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

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Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response. / Hedegaard, Rasmus Elbæk; Kristensen, Martin Heine; Pedersen, Theis Heidmann; Brun, Adam; Petersen, Steffen.

I: Applied Energy, Bind 242, Nr. May, 2019, s. 181-204.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Hedegaard, Rasmus Elbæk ; Kristensen, Martin Heine ; Pedersen, Theis Heidmann ; Brun, Adam ; Petersen, Steffen. / Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response. I: Applied Energy. 2019 ; Bind 242, Nr. May. s. 181-204.

Bibtex

@article{dd4bb092b7a84c218b314c1a04ef0f56,
title = "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response",
abstract = "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. ",
keywords = "Bayesian calibration; Urban scale bottom-up modelling; Demand response; Space heating; Domestic hot water; Smart meter data",
author = "Hedegaard, {Rasmus Elb{\ae}k} and Kristensen, {Martin Heine} and Pedersen, {Theis Heidmann} and Adam Brun and Steffen Petersen",
year = "2019",
doi = "10.1016/j.apenergy.2019.03.063",
language = "English",
volume = "242",
pages = "181--204",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Pergamon Press",
number = "May",

}

RIS

TY - JOUR

T1 - Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response

AU - Hedegaard, Rasmus Elbæk

AU - Kristensen, Martin Heine

AU - Pedersen, Theis Heidmann

AU - Brun, Adam

AU - Petersen, Steffen

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Bayesian calibration; Urban scale bottom-up modelling; Demand response; Space heating; Domestic hot water; Smart meter data

U2 - 10.1016/j.apenergy.2019.03.063

DO - 10.1016/j.apenergy.2019.03.063

M3 - Journal article

VL - 242

SP - 181

EP - 204

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

IS - May

ER -