An MILP Based Energy Optimization in a Multi Source EV Charging Station

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Ali Kazmi, SN
Yang, F
Sanjari, MJ
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2024
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Pattaya, Thailand

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Abstract

The global rise in Electric Vehicle (EV) adoption to reduce the carbon footprint has intensified the need for efficient energy management strategies in grid-connected EV charging stations. This paper presents an optimal energy management approach using Mixed-Integer Linear Programming (MILP) to maximize the profit for the charging station owners (CSOs), a key factor in driving further investment in the charging infrastructure and accelerating widespread adoption of EVs. By leveraging MILP, the proposed framework enables charging station operators to optimize EV charging schedules, accounting for dynamic time-of-use (TOU) electricity rates, PV generation variability, and fluctuating EV demand. The objective is to maximize profit while ensuring sustainable and cost-effective operation. Simulation results highlight the effectiveness of this approach in improving profitability and reducing operational costs. The study underscores the potential of MILP to enhance the efficiency and sustainability of EV charging infrastructure, accelerating the shift towards smart and resilient urban mobility solutions.

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2024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)

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This work is covered by copyright. You must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a specified licence, refer to the licence for details of permitted re-use. If you believe that this work infringes copyright please make a copyright takedown request using the form at https://www.griffith.edu.au/copyright-matters.

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Ali Kazmi, SN; Yang, F; Sanjari, MJ, An MILP Based Energy Optimization in a Multi Source EV Charging Station, 2024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), 2024, pp. 1-6