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Increasing the Observability of Electrical Distribution Systems using Smart Meters (IOSM)

使用智能电表 (IOSM) 提高配电系统的可观测性

基本信息

批准号：
EP/J00944X/1
负责人：
Jianzhong Wu
金额：
$ 12.71万
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ00944X%2F1
关键词：
Increasing Observability Electrical Distribution Systems

项目摘要

Real-time monitoring and control of distribution systems is very limited due to the lack of sensors and communication systems. Hence the distribution system can be described as under-determined with the number of measurements insufficient to make the system observable. Once the complete system state is available, then any quantity in the system can be calculated. The observability and controllability of a system are mathematical duals, which means an unobservable system cannot be fully controlled. Distributed energy resources introduce significant uncertainties and, at high penetrations, may lead to operational difficulties in a network. Therefore the provision of accurate system state information to the network operators is critical for them to operate the system in a safe, prompt, and cost-effective manner, and also to make best use of the assets. Smart metering is widely recognised as the first step towards a Smart Grid future and the UK is committed to the full deployment of smart meters by 2019. Smart meters and the associated ICT (information and communication) infrastructure can greatly improve observability. Therefore there is a need to investigate the technical feasibility and key technologies of using smart metering to increase the observability of the distribution system through state estimation techniques.The research programme is structured around three challenges: Research Challenge 1: The load demand needs to be aggregated at the MV nodes using data from smart meters connected to the low voltage (LV) nodes. A big challenge is how a state estimator deals with both various kinds of measurement errors with non-normal distribution and the influence of the measurement configuration (type, location, accuracy of measurements) effectively and provides accurate estimation on the system state.We will improve the distribution state estimation to make it robust to the influence of both the measurement error distribution and the measurement configuration of a distribution system.Research Challenge 2: Smart metering may change the behaviour of energy consumers and thus lead to more dynamic demand (e.g. load that is sensitive to price). Therefore the second challenge is how to model extremely dynamic load and to provide pseudo measurements to the state estimator under conditions of large latency or failure of the ICT infrastructure or if there are un-monitored quantities. We will provide a theoretical contribution to MV nodal load modelling through investigating a new machine learning method which is able to obtain knowledge from past experience (e.g. past smart meter data). Research Challenge 3: What and where additional real-time measurements should be placed, in addition to the smart meters, to make the estimated system states accurate enough for particular Smart Grid functions and reduce the impact of the measurement configuration. We will develop an optimal meter location method considering impact from both measurement errors and measurement configurations while minimising the extra metering cost.The research will benefit from close collaboration with national and international industrial partners, and will gain insight and make contribution to the research challenges through both theoretical study of using smart meter information to increase the observability of distribution systems, and technical demonstration via small scale test facility, i.e. the Smart Metering test rig and the Smart Grid test rig developed at Cardiff; medium scale test facility in RSE, Italy; and practical case study using a BC Hydro network. The impact on potentiol beneficiaries will be delivered through collaboration, communication, and commercialisation. We will also utilise EPSRC HubNet as a dissemination platform to facilitate a wider communication.

由于缺乏传感器和通信系统，对配电系统的实时监测和控制非常有限。因此，分配系统可以被描述为欠确定，测量的次数不足以使系统可见。一旦完整的系统状态可用，那么系统中的任何数量都可以计算出来。一个系统的可观察性和可控性是数学上的对偶，这意味着一个不可观察系统不能被完全控制。分布式能源带来了巨大的不确定性，并且在高渗透时，可能导致网络中的操作困难。因此，向网络运营商提供准确的系统状态信息对于他们以安全、及时、经济有效的方式操作系统以及充分利用资产至关重要。智能电表被广泛认为是迈向智能电网未来的第一步，英国承诺到2019年全面部署智能电表。智能电表和相关的ICT（信息和通信）基础设施可以大大提高可观测性。因此，有必要研究利用智能电表技术通过状态估计技术来提高配电系统的可观测性的技术可行性和关键技术。研究计划围绕三个挑战进行：研究挑战1：需要使用连接到低压（LV）节点的智能电表的数据在MV节点上汇总负载需求。状态估计器如何有效地处理非正态分布的各种测量误差和测量配置（测量类型、位置、精度）的影响，并提供对系统状态的准确估计是一个很大的挑战。我们将改进分布状态估计，使其对测量误差分布和配电系统测量配置的影响都具有鲁棒性。研究挑战2：智能电表可能会改变能源消费者的行为，从而导致更多的动态需求（例如，对价格敏感的负荷）。因此，第二个挑战是如何在大延迟或ICT基础设施故障或存在未监测数量的情况下对极端动态负载进行建模并为状态估计器提供伪测量。我们将通过研究一种新的机器学习方法为MV节点负载建模提供理论贡献，该方法能够从过去的经验中获得知识（例如，过去的智能电表数据）。研究挑战3：除了智能电表之外，应该在哪里放置额外的实时测量，以使估计的系统状态足够准确，以满足特定的智能电网功能，并减少测量配置的影响。考虑到测量误差和测量配置的影响，我们将开发一种最佳的仪表位置方法，同时最大限度地减少额外的计量成本。该研究将受益于与国内和国际工业合作伙伴的密切合作，并将通过使用智能电表信息的理论研究来增加配电系统的可观察性，以及通过小型测试设施（即智能电表测试平台和智能电网测试平台）进行技术演示，获得洞察力并为研究挑战做出贡献。位于意大利RSE的中型测试设施；以及使用卑诗省水电网络的实际案例研究。对潜在受益者的影响将通过合作、沟通和商业化来实现。我们亦会利用EPSRC HubNet作为传播平台，促进更广泛的沟通。