Abstract
A large potential to shift the electricity consumption to adapt to the stochastic renewable electricity generation is identified through the utilisation of a combination of Heat Pumps (HP) and local Thermal Energy Storage (TES) devices in building heating systems. In this paper, a building heating system coupled with an active Phase Change Material (PCM) TES device and a HP is simulated to characterise its potential for Demand Response (DR) applications. A control-oriented numerical model for the PCM TES is developed and previously validated numerical models of the building, HP and hot water radiators are integrated to simulate the dynamics of the coupled building heating system. A Genetic Algorithm (GA) based control strategy is designed to optimise the building heating energy consumption and operational cost with respect to time-varying electricity price signals. The developed control strategy is successfully implemented to utilise the TES capability of the PCM and the thermal inertia of the building to intelligently shift the electrical load of the HP to low price periods, while satisfying the specified indoor comfort requirements. In comparison to a reference case utilising a sensible TES, cost savings and consumption reductions of more than 40% and 30%, respectively, are attained with the active PCM TES. Simulation results indicate that utilising an active PCM TES over a sensible TES offers significant advantages for DR applications in building heating systems in terms of load shift flexibility, energy costs and consumption.
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