This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and CO2 emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s CO2 emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs.

Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station

Cabrera-Tobar, Ana
;
Blasuttigh, Nicola;Massi Pavan, Alessandro;Lughi, Vanni;
2022-01-01

Abstract

This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and CO2 emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s CO2 emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs.
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https://www.mdpi.com/2079-9292/11/23/3948
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3035382
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