Energy Asset Optimisation
The challenge centres on four SSE Heat Networks powered by Combined Heat and Power Plants (CHPs). Historically running according to fixed schedules using fixed energy pricing. But with recent energy market volatility, the challenge was how to optimise energy assets to lower energy costs and reduce net carbon grid intensity.
Our team worked closely with SSE Heat Networks to understand how the energy system operates in the real world and then use this as the basis to create a digital twin. This digital simulation mirrors the physical CHP asset with data constantly flowing from the real to the digital version.
The digital twins of the CHPs from the basis for optimisation. We use machine learning to optimise the energy asset's operating schedule and then deploy this onsite in the real world. We're now making the CHP think how best to optimise performance to reduce energy costs and lower carbon intensity.