Resilient Operation of Sustainable Energy Systems (ROSES)

英国能源系统的升级是政府产业战略中制定的智能系统和灵活性计划的核心。北京、上海、广州和深圳正致力于建立世界级的分销网络，因为它们是出口促进的目标地区之一。在英国和中国，可再生能源的大量使用带来了巨大的挑战，需要昂贵的平衡机制。只有通过专注于应对这些挑战的基础研究，我们的能源未来才能在2050年后发生真正的变革性变化。这项建议的目的是进行基础研究，目的是开发工具，使我们的电力供应系统具有弹性和可持续性。这将是一个由双方技术专家组成的强大财团的数据和模型驱动的活动。数据驱动机器学习工具将提供系统中各个组件的运行状况指数。它将使用动态状态估计来开发新的网络自动化程序，以从系统的非同步发电和同步发电之间的不利交互中确定网络运行的足够稳定裕度。它将探索新的控制和保护技术，以安全地保护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发电厂和网络建模、大数据、机器学习、系统动力学、估计、控制和互联电网运行和保护方面的电力电子方面的一系列专业知识。工作方案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。通过阻抗传递函数实现更快、更准确地识别电缆中的故障位置是革命性的，用于故障定位中传感器数据优化的ML方法也是革命性的。这是一个由来自不同学科、不同研究环境和文化的学术和工业合作伙伴组成的联盟。项目督察建议成立一个由所有调查员组成的联合领导的项目管理小组。所有的工作方案都涉及来自双方的研究人员，要求定期交换研究人员以继续进行技术任务。RAS和调查人员每次与合作伙伴的组织一起探望中国时，都会花两周的时间。每个工作包都有联合WP领导，他们将在他/她的研究小组内进行协调，并向PI报告。这两家私人投资机构都领导着规模更大的跨国财团，而且它们之间的规模也更大。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的成员。PAB将帮助促进探索与工业界和其他研究成果使用者接触的机会。双方的私人投资促进机构将透过一个高层委员会与其他获批准的项目联网，该委员会由所有私人投资机构及RCUK和NSFC的代表组成。将通过在电力技术、ISGT、CIGRE、Cired、电力和能源学会大会(PESGM)和参与低碳网络创新(LCNI)等大型会议上赞助会议和技术论文，进一步建立联系。有意义的数据的可用性有时是具有挑战性的。我们应对此类挑战的战略将是利用行业支持者承诺的公共领域可用模型的模拟数据，并根据电力工程背景下大数据分析领域的趋势和实践引入噪声、污染和遗漏数据，借鉴行业合作伙伴的经验和见解。

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.