Open Access
Open access
Energies, volume 16, issue 16, pages 6048

Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators

Nikoleta Andreadou 1
Dimitrios Thomas 2
Antonio De Paola 2
Evangelos Kotsakis 2
Gianluca Fulli 2
1
 
Seidor Italy, 21027 Ispra, Italy
2
 
Energy Security, Distribution and Markets Unit, Energy, Transport and Climate Directorate, Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy
Publication typeJournal Article
Publication date2023-08-18
Journal: Energies
scimago Q1
SJR0.651
CiteScore6.2
Impact factor3
ISSN19961073
Electrical and Electronic Engineering
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Building and Construction
Control and Optimization
Engineering (miscellaneous)
Energy (miscellaneous)
Abstract

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.

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