Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices

TL;DR

This paper introduces an adaptive, data-driven demand response scheme for residential devices that estimates user preferences from consumption data, improving participation and balancing energy and user satisfaction.

Contribution

It proposes a novel online utility estimation method and a probabilistic flexibility model, enhancing user-oriented load control without requiring surveys or daily input.

Findings

Significant financial benefits achieved with the scheme.

High user satisfaction maintained through adaptive scheduling.

Effective modeling of user flexibility under uncertainty.

Abstract

Demand Response (DR) schemes are effective tools to maintain a dynamic balance in energy markets with higher integration of fluctuating renewable energy sources. DR schemes can be used to harness residential devices' flexibility and to utilize it to achieve social and financial objectives. However, existing DR schemes suffer from low user participation as they fail at taking into account the users' requirements. First, DR schemes are highly demanding for the users, as users need to provide direct information, e.g. via surveys, on their energy consumption preferences. Second, the user utility models based on these surveys are hard-coded and do not adapt over time. Third, the existing scheduling techniques require the users to input their energy requirements on a daily basis. As an alternative, this paper proposes a DR scheme for user-oriented direct load-control of residential appliances operations. Instead of relying on user surveys to evaluate the user utility, we propose an online data-driven approach for estimating user utility functions, purely based on available load consumption data, that adaptively models the users' preference over time. Our scheme is based on a day-ahead scheduling technique that transparently prescribes the users with optimal device operation schedules that take into account both financial benefits and user-perceived quality of service. To model day-ahead user energy demand and flexibility, we propose a probabilistic approach for generating flexibility models under uncertainty. Results on both real-world and simulated datasets show that our DR scheme can provide significant financial benefits while preserving the user-perceived quality of service.