Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Abstract

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

“…Every CS contains multiple charging connectors (i.e., normal charging or fast charging) and a Hybrid Energy Storage System (HESS) made up of a Lithium battery and a Redox Flow Battery (RFB). Two sources of power are used to supply the HESS, i.e., the grid and the Photovoltaic powers [12,13]. Our main target is to determine the optimal time slots to charge the EV, based on a pre-selected location, and the initial EV SOC while considering the availability of the charging connector type at a specific part of the day.…”

Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models

“…Additionally, EV Owners can also benefit from reduced utility rates for off-peak charging and exemptions from congestion charges in some cities. With the introduction of the latest EV models, battery technology in the automotive sector has undergone a rapid evolution [2]. To promote truly sustainable road transport, electric vehicles also play a key role in harnessing renewable energy sources (RES) [1], which are abundant and do not produce greenhouse gases [3].…”