Trade-offs between Thermal Comfort, Storage Sizing, and Marginal Economics
The critical synthesis of recent techno-economic evidence demonstrates that deploying thermal energy storage alongside residential heat pumps fundamentally alters domestic load shapes under dynamic pricing structures. Optimisation frameworks applied to domestic space heating reveal that predictive control algorithms utilizing costate estimation can capture substantial operational cost savings under time-of-use tariffs, approaching the theoretical benchmark established by dynamic programming with perfect foresight (2025). Concurrently, empirical evaluations of dynamic tariff implementations confirm that price signals can effectively halve household electricity consumption during peak evening periods, validating the technical feasibility of shifting thermal loads on cold days across diverse building archetypes (2024). However, a pronounced research gap persists regarding the divergence between deterministic optimal control performance and realisable flexibility within uncoordinated residential clusters. While mathematical formulations demonstrate significant demand shifting (2025), empirical realisations depend heavily on automated response fidelity and sustained consumer tariff engagement (2024). Most existing analytical models presume uninterrupted consumer adherence to automated setpoint alterations and overlook the physical degradation of storage efficiency over prolonged cold weather periods. Furthermore, the present modelling framework is subject to methodological limitations, particularly the reliance on static building envelope thermal parameters and idealised household occupancy profiles that omit stochastic occupant behaviour. Addressing these identified limitations requires future research to integrate adaptive socio-technical feedback loops, thereby preventing uncoordinated rebound peaks when aggregated flexible heating assets resume normal thermal regulation simultaneously across local distribution networks.