Summary
Presenting how digital twins can be used to help understand and improve energy systems.
By Professor Brent Young, Department of Chemical and Materials Engineering, Faculty of Engineering and Design, University of Auckland
Digital Twins and their variants
The term digital twins are a digital technology that was coined less than twenty years ago and have become an important part of the lexicon of science and technology ever since. In fact, arguably, since engineers and scientists began applying computer models, digital twins have been employed. At its most basic level, a digital twin is a form of digital model of a real physical entity that is being twinned, i.e., a physical twin. Think computer coded equations, either mechanistic or data driven (e.g., via AI) that mimic the behaviour of the physical twin. Depending on the nature of these equations, the digital twin model will be able to mimic physical twin behaviour to various degrees of fidelity, from a single steady state to multiple steady states, to dynamic temporal behaviour in one, two, or three dimensions. Such digital twin models can be used offline for analysis, case studies, prediction and optimisation “what-if” type studies. The next level of sophistication in the architecture of a digital twin is a digital model that receives data directly on-line from a physical twin – this is termed a digital shadow. The most sophisticated architectural form of digital twin, sometimes also refereed to as a digital manager, is a digital twin that is fully connected on-line to a physical twin to allow closed loop decision making and control. All these forms of digital twin may have interfaces of varying degrees of sophistication that look like the physical system in some way, from simple dialog boxes to graph interfaces to virtual reality headsets and data groves, depending on the user need and application. The various digital twin architecture classes are shown next:
Digital Twins and their application to energy digital ecosystems
Digital twins can be of great use for studying real energy systems at all scales to understand, design, retrofit, operate, control and optimise them.
At the community scale, we are currently developing energy digital twin models for rural, off and on grid communities in Ruatoria to assist them to understand and optimse their energy needs now and into future. These models are one-dimensional multiple steady state spreadsheet-based digital twin models that can run offline and enable the users to input current and planned household activities and services and run what-if scenarios to plan.
At the industrial plant scale, we have developed energy digital twin models for large scale industrial energy users to study how they can re-engineer the way they use, convert and provision energy for process heat for process heat decarbonisation as part of the Ahuora Centre for Smart Energy Systems. These digital twin tools are typically digital twin models, and sometimes digital shadows (guidance control) or full digital twins (model predictive control) that correspond to two-dimensional process flow diagrams of the plant and can either be multi-steady state or dynamic depending on whether the study is for design and retrofit, or control and optimisation. The following image shows an example of a process flow diagram from a validated, multi-steady state, two-dimensional digital twin model. This digital-twin model was used to assess the feasibility of building a new section of a NZ geothermal flash plant to removal non-condensable gases (NCG) to improve power output.
At the national scale, digital twin models are in major use in the energy sector to run, model and optimise the system. As the power system operator, the Electricity Authority runs the Scheduling, Pricing, and Dispatch (SPD) model to run the wholesale energy system and to operate the power market. This system is arguably a connected digital twin which also has its own digital twin model that can be run offline, vSPD, which is a web-based implementation of SPD. vSPD can be used to simulate and analyse the New Zealand electricity market offline for scenario and what-if type optimisation analysis.
Our current work is looking at how we can combine and use these digital twin (DT) models and others at the industrial, regional, and national scales to optimise the design and operation of NZ’s major energy users to enable them to run their plants flexibly and remain economically viable under the challenge of increasing volatility of electricity prices. We are looking at how we might be able to use industrial sites as virtual dynamic batteries (VDB) to store energy and enable more energy flexibility in the system as described in the simple schematic shown on the right.
Conclusions
In conclusion, digital twins have been with us for many years, though they are new term for some. They are incredibly useful for the analysis, design, operation, and optimisation of energy systems from the household to community to industrial, to regional, to national levels.