Definitions of Demand Flexibility for Aggregate Residential Loads

Intisar Ali Sajjad, Gianfranco Chicco, Roberto Napoli

IEEE Transactions on Smart Grid · 2016 · 106 citations · 32 references

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TL;DR

Enhanced knowledge of electricity demand via smart meters and advanced data analysis has led to a need to characterize demand flexibility, yet existing mathematical definitions fail to capture time variation in aggregate demand. The study aims to develop definitions of expected flexibility for aggregate residential demand that can support demand response participation. The authors analyze time‑variable patterns of aggregate residential customers, assessing positive and negative pattern variations to derive flexibility definitions based on collective user behavior. Results demonstrate how flexibility metrics vary with the number of customers and the averaging time step used for load pattern representation.

Abstract

Nowadays, enhanced knowledge of the nature of the electricity demand is achieved through the progressively increasing deployment of smart meters and advanced data analysis techniques. One of the major challenges is to exploit this knowledge to support the introduction of strategies to modify the demand according to relevant objectives to be achieved, like users' participation in demand response programmes. A key point for facing this challenge is to characterize the demand flexibility. In spite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. This paper starts from the analysis of time-variable patterns of aggregate residential customers, ending up with suitable definitions of expected flexibility for aggregate demand. These definitions are based on assessing positive and negative pattern variations and are identified from the analysis of the collective behavior of the aggregate users. A set of results is shown for different numbers of aggregate customers, by considering different values of the averaging time step for load pattern representation.

References

32