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Business-oriented optimization of EV-to-building energy flows: Predictive modeling and scenario evaluation

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  • Dai, Jie
  • Yuan, Qiong
  • Cai, Helen Huifen
  • Zhang, Vince
  • Hasanuzaman, Md.
  • Selvaraj, J.

Abstract

This study presents a hybrid electric vehicle-to-building (EV-t-B) energy management system that integrates high-resolution demand forecasting with real-world scheduling constraints to reduce peak electricity loads in institutional buildings. The framework comprises two components: (1) a machine learning module that forecasts next-day electricity demand at 15-min intervals using historical sub-metering and environmental data, and (2) a mixed-integer linear programming (MILP) optimization model that generates feasible charging/discharging schedules for EVs under multiple operational constraints.

Suggested Citation

  • Dai, Jie & Yuan, Qiong & Cai, Helen Huifen & Zhang, Vince & Hasanuzaman, Md. & Selvaraj, J., 2025. "Business-oriented optimization of EV-to-building energy flows: Predictive modeling and scenario evaluation," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225030981
    DOI: 10.1016/j.energy.2025.137456
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