Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Abstract

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Keywords:

• power distribution systems
• electric vehicle charging stations
• mixed-integer linear programming
• expansion planning
• uncertainty
• stochastic model
• Chance-Constraint Programming

ACKNOWLEDGMENT

None.

AUTHORS’ CONTRIBUTION

Peyman Zare: Conceptualization, Visualization, Methodology, Software, Writing - Data Curation, Formal analysis, Investigation, Writing-original Draft, Writing - Review & Editing, Resources. Abdolmajid Dejamkhooy: Project administration, Review & Editing, Validation, Editing, Supervision .: Sajjad Shoja Majidabad: Formal analysis, Validation, Writing-Review & Editing .: Iraj Faraji Davoudkhani: Validation, Writing-Review & Editing.

DATA AVAILABILITY STATEMENT

The data used in this article will be accessible upon request.

DISCLOSURE STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. During the preparation of this work, the authors used AI and AI-assisted technologies in only order to improve the readability and language of the work. After using these tools, the authors reviewed and edited the content as needed and took full responsibility for the publication’s content.

Additional information

Notes on contributors

Peyman Zare

Peyman Zare, born in Ardabil, Iran, in 1995. He earned his B.Sc. degree in Biomedical Engineering from Islamic Azad University, Ardabil, Iran, in 2019, and holds a second B.Sc. degree in Electrical Engineering from Islamic Azad University, Khalkhal, Iran, conferred in 2023. He furthered his academic journey by obtaining an M.S. in Power Electrical Engineering from Mohaghegh Ardabili University in 2021. He is working dedicated researcher towards Ph.D. candidate in Power Electrical Engineering at Mohaghegh Ardabili University, Ardabil, Iran. His research focuses on the diverse applications of power electrical engineering, with a particular emphasis on maritime grid power systems, onshore/offshore (or mobile) microgrids, hub energy, energy management, load frequency control, renewable energy, and motors. He actively explores the integration of artificial intelligence and optimization algorithms in these fields to enhance efficiency and performance. Beyond his primary research areas, he maintains a keen interest in the broader aspects of power networks, including generation, transmission, and distribution.

Abdolmajid Dejamkhooy

Abdolmajid Dejamkhooy was born in Ardabil, Iran, in 1983. He received the B.S. degree from the University of Tabriz, Tabriz, Iran, in 2006, and the M.S. and Ph.D. degrees in electrical engineering from the Shahrood University of Technology, Shahrood, Iran, in 2009 and 2014, respectively. He is currently with the Department of Electrical Engineering. University of Mohaghegh Ardabili, Ardabil, Iran. His research interests include power system optimiation, power quality, and processing of power system signals.

Sajjad Shoja Majidabad

Sajjad Shoja-Majidabad received the B.Sc. degree from the Sahand University of Technology, Tabriz, Iran, in 2008, and the M.Sc. and Ph.D. degrees in electrical engineering from the Shahrood University of Technology, Shahrood, Iran, in 2010 and 2014, respectively. His research interests include nonlinear robust control, power system analysis and control, and fractional-order robust control.

Iraj Faraji Davoudkhani

Iraj Faraji Davoudkhani received his, B.Sc degree from Birjand University in 2009, an M.Sc degree from Lorestan University in 2016, and a Ph.D degree from Mohaghegh Ardabili University in 2023 respectively, all in electrical engineering. He was an active researcher and lecturer at the Islamic Azad University, Khalkhal in electrical engineering from 2016 to 2023 in Iran. His research interests include Energy Management, Renewable Energy Sources, Smart Grid, Hybrid REN Systems, Optimization Algorithms, and dynamic stability of power systems. He has also published over 70 articles in prestigious and international journals and conferences. He is also a member of the Iran Energy Association and IEEE.