Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data. / Ziras, Charalampos; Heinrich, Carsten; Pertl, Michael; Bindner, Henrik W.

In: Applied Energy, Vol. 242, 15.05.2019, p. 1407-1421.

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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@article{b04eeb9ad90d4b74a7687c2b5e4d307a,
title = "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data",
abstract = "Thermal loads are an important source of flexibility at a residential customer level. The uncertain economic value of residential demand response (DR), and the rising customer data privacy concerns, require non-intrusive and economical approaches to harness flexibility. Baselines are essential for evaluating DR activations, however, the frequent use of flexibility makes them less accurate. In this paper, we first propose a baseline estimation method based solely on aggregated behind the meter data, which does not require additional knowledge of the portfolio's parameters. It is suited for frequent DR activations, and relies on a combination of linear interpolation, forward-backward autoregression and load decomposition. The method is then used to evaluate DR activations, in order to construct, and continuously update, a model for the response and the rebound behavior of the loads. A portfolio of 138 real residential customers equipped with electric heaters, and a large number of DR experiments, were used to verify the proposed approach. The response model, fitted with the experimental results, shows a strong dependency of the load reduction potential on time of day and ambient temperature, with a maximum load reduction equal to 1.2 kW per household. Validation results confirm that the fitted model can be used to estimate the response with a good accuracy. Finally, a model to describe and shape the rebound behavior of the loads is proposed and validated with real experiments.",
keywords = "Aggregation size, Baseline, Demand response, Experiments, Flexibility model, Rebound, Thermal loads",
author = "Charalampos Ziras and Carsten Heinrich and Michael Pertl and Bindner, {Henrik W.}",
year = "2019",
month = "5",
day = "15",
doi = "10.1016/j.apenergy.2019.03.156",
language = "English",
volume = "242",
pages = "1407--1421",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

AU - Ziras, Charalampos

AU - Heinrich, Carsten

AU - Pertl, Michael

AU - Bindner, Henrik W.

PY - 2019/5/15

Y1 - 2019/5/15

N2 - Thermal loads are an important source of flexibility at a residential customer level. The uncertain economic value of residential demand response (DR), and the rising customer data privacy concerns, require non-intrusive and economical approaches to harness flexibility. Baselines are essential for evaluating DR activations, however, the frequent use of flexibility makes them less accurate. In this paper, we first propose a baseline estimation method based solely on aggregated behind the meter data, which does not require additional knowledge of the portfolio's parameters. It is suited for frequent DR activations, and relies on a combination of linear interpolation, forward-backward autoregression and load decomposition. The method is then used to evaluate DR activations, in order to construct, and continuously update, a model for the response and the rebound behavior of the loads. A portfolio of 138 real residential customers equipped with electric heaters, and a large number of DR experiments, were used to verify the proposed approach. The response model, fitted with the experimental results, shows a strong dependency of the load reduction potential on time of day and ambient temperature, with a maximum load reduction equal to 1.2 kW per household. Validation results confirm that the fitted model can be used to estimate the response with a good accuracy. Finally, a model to describe and shape the rebound behavior of the loads is proposed and validated with real experiments.

AB - Thermal loads are an important source of flexibility at a residential customer level. The uncertain economic value of residential demand response (DR), and the rising customer data privacy concerns, require non-intrusive and economical approaches to harness flexibility. Baselines are essential for evaluating DR activations, however, the frequent use of flexibility makes them less accurate. In this paper, we first propose a baseline estimation method based solely on aggregated behind the meter data, which does not require additional knowledge of the portfolio's parameters. It is suited for frequent DR activations, and relies on a combination of linear interpolation, forward-backward autoregression and load decomposition. The method is then used to evaluate DR activations, in order to construct, and continuously update, a model for the response and the rebound behavior of the loads. A portfolio of 138 real residential customers equipped with electric heaters, and a large number of DR experiments, were used to verify the proposed approach. The response model, fitted with the experimental results, shows a strong dependency of the load reduction potential on time of day and ambient temperature, with a maximum load reduction equal to 1.2 kW per household. Validation results confirm that the fitted model can be used to estimate the response with a good accuracy. Finally, a model to describe and shape the rebound behavior of the loads is proposed and validated with real experiments.

KW - Aggregation size

KW - Baseline

KW - Demand response

KW - Experiments

KW - Flexibility model

KW - Rebound

KW - Thermal loads

U2 - 10.1016/j.apenergy.2019.03.156

DO - 10.1016/j.apenergy.2019.03.156

M3 - Journal article

VL - 242

SP - 1407

EP - 1421

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

ER -