SiemensEPSRC Digital Twin with Data-Driven Predictive Control: Unlocking Flexibility of Industrial Plants for Supporting a Net Zero Electricity System
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: Sch of Engineering
Abstract
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.
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.
Publications
Su P
(2023)
Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span
in Journal of Cleaner Production
| Description | Electrification is one of the key ways to achieve the net zero transition, but brings significant challenges as well, including soaring peak and total electricity demand and increasing impact of the volatility from renewable power generation. Industrial plants, with high energy consumption, are an important part of electrification and can contribute to addressing the challenges in the process of electrification. The key way in which industrial plants can contribute is to adjust their electricity generation/consumption patterns in response to the needs of themselves and also those of bulk power systems (i.e., unlocking and utilising their flexibility). As a result, the electricity costs as well as the emissions of the industrial plants can be reduced, and the pressure on the bulk power networks can be relieved. Nevertheless, utilising flexibility, via controlling the flexible power generation, energy storage and demand in industrial plants, is faced with two major challenges - uncertainties (in renewable power generation and industrial processes) and computational complexity. In this project, we found that the challenges can be well addressed via data-driven methods. A support vector clustering method with the generalised intersection kernel was found to be able to characterise the uncertainty set more accurately and effectively, thus better addressing the challenges of uncertainty. An artificial neutral network method was used to generate real-time operational decisions, instead of solving time-consuming optimisation problems, thus better addressing the challenges of computational complexity. We tested the two methods, tackling uncertainty and computational complexity, in two case studies - an industrial site with multiple separate bitumen tanks and a paper mill involving continuous production process. The two methods performed well in both case studies. |
| Exploitation Route | The findings will be published on academic journals so others can access the outcomes and further develop them in their own ways. In our view, our current artificial neural network method may not very well deal with the situations where the final schedules obtained violate some production constraints. This may be a significant issue for large complex industrial plants with continuous processes (like steelmaking plants). This may be a future research direction. Furthermore, the research still stays at a low Technology Readiness Level (TRL). Further work can be done to see how the methods can be integrated in real industrial plants considering their existing digital and control infrastructure. |
| Sectors | Digital/Communication/Information Technologies (including Software) Energy Manufacturing including Industrial Biotechology |
| Description | Collaboration with SUSTAIN (UK Future Steelmaking Research Hub) |
| Organisation | Liberty Steel |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Energy issues are of great importance for future steel manufacturing in the UK especially in the transition to net zero, but the original future steel manufacturing research community of the UK (i.e., SUSTAIN) does not have much expertise or research in this area. I brought this expertise and reserch to the community, help facilitate the development of UK steelmaking research and industry in this regard. Specifically, I developed digital twin approach for smart and flexible operation of steelmaking plants, in the context of net zero transition of UK's electricity system. |
| Collaborator Contribution | SUSTAIN provided me with the funding and a very good environment to develop my research in smart and flexible steelmaking using digital twin approaches. Libterty Steel arranged a site visit for us to one of its steelmaking sites, providing good information regarding the practical situation and need, which further inspired and supported our research. |
| Impact | Research paper published: [1] Pengfei Su, Yue Zhou, Jianzhong Wu, "Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span," Journal of Cleaner Production, vol. 428, 139350, 20 November 2023. [2] Pengfei Su, Yue Zhou, "Demand Response from Steelmaking Process Coordinated with Energy Storage Systems," 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), Grenoble, France, 2023, pp. 1-5, doi: 10.1109/ISGTEUROPE56780.2023.10407210. |
| Start Year | 2022 |
| Description | A presentation at 7th IEEE Conference on Energy Internet and Energy System Integration |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Pengfei Su (the PhD student of the PI Yue Zhou) presented his research on "Optimal Scheduling of Steelmaking Process Considering Multiple Electricity Bill Components" at the 7th IEEE Conference on Energy Internet and Energy System Integration, held in China during 15th-18th December, 2023.. This series of conferences has been one of the top conferences in the power engineering domain in China, and affect many researchers, practioners, industry, policy makers and other stakeholders across China and even globally. |
| Year(s) Of Engagement Activity | 2023 |
| Description | A presentation at EPSRC SUSTAIN Future Steel Manufacturing Research Hub |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | The conference was organised by EPSRC SUSTAIN Future Steel Manufacturing Research Hub, and was attended by experts from both the academia and industry in the steelmaking sector. I gave the presentation on how the digital twin and data driven approaches can help steelmaking plants to utilised their flexibility to reduce electricity costs, reduce carbon emissions and also support the net zero transition of the bulk power system. My presentation generated a lot of interests from the audience - we received many questions and discussions during the conference. Liberty Steel also showed interests in further collaborating with us in using our approaches for their plants. |
| Year(s) Of Engagement Activity | 2022 |
| Description | A presentation at IEEE Innovative Smart Grid Technologies (ISGT) EUROPE 2023 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Pengfei Su (the PhD student of the PI Yue Zhou) presented his research on "Demand Response from Steelmaking Process Coordinated with Energy Storage Systems" at IEEE Innovative Smart Grid Technologies (ISGT) EUROPE 2023, which was held in France during 23rd-26th October, 2023. This series of conferences has been one of the top conferences in the power engineering domain in Europe, and affect many researchers, practioners, industry, policy makers and other stakeholders across the Europe and even globally. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.linkedin.com/posts/activity-7124286458256896000-q3FS?utm_source=share&utm_medium=member_... |