An evolutionary approach to modeling and control of space heating and thermal storage systems

Abstract Home Energy Management systems are in a rapid development curve, supported by the advancements in computational intelligence, smart appliances, and new smart-grid frameworks. These systems are a fundamental part to implement demand-side management strategies and to shift local energy demand to periods of lower consumption effectively. In this paper, we explore the application of distributed co-evolutionary optimization algorithms and an agent-based architecture to reduce the consumption profile signature of the heating system during the critical peak demand periods, by reducing costs and respecting the comfort constraints of the occupants. The proposed control architecture targets the typical baseboard space heating systems and electrical thermal storage systems, as these represent a large portion of the energy usage in Nordic countries and are commonly controlled by room independent thermostats, which could be easily replaced by smart devices running an algorithm as the one presented in this work. Results prove the strategy proposed getting a cost reduction of up to 23% and a peak-to-average ratio decrease of up to 25% for reference scenarios. Also, an emulation Simulink model is developed to recreate a house and the different heating loads studied in this paper and an experimental test bed is built to model a real ETS system, two different complexity degree RC models are proposed to describe such systems.