A deep learning approach for prediction of electrical vehicle charging stations power demand in regulated electricity markets: The case of Morocco

Boulakhbar, Mouaad; Farag, Markos; Benabdelaziz, Kawtar; Kousksou, T.; Zazi, Malika

doi:10.1016/j.cles.2022.100039

Cited by 22 publications

(16 citation statements)

References 28 publications

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“…RNN models may more accurately capture input data characteristics because of their dynamic nature and natural structure. [ 44 ]…”

Section: Types Of Techniques Used In Ev Chargingmentioning

confidence: 99%

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

Bishla,

Khosla

2023

Energy Tech

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As a sustainable mode of transportation, electric vehicles (EVs) are gaining popularity, and their integration with the power grid and photovoltaic (PV) solar systems is essential for their widespread adoption. Solar PV and EV systems have improved based on the generation and demands of the power grid. However, incorporating EV chargers with the grid and PV solar systems can cause power quality issues and stability problems that can impact the charger's performance and affect the charging procedure. EV technologies are divided into two major components: electric propulsion systems and EV charging systems. The electric propulsion system delivers the necessary energy for the EV motor in an EV system. In addition, with higher penetration, EVs and PVs are anticipated to emerge as new participants in the energy market operation and can play a crucial role in redefining the market. Many have integrated into the electricity network to reach renewable energy sources (RES), such as wind and solar PV. The study concludes that continued research and development are necessary to upgrade the power quality and solidness of EV charging frameworks, to ensure the seamless integration of PV solar systems and electric vehicles into the grid, and to promote sustainable transportation.

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“…RNN models may more accurately capture input data characteristics because of their dynamic nature and natural structure. [ 44 ]…”

Section: Types Of Techniques Used In Ev Chargingmentioning

confidence: 99%

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

Bishla,

Khosla

2023

Energy Tech

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“…The drawback of that study was the lack of data during holidays and semester breaks. Lastly, the paper [22] compared the following algorithms: artificial neural networks (ANN), RNN, gated recurrent unit (GRU), and LSTM, and proved a singlehidden-layer GRU outperformed others in predicting EV load demand and providing insight into weekly consumption patterns. In this paper, we propose a three-stage-based approach to find an optimal charging schedule for an HESS to achieve a clean, cost-effective, sustainable, and efficient EV charging station.…”

Section: Introductionmentioning

confidence: 99%

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Erdogan,

Fekih Hassen

2023

Energies

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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%.

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“…The travel behaviour of private electric vehicles and taxi owners was considered along with the roads' velocity and network. Authors in [22] compared four different deep learning methods for a real case in Marocco to predict charging demand of an EV station. In [23], a data-driven approach using machine learning regression methods is adopted for a public charging station.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

et al. 2023

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Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

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A deep learning approach for prediction of electrical vehicle charging stations power demand in regulated electricity markets: The case of Morocco

Cited by 22 publications

References 28 publications

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

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