twinCity -- Physics Informed Digital Twins for Urban Powerplants

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Solar energy is a clean renewable energy source, however it comes with significant challenges to deploy at scale. This is because energy production does not always align with energy demand, and excessive energy cannot either be easily fed back into the larger electric grid, or the viability of many energy storage solutions is not clear due to losses at conversion or cost. Solving this challenge is central to meeting UK's commitment to generating all our power renewably by 2035\.In this project digiLab will develop a novel digital pipeline to accurately and efficiently optimize the design of urban powerplants using physics informed digital twins. The solution will use self -learning algorithms to optimal balance, production / demand and storage. The innovative approach provide easy to train and fast deployable solutions not currently available in the market, using bleeding edge methods in so-called "Physics Informed digital twins".Whilst the deployment of urban solar production and intelligent control has broad application across the UK and internationally. In this Fast Start project we will focus on a 30MW solar farm on the roof tops of Marsh Barton Industrial Estate, on the edge of Exeter. UPS is looking to generate sufficient power from local solar to take 150 electric delivery vehicles off grid. The opportunity for the particular case study considered at Marsh Baton is clear. By electrifying 150 vehicles with renewable solar, it is equivalent to the equivalent carbon absorption of 200,000 trees annually. Yet such a solution is not possible without our intelligent control algorithm living beneath.This project paves the way to using smart digital technologies to maximise the economic viability of locally balanced energy solution - a necessary step in our net zero journey.