Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response

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Several studies have indicated a potential to exploit the thermal inertia of individual residential buildings for demand response purposes using model predictive control and time-varying prices. However, studies that investigate the response obtained from applying these techniques to larger groups of buildings, and how this response affects the aggregated load profile, are needed. In this study, we propose a methodology for modelling residential buildings that enables bottom-up modelling of entire urban areas. The methodology is based on the thermal model described in ISO 13790, which was extended to a second order model to improve its capability to describe the thermodynamic behaviour of buildings under dynamic conditions, and a Bayesian statistical framework used for the inference of model parameters. The methodology utilizes three sources of information for model calibration, namely public building registers, weather measurements, and hourly smart-meter consumption data. The methodology was tested through the modelling of a residential neighbourhood consisting of 159 single-family houses in the city of Aarhus, Denmark. The aggregated model was capable of predicting the aggregated district heating consumption in a previously unseen validation period with high accuracy: CVRMSE of 5.58% and NMBE of -1.39%. The model was then used to investigate the effectiveness of a DR scheme with the objective of reducing the daily fluctuations in the district heating consumption due to periods with increased domestic hot water consumption. The results showed that a commonly applied price-based demand response scheme incentivizing consumers through time-of-use energy prices would lead to the formation of new, undesirable peaks. To avoid this, a requirement for a more distributed response from the individual consumers was added to the DR scheme. This significantly improved effectiveness of the DR scheme as the size of two investigated peaks was reduced by 6.3% and 4.3%, respectively, without generating new peaks. This suggests that future research exploring and comparing various DR schemes on their effectiveness and efficiency at addressing various system performance objectives is needed. The methodology presented in this paper seems well-suited for such analysis.
TidsskriftApplied Energy
Sider (fra-til)181-204
Antal sider24
StatusUdgivet - 2019

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