Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes

Abstract

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 socalled 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.

Aggregation of Demand-Side Flexibilities: A Comparative Study of Approximation Algorithms

“…First, renewable energy generation can be subsumed into the demand profiles. The inclusion of self-discharge rates < 1 changes the submatrix Γ in Equation (9) and results in possibly different A matrices for different households. This can be transformed in our model with equal A-matrices by introducing redundant constraints through PC, cf.…”

“…[21]. We identify the left matrix in (9) as A and the right-hand side vector as b i . Hence, the flexibility of household i can be succinctly written as…”

“…These methods range from one step ahead to multi-step ahead and from bottom-up to top-down approaches. Top-down approaches attempt to directly capture the aggregated flexibility, for example, by using probability distributions or Markov transition matrices [6][7][8][9]. In this paper, we consider bottom-up multi-step ahead approaches which start from individual flexibility sets.…”

Exploring Operational Flexibility of Active Distribution Networks with Low Observability

“…OPF-based approaches ignore the dynamics of (1) and assess the steady-state performance. The OPF considers the complete network, thus ignoring (2). The output of these approaches aims only at objective (5).…”

“…Flexibility in power systems is a term used from various directions. These directions include market design [1], a specific resource type [2], or the aggregated distribution network (DN) flexibility offered at the TSO-DSO interconnection [3]- [5]. This paper focuses on the flexibility of the TSO-DSO interconnections.…”