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SiemensEPSRC Digital Twin with Data-Driven Predictive Control: Unlocking Flexibility of Industrial Plants for Supporting a Net Zero Electricity System

具有数据驱动预测控制功能的西门子 EPSRC 数字孪生：释放工业工厂的灵活性，支持净零电力系统

基本信息

批准号：
EP/W028573/1
负责人：
Yue Zhou
金额：
$ 6.42万
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FW028573%2F1
关键词：
SiemensEPSRC Digital Twin Data Driven

项目摘要

In the net-zero transition of the UK by 2050, electricity demand will increase and more renewable power generation will be installed in industrial plants. The bulk electricity system also faces the challenges of increased total and peak demand, increased difficulty in balancing supply and demand, and increased network issues. The flexibility of industrial plants, i.e., the ability to change the normal electricity generation/consumption patterns, can be utilised to address these challenges, through improving the utilisation of renewable power generation onsite and providing balancing and network services to the bulk electricity system. However, the scheduling and control for tapping this flexibility are subject to great difficulty due to significant uncertainties and computational complexity.Digital twins are systems of advanced sensing, communication, simulation, optimisation and control technologies, and can provide updating system states and prediction, based on which data-driven approaches can be developed to tackling the uncertainties and computational complexity in scheduling and control. Specifically, a kernel-learning based method is proposed to characterise the uncertainty sets, and an artificial neutral network based method is proposed for predictive control of industrial plants in real-time operation.A test digital twin platform is established in the lab to demonstrate and assess the proposed data-driven solutions. The platform adopts a two-level structure, with the upper-level global digital twin for whole-plant level predictive control and lower-level local digital twins representing industrial processes, renewable power generation and energy storage systems. The measurements are taken from sensors or a data generator which produces mimic data flow. Two industrial case studies with real data are tested on the platform. One case is an industrial site with a number of bitumen tanks and PV panels, and the other is a paper mill with onsite wind turbines and battery storage.

在英国到2050年的净零过渡中，电力需求将会增加，工业工厂将安装更多的可再生能源发电。大宗电力系统还面临总需求和峰值需求增加、供需平衡难度增加以及网络问题增加的挑战。可以利用工业工厂的灵活性，即改变正常发电/消费模式的能力，通过提高现场可再生能源发电的利用率，并为大型电力系统提供平衡和网络服务，来应对这些挑战。然而，由于具有很大的不确定性和计算复杂性，利用这种灵活性的调度和控制是非常困难的。数字孪生系统是先进的传感、通信、仿真、优化和控制技术的系统，可以提供系统状态和预测的更新，在此基础上可以开发数据驱动的方法来解决调度和控制中的不确定性和计算复杂性。具体地说，提出了一种基于核学习的方法来表征不确定集，提出了一种基于人工神经网络的实时运行的工业对象预测控制方法，并在实验室建立了一个测试数字孪生平台来演示和评估所提出的数据驱动的解决方案。该平台采用两级结构，上层全局数字孪生节点用于全厂级预测控制，下层局部数字孪生节点代表工业过程、可再生发电和储能系统。测量数据来自传感器或产生模拟数据流的数据生成器。在该平台上对两个具有真实数据的工业案例进行了测试。一个案例是一个工业现场，有许多沥青罐和光伏电池板，另一个是一个造纸厂，现场有风力涡轮机和电池储存。