This paper addresses the challenge of the charging control of Electric Busses (EBs) and implications on network demand. Present literature has already confirmed the possibility to do this type of service and its benefits, but the solutions proposed require a complex communication infrastructure. Moreover, the Distribution Network (DN) must be ready to an increased prevalence for reverse power flow manifest by mainstreaming of EVs. In this context, the paper proposes a transitional solution to host the EBs until the required communication infrastructure is mature enough. The Smart Charging (SC) method proposed here relies instead on the Day-Ahead Energy Market to forecast the network working conditions. The method also facilitates distributed photovoltaic (PV) production so that network demand reference is based on net demand. The algorithm focuses on load-levelling or peak-shaving as the primary objective, in the optimisation of individual charger current per vehicle and per time step to realise an overall charging strategy for the charging station. The strategy seeks to control fleet charging by managing how individual vehicle charging is interchangeable based on an 80% vehicle state-of-charge objective. The algorithm achieves a scheduling capability for the EBs that transit through the Charging Station (CS) through optimum load-levelling/peak-shaving based on the size of the fleet.

Electric Bus Demand Management through Unidirectional Smart Charging

Turri, Roberto;
2022

Abstract

This paper addresses the challenge of the charging control of Electric Busses (EBs) and implications on network demand. Present literature has already confirmed the possibility to do this type of service and its benefits, but the solutions proposed require a complex communication infrastructure. Moreover, the Distribution Network (DN) must be ready to an increased prevalence for reverse power flow manifest by mainstreaming of EVs. In this context, the paper proposes a transitional solution to host the EBs until the required communication infrastructure is mature enough. The Smart Charging (SC) method proposed here relies instead on the Day-Ahead Energy Market to forecast the network working conditions. The method also facilitates distributed photovoltaic (PV) production so that network demand reference is based on net demand. The algorithm focuses on load-levelling or peak-shaving as the primary objective, in the optimisation of individual charger current per vehicle and per time step to realise an overall charging strategy for the charging station. The strategy seeks to control fleet charging by managing how individual vehicle charging is interchangeable based on an 80% vehicle state-of-charge objective. The algorithm achieves a scheduling capability for the EBs that transit through the Charging Station (CS) through optimum load-levelling/peak-shaving based on the size of the fleet.
2022
2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings
978-1-6654-5505-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3507792
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