Centralized MPC for Home Energy Management With EV as Mobile Energy Storage Unit

Electric vehicle (EV) is commonly considered as an electric load in a residential energy network. However, the large capacity EV battery can be used as electric storage when the EV is plugged in at home. While model predictive control (MPC) offers an efficient and reliable control mechanism for a home energy management system (HEMS), the uncertainty related to the availability of an EV at home poses an interesting challenge for the MPC optimization strategy. In this article, the thermal comfort and energy management performance of a centralized MPC-based HEMS is presented for such a scenario where an EV is used as a mobile energy storage unit in a home energy network. The MPC-based HEMS simultaneously controls the zone-based heating system consisting of a heat pump and baseboard units along with the energy flow among the different components of the home energy network. This network comprises the home baseload, charging and discharging of the home-battery and the EV battery, in-house solar energy generation and storage. The MPC-based HEMS optimizes the buying/selling of energy from/to the grid based on time of use electricity rates. A Monte-Carlo based uncertainty analysis is presented here to estimate the robustness of the MPC against the randomness in the EV arrival and departure schedule. Finally, the responsiveness of the centralized MPC is showcased using a vehicle to home communication scenario where the owner notifies the home network regarding their arrival within a short interval, along with the preferred indoor temperature upon arrival.

Automated summary

This article presents a centralized MPC-based home energy management system (HEMS) that utilizes an electric vehicle (EV) as a mobile energy storage unit. The system controls the zone-based heating system and manages the energy flow among various components including home baseload, home-battery and EV battery charging and discharging, in-house solar energy generation, and storage. A Monte-Carlo based uncertainty analysis is used to evaluate the robustness of the MPC against the randomness in EV arrival and departure schedule. The responsiveness of the centralized MPC is demonstrated through a vehicle to home communication scenario.