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: Imperial College London
• 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
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR045518%2F1
• 关键词: Integrated; Development; Low; Carbon; Energy

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.

能源系统的长期演变取决于上游资源公司、发电厂运营商、网络基础设施提供商、车主、运输系统运营商以及建筑开发商和住户等许多行为者的投资决定。但这些决定是由地方和国家政府为实现能源、空气质量、经济增长等政策目标而设计的市场和激励措施刻意塑造的。因此，很明显，政府和企业需要详细可靠的证据，证明可以实现什么目标，我们应该以什么形式的能源系统为目标，需要鼓励什么新技术，以及能源系统如何成为新产品和服务的工业战略的一部分。人们普遍认为，需要一个整体的能源观，不仅包括多个能源部门（天然气，热力，电力和运输燃料），而且还包括个人和组织在能源消费部门（如运输和建筑环境）的行为。这意味着在未来的集成系统中对能源生产、输送和使用进行建模是非常复杂和具有分析挑战性的。为了向政府和企业提供关于优化的未来系统可能是什么样子的证据，人们必须应对这些建模挑战。仅就电力系统而言，已有既定模型可以在给定一些假设（和敏感性）的情况下优化安全性、成本和排放，这些模型已被用于提供政策和业务战略证据。然而，这样的模型不存在的综合系统的复杂的相互作用，而不是在详细的水平需要暴露特别困难的操作条件。我们的愿景是通过将多物理优化技术经济模型与人类行为的机器学习和新兴多载波网络和转换技术的操作模型相结合，解决集成能源系统所需的非常具有挑战性的建模问题。我们希望采取的方向是明确的，但在此过程中存在许多具体的挑战，需要高度创新的解决方案来克服所遇到的障碍。计划补助金结构使我们能够在许多学科中组建一支出色的专家团队。例如，有一些新的令人兴奋的机会，可以应用机器学习以定量的方式确定消费者行为和对激励措施的反应模型，这些模型可以帮助探索综合能源系统中需求方的灵活性。我们已经与来自能源系统互补部门的四个主要合作伙伴进行了合作，他们将为该计划提供大量资金（约35%的额外资金），更重要的是，他们将与我们以及彼此合作，分享见解，挑战，数据和案例研究。EDF Energy提供能源零售业务的视角和对智能电表跟踪数据的访问。壳牌提供了对未来用于运输和建筑服务的燃料的见解。国家电网（系统运营商）从灵活性和新服务提案的使用角度出发，以实现高效的系统运营。ABB是数据采集和控制系统的供应商，并提供分散控制的工业视角。ABB承诺提供大量设备和资源，以建立分散控制的验证和演示设施。我们也在与新进入者互动，通常是拥有潜在颠覆性技术和商业模式的小公司，他们将参与并分享他们的一些见解。

项目成果

Heterogeneous network flow and Petri nets characterize multilayer complex networks.

• 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

• 发表时间: 2020
• 作者: Al Kindi AA

Thermo-economic assessment of flexible nuclear power plants in future low-carbon electricity systems: Role of thermal energy storage

• 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.

Thermodynamic assessment of steam-accumulation thermal energy storage in concentrating solar Power plants

聚光型太阳能发电厂蒸汽蓄热储热的热力学评估

• 发表时间: 2019
• 作者: Al Kindi A

Fuel cells as combined heat and power systems in commercial buildings: A case study in the food-retail sector

• DOI: 10.1016/j.energy.2020.118046
• 发表时间: 2020-09-01
• 期刊: ENERGY
• 影响因子: 9
• 作者: Acha, Salvador;Le Brun, Niccolo;Shah, Nilay
• 通讯作者: Shah, Nilay