Predictive energy scheduling of smart parking infrastructure with solar-powered electric vehicles

Abstract

This paper presents a novel model predictive control framework for managing energy flow in smart parking infrastructures with renewable energy facilities, electric vehicles, and solar-powered electric vehicles. The proposed control framework minimizes the energy costs for the parking lot operators, ensuring the user-defined charge levels for vehicles at departure, and protecting the charging infrastructure during operation. Field validation on Lonsdale Street, Melbourne (Australia)—using real data on vehicle behavior, solar irradiance, and energy prices—shows significant grid load reduction even with partial solar production. Compared to a rule-based strategy, the MPC approach reduces operational costs by 15.32% and energy demand by 6.12%. Lastly, we show that the proposed framework is robust under forecast uncertainty, supporting its practical deployment in dynamic real-world environments.