This study presents the initialisation of a hybrid alternating current/direct current(AC/DC)power system to determine the operation point around which the system is linearised as a starting point for an electromagneti...This study presents the initialisation of a hybrid alternating current/direct current(AC/DC)power system to determine the operation point around which the system is linearised as a starting point for an electromagnetic or harmonic stability analysis.To this end,each AC or DC power system component is modelled in the Fourier transform domain and further adjusted to a simplified representation compatible with a power flow formulation.The study also presents how voltage source converter(VSC)high-voltage DC-based systems can be analysed when the different VSC controls are applied.The result of the power flow solution is then used for the power converters’initialisation.展开更多
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
基金supported by the Energy Transition Fund,FOD Economy,Belgium.This work was supported by the Research Foundation Flanders(FWO)under grant no.G0D2319N.The research of J.Beerten was funded by the FWO under grant no.12D1117N.
文摘This study presents the initialisation of a hybrid alternating current/direct current(AC/DC)power system to determine the operation point around which the system is linearised as a starting point for an electromagnetic or harmonic stability analysis.To this end,each AC or DC power system component is modelled in the Fourier transform domain and further adjusted to a simplified representation compatible with a power flow formulation.The study also presents how voltage source converter(VSC)high-voltage DC-based systems can be analysed when the different VSC controls are applied.The result of the power flow solution is then used for the power converters’initialisation.
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.
