IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i16p4036-d1456466.html
   My bibliography  Save this article

Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Author

Listed:
  • Rafael A. Núñez-Rodríguez

    (School Electronic Engineering, Unidades Tecnológicas de Santander, Bucaramanga 680005, Colombia)

  • Clodomiro Unsihuay-Vila

    (Department of Electrical Engineering, Universidade Federal do Paraná, Curitiba 81531-980, Brazil)

  • Johnny Posada

    (Department of Electronics Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia)

  • Omar Pinzón-Ardila

    (School of Electronic Engineering, Universidad Pontificia Bolivariana, Floridablanca 681007, Colombia)

Abstract

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions.

Suggested Citation

  • Rafael A. Núñez-Rodríguez & Clodomiro Unsihuay-Vila & Johnny Posada & Omar Pinzón-Ardila, 2024. "Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch," Energies, MDPI, vol. 17(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4036-:d:1456466
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/16/4036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/16/4036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
    2. Huang, Zhanghao & Zhang, Yachao & Xie, Shiwei, 2022. "Data-adaptive robust coordinated optimization of dynamic active and reactive power flow in active distribution networks," Renewable Energy, Elsevier, vol. 188(C), pages 164-183.
    3. Pinto, Rafael S. & Unsihuay-Vila, Clodomiro & Tabarro, Fabricio H., 2021. "Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties," Applied Energy, Elsevier, vol. 297(C).
    4. Zhang, Yachao & Le, Jian & Zheng, Feng & Zhang, Yi & Liu, Kaipei, 2019. "Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration," Renewable Energy, Elsevier, vol. 135(C), pages 122-135.
    5. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    6. Qiu, Haifeng & Gu, Wei & Pan, Jing & Xu, Bin & Xu, Yinliang & Fan, Miao & Wu, Zhi, 2018. "Multi-interval-uncertainty constrained robust dispatch for AC/DC hybrid microgrids with dynamic energy storage degradation," Applied Energy, Elsevier, vol. 228(C), pages 205-214.
    7. Gupta, S. & Maulik, A. & Das, D. & Singh, A., 2022. "Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    Full references (including those not matched with items on IDEAS)

    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. Zhou, Bo & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2019. "Data-adaptive robust unit commitment in the hybrid AC/DC power system," Applied Energy, Elsevier, vol. 254(C).
    2. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    3. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    4. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    5. Zhou, Dengji & Yan, Siyun & Huang, Dawen & Shao, Tiemin & Xiao, Wang & Hao, Jiarui & Wang, Chen & Yu, Tianqi, 2022. "Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes," Energy, Elsevier, vol. 239(PA).
    6. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    7. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    8. Sun, Li & Li, Guanru & Hua, Q.S. & Jin, Yuhui, 2020. "A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control," Renewable Energy, Elsevier, vol. 147(P1), pages 1642-1652.
    9. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    10. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    11. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Pan, Bo & Qi, Shiqiang, 2020. "Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power," Applied Energy, Elsevier, vol. 275(C).
    12. Bao, Minglei & Hui, Hengyu & Ding, Yi & Sun, Xiaocong & Zheng, Chenghang & Gao, Xiang, 2023. "An efficient framework for exploiting operational flexibility of load energy hubs in risk management of integrated electricity-gas systems," Applied Energy, Elsevier, vol. 338(C).
    13. Jun Dong & Anyuan Fu & Yao Liu & Shilin Nie & Peiwen Yang & Linpeng Nie, 2019. "Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    14. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    15. Zhang, Yue & Wu, Qiong & Ren, Hongbo & Li, Qifen & Zhou, Weisheng, 2024. "Optimal operation of multi-microgrid systems considering multi-level energy-certificate-carbon coupling trading," Renewable Energy, Elsevier, vol. 227(C).
    16. Yang, Xiaohui & Wang, Xiaopeng & Deng, Yeheng & Mei, Linghao & Deng, Fuwei & Zhang, Zhonglian, 2023. "Integrated energy system scheduling model based on non-complete interval multi-objective fuzzy optimization," Renewable Energy, Elsevier, vol. 218(C).
    17. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    18. Zhang, Yachao & Liu, Yan & Shu, Shengwen & Zheng, Feng & Huang, Zhanghao, 2021. "A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources," Energy, Elsevier, vol. 216(C).
    19. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    20. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(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:jeners:v:17:y:2024:i:16:p:4036-:d:1456466. 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.