A predictive control strategy for optimal management of peak load thermal comfort energy storage and renewables in multi-zone buildings

Abstract

Buildings are responsible for about 40% of the global energy consumption where heating ventilation and air conditioning (HVAC) systems account for the most part of it. Continuous increase in the installation of new HVAC systems and higher penetration of renewables and energy storage in the building energy network require more sophisticated control approaches to realize the full potential of these systems. In this paper an optimal control framework to coordinate HVAC battery energy storage and renewable generation in buildings is developed. The controller aims to reduce peak load demand while achieving thermal comfort within industry standards. To facilitate this a simple lumped mathematical model that describes the zone transient thermal dynamics is structured with a minimal data from the building and is trained with actual thermal and electrical data. Next a model predictive control algorithm that takes into account building thermal dynamics battery state of charge renewable generation status and actual operational data and constraints is formulated to regulate HVAC demand battery power and building thermal comfort. The controller considers the changes in the outside dry-bulb air temperature electricity price required energy amount and comfort conditions simultaneously in order to find the proper optimal zone temperatures guaranteeing occupant comfort. The new controller was tested using data from a real building and preliminary results indicate that significant reduction in peak electrical power demand can be achieved by the proposed approach.