Resilient Operation of Sustainable Energy Systems (ROSES)

可持续能源系统的弹性运行（ROSES）

• 批准号: EP/T021713/1
• 负责人: Bikash Pal
• 金额: $ 98.85万
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT021713%2F1
• 关键词: Resilient; Operation; Sustainable; Energy; Systems

Upgrade of UK energy system is at the core of Smart systems and flexibility plan laid out in the Government's Industrial Strategy. Beijing, Shanghai, Guangzhou and Shenzhen are aiming to build up world-class distribution networks as they are among the target areas for export promotion. The massive uptake of renewable generations in both the UK and China offers a huge challenge requiring expensive balancing mechanism. It is only through fundamental research focused on addressing these challenges that truly transformative changes to our energy future beyond 2050 can happen. The purpose of this proposal is to carryout underpinning research with an objective to develop tools that will make our electricity supply system resilient as well as sustainable. It will be both data and model driven activities of a strong consortium of technical experts from both sides. The data driving machine learning tool will deliver operational health index of various components in the system. It will employ dynamic state estimation to develop new network automation procedure to ascertain adequate margin of stability of operation of the network from adverse interactions between the non-synchronous generation and synchronous generation of the system. It will explore novel control and protection technology to safe guard the integrity of the operation of the system with randomly fluctuating output from renewables. The technical competence of the team covers range of expertise in power plant and network modelling, big data, machine learning, system dynamics, estimation, control, and power electronics in the context of interconnected power network operation and protection. Tasks proposed in the program of work will explore several methods of data pre-processing, feature extraction and dimensionality reduction. Faster and accurate identification of the fault location in the cable through impedance transfer function enabled eigen-value approach is revolutionary and so is the ML approach to sensor data optimization in fault location. This is a consortium involving academic and industrial partners from a range of disciplines and different research environments and cultures. The PIs propose a jointly led project management team comprising of all the investigators. All the work packages involve researchers from both sides requiring regular exchange of researchers to carry on with the technical tasks. The RAs and investigators will spend two weeks in every visit to China with partner's organizations. Each work package has joint WP leaders who will coordinate within his/her group of researcher and reports to the PI. Both the PIs have led multinational consortia of even larger sizes and between them. A project advisory board (PAB) will be set up inviting the members from industry partners and technical experts from GEIRI, UKPN, Elin VERD, MHI, FTI Consulting. The PAB will help facilitate explore opportunity for engaging with industry and other user of the research outcome. The PIs from both sides will network with other approved projects through a high-level board comprising of all PIs and representatives from RCUK and NSFC. There will be further networking through sponsoring session and technical paper in big conferences such as Power Tech, ISGT, CIGRE, CIRED, Power and Energy Society general meeting (PESGM), and participating in low carbon network innovation (LCNI). The availability of meaningful data is at times challenging. Our strategy to manage such challenge will be to work on simulation data from model available in public domain, promised by industry supporter and introduce noise, contamination and missed data based on trend and practice in big data analytic domain in the context of power engineering drawing upon the experience and insight of the industry partners.

英国能源系统的升级是政府工业战略中制定的智能系统和灵活性计划的核心。北京、上海、广州和深圳是促进出口的目标地区之一，它们的目标是建立世界级的分销网络。英国和中国对可再生能源发电的大规模吸收带来了巨大的挑战，需要昂贵的平衡机制。只有通过专注于解决这些挑战的基础研究，才能实现2050年以后能源未来的真正变革。该提案的目的是开展基础研究，旨在开发使我们的电力供应系统具有弹性和可持续性的工具。这将是双方技术专家组成的强大联盟的数据和模型驱动活动。数据驱动机器学习工具将提供系统中各个组件的运行健康指数。该系统会采用动态状态估计法，发展新的网络自动化程序，以确定网络运作的足够稳定裕度，免受系统的非同步发电机与同步发电机之间的不利相互作用影响。它将探索新的控制和保护技术，以确保可再生能源输出随机波动的系统运行的完整性。该团队的技术能力涵盖互联电网运营和保护背景下的发电厂和网络建模、大数据、机器学习、系统动态学、估计、控制和电力电子领域的一系列专业知识。工作计划中提出的任务将探索数据预处理、特征提取和降维的几种方法。通过阻抗传递函数使能的特征值方法更快、更准确地识别电缆中的故障位置是革命性的，ML方法用于故障定位中的传感器数据优化也是革命性的。这是一个涉及来自一系列学科和不同研究环境和文化的学术和工业合作伙伴的联盟。主要研究人员提议成立一个由所有调查人员组成的联合领导的项目管理小组。所有工作包都涉及双方的研究人员，需要定期交流研究人员，以继续进行技术任务。RA和研究者每次访问中国时将与合作伙伴组织一起度过两周时间。每个工作包都有联合WP领导人，他们将在他/她的研究人员小组内进行协调，并向PI报告。这两个私人投资者都领导着规模更大的跨国财团。将成立一个项目咨询委员会（PAB），邀请来自GEIRI、UKPN、Elin VERD、MHI、FTI Consulting的行业合作伙伴和技术专家。PAB将有助于促进探索与行业和研究成果的其他用户接触的机会。 双方的项目研究者将通过一个由所有项目研究者和英国皇家科学院和国家自然科学基金委员会代表组成的高级别委员会与其他批准的项目建立联系。将通过在Power Tech，ISGT，CIGRE，CIRED，Power and Energy Society General meeting（PESGM）等大型会议上赞助会议和技术论文以及参与低碳网络创新（LCNI）来进一步建立网络。提供有意义的数据有时具有挑战性。我们应对这一挑战的策略将是利用行业支持者承诺的公共领域可用模型中的模拟数据，并根据电力工程背景下大数据分析领域的趋势和实践，利用行业合作伙伴的经验和洞察力，引入噪声，污染和缺失数据。

项目成果

Global Optimality of Inverter Dynamic Voltage Support

• DOI: 10.1109/tpwrs.2021.3133715
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Yifei Guo;B. Pal;R. Jabr;H. Geng

Transient Stability of Power Systems Integrated With Inverter-Based Generation

• DOI: 10.1109/tpwrs.2020.3033468
• 发表时间: 2020-06
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Xiuqiang He;H. Geng

Model-Free Optimal Control of Inverter for Dynamic Voltage Support

用于动态电压支持的逆变器无模型优化控制

• DOI: 10.1109/tpwrs.2022.3226945
• 发表时间: 2023
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Guo Y

On the Optimality of Voltage Unbalance Attenuation by Inverters

• DOI: 10.1109/tste.2022.3163074
• 发表时间: 2021-09
• 期刊: IEEE Transactions on Sustainable Energy
• 影响因子: 8.8
• 作者: Yifei Guo;B. Pal;R. Jabr

A Generalized Phase-Shift PWM Extension for Improved Natural and Active Balancing of Flying Capacitor Multilevel Inverters

用于改进飞电容多电平逆变器的自然和主动平衡的通用相移 PWM 扩展

• DOI: 10.1109/ojpel.2022.3209540
• 发表时间: 2022
• 期刊: IEEE Open Journal of Power Electronics
• 影响因子: 5.8
• 作者: Kampitsis G