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

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.