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

Abstract

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 uses Value of Information analysis (VoI) to quantify the economic benefit of practical building monitoring for supporting energy system design decisions, and determine if its benefits outweigh its cost. An extension of the VoI 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 significantly impact both system operating costs ($\pm$30%) and the optimal system design ($\pm$20%). However, using building monitoring data to improve the design of the district reduces overall costs by less than 1.5% on average. As this is less than the cost of measurement, using monitoring is not economically worthwhile in this case. 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 most of the available decision support benefit, meaning using hourly measurement data provides little benefit for energy retrofit design.