IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p7911-d687649.html
   My bibliography  Save this article

Equivalent Modeling of Microgrids Based on Optimized Broad Learning System

Author

Listed:
  • Lin Wang

    (School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
    School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

  • Anke Xue

    (School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

The DC microgrid is an important structure of microgrids. Aiming at the problem of the grid-connected DC microgrid modeling, a grid-connected DC microgrid equivalent modeling method based on the optimized Broad Learning System (BLS) is proposed. Taking the electrical parameter data of the grid-connected DC microgrid access point as the training data set of BLS, the equivalent model of the grid-connected equivalent model is constructed. In order to further improve the accuracy and generalization performance of the model, the shark smell optimization (SSO) algorithm is used to optimize the input weights and thresholds of the BLS. Furthermore, the shark smell optimization-Broad Learning System (SSO-BLS) algorithm is proposed. SSO-BLS is compared with RBF, BLS, BFO-ELM, and other algorithms. The results show that the grid-connected DC microgrid model based on SSO-BLS has good accuracy and generalization characteristics.

Suggested Citation

  • Lin Wang & Anke Xue, 2021. "Equivalent Modeling of Microgrids Based on Optimized Broad Learning System," Energies, MDPI, vol. 14(23), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7911-:d:687649
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/7911/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/7911/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Massimiliano Luna & Antonino Sferlazza & Angelo Accetta & Maria Carmela Di Piazza & Giuseppe La Tona & Marcello Pucci, 2021. "Modeling and Performance Assessment of the Split-Pi Used as a Storage Converter in All the Possible DC Microgrid Scenarios. Part I: Theoretical Analysis," Energies, MDPI, vol. 14(16), pages 1-16, August.
    2. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    3. Massimiliano Luna & Antonino Sferlazza & Angelo Accetta & Maria Carmela Di Piazza & Giuseppe La Tona & Marcello Pucci, 2021. "Modeling and Performance Assessment of the Split-Pi Used as a Storage Converter in All the Possible DC Microgrid Scenarios. Part II: Simulation and Experimental Results," Energies, MDPI, vol. 14(18), pages 1-22, September.
    4. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    5. Vibhu Jately & Balaji Venkateswaran V. & Stefan Azzopardi & Brian Azzopardi, 2021. "Design and Performance Investigation of a Pilot Micro-Grid in the Mediterranean: MCAST Case Study," Energies, MDPI, vol. 14(20), pages 1-32, October.
    6. Ahmadigorji, Masoud & Amjady, Nima, 2016. "A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm," Energy, Elsevier, vol. 102(C), pages 199-215.
    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. Grzegorz Maślak & Przemysław Orłowski, 2022. "Microgrid Operation Optimization Using Hybrid System Modeling and Switched Model Predictive Control," Energies, MDPI, vol. 15(3), pages 1-21, January.

    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. Massimiliano Luna, 2022. "High-Efficiency and High-Performance Power Electronics for Power Grids and Electrical Drives," Energies, MDPI, vol. 15(16), pages 1-6, August.
    2. Massimiliano Luna & Antonino Sferlazza & Angelo Accetta & Maria Carmela Di Piazza & Giuseppe La Tona & Marcello Pucci, 2023. "Modeling and Experimental Validation of a Voltage-Controlled Split-Pi Converter Interfacing a High-Voltage ESS with a DC Microgrid," Energies, MDPI, vol. 16(4), pages 1-23, February.
    3. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    4. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    5. Mojtaba Nasiri & Saleh Mobayen & Quan Min Zhu, 2019. "Super-Twisting Sliding Mode Control for Gearless PMSG-Based Wind Turbine," Complexity, Hindawi, vol. 2019, pages 1-15, April.
    6. Anto Anbarasu Yesudhas & Young Hoon Joo & Seong Ryong Lee, 2022. "Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed," Energies, MDPI, vol. 15(9), pages 1-17, April.
    7. Huazhen Cao & Chong Gao & Xuan He & Yang Li & Tao Yu, 2020. "Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow," Energies, MDPI, vol. 13(18), pages 1-22, September.
    8. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    9. dos Santos, L.L.C. & Canha, L.N. & Bernardon, D.P., 2018. "Projection of the diffusion of photovoltaic systems in residential low voltage consumers," Renewable Energy, Elsevier, vol. 116(PA), pages 384-401.
    10. Mulusew Ayalew & Baseem Khan & Issaias Giday & Om Prakash Mahela & Mahdi Khosravy & Neeraj Gupta & Tomonobu Senjyu, 2022. "Integration of Renewable Based Distributed Generation for Distribution Network Expansion Planning," Energies, MDPI, vol. 15(4), pages 1-17, February.
    11. Yang, Bo & Zeng, Chunyuan & Li, Danyang & Guo, Zhengxun & Chen, Yijun & Shu, Hongchun & Cao, Pulin & Li, Zilin, 2022. "Improved immune genetic algorithm based TEG system reconfiguration under non-uniform temperature distribution," Applied Energy, Elsevier, vol. 325(C).
    12. Wollz, Danilo Henrique & da Silva, Sergio Augusto Oliveira & Sampaio, Leonardo Poltronieri, 2020. "Real-time monitoring of an electronic wind turbine emulator based on the dynamic PMSG model using a graphical interface," Renewable Energy, Elsevier, vol. 155(C), pages 296-308.
    13. Yang, Bo & Wang, Junting & Zhang, Xiaoshun & Yu, Lei & Shu, Hongchun & Yu, Tao & Sun, Liming, 2020. "Control of SMES systems in distribution networks with renewable energy integration: A perturbation estimation approach," Energy, Elsevier, vol. 202(C).
    14. Yang, Bo & Zhu, Tianjiao & Zhang, Xiaoshun & Wang, Jingbo & Shu, Hongchun & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao, 2020. "Design and implementation of Battery/SMES hybrid energy storage systems used in electric vehicles: A nonlinear robust fractional-order control approach," Energy, Elsevier, vol. 191(C).
    15. Liao, Shiwu & Yao, Wei & Han, Xingning & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & He, Haibo, 2019. "An improved two-stage optimization for network and load recovery during power system restoration," Applied Energy, Elsevier, vol. 249(C), pages 265-275.
    16. Longda Wang & Xingcheng Wang & Zhao Sheng & Senkui Lu, 2020. "Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation," Energies, MDPI, vol. 13(3), pages 1-25, February.
    17. Zhang, Xiaoshun & Tan, Tian & Yang, Bo & Wang, Jingbo & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao & Sun, Liming, 2020. "Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution," Applied Energy, Elsevier, vol. 260(C).
    18. Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.
    19. Massimiliano Luna & Antonino Sferlazza & Angelo Accetta & Maria Carmela Di Piazza & Giuseppe La Tona & Marcello Pucci, 2021. "Modeling and Performance Assessment of the Split-Pi Used as a Storage Converter in All the Possible DC Microgrid Scenarios. Part II: Simulation and Experimental Results," Energies, MDPI, vol. 14(18), pages 1-22, September.
    20. Canizes, Bruno & Soares, João & Lezama, Fernando & Silva, Cátia & Vale, Zita & Corchado, Juan M., 2019. "Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generation," Renewable Energy, Elsevier, vol. 138(C), pages 937-954.

    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:14:y:2021:i:23:p:7911-:d:687649. 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.