LAPSE:2023.34638v1
Published Article

LAPSE:2023.34638v1
Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load
April 27, 2023
Abstract
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.
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Keywords
baselines, demand response, flexibility: decarbonisation, H2020 projects, smart grids
Subject
Suggested Citation
Valentini O, Andreadou N, Bertoldi P, Lucas A, Saviuc I, Kotsakis E. Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load. (2023). LAPSE:2023.34638v1
Author Affiliations
Valentini O: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy; Department of Science, Technology and Society, University School for Advanced Studies IUSS, 27100 Pavia, Italy; Department of Economics, University of Insubria, Via Mon
Andreadou N: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy [ORCID]
Bertoldi P: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy
Lucas A: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy; Institute for Systems and Computer Engineering, Technology and Science—INESC TEC, 4200-465 Porto, Portugal [ORCID]
Saviuc I: Department of Engineering Management, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium [ORCID]
Kotsakis E: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy
Andreadou N: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy [ORCID]
Bertoldi P: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy
Lucas A: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy; Institute for Systems and Computer Engineering, Technology and Science—INESC TEC, 4200-465 Porto, Portugal [ORCID]
Saviuc I: Department of Engineering Management, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium [ORCID]
Kotsakis E: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy
Journal Name
Energies
Volume
15
Issue
14
First Page
5259
Year
2022
Publication Date
2022-07-20
ISSN
1996-1073
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Original Submission
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PII: en15145259, Publication Type: Review
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https://doi.org/10.3390/en15145259
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