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Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

Author

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  • Alexandre Lucas

    (European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), Italy)

  • Luca Jansen

    (European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), Italy)

  • Nikoleta Andreadou

    (European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), Italy)

  • Evangelos Kotsakis

    (European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), Italy)

  • Marcelo Masera

    (European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), Italy)

Abstract

Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

Suggested Citation

  • Alexandre Lucas & Luca Jansen & Nikoleta Andreadou & Evangelos Kotsakis & Marcelo Masera, 2019. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector," Energies, MDPI, vol. 12(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2725-:d:248953
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    References listed on IDEAS

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    Cited by:

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    5. Angelidis, O. & Ioannou, A. & Friedrich, D. & Thomson, A. & Falcone, G., 2023. "District heating and cooling networks with decentralised energy substations: Opportunities and barriers for holistic energy system decarbonisation," Energy, Elsevier, vol. 269(C).
    6. Francesco Mancini & Jacopo Cimaglia & Gianluigi Lo Basso & Sabrina Romano, 2021. "Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Ottavia Valentini & Nikoleta Andreadou & Paolo Bertoldi & Alexandre Lucas & Iolanda Saviuc & Evangelos Kotsakis, 2022. "Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load," Energies, MDPI, vol. 15(14), pages 1-36, July.
    8. Antonio Moretti & Charalampos Pitas & George Christofi & Emmanuel Bué & Modesto Gabrieli Francescato, 2020. "Grid Integration as a Strategy of Med-TSO in the Mediterranean Area in the Framework of Climate Change and Energy Transition," Energies, MDPI, vol. 13(20), pages 1-22, October.
    9. Adam Lesniak & Dawid Chudy & Rafal Dzikowski, 2020. "Modelling of Distributed Resource Aggregation for the Provision of Ancillary Services," Energies, MDPI, vol. 13(18), pages 1-16, September.

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