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Robust Online Electric Vehicle Control at a Charging Hub

Reuling, Jacco (2023) Robust Online Electric Vehicle Control at a Charging Hub.

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Abstract:The transportation and mobility sector is central to the energy transition from fossil fuels to sustainable sources. Given the European Union's impending ban on gasoline and diesel vehicles by 2035, electric vehicle (EV) sales are soaring, presenting challenges in charging infrastructure and power grid management. As EV adoption increases, so does the need for widespread charging infrastructure and strategies to manage the strain of EV charging on power grids. Moreover, the variable nature of energy prices introduces the need for optimizing energy consumption during cheaper rates, pushing businesses towards dynamic energy pricing. Addressing these challenges, Energy Management Systems (EMS) have emerged, aiming to control EV charging, especially with increasing interest in photovoltaic (PV) systems integrated with charging facilities. The "Energy Scheduler" is a pioneering approach to determine optimal charging schedules, balancing costs, grid load, and EV user needs. However, the system, as it stands, doesn't account for real-time deviations from solar power predictions. This thesis seeks to assess the performance of the Energy Scheduler and enhance it by integrating a Real-Time Control mechanism, making charging more adaptable to real-time conditions. The research also delves into gathering EV flexibility information, used for charging scheduling. It explores the potential of a charger integrated user interface (UIs). Additionally, to validate EV control strategies, a comprehensive Evaluation Framework is proposed, serving both simulated and real-world testing environments. The Energy Scheduler demonstrated an effective EV charging schedule management based on PV power forecasts and EV flexibility. Significant achievements include a 67.47\% reduction in summer electricity costs and a 40.97\% reduction in peak grid loads. However, the system's efficiency waned during winter due to lower PV generation. Implementing a real-time control mechanism provided an effective response to unforeseen solar power forecast fluctuations. Although costs slightly rose, the mechanism significantly reduced peak grid loads with an extra 5\%, and improved self-consumption and self-sufficiency rates. While robust in simulated conditions, real-world complexities posed challenges to the mechanism's performance. Simulations showed that an UI has potential for improved scheduling effectiveness, offering potential enhancements in self-sufficiency, self-consumption, cost-effectiveness, and grid stability. Challenges remain in user engagement and advocating the benefits of active UI use. The Evaluation Framework enabled rigorous testing of solutions under various conditions. It ensures adaptability for simulation and real-world scenarios. However, limitations surfaced, including high-speed simulation challenges and the inability to perfectly replicate real-world hardware behaviors. This research proposes enhancements to the EV charging system for a sustainable, cost-effective, and grid-protective future. Key recommendations include refining the Real-Time Control mechanism to consider grid load discrepancies, and reevaluating the Cost Optimization strategy to prevent grid strain. Furthermore, boosting user engagement with the UI, and leveraging sophisticated predictive modeling could be beneficial for EV scheduling. Extended real-world testing across seasons is advised to ensure system robustness. Implementing these measures promises a holistic improvement in EV charging infrastructure.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology
Programme:Sustainable Energy Technology MSc (60443)
Link to this item:https://purl.utwente.nl/essays/97980
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