ROCHD, Abdelilah (ENSET Mohammedia, Hassan II University of Casablanca, Morocco), HOURAN, Nouriddine (Innovative Technologies Laboratory, ENSA, Sidi Mohamed Ben Abdel), BENAYAD, Mohamed (Geosciences Laboratory, Faculty of Sciences-Ain Chock, Hassan II), LAAMIM, Mohamed (Laboratoire des Sciences de l’Ingénieur & Biosciences (LSIB), Fa), Kissaoui, Mohammed (Hassan II University of casablanca, Morocco), RAIHANI, Abdelhadi (Hassan II university of Casablanca (Morocco), ENSET Mohammedia (), ALLOUHI, Amine (Innovative Technologies Laboratory, ENSA, Sidi Mohamed Ben Abdel), SUN, Hongjian (Department of Engineering, Durham University, Durham, DH1 3LE, U)

Towards Smart EV Charging: Assessing the Flexibility Provision Potential of Electric Vehicle Charging Stations for Cost-Effective Grid Responsiveness

Scheduled for presentation during the Invited Session "Advanced Control Techniques for Energy Conversion Systems-2" (ThuS3T1), Thursday, July 11, 2024, 16:50−17:10, Salle des conférences

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

In the dynamic shift towards a sustainable global energy landscape, electric vehicle (EV) charging stations emerge as crucial components, fostering clean mobility while holding untapped potential for grid support. This study investigates the flexibility potential of charging stations, aiming to provide ancillary services to the grid. The methodology, encompassing diverse factors from parking scenarios to technical regulations, serves a dual purpose: predicting the station’s service engagement and assessing its capacity to enhance grid responsiveness. Building on prior research statistically assessing the dynamics of charging patterns with a two-year dataset from a specific charging station, the methodology creates a comprehensive framework to estimate EV charging management benefits and services. As a continuity, the study employs a variety of statistical analyses and machine learning models, including XGBoost, to explore time-based, energy-based, and behavior-based perspectives. The study presents simulation algorithms for ancillary service potential, revealing the impact of an EV charging management strategy on daily power consumption, client satisfaction, net profit, and pricing dynamics. The intricate interplay between charging stations, electric grid providers, and users underscores the need for strategic considerations to optimize economic viability and user satisfaction in the evolving electric vehicle charging landscape. As the electric mobility era unfolds, this study provides a crucial approach for forecasting capabilities and estimating economic gains, contributing to the sustainable integration of electric vehicles into the global energy ecosystem.