3 research outputs found
Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes
Real-time quantification of residential building energy flexibility is needed
to enable a cost-efficient operation of active distribution grids. A promising
means is to use the so-called flexibility envelope concept to represent the
time-dependent and inter-temporally coupled flexibility potential. However,
existing optimization-based quantification entails high computational burdens
limiting flexibility utilization in real-time applications, and a more
computationally efficient quantification approach is desired. Additionally, the
communication of a flexibility envelope to system operators in its original
form is data-intensive. In order to address the computational burdens, this
paper first trains several machine learning models based on historical
quantification results for online use. Subsequently, probability distribution
functions are proposed to approximate the flexibility envelopes with
significantly fewer parameters, which can be communicated to system operators
instead of the original flexibility envelope. The results show that the most
promising prediction and approximation approaches allow for a minimum reduction
of the computational burden by a factor of 9 and of the communication load by a
factor of 6.6, respectively