Thermostatic control for demand response using distributed averaging and deep neural networks
This work addresses load management for power grids using smart buildings, but it appears incremental as it combines existing distributed averaging with deep learning extensions.
The authors tackled the problem of managing thermostatically controlled loads (TCLs) in smart buildings to achieve desired aggregate power for grid ancillary services, using a distributed averaging protocol with deep neural networks, resulting in reduced power system oscillations and verified feasibility through hardware-based results.
Smart buildings are the need of the day with increasing demand-supply ratios and deficiency to generate considerably. In any modern non-industrial infrastructure, these demands mainly comprise of thermostatically controlled loads (TCLs), which can be manoeuvred. TCL loads like air-conditioner, heater, refrigerator, are ubiquitous, and their operating times can be controlled to achieve desired aggregate power. This power aggregation, in turn, helps achieve load management targets and thereby serve as ancillary service (AS) to the power grid. In this work, a distributed averaging protocol is used to achieve the desired power aggregate set by the utility using steady-state desynchronization. The results are verified using a computer program for a homogeneous and heterogeneous population of TCLs. Further, load following scenario has been implemented using the utility as a reference. Apart from providing a significant AS to the power grid, the proposed idea also helps reduce the amplitude of power system oscillations imparted by the TCLs. Hardware-based results are obtained to verify its implementation feasibility in real-time. Additionally, we extend this idea to data-driven paradigm and provide comparisons therein.