Two-Stage Energy Management for Office Buildings With Workplace EV Charging and Renewable Energy

Workplace electric vehicle (EV) charging is now supported by more and more companies to encourage EV adoption. In the meantime, renewable energies are becoming an important power source. To participate in the day-ahead power market, decisions have to be made before knowing the actual power demand. This paper addresses the challenges of energy scheduling in office buildings integrated with photovoltaic systems and workplace EV charging. It proposes to leverage day-ahead power market and time-of-use electricity, and uses stochastic programming to address the uncertainties in EV charging demand. Two computationally efficient control algorithms, stochastic programming and load forecasting for energy management with two stages (SPLET) and sample average approximation-based SPLET, are proposed. Both algorithms contain two stages: day-ahead scheduling and real-time operation. First, they try to find the amount of power to purchase from the day-ahead power market while leveraging the flexibility of the load. Then, the real-time demand is satisfied while incorporating the uncertainties realization. Case study based on real-world data shows the proposed two algorithms could provide 7.2% and 6.9% average cost reduction, respectively. Vehicle-to-building and stand-alone battery system can serve as countermeasures for the mismatch between the day-ahead scheduling and real-time demand to further reduce the operation cost.