Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector
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 regulator...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/14/2725 |
| _version_ | 1811187326373593088 |
|---|---|
| author | Alexandre Lucas Luca Jansen Nikoleta Andreadou Evangelos Kotsakis Marcelo Masera |
| author_facet | Alexandre Lucas Luca Jansen Nikoleta Andreadou Evangelos Kotsakis Marcelo Masera |
| author_sort | Alexandre Lucas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:01:24Z |
| format | Article |
| id | doaj.art-4f7d12c33dcb4fc68df9c981a5424be7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T14:01:24Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4f7d12c33dcb4fc68df9c981a5424be72022-12-22T04:20:07ZengMDPI AGEnergies1996-10732019-07-011214272510.3390/en12142725en12142725Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential SectorAlexandre Lucas0Luca Jansen1Nikoleta Andreadou2Evangelos Kotsakis3Marcelo Masera4European Commission, Joint Research Centre (JRC), 21027 Ispra (VA), ItalyEuropean Commission, Joint Research Centre (JRC), 21027 Ispra (VA), ItalyEuropean Commission, Joint Research Centre (JRC), 21027 Ispra (VA), ItalyEuropean Commission, Joint Research Centre (JRC), 21027 Ispra (VA), ItalyEuropean Commission, Joint Research Centre (JRC), 21027 Ispra (VA), ItalyDemand 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.https://www.mdpi.com/1996-1073/12/14/2725flexibility forecastdemand responseSTORdisaggregated loadsnon-intrusive monitoring |
| spellingShingle | Alexandre Lucas Luca Jansen Nikoleta Andreadou Evangelos Kotsakis Marcelo Masera Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector Energies flexibility forecast demand response STOR disaggregated loads non-intrusive monitoring |
| title | Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector |
| title_full | Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector |
| title_fullStr | Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector |
| title_full_unstemmed | Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector |
| title_short | Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector |
| title_sort | load flexibility forecast for dr using non intrusive load monitoring in the residential sector |
| topic | flexibility forecast demand response STOR disaggregated loads non-intrusive monitoring |
| url | https://www.mdpi.com/1996-1073/12/14/2725 |
| work_keys_str_mv | AT alexandrelucas loadflexibilityforecastfordrusingnonintrusiveloadmonitoringintheresidentialsector AT lucajansen loadflexibilityforecastfordrusingnonintrusiveloadmonitoringintheresidentialsector AT nikoletaandreadou loadflexibilityforecastfordrusingnonintrusiveloadmonitoringintheresidentialsector AT evangeloskotsakis loadflexibilityforecastfordrusingnonintrusiveloadmonitoringintheresidentialsector AT marcelomasera loadflexibilityforecastfordrusingnonintrusiveloadmonitoringintheresidentialsector |