Evaluating the uncertainty in the performance of small scale renewables

The successful delivery of low-carbon housing (both new and retrofitted) is a key aspect of the UK s commitment to an 80% reduction in carbon emissions by 2050. In this context, the inclusion of small-scale building-integrated renewable energy technologies is an important component of low carbon design strategies, and is subject to numerous regulation and incentive schemes (including the Renewable Heat Incentive (RHI)) set up by government to encourage uptake and set minimum performance benchmarks. Unfortunately there are numerous examples of in-use energy and carbon performance shortfalls for new and retrofitted buildings this is termed the performance gap . Technical and human factors associated with building subsystem performance, which are often not considered in design tools used to predict performance, are the root cause of performance uncertainty. The research presented in this doctoral thesis aims to develop and apply a novel probabilistic method of evaluating the performance uncertainty of solar thermal systems installed in the UK. Analysis of measured data from a group of low carbon retrofitted dwellings revealed that the majority of buildings failed to meet the designed-for carbon emissions target with an average percentage difference of 60%. An in-depth case study technical evaluation of one of these dwellings showed significant dysfunction associated with the combined ASHP/solar thermal heating system, resulting in a performance gap of 94%, illustrating that the performance gap can be regarded as a whole-system problem, comprising a number of subsystem causal factors. Using a detailed dataset obtained from the UK s largest field trial of domestic solar thermal systems, a cross-cutting evaluation of predicted vs. measured performance similarly revealed a discrepancy with a mean percentage difference in predicted and measured annual yield of -24%. Having defined the nature and extent of underperformance for solar thermal technology in the UK, causal factors influencing performance were mapped and the associated uncertainty quantified using a novel knowledge-based Bayesian network (BN). In addition, the BN approach along with Monte Carlo sampling was applied to the well-established BREDEM model in order to quantify performance uncertainty of solar thermal systems by producing distributions of annual yield. As such, the modified BN-based BREDEM model represents a significant improvement in the prediction of performance of small-scale renewable energy technologies. Finally, financial analysis applied to the probabilistic predictions of annual yield revealed that the current UK RHI scheme is unlikely to result in positive returns on investment for solar thermal systems unless the duration of the payments is extended or electricity is the primary source of heating.