Distributed model predictive control for thermal house comfort with auction of available energy

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. The limited energy resource is shared by a fixed order established from a previously done auction wherein the bids are made by each agent based on the energy price that they are willing to pay each day. The developed system is applied and simulated with three houses.