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Integrated Development of Low-Carbon Energy Systems (IDLES): A Whole-System Paradigm for Creating a National Strategy

Integrated Development of Low-Carbon Energy Systems (IDLES): A Whole-System Paradigm for Creating a National Strategy
低碳能源系统综合发展(IDLES):制定国家战略的全系统范式
批准号:
EP/R045518/1
负责人:
Tim Green
金额:
$898.01万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2018
资助国家:
英国
项目状态:
未结题
起止时间:
2018 至 --

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中文摘要
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英文摘要
The long-term evolution of energy systems is set by the investment decisions of very many actors such as up-stream resource companies, power plant operators, network infrastructure providers, vehicle owners, transport system operators and building developers and occupiers. But these decisions are deliberately shaped by markets and incentives that have been designed by local and national governments to achieve policy objectives on energy, air-quality, economic growth and so on. It is clear then that government and businesses need detailed and dependable evidence of what can be achieved, what format of energy system we should aim for, what new technologies need to be encouraged, and how energy systems can form part of an industrial strategy to new goods and services. It is widely accepted that a whole-system view of energy is needed, covering not only multiple energy sectors (gas, heat, electricity and transport fuel) but also the behaviour of individuals and organisations within the energy consuming sectors such as transport and the built environment. This means that modelling energy production, delivery and use in a future integrated system is highly complex and analytically challenging. To provide evidence to government and business on what an optimised future system may look like, one has to rise to these modelling challenges. For electricity systems alone, there are established models that can optimise for security, cost and emissions given some assumptions (and sensitivities) and these have been used to provide policy and business strategy evidence. However, such models do not exist for the complex interactions of integrated systems and not at the level of fine detailed needed to expose particularly difficult operating conditions. Our vision is to tackle the very challenging modelling required for integrated energy systems by combining multi-physics optimising techno-economic models with machine learning of human behaviour and operational models emerging multi-carrier network and conversion technologies. The direction we wish to take is clear but there are many detailed challenges along the way for which highly innovative solutions will be needed to overcome the hurdles encountered. The programme grant structure enables us to assemble an exceptional team of experts across many disciplines. There are new and exciting opportunities, for instance, to apply machine learning to identify in a quantitative way models of consumer behaviour and responsiveness to incentives that can help explore demand-side flexibility within an integrated energy system. We have engaged four major partners from complementary sectors of the energy system that will support the programme with significant funding (approximately 35% additional funding) and more importantly engage with us and each other to share insights, challenges, data and case studies. EDF Energy provide the perspective on an energy retail business and access to smart meter trail data. Shell provide insights into the future fuels to be used in transport and building services. National Grid (System Operator) give the perspective of the use of flexibility and new service propositions for efficient system operations. ABB are a provider of data acquisition and control systems and provide industrial perspective of decentralisation of control. ABB have committed to providing substantial equipment and resource to build a verification and demonstration facility for decentralised control. We are also engaging examples of the new entrants, often smaller companies with potentially disruptive technologies and business models, who will engage and share some of their insights.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1038/s41598-022-07249-6
发表时间: 2022-03-03
期刊: Scientific reports
影响因子: 4.6
作者: [Ademovic Tahirovic A, Angeli D, Strbac G]
通讯作者: Strbac G
Optimal system configuration and operation strategies of flexible hybrid nuclear-solar power plants
灵活核太阳能混合电站优化系统配置及运行策略
DOI: --
发表时间: 2020
期刊:
影响因子: --
作者: [Al Kindi AA]
通讯作者: Al Kindi AA
DOI: 10.1016/j.enconman.2022.115484
发表时间: 2022-03-24
期刊: ENERGY CONVERSION AND MANAGEMENT
影响因子: 10.4
作者: [Aunedi, Marko, Pantaleo, Antonio M. M., Al Kindi, Abdullah A.]
通讯作者: Al Kindi, Abdullah A.
DOI: --
发表时间: 2019
期刊:
影响因子: --
作者: [Al Kindi A]
通讯作者: Al Kindi A
9
    Technology Transformation to Support Flexible and Resilient Local Energy Systems
    • 批准号:
      EP/T021780/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $103.05万
    • 财政年份:
      2020
    • 负责人:
      Tim Green
    • 依托单位:
    DC networks, power quality and plant reliability
    • 批准号:
      EP/T001623/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $45.65万
    • 财政年份:
      2019
    • 负责人:
      Tim Green
    • 依托单位:
    HubNet: Research Leadership and Networking for Energy Networks (Extension)
    • 批准号:
      EP/N030028/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $261.2万
    • 财政年份:
      2016
    • 负责人:
      Tim Green
    • 依托单位:
    RHYTHM: Resilient Hybrid Technology for High-Value Microgrids
    • 批准号:
      EP/N034570/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $125.54万
    • 财政年份:
      2016
    • 负责人:
      Tim Green
    • 依托单位:
    国内基金
    海外基金
    水稻边界发育缺陷突变体abnormal boundary development(abd)的基因克隆与功能分析
    Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
    • 批准号:
      --
    • 项目类别:
      --
    • 资助金额:
      40万元
    • 批准年份:
      2020
    • 负责人:
      Vikrant Gupta
    • 依托单位: