Scalable and Anonymous Modeling of Large Populations of Flexible Appliances

TL;DR

This paper introduces a scalable, privacy-preserving stochastic hybrid model for large populations of flexible appliances, enabling effective demand response management in power grids.

Contribution

It presents a novel medium-grained model that balances accuracy and tractability, modeling heterogeneous appliances without revealing individual identities.

Findings

Model enables scalable load control for large appliance populations.

Preserves customer privacy while maintaining control accuracy.

Suitable for both planning and real-time demand response.

Abstract

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances.