Economic model predictive control (EMPC) is an advanced control strategy that uses a dynamic model of the system to predict future system behavior and determines the control actions that minimize an economic cost function while satisfying operational limitations (constraints). While numerous simulation studies have demonstrated EMPC’s effectiveness in shifting HP loads to reduce energy costs [5, 6], field demonstration of EMPC in residential buildings with air-to-air HPs remains limited [7-10]. More field demonstrations in different climate zones are needed with EMPC to fully validate its performance in real-world applications.
To this end, we present closed-loop results from a recent field demonstration of our cloud-based supervisory EMPC framework, designed to co-optimize expected electricity costs and grid-associated greenhouse gas emissions from HP operation. Because multiple setpoint trajectories often yield similar HP behavior, this flexibility can lead to frequent setpoint changes, which may be undesirable from an occupant comfort perspective. To mitigate this, we formulate the EMPC with additional penalties to limit setpoint changes. We present the cloud-based EMPC architecture and detail how it addresses practical challenges such as intermittent connectivity and manual setpoint overrides by residents. We conducted the field demonstration in two multi-family residential units in California Climate Zone 12, each equipped with cloud-connected thermostats. To validate the performance of the EMPC framework, we compare data collected under the EMPC framework to data collected under a fixed-setpoint RBC strategy, evaluating electricity cost, temperature comfort violations, and the frequency of setpoint changes.
References:
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