System Architecture Challenges: Supergen+ for HubNet

HubNet是一个多所大学合作的项目，该项目为期五年，旨在探索未来能源网络的形式。特别是，我们正在探索使用智能电网技术和高压直流输电技术，将可再生能源的高渗透率整合到电力系统中，并研究如何管理私人车辆和建筑供暖的能源供应变化。它通过自己的核心研究方案，并通过汇集这一领域的大挑战项目的研究和其他研究理事会活动来做到这一点。本提案旨在扩展中心网的核心研究，以应对原提案中未确定的挑战。在任何关于智能电网的讨论中都会出现两个主题：（1）消费者变得更加积极的机会，以便他们从交易其能源需求和生产中受益，以及（2）创建一个ICT系统，该系统可以生成大量的电力流和电压数据，可以利用这些数据来更有效地运行系统。尽管这是一个众所周知的话题，也是特定方面研究项目的主题，但对于客户如何发挥作用（在他们可以做出的选择、帮助的技术和为所有参与者提供价值的市场方面），仍然缺乏共识。电力系统运行中的挑战究竟是什么，“大数据”技术是答案，这一点仍然缺乏明确性。我们希望透过持份者参与活动，进一步了解这些范畴。我们建议透过变压器及电缆状况监测数据的个案研究，进一步探讨“大数据”的机会，以准确预测变压器及电缆的寿命及初期故障。电力系统的一个关键特征是低停电率和现代社会对电力的高度依赖。从一个主要通过发电站的行动进行控制的系统转向一个鼓励消费者改变其行为并成为控制的一部分的系统，以及一些电源（例如风能和光伏）不容易控制的系统，从根本上改变了系统的风险状况。我们建议开发新的分析技术来模拟这一点，并规划未来的系统。将深入分析的一个具体控制问题是从系统中逐渐消除传统发电站发电机旋转质量所提供的惯性。这种惯性有助于将系统频率保持在50 Hz。爱尔兰已经限制了风力涡轮机的生产，以便保持足够的惯性。我们将探讨英国的极限在哪里，以及什么样的创新可能会推动这一极限。最后，为了解决与系统运行中不确定性增加相关的问题，并管理系统变得不那么可控和易受外部干扰的风险，需要开发新的方法来量化未来电力系统的风险状况，以及这种状况如何受到系统模型和操作中的各种认识（数据短缺或模型简化）和偶然（固有的随机行为）不确定性的影响。

HubNet is a multi-university collaborative project which is three years into a five-year programme of exploring the future form of energy networks. In particular we are exploring the use of smart grid technologies and HVDC to integrate high penetrations of renewable energy into the electricity system and investigating how to manage changes in energy supply to private vehicles and building heating. It does this through its own programme of core research and through drawing together the research of the Grand Challenges projects in this area and other Research Council activities. This proposal is for an extension to the HubNet's core research in order to address challenges not identified in the original proposal. Two themes appear in any discussion of the smart grid: (1) the opportunities for consumers to be become more active so that they benefit from trading their energy needs and production, and (2) the creation of an ICT system that generates very large volume of power flow and voltage data that can be harnessed to run the system more efficiently. Despite being a well known topic and the subject of research projects on specific aspects, there is still a lack of consensus over precisely how customers can play their role (in terms of choices they can make, technology to assist and markets that deliver value to all participants). There also remains a lack of clarity over what exactly is the challenge in power system operation to which "big data" technology is the answer. We seek to gain further understanding in these areas through stakeholder engagement activities.We propose to explore further the opportunities of "big data" through a case study of data used in condition monitoring of transformers and cables to predict accurately lifetime and incipient failure. A key feature of the electricity system is the low rate power cuts and the high dependence placed on electricity by modern society. Moving from a system where control is exercised primarily by actions in power stations to a system where consumers are encouraged to modify their behaviour and become part of the control and where some power sources (wind and PV for example) are not readily controlled radically changes the risk profile of the system. We propose to develop new analytical techniques to model this and plan future systems. A specific control problem that will be analysed in depth is the gradual removal from the system of the inertia provided by the spinning masses of conventional power station generators. This inertia helps keep the system frequency at 50 Hz. Already Ireland limits the production by wind turbines so that sufficient inertia is retained. We will explore where the limit lies for Great Britain and what innovations might advance that limit. Finally to address the issues associated with increased uncertainties in system operation and mange the risk of system becoming less controllable and vulnerable to external disturbances, new methodologies will need to be developed to quantify the risk profile of a future power system and how this profile is affected by various epistemic (data shortage or model simplification) and aleatory (inherent random behaviour) uncertainties in system model and operation.