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Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information

Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for improved designs with less compromise to be identified, reducing the cost of decarbonizing energy usage. However, the building monitoring required to reduce load uncertainty is costly. This study quantifies the economic benefit of practical building monitoring for supporting energy system design decisions, to determine if its benefits outweigh its cost. Value of Information analysis (VoI) is a numerical framework for quantifying the benefit of uncertainty reduction to support decision making. An extension of the framework, termed 'On-Policy' VoI, is proposed, which admits complex decision making tasks where decision policies are required. This is applied to a case study district energy system design problem, where a Linear Program model is used to size solar-battery systems and grid connection capacity under uncertain building loads, modelled using historic electricity metering data. Load uncertainty is found to have a significant impact on both system operating costs (\pm30%) and the optimal system design (\pm20%). However, using building monitoring is found to reduce overall costs by less than 2% on average, less than the cost of measurement, and is therefore not economically worthwhile. This provides the first numerical evidence to support the sufficiency of using standard building load profiles for energy system design. Further, reducing only uncertainty in mean load is found to provide all available decision support benefit, meaning using hourly measurement data provides no benefit for energy retrofit design.

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