An Optimal Interaction Model of Reconfigurable Smart Distribution System and Parking Lot Operators

Document Type : Research paper

Authors

Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.

Abstract

By leveraging the capabilities of Internet of Things (IoT) technology in conjunction with the smart grid concept and cloud-based data sharing, distribution system operators (DSOs) and parking lot operators (PLOs) can coordinate collaboratively to optimize techno-economic interactions. The integration of smart devices for data acquisition, monitoring, and control, along with cloud-based platforms for data storage, analysis, and collaboration, facilitates more efficient energy management, cost-effectiveness, and overall performance improvements. Building on these technological advancements, this study examines the daily operational planning of a smart distribution system in collaboration with PLOs, utilizing the Equilibrium Optimizer (EO) algorithm. Considering the potential of parking lots, the DSO aims to optimize both economic objectives and load leveling goals simultaneously, benefiting from structural reconfiguration for additional technical and financial gains. The model effectively incorporates constraints related to the expected and reliable operation of parking lots, as well as the security and radiality of the distribution system. By analyzing various objective functions and perspectives, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the optimal state across all scenarios, achieving 60.3$, 5306.6 kW, and 716.8 kW for the first, second, and third objective functions, respectively. Numerical studies and simulation validations are conducted to evaluate the proposed model's performance, with results discussed in detail.

Keywords

Main Subjects

References

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Journal of Operation and Automation in Power Engineering
Volume 12, Special Issue (Open)
Advanced Technologies for Resilient and Efficient Microgrid Management: Innovations in Energy Optimization, Security, and Integration
2024

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History

  • Receive Date: 17 July 2024
  • Revise Date: 02 September 2024
  • Accept Date: 11 September 2024
  • First Publish Date: 11 September 2024

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