Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehi...Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.展开更多
文摘Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.
