IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p4964-d798338.html
   My bibliography  Save this article

Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations

Author

Listed:
  • Hyeon Woo

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Yongju Son

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Jintae Cho

    (Korea Electric Power Research Institute, Daejeon 34056, Korea)

  • Sungyun Choi

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

The increased penetration of electric vehicles (EVs) and distributed generator (DG) units has led to uncertainty in distribution systems. These uncertainties—which have not been adequately considered in the literature—can entail risks in the optimal sizing of EV charging stations (EVCSs) and DG units in active distribution network planning. This paper proposes a method for obtaining the optimal sizing of DG units and EVCSs (considering uncertainty), to achieve exact power system analysis and ensure EV driver satisfaction. To model uncertainties in optimal sizing planning, this study first generates scenarios for each system asset using a probability distribution that considers the asset characteristics. In this step, the wind-turbine (WT), PV, and EVCS are modeled applying the Weibull, exponential, and kernel density estimation (KDE), and scenarios for each asset are generated using random sampling. Then, the k-means clustering is carried out for scenario reduction and the representative scenario abstract. The probability of occurrence for each representative scenario is assigned depending on the number of observations within each cluster. The representative scenarios for each asset are integrated into the scenario for all assets through the joint probability. The integrated scenarios are applied in the optimization problem for optimal sizing of the system asset framework. The optimal sizing of the system assets problem is proposed (to minimize the line loss and voltage deviation) and formulated via stochastic second-order conic programming, to reflect the uncertainty under an AC power flow; this is a convex problem that can be solved in polynomial time. The proposed method is tested on a modified IEEE 15 bus system, and the simulation is performed with various objective functions. The simulation results demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Hyeon Woo & Yongju Son & Jintae Cho & Sungyun Choi, 2022. "Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4964-:d:798338
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/4964/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/4964/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    2. Luo, Lizi & Gu, Wei & Wu, Zhi & Zhou, Suyang, 2019. "Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation," Applied Energy, Elsevier, vol. 242(C), pages 1274-1284.
    3. Bahrami, Shahab & Amini, M. Hadi, 2018. "A decentralized trading algorithm for an electricity market with generation uncertainty," Applied Energy, Elsevier, vol. 218(C), pages 520-532.
    4. Kabir A. Mamun & F. R. Islam & R. Haque & Aneesh A. Chand & Kushal A. Prasad & Krishneel K. Goundar & Krishneel Prakash & Sidharth Maharaj, 2022. "Systematic Modeling and Analysis of On-Board Vehicle Integrated Novel Hybrid Renewable Energy System with Storage for Electric Vehicles," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    5. Fan, Vivienne Hui & Dong, Zhaoyang & Meng, Ke, 2020. "Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles," Applied Energy, Elsevier, vol. 278(C).
    6. Yuwei Chen & Ji Xiang & Yanjun Li, 2018. "SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks," Energies, MDPI, vol. 11(11), pages 1-17, November.
    7. Kandil, Sarah M. & Farag, Hany E.Z. & Shaaban, Mostafa F. & El-Sharafy, M. Zaki, 2018. "A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems," Energy, Elsevier, vol. 143(C), pages 961-972.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexander Mutiso Mutua & Ruairí de Fréin, 2024. "Sustainable Mobility: Machine Learning-Driven Deployment of EV Charging Points in Dublin," Sustainability, MDPI, vol. 16(22), pages 1-37, November.
    2. Haelee Kim & Hyeon Woo & Yeunggurl Yoon & Hyun-Tae Kim & Yong Jung Kim & Moonho Kang & Xuehan Zhang & Sungyun Choi, 2024. "An Enhanced Continuation Power Flow Method Using Hybrid Parameterization," Sustainability, MDPI, vol. 16(17), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luo, Lizi & Gu, Wei & Wu, Zhi & Zhou, Suyang, 2019. "Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation," Applied Energy, Elsevier, vol. 242(C), pages 1274-1284.
    2. Luo, Lizi & Wu, Zhi & Gu, Wei & Huang, He & Gao, Song & Han, Jun, 2020. "Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction," Energy, Elsevier, vol. 192(C).
    3. Guido C. Guerrero-Liquet & Santiago Oviedo-Casado & J. M. Sánchez-Lozano & M. Socorro García-Cascales & Javier Prior & Antonio Urbina, 2018. "Determination of the Optimal Size of Photovoltaic Systems by Using Multi-Criteria Decision-Making Methods," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
    4. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    5. Cai, Hanmin & You, Shi & Wu, Jianzhong, 2020. "Agent-based distributed demand response in district heating systems," Applied Energy, Elsevier, vol. 262(C).
    6. Li, Yinxiao & Wang, Yi & Chen, Qixin, 2020. "Study on the impacts of meteorological factors on distributed photovoltaic accommodation considering dynamic line parameters," Applied Energy, Elsevier, vol. 259(C).
    7. Raja S, Charles & Kumar N M, Vijaya & J, Senthil kumar & Nesamalar J, Jeslin Drusila, 2021. "Enhancing system reliability by optimally integrating PHEV charging station and renewable distributed generators: A Bi-Level programming approach," Energy, Elsevier, vol. 229(C).
    8. Xie, Shiwei & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "The optimal planning of smart multi-energy systems incorporating transportation, natural gas and active distribution networks," Applied Energy, Elsevier, vol. 269(C).
    9. Abdullah M. Shaheen & Ehab E. Elattar & Nadia A. Nagem & Asmaa F. Nasef, 2023. "Allocation of PV Systems with Volt/Var Control Based on Automatic Voltage Regulators in Active Distribution Networks," Sustainability, MDPI, vol. 15(21), pages 1-23, November.
    10. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    11. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    12. Artis, Reza & Shivaie, Mojtaba & Weinsier, Philip D., 2024. "A flexible urban load density-dependent framework for low-carbon distribution expansion planning in the presence of hybrid hydrogen/battery/wind/solar energy systems," Applied Energy, Elsevier, vol. 364(C).
    13. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    14. Feifeng Zheng & Zhaojie Wang & Ming Liu, 2022. "Overnight charging scheduling of battery electric buses with uncertain charging time," Operational Research, Springer, vol. 22(5), pages 4865-4903, November.
    15. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    16. Gao, Yan & Jiang, Chen & Yu, Dahai & Ahmad, Maiwand, 2023. "A novel electric differential and synchronization control method for 4WD/4WS electric vehicles based on fictitious master," Energy, Elsevier, vol. 274(C).
    17. Reza Sirjani, 2018. "Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    18. Mostafa Elshahed & Mohamed A. Tolba & Ali M. El-Rifaie & Ahmed Ginidi & Abdullah Shaheen & Shazly A. Mohamed, 2023. "An Artificial Rabbits’ Optimization to Allocate PVSTATCOM for Ancillary Service Provision in Distribution Systems," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
    19. Li, Rui & Wang, Wei & Wu, Xuezhi & Tang, Fen & Chen, Zhe, 2019. "Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis," Energy, Elsevier, vol. 168(C), pages 30-42.
    20. Eltoumi, Fouad M. & Becherif, Mohamed & Djerdir, Abdesslem & Ramadan, Haitham.S., 2021. "The key issues of electric vehicle charging via hybrid power sources: Techno-economic viability, analysis, and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4964-:d:798338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.