Co-designing Intelligent Control of Building HVACs and Microgrids

TL;DR

This paper explores the co-design of microgrid power dispatch and building HVAC control using Deep Reinforcement Learning to achieve effective temperature regulation with minimized costs, considering various levels of system model knowledge.

Contribution

It introduces DRL-based control architectures for microgrid and HVAC systems, accommodating different levels of model information and demonstrating their advantages.

Findings

DRL effectively manages temperature control and cost reduction.

Control architectures adapt to varying system model knowledge.

DRL offers advantages over traditional control methods.

Abstract

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. Therein, renewable resource-based microgrids offer a greener and cheaper alternative. This communication explores the possible co-design of microgrid power dispatch and building HVAC (heating, ventilation and air conditioning system) actuations with the objective of effective temperature control under minimised operating cost. For this, we attempt control designs with various levels of abstractions based on information available about microgrid and HVAC system models using the Deep Reinforcement Learning (DRL) technique. We provide control architectures that consider model information ranging from completely determined system models to systems with fully unknown parameter settings and illustrate the advantages of DRL for the design prescriptions.