Model Predictive Control for Price-Based Demand-Responsive Building Control by Leveraging Active Latent Heat Storage

Active latent heat storage (ALHS) involving phase-change materials constitutes a promising energy-efficient solution for building energy management (BEM). Current BEM systems based on conventional reactive control lack the level of control delicacy required to exploit the full potential of ALHS for BEM. This study proposes a smart model predictive control (MPC) approach for BEM to minimize energy costs while maintaining indoor climate by fully applying ALHS. An MPC framework considering ALHS dynamics and dynamic electricity prices is proposed. A case study entailing a set of simulations is designed based on a single-family house with a space heating system integrated with ALHS. The proposed MPC approach, compared to conventional reactive control, enables more than 70% of reductions in electricity costs. Further analysis reveals that coupling ALHS with MPC is critical to exploiting the ALHS potential for BEM: while conventional reactive control of an ALHS-equipped building increases the electricity cost, an MPC-enabled building could reduce the electricity cost by 45.1% due to ALHS adoption.