Optimal Day-ahead Scheduling of Heat Pump Heating Systems Partially Fed by Renewable Generation

Buildings are responsible for a large amount of CO
2
emissions and reducing their footprint can play an important role to contrast climate change. When designing heating systems, two aspects are usually considered as the most relevant: the energy consumption and the thermal comfort that the system must guarantee. The thermal comfort of a building and the energy consumption are thus connected with the heating power provided and demanded by the heating system. In a scenario of increasing self-generation of renewable energy for electricity production, it is necessary to take advantage of energy storage capacities to reduce the coupling between heating demand and the electricity demand needed to maintain comfort. In this work, this problem is solved as a multi-objective optimization problem aiming to schedule the best temperature set-point to maintain comfort with the minimal power consumption. A Particle Swarm optimization algorithm was applied to find the optimum operation point.