual (REF) and four counterfactual scenarios to explore the priority and best-ranked ordering of both EVs' penetration and high energy-efficient ICEVs in 31 Chinese provinces to achieve the most environmental and human health benefits from 2011 to 2018. The results demonstrate that electrification of the passenger fleet, which is charged by a slightly cleaner power source relative to 2011, yields significant co-benefits of CO2 reduction and air quality improvement. Compared with REF, the fleet electrification scenario would lead to 3167 cases of avoided mortality and attain US$4.269 billion of health benefits in 2018, accounting for 0.03% of China's gross domestic product. Nonetheless, highly efficient ICEVs are found to harbor decarbonization potential and health benefits in northern China. Based on these results, Sichuan, Hebei and seven other provinces in east China should promote EVs imminently; conversely, eight provinces with a high share of thermal power must continually advance their implementation of ICEVs in the near future. Such prioritization of EVs and ICEV development at the provincial level provides timely insights for devising tailored policies regarding passenger car transition and for maximizing climate and health benefits based on regional heterogeneity.
... The truck travel survey data to simulate BET charging demand is based on the method in Li et al. [24,25]. As motivated in Section 2, there are two charging strategies of BETs. ...
To address environmental challenges resulting from transportation sector's carbon emissions, replacing conventional internal combustion engine vehicles with electric vehicles is a key solution. Comprehensive electrification of heavy-duty trucks can promote the decarbonization of the freight transportation. The mismatch of supporting infrastructures is one of the major barriers to the deployment of battery-electric trucks (BET). BET charging behaviors and vehicle daily operations could influence the charging station planning and electricity distribution systems, which will cause the costs of upgrading distribution grid. This paper studies centralized BET charging station planning, considering the impacts of plug-in fast charging mode on the distribution systems. First, the BET model is proposed to capture BETs charging load profiles based on real-world operating data. Then, a mixed-integer programming model is formulated for charging station planning considering the upgrade requirements for distribution systems based on the BET model. After the co-optimization of centralized charging station planning and charging strategies of BET, the size of charging stations and charging schedules are determined. The results of numerical experiments indicate that charging demand from 50 BET can be met at current charging infrastructure technologies (>60 kW per charger) with a 37% reduction in the upgrade of distribution systems if incorporating managed charging strategy.
... So, equation (6) use to calculate the emission reduction ratio in past five years (B. Li et al., 2021). ...
China is working to boost the manufacture, market share, sales, and use of NEVs to replace fuel vehicles in transportation sector to get carbon reduction target by 2060. In this research, using Simapro life cycle assessment software and Eco-invent database, the market share, carbon footprint, and life cycle analysis of fuel vehicles, NEVs, and batteries were calculated from the last five years to next 25 years, with a focus on the sustainable development. Results indicate globally, China had 293.98 m vehicles and 45.22% worldwide highest market share, followed by Germany with 224.97 m and 42.22% shares. Annually China's NEVs production rate is 50%, and sales account for 35%, while the carbon footprint will account for 5.2 E+07 to 4.89 E+07 kgCO2e by 2021-2035. The power battery production 219.7 GWh reaches 150%-163.4%, whereas carbon footprint values in production and use stage of 1 kWh of LFP 44.0 kgCO 2 eq, NCM-146.8 kgCO 2 eq, and NCA-370 kgCO 2 eq. The single carbon footprint of LFP is smallest at about 5.52 E+09, while NCM is highest at 1.84 E+10. Thus, using NEVs, and LFP batteries will reduce carbon emissions by 56.33%-103.14% and 56.33% or 0.64 Gt to 0.006 Gt by 2060. LCA analysis of NEVs and batteries at manufacturing and using stages quantified the environmental impact ranked from highest to lowest as ADP > AP > GWP > EP > POCP > ODP. ADP(e) and ADP(f) at manufacturing stage account for 14.7%, while other components account for 83.3% during the use stage. Conclusive findings are higher sales and use of NEVs, LFP, and reduction in coal-fired power generation from 70.92% to 50%, and increase in renewable energy sources in electricity generation expectedly will reduce carbon footprint by 31% and environmental impact on acid rain, ozone depletion, and photochemical smog. Finally, to achieve carbon neutrality in China, the NEVs industry must be supported by incentive policies, financial aid, technological improvements, and research and development. This would improve NEV's supply, demand, and environmental impact.
... Flertalet studier (Muratori, 2021;Li, 2021) har visat att fordonselektrifiering sannolikt inte kommer att utgöra en existentiell utmaning för högspänningsnätet. Istället förväntas den större utmaningen ligga närmare källan för den utökade efterfrågan, det vill säga i lågspänningsnäten. ...
The government has commissioned the Swedish National Road and Transport Research Institute (VTI) to “contribute to building knowledge around a fast, smart, and economically efficient transport sector electrification.” This report focuses on the part of the mission that deals with conducting pilot projects and developing models for how data, in practice, can be made available, shared, and utilized in the best way to optimize planning, development, and operation for charging infrastructure and business models. The report describes existing technologies for charging electric vehicles, important user perspectives, and how business models and systems for charging infrastructure can be modeled. The report focuses on data sharing and describes how actors today share data and the difficulties they see with it. This includes, among other things, data availability, sharing, and utilization, as well as how the actors want it to work going forward. A major challenge concerns data availability, where actors partly see problems with getting access to data and partly are hesitant to want to share their own data. Often, it is about privacy issues and regulation according to the GDPR. The importance of a well-functioning collaboration between the energy and transport sectors has been highlighted in previous reports from this assignment. The importance of digitalization and digital infrastructure that connects these sectors is particularly emphasized in this work. Digitalization is needed to streamline the planning, development, and operation of the infrastructure that an electrified transport system requires. The modeling done in this part of the assignment deals with transport modeling and energy modeling and development to make the models interact.
Keywords: Electrification, electric vehicles, energy systems, charging infrastructure, conversion, transport systems, digitization, data sharing, data areas, regulations.
... Alternative fuels such as hydrogen and biofuels may also have a role to play where the electrification potential is less (e.g., aviation, heavy road transport). There are various government policies and subsidies that could support EV uptake and reduce the up front purchase cost [63,64,65,66]. Additionally, one study assessed an alternative way of decarbonising the Danish transport sector based on the increase in travel time budget (TTB), flexible driving patterns (Flex DP), increase of the speed of bus mode, and the combination of an increase of TTB & Flex DP); it found that increased speed for public buses and a less strict time budget for passengers could lead to lower-cost decarbonisation by 2050 [67]. ...
Deep decarbonisation pathways can enable the state of New South Wales (NSW) in Australia to reach a net-zero emissions reduction goal and contribute to global mitigation efforts to limit temperature rise to 1.5°C by mid-century. This paper explores minimum cost solutions for achieving the corresponding greenhouse gas (GHG) emissions reduction target for NSW, using an Australian implementation of the TIMES (The Integrated MARKAL-EFOM System) energy system modelling framework. This paper investigated possible decarbonisation pathways and available technology options to reach the target. It includes both a higher emissions reference case scenario and a scenario implementing the NSW state government's target of net-zero emissions by 2050 under the NSW Climate Change Policy Framework, consistent with the international Paris Agreement on climate change, with available and viable well-developed technologies. The findings show that the NSW energy system can continue its shift from fossil fuels to renewables like solar, wind, and hydro and can entirely phase out coal- and gas-fired electricity generation by 2050. The deployment of zero-emissions technologies along with policy supports are crucial to achieving deep decarbonisation of the NSW economy by 2050. In addition, electrification and energy efficiency improvements play a significant role in the end-use sector's energy consumption reduction in the coming decades. This paper shows that the electricity sector is the dominant contributor to emission reductions up to the year 2030, while transport, buildings, and industry sectors are set to decarbonise later in the projection period (2030-2050) along this least-cost trajectory. However, the NSW government's aspirational target of net-zero emissions by 2050 can be achieved by 2039 by offsetting negative emissions.
... Nowadays, the development of electric vehicles (EVs) is ongoing as a critical action for the direct reduction of CO2 emissions. The main drivers for developing EVs are the energy crisis [3] and environmental issues [4], including global warming and local air pollution, particularly in cities [5]. The research outlines the role of EVs in electrifying active buildings as one of the decarbonization approaches [6]. ...
The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers.
... Power converters are widely used with the rapid development of renewable energy [1]. As the key component of power converters, insulated gate bipolar transistors (IGBTs) are the most adopted power devices [2] according to the demand for operational reliability, energy efficiency, and cost competitiveness. ...
Bond wire lift-off is one of the major failure mechanisms in the insulated gate bipolar transistor (IGBT) modules. Detecting the fault of bond wires is important to avoid the open-circuit fault of IGBT to ensure the reliable operation of power converters. In this paper, we propose a novel bond wire fatigue detection method for IGBT, which could be used in normal working conditions. Firstly, we investigated the dependence of bond wire fatigue on heatsink thermal resistance. An aging rate K was proposed to compare the measured thermal resistance with the initial value, which could indicate the bond wire fatigue. Then, this proposed method was verified by simulation and experimental results under different current levels. Finally, a power cycling test was used to show the aging process of the IGBT module, which shows the feasibility of proposed method.
... However, Zhuge, et al. [60] predict that Battery EVs are preferable to hybrid EVs in Beijing, and their charging demand may account for 4% of Beijing's residential electricity demand in 2020. From a longer-time perspective, China's CO 2 reduction brought by the aggressive deployment of EVs may reach 725 Mt by 2050, about 10% of national CO 2 emissions from the power sector in the business-as-usual scenario [61]. ...
... The traditional dispatch problems focus on active power scheduling of generation units from the power supply side, aiming to minimize fuel costs. Currently, the increasing attention in environmental protection gives rise to grid dispatch solutions that can simultaneously increase economic and environmental benefits (Li et al. 2021). ...
A dynamic dispatch program schedules the controllable generation units during the entire dispatch period to minimize the economic cost and increase other benefits. On the other hand, demand response (DR) improves energy utilization and increases power quality. The coordination of dynamic dispatch with DR is potentially important to increase the benefits to distribution network areas. In this paper, a dynamic multiobjective optimal dispatch (DMOD) problem is developed to effectively integrate with the time of use (TOU)-based DR for optimal distribution network dispatch. DR is concerned with the optimal pricing during peak, off-peak, and valley periods such that the load curve of the dispatch is optimally shaped. In a combined dispatch procedure, dynamic multistage generators, energy storage operations, and power exchanged between upstream grid dispatches are simultaneously optimized to decrease the system operational cost and the active power loss along with the responsive load based on a DR scheme. In addition, a hybrid teaching-learning multiobjective particle swarm optimization algorithm (HTL-MOPSO) is proposed for solving the DMOD incorporation with the DR model. The proposed method is validated in a simulation study using an IEEE 33 bus system. The comprehensive numerical results reveal not only the efficiency of the proposed combined DMOD with the DR model but also the practical DR benefits to customers and distribution network operators.
... По некоторым оценкам, обеспечение электромобилей электроэнергией на основе НВИЭ потребует дополнительных издержек для интеграции этих источников в электроэнергетическую систему в размере 228-352 долл. на электромобиль [Chen, Kammen, 2021]. ...
Китайская Народная Республика – лидер в продажах легкового дорожного транспорта, электромобилизацию которого ускорила пандемия COVID-19. В работе оцениваются перспективы продвижения электромобилей в Китае и влияние этого процесса на потребление нефти внутри страны. Согласно прогнозам, Китай может пройти пик спроса на нефть около 2030 г., и еще до 2025 г. обострится конкуренция за его рынок между Россией, Саудовской Аравией, США, Ливией и, при снятии санкций, Ираном. Дополнительным риском успешной политики Китая в продвижении электромобилизации может стать значительный рост экспорта дешевых электромобилей, что подстегнет процесс электромобилизации в других странах и приблизит глобальный пик нефтепотребления.
... However, most research on investment planning in power systems is focused on the physical characteristics of the electricity system, while influences from market factors are ignored by most studies. Typical simulation software for investment planning for power systems include Energy Plan [68], SWITCH [69][70][71], NEPLAN [72] and PLEXOS [73]. Only PLEXOS takes into account market factors (including recent and realtime market price movements, changes in demand, market risks, etc.), ancillary services and system flexibility. ...
To reach the “30·60” decarbonization target (where carbon emissions start declining in 2030 and reach net zero in 2060), China is restructuring its power system to a new energy-based one. Given this new situation, this paper reviews previous studies on the power market and highlights key issues for future research as we seek to adapt to the new power system (NPS). Based on a systematic literature review, papers on the operational efficiency of the power market, participants’ bidding strategies and market supervision were identified. In a further step, papers with high relevance were analyzed in more detail. Then, key studies that focused on market trading under China’s new power system were picked out for further discussion. New studies were searched for that pertained to new energy mechanisms and bidding, the transition from coal-fired power, flexible resources and the technical applications of simulations. The quantitative analysis supports the construction of a basic paradigm for the study of power markets that is suitable for the new power system. Finally, the theoretical basis and application suggestions for power market simulations are introduced. This study summarized the existing research on the power market and further explored the key issues relating to the power market as it adapts to the NPS, hoping to inspire better research into China’s power sector, and promote safe, low-carbon, and sustainable development in China’s power industry.
This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.
Undoubtedly, the transition to electromobility with several million new, efficient charging points will have consequences for the energy industry, and in particular for network operators of the distribution infrastructure. At the same time, in the coming years the energy landscape will change into a system in which an increase in decentralized systems based on renewable energy sources will take over the leading function. This transformation process will further increase the complexity and overall pressure for change in energy systems over the next decade. In order to be able to ensure the energy supply and the reliable system operation of the grids in the future as well, communicative networking of generators, storage systems, electrical consumers and grid equipment is indispensable. This study aims to investigate the consequences of including electric vehicles in Istanbul’s power system using a unit commitment simulation model. The presented considerations analyze how uncertain and managed charging strategies affect the power system in terms of operating costs and renewable resources. The presented simulations indicate that, in economic terms, the use of a managed charging strategy saves 2.3%, reducing the total cost from USD 66.71 million to USD 65.18 million. The recipients of the presented research are both the demand and supply sides of the future energy transformation based on the idea of synergy of electromobility and renewable energy sources within the framework of the smart city idea.
Since the possibility of grid-side use of electrically driven vehicles was first proposed, numerous valuable investigations have been conducted to demonstrate the beneficial capabilities of such technologies. However, there are still uncertainties surrounding the integration of electric vehicles into the power grid. These uncertainties include the number of electric vehicles that will be connected to the grid at any given time, the amount of energy stored in their batteries during both daytime and nighttime, and the impact of their charging profiles on the overall power grid load. Furthermore, there are several other unanswered questions that require attention. This paper introduces a novel model based on a non-stationary discrete Markov chain, which effectively tackles these uncertainties. Through a case study, the model's outputs provide intriguing insights into the number of electric vehicles connected and the energy they reserve over a 24-hour period. Additionally, this paper evaluates the model's ability to accurately represent the load modeling of electric vehicles charging.
As countries around the world race to decarbonize their power systems in an effort to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although many commercially available technologies are typically included in these studies, only a few of them have considered offshore wind and wave energy as contenders in future zero emissions grids. We model, for the first time, offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050 and a high geographical and temporal resolution. Our key contributions are to identify cost targets for offshore wind and wave energy to become cost effective, to observe a 17% of reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and to show how total curtailment decreases as offshore wind increases its deployment.
The constantly increasing amount of road vehicles causes massive exhaust emissions of pollutants, including polycyclic aromatic hydrocarbons (PAHs), necessitating a global responsibility to implement the policy of the ban on the sale of new petrol and diesel cars. Here, we assessed the policy control efficiency on marine pollution of PAHs in China through scenario modeling and prediction models, based on pollution monitoring, risk assessment, and source apportionment of PAHs in typical bays of Shandong Province. The results showed that in 2021, the pollution risk levels were relatively low (HI: 0.008-0.068, M-ERM-Q: 0.001-0.016, IBR: 1.23-2.69, ILCR: 8.11 ×10-6-1.99 ×10-5), and PAHs were mainly derived from traffic emissions (24.9%-35.2%), coal combustion (25.2%-32.9%), petroleum (17.2%-28.9%), and biomass combustion (17.6%-22.8%). In 2050, the predicted decrease of pollution risk values after the implementation of the policy was significant (12%-26%), and the gap between 2021 and 2050 was also significantly huge (18%-85%) without considering possible substitution of conventional energy. Collectively, this study built systematic approaches for assessing prospective marine pollution of PAHs. However, due to the particularity of Shandong Province, i.e., its national predominance of conventional energy consumption, the policy may be more effective when it comes to other coastal areas worldwide, calling for a larger scale research.
The proposal of “double carbon” goal increases the pressure of power structure transformation. This paper sets up two scenarios according to the timing progress of realizing the “double carbon” goal and explores the transformation planning schemes of China’s power structure. The conclusions are as follows: (1) Technological progress and policy support will greatly reduce the levelized cost of electricity (LCOE) of onshore wind power, offshore wind power, photovoltaic power, and photothermal power. The rapid rise in carbon price will lead to the LCOE of coal power in 2060 rising to 2 CNY/kWh. (2) The power consumption of the whole society in the baseline scenario can reach 17,000 TWh in 2060. In the acceleration scenario, this value may triple that in 2020 to 21,550 TWh. (3) The acceleration scenario will pay more newly added power costs and coal power stranded scale than the baseline scenario but can achieve carbon peak and negative emissions earlier. (4) More attention should be paid to the flexible level of power system, improve the allocation proportion and requirements of new energy storage on the power supply side, help the steady exit of coal power, and ensure the safety of low-carbon transformation of power structure.
The ongoing electrification of road vehicles needs to be supported by proper growth of charging infrastructure. In this context, dynamic wireless charging can provide a number of advantages, main being the possibility to extend vehicle range without increasing on board battery capacity, potentially reducing cost, mass, and tank-to-wheel energy consumption. The development of such solutions however poses various questions, including the acceptance and the capability of users in driving according to infrastructure characteristics; in particular, the misalignment reduction while a driver follows a straight path positively influence the charging efficiency in dynamic wireless technology. In this paper, authors describe a tests campaign to determine driving performances using both a simulator and a real world equipped vehicle. The research question of this paper is to assess and quantify differences between the two approaches. To reach this objective, in a first phase, data have been collected through a driving simulator (i.e. a full car body mounted on a parallel linked kinematics with a large screen, proposing a virtual city scenario), and in a second phase through a car equipped with a camera. As a post processing phase, statistical tools have been used to describe driving performance indexes and related impacts on wireless charging infrastructure by determining the secondary voltage on the vehicle. Data coming from the activity will be functional to be used by scenario analyst to develop characterization tests only with a simulative approach to decrease costs.
Beijing's power system has experienced a significant transition by eliminating coal-fired power generation and increasing the extent of electrification over the past two decades. This study uses a plant-level dataset, pair-wise energy flow, and customized index decomposition analysis to investigate the driving forces behind Beijing's rapid electrification and carbon mitigation effects. Our assessment approaches can reveal the role of electrification in driving city-level emissions and identify a reasonable timeline to accelerate electrification for cities. We find that electrification-related factors are responsible for 21.8% of carbon emission changes in Beijing between 1995 and 2019. The extent of electrification in Beijing has doubled from 9.5% in 1995 to 19% in 2019, while the carbon intensity of electricity consumption has reduced by 26%. Particularly, the average emission intensity of Beijing's local power generation has dropped from 860 tCO2e/GWh in 2000 to 370 tCO2e/GWh in 2018, which is far below a critical level at which the high penetration of electricity in transportation and housing should be prioritized for decarbonization. Our results confirm that Beijing is in the best position to accelerate electrification to meet its carbon neutrality goals; however, this will increasingly rely on regionally collaborative decarbonization efforts with surrounding regions.
With the development of smart cities, new requirements have been put forward for the control of carbon emissions (CEs) in the transportation system. Intelligent transportation systems (ITS) provide a solution to the problems of traffic congestion and CEs caused by the rapid increase in the number of vehicles. This work aims at the impact of ITS on the energy conservation and emission reduction (ECER) of transportation networks. This work screened more than 100 pieces of relevant literature on ECER from Google Scholar and classified and analyzed them one by one. First, the development of the transportation system is studied. The monitoring of the transportation network is developing in a brilliant direction, from the initial induction coil detector to the current ITS. Then, the realization path of the ECER transportation system is studied from three aspects: transportation, transportation organization and management, and energy upgrading and replacement. Finally, the impact of ITS on ECER in different transportation domains is summarized. The results indicate that the traffic system has undergone traditional video surveillance and has become increasingly intelligent. Visually presenting traffic data in the management system plays a very influential role in alleviating traffic congestion and vehicle ECER. However, selecting the best vehicle parameters is necessary to realize ECER for the transport system. At the same time, the implementation of these energy-saving measures requires the traction and promotion of government policies in order for ECER transportation to fulfill its potential role.
The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments.
Power system dynamic state estimation plays an important role. However, rapid changes in states cause state estimation to become very hard. To reduce the residual between pseudo and real measurement, prediction models are adopted, which are strongly associated with the convergence rates and accuracies of estimation methods. In this paper, to improve the estimation accuracy, a prediction model that consists of the convolutional neural network and long short-term memory (CNN-LSTM) is employed and then integrated into the unscented Kalman filter (UKF). In the proposed UKF with CNN-LSTM, state vectors are considered as time-series data, so CNN performs feature extraction for data pre-processing first, and then the features go through LSTM to improve its forecast accuracy in real-time. Next, online training and error normalization are introduced to UKF, which increases the estimation accuracy effectively. Finally, simulations are carried out on the IEEE 33-bus system. Simulation results show that the accuracies of the CNN-LSTM prediction model are much higher than those of conventional methods. Furthermore, compared to widely used state estimation methods, our method decreases RMSE and MAPE by about 2 multiples.
China has pledged for their carbon emissions to have peaked by 2030 and to achieve carbon neutrality by 2060. However, this requires restructuring of their entire energy system. This study establishes a bottom-up low-carbon transition model for electric and heating coupled systems. The power system's adequacy with high penetration of renewable energy is considered via an iterative calculation approach, the layout of the power and heat system and the future of coal power under carbon emission constraints are discussed. Different from other research, our results show that the capacity of coal power would further increase to support a reliable transition of power system towards a higher proportion of renewable electricity, and the current target of 1200 GW of wind and solar in 2030 is not enough to achieve rapid carbon reductions, and would result in an extra 1.6 to 2.2 billion t CO2 emitted compared to 2020. Achieving 50% of non-fossil electricity generation by 2030 is feasible, and the target needs to be raised to 1660 to 1850 GW at only 3% increase of total cost. Not building new coal power plants would result in a huge expansion of electricity storage in China, which is hard to realize because of extremely high investment cost.
Despite the increase in vehicle electrification in recent years, the transport sector is still a major contributor to the rise in global carbon dioxide (CO 2 ) emissions. Using dynamic product lifecycle (LC) models, our study analyzes the relationship between lifecycle CO 2 (LC-CO 2 ) emissions and the proportion of electric vehicle sales in Japan. We consider the contributions of fuel efficiency and vehicle lifetime to LC-CO 2 emissions in three scenarios: changes in sales, improvement in fuel efficiency, and changes in vehicles’ lifetimes. Our findings show that promoting electric vehicles and decarbonization of electricity sector will decrease CO 2 emissions from the driving phase. However, even if the energy mix follows the net zero emission target, emissions from the vehicle manufacturing phase will largely remain, and the manufacturing emissions from electric vehicle accounts for more than 50% of total emission in 2050 even in the case of the vehicle lifetime is extended by 5 years. Decarbonization of power sector is effective to reduce driving phase emissions, however it is insufficient for reducing LC-CO 2 emissions. Thus, for reducing LC-CO 2 emissions including the manufacturing phase, the Japanese government need to focus on the decarbonization of supply chain as well as reducing the driving phase emissions.
The power sector in China, which is the main CO2 emission contributor in the country, plays an essential role in achieving the 2060 carbon neutrality goal. Notably, there are scientific gaps regarding the decarbonization plan to achieve this goal and the future power supply structure. The objective of this study is to systematically explore and evaluate the feasibility of constructing a carbon-neutral power sector. Considering the power source potential, power supply characteristics, and advanced technologies, methodological steps were developed for the design and assessment of China's power sector. In particular, an evaluation indicator system was included to assess the decarbonization of the power sector and make it comparable in the international context. The results indicated that it is possible for the country's power sector to achieve carbon neutrality by 2060, using available domestic energy resources. The total cost of the 100% non-fossil power sector was the lowest, accounting for 87.3% of that of the business-as-usual (BAU) power sector. Compared with the BAU power sector, the renewable power sectors had abatement costs of −0.12–0.43 kCNY/t. The negative abatement cost indicated that power sector decarbonization could be cost-effective in China. In the international context, the cost of electricity of the future China power sector (∼0.42 CNY/kWh) was comparable to that in other regions, while the CO2 abatement cost was lower than that in most regions. The proposed methodological steps can be beneficial for CO2 emissions reduction and energy structure conversion in the power sector of any region.
Multichip IGBT module has been widely used in high-power converters. Detecting the status of the IGBT chip is a cost-effective approach to improving the reliability of power devices. This article proposes a novel method based on turn-off delay time to detect chip fault in multichip IGBT modules. First, the effect of chip failure on the turn-off process is analyzed, which causes a decrease in turn-off delay time. Then, a detection method of chip failure based on turn-off delay is proposed, which is validated by experimental results. Furthermore, the effects of chip failure on turn-off delay time in different working conditions are investigated through effective experimental results. The tested results show that the proposed method not only has high linearity and sensitivity to chip failure, but can also eliminate the influence of working environments on detection. The proposed method can be used to monitor the health status of multichip modules and is of great significance for enhancing operation reliability.
Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are 113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.
To meet the 2060 carbon neutrality target, China will need to phase out existing coal-fired power plants by 2050 or before. The electricity supply sector directly employs over 2 million and an additional 2 million when indirect employment including product and machinery production are included. To investigate the policy options and pathways available to meet the national climate goal while transitioning this jobs-intensive and economically powerful sector, we developed a plant-level job accounting model, and combine it with a power sector optimization model to assess job loss in the coal sector, as well as job creation in the burgeoning renewable power sector in China. We find that national and provincial policy actions to support an early and managed transition help to ensure a job-rich and both geographically and socioeconomically equitable shift from coal to clean energy. Specifically, the projected decline in fossil-fuel jobs can be fully offset by job new creation in the expanding renewable energy sector. Current COVID-19 economic stimulus plans include a potential new coal boom, where China could build up to 247 GW of additional plants, and thus delay the transition by a decade or more. We find that this action would result in up to 90% of coal-fired workers losing jobs between 2030 and 2040 without a clear pathway to absorb these workers in what will be an already mature clean energy economy. Provinces with massive coal fleets and limited renewable energy resources, notably Anhui, Henan, Hebei and Shandong, etc., would face a particularly disruptive mismatch of job gains and losses.
Deep carbon mitigation and water resources conservation are two interacted environmental challenges that China's power sector is facing. We investigate long-term transition pathways (2020–2050) of China's power sector under carbon neutrality target and water withdrawal constraint using an integrated capacity expansion and dispatch model: SWITCH-China. We find that achieving carbon neutrality before 2060 under moderate cost decline of renewables by 10–20% depends heavily on large scale deployment of coal-fired power generation with carbon capture and storage (CCS) since 2035 in China's water-deficient northwestern regions, which may incur significant water penalties in arid catchments. Introducing water withdrawal constraints at the secondary river basin level can reduce the reliance on coal-CCS power generation to achieve carbon neutrality, promote the application of air-cooling technology, and reallocate newly built coal power capacities from northwestern regions to northeastern and southern regions. If levelized cost of renewables can decline rapidly by about 70%, demand for coal power generation with CCS will be significantly reduced by more than 80% and solar photovoltaic (PV) and wind could account for about 70% of the national total power generation by 2050. The transition pathway under low-cost renewables also creates water conservation co-benefits of around 10 billion m³ annually compared to the reference scenario.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The costs for solar photovoltaics, wind, and battery storage have dropped markedly since 2010, however, many recent studies and reports around the world have not adequately captured such dramatic decrease. Those costs are projected to decline further in the near future, bringing new prospects for the widespread penetration of renewables and extensive power-sector decarbonization that previous policy discussions did not fully consider. Here we show if cost trends for renewables continue, 62% of China’s electricity could come from non-fossil sources by 2030 at a cost that is 11% lower than achieved through a business-as-usual approach. Further, China’s power sector could cut half of its 2015 carbon emissions at a cost about 6% lower compared to business-as-usual conditions.
Current Chinese policy promotes the development of both electricity-propelled vehicles and carbon-free sources of power. Concern has been expressed that electric vehicles on average may emit more CO2 and conventional pollutants in China. Here, we explore the environmental implications of investments in different types of electric vehicle (public buses, taxis and private light-duty vehicles) and different modes (fast or slow) for charging under a range of different wind penetration levels. To do this, we take Beijing in 2020 as a case study and employ hourly simulation of vehicle charging behaviour and power system operation. Assuming the slow-charging option, we find that investments in electric private light-duty vehicles can result in an effective reduction in the emission of CO2 at several levels of wind penetration. The fast-charging option, however, is counter-productive. Electrifying buses and taxis offers the most effective option to reduce emissions of NOx, a major precursor for air pollution.
This paper describes Switch 2.0, an open-source modeling platform for planning transitions to low-emission electric power grids, designed to satisfy 21st century grid planning requirements. Switch is capable of long-, medium- and short-term planning of investments and operations with conventional or smart grids, integrating large shares of renewable power, storage and/or demand response. Applications include integrated resource planning, investment planning, economic and policy analyses as well as basic research. Potential users include researchers, educators, industry and regulators. Switch formulates generation and transmission capacity planning as a mixed integer linear program where investment and operation are co-optimized across sampled time series during multiple investment periods. High-resolution production cost modeling is supported by freezing investment decisions and including longer time series and more operational details. Modeling features include unit commitment, part-load efficiency, planning and operating reserves, fuel supply curves, storage, hydroelectric networks, policy constraints and demand response. Switch has a modular architecture that allows users to flexibly compose models by choosing built-in modules 'a la carte' or writing custom modules. This paper describes the software architecture and model formulation of Switch 2.0 and provides a case study in which the model was used to identify the best options for obtaining load-shifting and reserve services from batteries and demand response in a 100% renewable power system.
Vehicle-grid integration (VGI) uses the interaction between electric vehicles and the electrical grid to provide benefits that may include reducing the cost of using intermittent renwable electricity or providing a financial incentive for electric vehicle ownerhip. However, studies that estimate the value of VGI benefits have largely ignored how consumer behaviour will affect the magnitude of the impact. Here, we simulate the long-term impact of VGI using behaviourally realistic and empirically derived models of vehicle adoption and charging combined with an electricity system model. We focus on the case where a central entity manages the charging rate and timing for participating electric vehicles. VGI is found not to increase the adoption of electric vehicles, but does have a a small beneficial impact on electricity prices. By 2050, VGI reduces wholesale electricity prices by 0.6-0.7% (0.7 MWh⁻¹, 2010 CAD) relative to an equivalent scenario without VGI. Excluding consumer behaviour from the analysis inflates the value of VGI.
We present an integrated model, SWITCH-China, of the Chinese power sector which we use to analyze the economic and technological implications of a medium to long-term decarbonization scenario while accounting for very short-term renewable variability. Based on the model and assumptions used, we find that the announced 2030 carbon peak can be achieved with a carbon price of ~$40/tCO2. Current trends in renewable energy price reductions alone are insufficient to replace coal, however, an 80% carbon emission reduction by 2050 is achievable in the IPCC Target Scenario with an optimal electricity mix in 2050 including nuclear (14%), wind (23%), solar (27%), hydro (6%), gas (1%), coal (3%), CCS coal (26%). The co-benefits of carbon-price strategy would offset 22% to 42% of the increased electricity costs if the true cost of coal and social cost of carbon are incorporated. In such a scenario, aggressive attention to research and both technological and financial innovation mechanisms are crucial to enabling the transition at reasonable cost, along with strong carbon policies.
Large-scale plug-in electric vehicles (PEVs) utilizing vehicle-to-grid (V2G) technology can collectively behave as a storage system under the control of an aggregator, e.g., arbitraging in the energy market and providing ancillary services to the grid. Quantitatively evaluating V2G capacity, i.e., charging and discharging power ranges, for a PEV fleet utilizing V2G technology (which is referred to as a V2G fleet in this paper) ahead of time is of fundamental importance for V2G implementation. However, because of the stochastic characteristics of PEV driving behaviors, charging demands are difficult to forecast, which makes evaluating V2G capacity technically difficult. This paper first establishes an aggregate model of a V2G fleet that employs aggregated parameters to represent energy and power constraints of the entire V2G fleet and, therefore, reduces the difficulty of forecasting. Then, an evaluation method for V2G capacity of large-scale PEVs is developed based on the proposed aggregate model. To make the V2G capacity evaluated in advance achievable while guaranteeing charging demands during real-time operation, a heuristic smart charging strategy is designed. The application of the evaluation method in optimal charge and discharge scheduling for a V2G fleet providing power reserves is illustrated. Numerical simulations are conducted to validate the proposed method.
This work investigates different scenarios for electric vehicle (EV) deployment in China and explores the implications thereof with regard to energy portfolio, economics and the environment. Specifically, we investigate how to better deliver the value of EVs by improving designs in the power system and charging strategies, given expected developments by 2030 in both the power system and EV penetration levels. The impact of EV charging is quantified by applying an integrated transportation-power system model on a set of scenarios which represent uncertainties in charging strategies. We find that deploying EVs essentially shifts the use of gasoline to coal-fired power generation in China, thus leading to more coal consumption and CO2 emissions of the power system. Economically, EVs outperform gasoline-powered vehicles in terms of average fueling costs. However, the impact of EVs in terms of CO2 emissions at the national level largely depends on the charging strategy. Specifically, controlled charging results in more CO2 emissions associated with EVs than uncontrolled charging, as it tends to feed EVs with electricity produced by cheap yet low-efficiency coal power plants located in regions where coal prices are low. Still, compared with uncontrolled charging, controlled charging shows absolute advantages in: (1) mitigating the peak load arising from EV charging; (2) facilitating RES generation; and (3) reducing generation costs and EV charging costs. Hence, in light of this trade-off of controlled charging with the goals of energy security, economic efficiency and reducing environmental impacts, policy interventions in the Chinese power system should opt for controlled charging strategies in order to best realize the benefits of EVs. Accordingly, this paper proposes that increasing the use of cleaner forms of electricity generation, such as RES power and gas power, and establishing energy efficiency and CO2 emission regulations in power dispatch are critical for China. Lastly, this work illustrates what the optimized charging profiles from the power system perspective look like for different regions. These results can inform Chinese policy makers in creating a better integration of the transportation and the power system.
Plug-in electric vehicle (PEV) technology has drawn increasing amounts of attention in the last decade. As the world's largest automotive market, China has recently made the electrification of transportation central to its national strategic plan. Because of the unique nature of the vertically regulated power industry, China's massive deployment of PEVs has to face unique challenges that may not be encountered by any other country/region. Therefore, a comprehensive coordinated PEV charging scheme is urgently needed to facilitate the smooth grid integration of PEVs at all levels (e.g., transmission systems , distribution systems, and charging stations). This paper presents detailed mathematical modeling of a novel hierarchical framework for coordinated PEV charging at multiple timescales (i.e., day-ahead and real-time). The proposed three-level (e.g., provincial level, municipal level, and charging station level) PEV charging strategy jointly optimizes system load profile and charging costs while satisfying customer charging requirements. The interrelationships between various levels in terms of energy transaction and information exchange are clearly identified. Case studies on Guangdong Province, China, are carried out and simulation results demonstrate the effectiveness of our proposed hierarchical control framework in reducing system peak demand and charging costs.
Sustainable biomass can play a transformative role in the transition to a decarbonized economy, with potential applications in electricity, heat, chemicals and transportation fuels. Deploying bioenergy with carbon capture and sequestration (BECCS) results in a net reduction in atmospheric carbon. BECCS may be one of the few cost-effective carbon-negative opportunities available should anthropogenic climate change be worse than anticipated or emissions reductions in other sectors prove particularly difficult. Previous work, primarily using integrated assessment models, has identified the critical role of BECCS in long-term (pre- or post-2100 time frames) climate change mitigation, but has not investigated the role of BECCS in power systems in detail, or in aggressive time frames, even though commercial-scale facilities are starting to be deployed in the transportation sector. Here, we explore the economic and deployment implications for BECCS in the electricity system of western North America under aggressive (pre-2050) time frames and carbon emissions limitations, with rich technology representation and physical constraints. We show that BECCS, combined with aggressive renewable deployment and fossil-fuel emission reductions, can enable a carbon-negative power system in western North America by 2050 with up to 145% emissions reduction from 1990 levels. In most scenarios, the offsets produced by BECCS are found to be more valuable to the power system than the electricity it provides. Advanced biomass power generation employs similar system design to advanced coal technology, enabling a transition strategy to low-carbon energy.
Electric vehicles offer the potential to reduce oil consumption, air pollutants, and greenhouse gas (GHG) emissions. To take advantage of electric vehicles and improve its urban environment, Beijing, as one of China’s most polluted cities, launched an electric vehicle promotion program that provided a generous subsidy for consumers who purchased battery electric vehicles (BEVs). In this study, we compare the levelized costs of a conventional vehicle (CV) versus a BEV using real data from the Beijing BEV subsidy program. Levelized cost for this study considers consumer driving patterns and vehicle age. For consumers with average driving profiles—i.e., an average driving distance of around 20 km per trip—the levelized cost of CVs decreases from 1.40 yuan/km for an 8-year vehicle lifetime to 1.04 yuan/km for a 15-year lifetime, while the levelized cost for BEVs decreases from 1.44 yuan/km for an 8-year vehicle lifetime to 1.01 yuan/km for a 15-year lifetime. BEVs are more cost competitive than CVs for consumers with medium and high driving profiles and a 12-year and 15-year lifetime. Under current conditions, the subsidy and tax incentives are necessary to make BEVs cost competitive. However, we project that, even if the subsidy is phased out in 2020, BEVs may become cost competitive with CVs because of the decrease in battery cost. Our study results suggest that the BEV subsidy should reflect changes in battery cost and gasoline prices to help continuing deployment of BEVs.
Vehicle-use intensity (kilometers traveled per vehicle per year or VKT) is important because it directly affects simulation results for vehicle fuel use and emissions, but the poor understanding of VKT in China could significantly affect the accuracy of estimation of total fuel use and CO2 emissions, and thus impair precise evaluation of the effects of associated energy and environmental policies. As an important component of our work on the Fuel Economy and Environmental Impacts (FEEI) model, we collected VKT survey data in China from available sources and conducted additional surveys during 2004 and 2010, from which we derived VKT values and VKT-age functions by vehicle type for China. We also projected the future VKT for China by examining the relationship of vehicle use to per-capita GDP in 20 other countries worldwide. The purpose of this work is to achieve a better understanding of vehicle-use intensity in China and to generate reliable VKT input (current and future VKT levels) for the FEEI model. The VKT results obtained from this work could also benefit other work in the field associated with vehicle energy use and emissions.
Today, electric vehicles (EVs) are being proposed in China as one of the potential options to address the dramatically increasing energy demand from on-road transport. However, the mass use of EVs could involve multiple environmental issues, because EVs use electricity that is generated primarily from coal in China. We examined the fuel-cycle CO(2), SO(2), and NO(x) emissions of EVs in China in both current (2008) and future (2030) periods and compared them with those of conventional gasoline vehicles and gasoline hybrids. EVs do not promise much benefit in reducing CO(2) emissions currently, but greater CO(2) reduction could be expected in future if coal combustion technologies improve and the share of nonfossil electricity increases significantly. EVs could increase SO(2) emissions by 3-10 times and also double NO(x) emissions compared to gasoline vehicles if charged using the current electricity grid. In the future, EVs would be able to reach the NO(x) emission level of gasoline vehicles with advanced emission control devices equipped in thermal power plants but still increase SO(2). EVs do represent an effective solution to issues in China such as oil shortage, but critical policy support is urgently needed to address the environmental issues caused by the use of EVs to make EVs competitive with other vehicle alternatives.
Due to economic development and urbanization, the number of car ownership continues to increase, making the urban passenger transport sector becoming an important contributor for energy consumption and CO2 emissions in China (accounting for around 20% of the total carbon emissions of the transport sector in 2016). Hence, how the urban passenger transport sector can contribute to peaking China's carbon dioxide emissions by 2030 is worthy of attention. To that end, we develop a National Energy Technology-Transport (NET-Transport) model to assess the impacts of shifting to alternative clean fuels, improving vehicle fuel efficiency, and promoting public transportation on the future energy demand and CO2 emissions for China's urban passenger transport sector. The results show that in the context of promoting the use of clean fuel vehicles and increasing vehicle fuel efficiency, CO2 emissions of China's urban passenger transport sector could reach a peak of 225 MtCO2 in 2030. If the mode share of public transport could further increase, the CO2 emissions from the urban passenger transport sector in China are possible to peak at around 2020 with the emissions ranging from 171 to 214 MtCO2.
With the rapid development of economy, China's electric power consumption has increased sharply. Its carbon emissions derived from power generation now accounts for more than 45% of the national emissions. This study employs a structural decomposition analysis based on input-output subsystem model to explore sources for emissions increments in China's power sector from 2007 to 2015. Under this approach, the influential factors are classified into four categories. Quite a few scenarios are designed to further assess the impacts of power mix and the levy of carbon tax. The results show that the consumption is the main driving growth factor of CO 2 emissions, and most of the emissions are driven by continuing expansion of large-scale infrastructure, and this trend seems going to change in the future; carbon tax and price policies may be the alternative for reducing the emissions. In addition, both the generation efficiency and internal industrial structure are critical factors in emission reduction. Besides, cleaner energy sources effectively lead to carbon emission reduction but this change performs a
relatively small effect. Finally, promoting the development of non-fossil energy power may lead to total CO2 emissions perform decrease trend before 2030
The development of electric vehicles (EVs) is not only essential to improving energy efficiency and reducing emissions in transportation sector, but also increases power system flexibility through demand response. However, the quantitative research on the potential and economics of smart charging for different types of EVs in China remains insufficient, which has been increasingly regarded as a critical barrier for associated policy decision marking. To address this issue, this paper conducts a survey on the charging behaviors of four types of EVs in Shanghai and evaluates the capacity potential and cost of EV smart charging. A Bass Diffusion model is adopted to forecast the stock growth of EVs and the potential of smart charging is investigated by simulating EV charging behavior under two scenarios. It finds that private and commercial EVs could deliver greater smart charging than that of electric bus and taxis. Business models and charging policies that could cost-effectively explore the potential of EV smart charging are further discussed. The paper concludes that charging service providers with time of use charging tariffs could realize the potential of smart charging in a cost-efficient way.
With the development of economic society and urban construction, it is outstanding that the disequilibrium of taxi supply-demand and low services level. It is essential that researches on characteristics of taxi operation, especially on taxi shift, which will contribute to observe taxi shift affects the supplying ability of taxi. Based on Beijing taxi data and spatial analysis algorithm for taxi shift, three indexes, which are the amount of vehicles for taxi shift, the percent of vehicles for taxi shift and detecting intensity for taxi shift, are designed to analyze the time space distribution characteristic of taxi shift by sample analysis. The result shows that the variation of the amount of vehicles for taxi shift in the day consistent with the change of travel demand, which the amount of vehicles for taxi shift are in the low level from 10 a. m. to 10 p. m. Location for taxi shift lies in the area around the freeway, which had significant area-central character. It is obvious that interval for taxi shift affect the time space distribution of taxi shift.
The growth of China’s road transport sector has driven huge increases in China’s oil demand and CO2 emissions over the last two decades, and these trends are likely to continue in the absence of specific measures to reduce the average carbon intensity of road vehicles. This paper describes a model, provided in full online, to undertake scenario analysis on the cost and CO2 emissions impact of substituting current vehicle drivetrain types with alternatives during the period 2010–2050. A detailed decomposition of the additional costs and CO2 emissions savings of each low-carbon vehicle type into their component parts is undertaken to calculate the marginal abatement cost of each vehicle and drivetrain type in 2050. The results indicate that passenger cars and heavy-duty trucks constitute the majority of future CO2 emissions savings potential, but that, using the central cost assumptions, alternative vehicle drivetrains are significantly more cost-effective for trucks than passenger cars. The low-carbon scenario sees demand for oil products (gasoline and diesel) more than 40% below the business-as-usual scenario in 2050. The total mitigation cost in 2050 is (US2010)$64 billion per year, or 1.3% of the total annual expenditure on road transport in China in 2050, using a discount rate of 5% to annualise vehicle purchase costs, although this cost increases with higher discount rates. A sensitivity analysis demonstrates that measures in addition to those assumed in the low-carbon scenario could achieve further emissions reductions, in some cases at negative costs. The availability and transparency of the model allows testing and development of a range of further scenarios and sensitivities, to aid in planning an optimal decarbonisation strategy for this highly carbon-intensive sector.
Electric vehicles (EVs) and renewable energy sources offer the potential to substantially decrease carbon emissions from both the transportation and power generation sectors of the economy. Mass adoption of EVs will have a number of impacts and benefits, including the ability to assist in the integration of renewable energy into existing electric grids. This paper reviews the current literature on EVs, the electric grid, and renewable energy integration. Key methods and assumptions of the literature are discussed. The economic, environmental and grid impacts of EVs are reviewed. Numerous studies assessing the ability of EVs to integrate renewable energy sources are assessed; the literature indicates that EVs can significantly reduce the amount of excess renewable energy produced in an electric system. Studies on wind–EV interaction are much more detailed than those on solar photovoltaics (PV) and EVs. The paper concludes with recommendations for future research.
Electric vehicles are commonly seen as one of the alternatives to reduce the oil dependency and the greenhouse gas emissions in the transport sector.
The aim of this paper is to evaluate the impact of different electric vehicle charging strategies on the national grid including the storage utilization of electric vehicles (V2G-vehicle to grid). Furthermore, an economic analysis of electric vehicle utilization is performed and the results are compared with the conventional diesel vehicle.
To accomplish this aim the availability of passenger cars in Germany to be plugged into the grid showed to be high at any time over the day (>89%), which is advantageous for the V2G concept.
The impact of the different electric vehicle charging strategies is investigated by employing three scenarios. The first scenario (unmanaged charging) shows that 1 mil. electric vehicles only impacts slightly on the daily peak electricity demand. In the second scenario (Grid stabilizing storage use) a maximum reductions of grid fluctuations of 16% can be achieved with the use of 1 mil. electric vehicles as storage. The last scenario (profit maximization by power trading) the maximum daily revenues from V2G activities are calculated to be 0.68 EUR2009.
This study investigates consequences of integrating plug-in hybrid electric vehicles (PHEVs) in a wind-thermal power system supplied by one quarter of wind power and three quarters of thermal generation. Four different PHEV integration strategies, with different impacts on the total electric load profile, have been investigated. The study shows that PHEVs can reduce the CO2-emissions from the power system if actively integrated, whereas a passive approach to PHEV integration (i.e. letting people charge the car at will) is likely to result in an increase in emissions compared to a power system without PHEV load. The reduction in emissions under active PHEV integration strategies is due to a reduction in emissions related to thermal plant start-ups and part load operation. Emissions of the power sector are reduced with up to 4.7% compared to a system without PHEVs, according to the simulations. Allocating this emission reduction to the PHEV electricity consumption only, and assuming that the vehicles in electric mode is about 3 times as energy efficient as standard gasoline operation, total emissions from PHEVs would be less than half the emissions of a standard car, when running in electric mode.
The rapid growth of vehicles has resulted in continuing growth in China’s oil demand. This paper analyzes future trends of both direct and life cycle energy demand (ED) and greenhouse gas (GHG) emissions in China’s road transport sector, and assesses the effectiveness of possible reduction measures by using alternative vehicles/fuels. A model is developed to derive a historical trend and to project future trends. The government is assumed to do nothing additional in the future to influence the long-term trends in the business as usual (BAU) scenario. Four specific scenarios are used to describe the future cases where different alternative fuel/vehicles are applied. The best case scenario is set to represent the most optimized case. Direct ED and GHG emissions would reach 734 million tonnes of oil equivalent and 2384 million tonnes carbon dioxide equivalent by 2050 in the BAU case, respectively, more than 5.6 times of 2007 levels. Compared with the BAU case, the relative reductions achieved in the best case would be 15.8% and 27.6% for life cycle ED and GHG emissions, respectively. It is suggested for future policy implementation to support sustainable biofuel and high efficient electric-vehicles, and the deployment of coal-based fuels accompanied with low-carbon technology.
Statistic Report of Transportation Sector
BTRC, 2018. Statistic Report of Transportation Sector. Beijing Transporation Research
Center (BTRC). https://www.bjtrc.org.cn/List/index/cid/7.html.
The number of motor vehicles in the country reached 340 million new energy vehicles, which increased by more than 70% year-on-year. Cent. People's Gov. People's Repub
CPGPRC, 2019. The number of motor vehicles in the country reached 340 million new
energy vehicles, which increased by more than 70% year-on-year. Cent. People's
Gov. People's Repub. China.
Transportation Sector Greenhouse Gas Emissions
EPA, 2019. Transportation Sector Greenhouse Gas Emissions 1990-2017.
Energy Technology Perspectives
IEA, 2017. Energy Technology Perspectives 2017. International Energy Agency (IEA)
Publications. https://doi.org/10.1787/energy_tech-2017-en.
Annual Energy Outlook 2019 with Projections to 2050
- U S Eia
U.S. EIA, 2019. Annual Energy Outlook 2019 with Projections to 2050. U.S. Annu.
Energy Outlook 2019 with Proj. to 2050. https://DOE/EIA-0383.
The number of motor vehicles in the country reached 340 million new energy vehicles, which increased by more than 70% year-on-year
- CPGPRC