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

到2050年，英国将实现净零过渡，电力需求将增加，更多的可再生能源发电将安装在工业工厂中。大容量电力系统还面临着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。工业设备的灵活性，即，通过提高现场可再生能源发电的利用率，并为大容量电力系统提供平衡和网络服务，可以利用改变正常发电/消费模式的能力来应对这些挑战。数字孪生是一种集先进的传感、通信、仿真、优化和控制技术于一体的系统，能够提供更新的系统状态和预测，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。具体而言，提出了基于核学习的不确定性集辨识方法和基于人工神经网络的工业对象实时预测控制方法，并在实验室搭建了数字孪生实验平台，对所提出的数据驱动解决方案进行了验证和评估。该平台采用两级结构，上层全局数字孪生子用于全厂级预测控制，下层局部数字孪生子代表工业过程、可再生能源发电和储能系统。测量值来自传感器或产生模拟数据流的数据发生器。在该平台上对两个具有真实的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，有现场风力涡轮机和电池存储。

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.