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Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm

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  • Md Jamal Ahmed Shohan

    (Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA
    Center for Advanced Power Systems (CAPS), Florida State University, Tallahassee, FL 32310, USA)

  • Md Maidul Islam

    (Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA
    Electric Power Research Institute (EPRI), Knoxville, TN 37932, USA)

  • Sophia Owais

    (Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA
    Center for Advanced Power Systems (CAPS), Florida State University, Tallahassee, FL 32310, USA)

  • Md Omar Faruque

    (Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA
    Center for Advanced Power Systems (CAPS), Florida State University, Tallahassee, FL 32310, USA)

Abstract

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

Suggested Citation

  • Md Jamal Ahmed Shohan & Md Maidul Islam & Sophia Owais & Md Omar Faruque, 2024. "Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm," Energies, MDPI, vol. 17(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5278-:d:1505009
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    References listed on IDEAS

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    1. Qingyuan Yan & Yang Gao & Ling Xing & Binrui Xu & Yanxue Li & Weili Chen, 2024. "Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&KANO and MVO in PV-Connected Distribution Network," Energies, MDPI, vol. 17(14), pages 1-36, July.
    2. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
    3. Marián Tomašov & Milan Straka & Dávid Martinko & Peter Braciník & Ľuboš Buzna, 2023. "A Feasibility Study of Profiting from System Imbalance Using Residential Electric Vehicle Charging Infrastructure," Energies, MDPI, vol. 16(23), pages 1-27, November.
    4. Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2021. "How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study," Applied Energy, Elsevier, vol. 283(C).
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