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

• 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

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.

到2050年英国的零净过渡中，电力需求将增加，并且将在工厂中安装更多可再生发电。批量电力系统还面临着总需求增加和高峰需求，平衡供应和需求的难度增加以及网络问题增加的挑战。工厂的灵活性，即改变正常的发电/消耗模式的能力，可以通过改善可再生能源发电现场的利用，并为散装电力系统提供平衡和网络服务，以应对这些挑战。但是，由于严重的不确定性和计算复杂性，攻击这种灵活性的调度和控制遇到了很大的困难。数字双胞胎是高级感应，沟通，模拟，仿真，优化和控制技术的系统，并且可以基于哪些数据驱动的方法来确定对不确定和计算和计算和计算和计算和控制和控制的更新系统状态和预测。具体而言，提出了一种基于内核学习的方法来表征不确定性集，并提出了一种基于人造的中性网络的方法来实时操作中的工业工厂的预测控制。该平台采用了两级结构，具有全工厂级别预测控制的高层全球数字双胞胎和代表工业流程，可再生发电和能源存储系统的下层本地数字双胞胎。测量取自传感器或产生模拟数据流的数据生成器。在平台上测试了两个具有实际数据的工业案例研究。一个案例是一个具有许多沥青罐和PV面板的工业场所，另一种是带有现场风力涡轮机和电池存储的造纸厂。

项目成果

Multi-Objective Production Scheduling of a Steel Plant With Electric Arc Furnaces

电弧炉钢厂多目标生产调度

• DOI: 10.46855/energy-proceedings-10185
• 发表时间: 2022
• 作者: Pengfei P

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

• DOI: 10.1016/j.jclepro.2023.139350
• 发表时间: 2023-10
• 期刊: Journal of Cleaner Production
• 影响因子: 11.1
• 作者: Pengfei Su;Yue Zhou;Jianzhong Wu

Demand Response from Steelmaking Process Coordinated with Energy Storage Systems

与储能系统协调的炼钢过程的需求响应

• DOI: 10.1109/isgteurope56780.2023.10407210
• 发表时间: 2023
• 作者: Su P