Stimulating Low-Carbon Vehicle Technologies
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Change , — Locked into Copenhagen pledges — implications of short-term emission targets for the cost and feasibility of long-term climate goals. Change 90 , 8—23 Noppers, E.
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Dymocks - Stimulating Low-Carbon Vehicle Technologies, , PaperBack book.
Smith, P. Table 6. Download references. Kolp, D. Huppmann and M. Riahi designed the research. Ramea contributed data for the modelling. Riahi, B. Correspondence to David L. Model assumptions for dis utility costs by non-financial attribute, consumer group, vehicle technology and region. Reprints and Permissions.
Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles. Nat Energy 3, — doi Download citation. Environmental Research Letters Energy Policy GCB Bioenergy Advanced search. Skip to main content.
Subjects Climate-change mitigation Energy and behaviour Energy economics Energy policy Energy supply and demand. Abstract Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Access through your institution. Buy or subscribe. Change institution. Rent or Buy article Get time limited or full article access on ReadCube.
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Article Google Scholar Google Scholar Baker Jr. Ethics declarations Competing interests The authors declare no competing interests. Supplementary information. Supplementary Data 1 Model assumptions for annual driving distances and consumer group splits by region. Supplementary Data 2 Model assumptions for dis utility costs by non-financial attribute, consumer group, vehicle technology and region. Rights and permissions Reprints and Permissions. About this article.
Cite this article McCollum, D. Grimes , Craig A. Nature Energy menu. Nature Research menu. Search Article search Search. Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing. Close banner Close. Smart DSP 28 can help to meet the challenges of flexible demand.
Stimulating Low-Carbon Vehicle Technologies
The challenge of demand side response DSR : the Thousand Flowers TF pathway in Spring [12 days mostly surplus, 10 days of deficit, 2 days surplus]. Figure Structural opportunities to control flexible demand, including an illustration of the roles of the transmission network operator TNO , distribution network operator DNO , and flexible prosumers. By that time the number of EVs in use may have reached over one million. Thus, the UK power system will be affected by an increasing imbalance, due to this rise in electricity demand from EVs and uncontrolled supply from wind.
Smart EV charging strategies 61 can therefore help the power system cope with high penetrations of local renewable energy sources RES. They adopted a Monte Carlo model together with state-of-charge SOC information, as part of a whole systems framework, in order to estimate EV charging profiles. Wind farm data was taken from operational sites in Scotland.
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It was found that the cost over several small EV charging events was essentially free, provided that the surplus wind was greater than 1 MW. Under these circumstances, the network may be required to deal with sudden frequency drops following a loss of generator. An advanced stochastic generation scheduling model was employed to quantify the frequency response requirements and the contribution that could be made by DSR.
These DSR benefits were found to have significant diurnal and seasonal variation, whereas an even more rapid near-instant delivery of frequency response from DSR could yield substantial additional value. It is critical to reflect how investors will take decisions to invest in or to retire generation plant within a market and policy context. Accounting for the incentives provided to companies through the trading arrangements is hence fundamental for modelling how investors take decisions going forward. As well as power market revenues, renewable and low-carbon generators are also reliant on subsidies to ensure their profitability, which is important for the investment decision-making process.
Quantitative modelling studies have therefore been conducted in order to evaluate the competitiveness of demand response against other technologies, using a range of GB network case studies related to the transition pathways. A holistic approach via the whole-electricity system investment model WeSIM 64 ; a successor to the HAPSO model 55 has been employed to assess the benefits of demand responses on power generation, transmission and distribution systems under each of the three pathways scenarios see Figure It was used to provide useful insights on the characteristics of different pathways in terms of the expected increase in future peak demand, driven primarily by electrification of heating and transport sectors, 61 , 62 as well as the consequences for future power system infrastructure requirements.
This approach 64 simultaneously optimised investment into new generation, network and storage capacity, while minimising system operation cost, and also considering reserve and security requirements. The analysis distinguished between bulk and distributed storage applications, while also considering the competition against other technologies, such as flexible generation, interconnection and DSR 64 see again Figure A key issue arising from these studies is that the postulated generation capacity under the pathways may not be sufficient to meet security standards.
This highlights the importance of considering the security of supply aspect in the development of future generation portfolios. Analysis of the electricity price characteristics of the three pathways showed that some generators with relatively very low load factors bring into question the feasibility of generation in an energy-only market.
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Annual versus peak electricity demand under the three UK transition pathways.