Abstract
Theincreasingvolatility of power prices,drivenbytheintegrationofrenewableenergy sources into the electricity mix, requires more efficient control strategies for electrified residential heating and cooling systems. This paper proposes an enhanced dynamic programming(EDP)algorithmdesignedforreal-timemodelpredictivecontrol(MPC) ofheatpump-drivenHVACsystemswith integratedthermalenergystorage(TES).The EDPintroducesabrute-force initialization and a smart cutoff approach to significantly reducecomputationalrequirements,whilepreservingtheglobaloptimalityofstandard dynamic programming (DP). The proposed method is benchmarked against standard DP, particle swarm optimization (PSO), and genetic algorithms (GA) in a residential usecasebasedonaGermanhomeandtime-varyingelectricityprices.Theresultsshow that the EDP finds the same cost-optimal control trajectories as the conventional DP but with up to 75% less computational time. Compared to metaheuristic methods, the EDP delivers superior cost savings while being up to two orders of magnitude faster, particularly for extended prediction horizons, hence enabling effective real-time MPC applications. The method is not limited to HVAC applications, but can be used for any DP-solvable MPC problem, enabling more intelligent and resource-efficient energy management under dynamic operating conditions