Digital Twin Services for Future Urban Energy Systems

This submission has open access
Submission Summary
Metropolitan areas are undergoing an energy transition of their own. Technologies such as rooftop solar PV and batteries, electric vehicles, electric heat pumps, and electric boilers transforming the urban energy landscape. In many areas, district heating is supplying cheap and reliable heat to residents by recycling waste heat from industries and data centers. However, unregulated, and explosive growth in the adoption of these technologies has outstripped the pace at which upgrades are needed in the electricity grid to support this transition as is seen in Amsterdam. This gives rise to smart solutions like demand side management to make the local grid more flexible. The potential and operation of grid-connected devices to provide such flexibility must be evaluated in a fair and transparent fashion.---A digital twin supplements the associated decision-making processes by providing independent facts and figures to the associated stakeholders. By using a data-driven continuously updated model of the energy system, various studies can be implemented to understand the system behaviour, mitigate unnecessary operating situations, and evaluate flexibility. Examples include: 1) weather forecast, 2) load demand scenarios, 3) congestion prediction, and 4) uncertainty quantification. A digital twin also acts as a testbed for testing different control strategies for flexible grid operation, and enables operators to be “active” in their operation, control, and maintenance.---This paper provides an overview of digital twin services and their needs in terms of data processing, modelling requirements, and simulation setup. It will be evaluated how EnergySim (https://pypi.org/project/energysim/) can support the services in a cloud setting. The results of this paper can be used by urban energy planners and developers to check out if digital twins can support their activities and will give a guideline on the assumptions to take.
Submission ID :
RTC-63

Associated Sessions

Ph.D. student
,
TU Delft
TU Delft // AMS Institute

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