Data-driven predictive direct load control of refrigeration systems

Abstract

A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model in the control implementation. As an important practical consideration, the control design relies on a cheap solution with available measurements than using the expensive mass flow meters. The results show successful implementation of the method on a large-scale non-linear simulation tool which is validated against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance.