IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4801-d1485658.html
   My bibliography  Save this article

Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization

Author

Listed:
  • Zhiming Gu

    (Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
    Yunnan Key Laboratory of Green Energy, Electric Power Measurement Digitalization, Control and Protection, Kunming 650217, China)

  • Tingzhe Pan

    (Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510000, China)

  • Bo Li

    (Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
    Yunnan Key Laboratory of Green Energy, Electric Power Measurement Digitalization, Control and Protection, Kunming 650217, China)

  • Xin Jin

    (Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510000, China)

  • Yaohua Liao

    (Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
    Yunnan Key Laboratory of Green Energy, Electric Power Measurement Digitalization, Control and Protection, Kunming 650217, China)

  • Junhao Feng

    (Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510000, China)

  • Shi Su

    (Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
    Yunnan Key Laboratory of Green Energy, Electric Power Measurement Digitalization, Control and Protection, Kunming 650217, China)

  • Xiaoxin Liu

    (China Southern Power Grid Industrial Investment Group Co., Ltd., Guangzhou 510000, China)

Abstract

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future.

Suggested Citation

  • Zhiming Gu & Tingzhe Pan & Bo Li & Xin Jin & Yaohua Liao & Junhao Feng & Shi Su & Xiaoxin Liu, 2024. "Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization," Energies, MDPI, vol. 17(19), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4801-:d:1485658
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4801/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/19/4801/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings," Applied Energy, Elsevier, vol. 213(C), pages 11-21.
    2. Zhang, Rufeng & Chen, Yan & Li, Zhengmao & Jiang, Tao & Li, Xue, 2024. "Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties," Applied Energy, Elsevier, vol. 371(C).
    3. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    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. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy," Energy, Elsevier, vol. 311(C).
    2. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    3. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    4. Beata Kurc & Xymena Gross & Natalia Szymlet & Łukasz Rymaniak & Krystian Woźniak & Marita Pigłowska, 2024. "Hydrogen-Powered Vehicles: A Paradigm Shift in Sustainable Transportation," Energies, MDPI, vol. 17(19), pages 1-38, September.
    5. Liu, Jia & Chen, Xi & Yang, Hongxing & Li, Yutong, 2020. "Energy storage and management system design optimization for a photovoltaic integrated low-energy building," Energy, Elsevier, vol. 190(C).
    6. Cheng, Qian & Liu, Pan & Ming, Bo & Yang, Zhikai & Cheng, Lei & Liu, Zheyuan & Huang, Kangdi & Xu, Weifeng & Gong, Lanqiang, 2024. "Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems," Energy, Elsevier, vol. 305(C).
    7. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    8. Jiyoung Eum & Yongki Kim, 2020. "Analysis on Operation Modes of Residential BESS with Balcony-PV for Apartment Houses in Korea," Sustainability, MDPI, vol. 13(1), pages 1-9, December.
    9. Mageswaran Rengasamy & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "The Motivation for Incorporation of Microgrid Technology in Rooftop Solar Photovoltaic Deployment to Enhance Energy Economics," Sustainability, MDPI, vol. 12(24), pages 1-27, December.
    10. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    11. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    12. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    13. Toopshekan, Ashkan & Yousefi, Hossein & Astaraei, Fatemeh Razi, 2020. "Technical, economic, and performance analysis of a hybrid energy system using a novel dispatch strategy," Energy, Elsevier, vol. 213(C).
    14. Heine, Karl & Thatte, Amogh & Tabares-Velasco, Paulo Cesar, 2019. "A simulation approach to sizing batteries for integration with net-zero energy residential buildings," Renewable Energy, Elsevier, vol. 139(C), pages 176-185.
    15. Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.
    16. Bandyopadhyay, Arkasama & Leibowicz, Benjamin D. & Webber, Michael E., 2021. "Solar panels and smart thermostats: The power duo of the residential sector?," Applied Energy, Elsevier, vol. 290(C).
    17. Koskela, Juha & Rautiainen, Antti & Järventausta, Pertti, 2019. "Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization," Applied Energy, Elsevier, vol. 239(C), pages 1175-1189.
    18. Zhong, Junjie & Zhao, Yirui & Cao, Yijia, 2024. "Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware," Energy, Elsevier, vol. 312(C).
    19. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Napolitano, Davide Ferdinando, 2019. "Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness," Applied Energy, Elsevier, vol. 254(C).
    20. Roxana Grigore & Aneta Hazi & Ioan Viorel Banu & Sorin Eugen Popa & Sorin Gabriel Vernica, 2024. "Enhancing the Energy Performance of a Gas Turbine: Component of a High-Efficiency Cogeneration Plant," Energies, MDPI, vol. 17(19), pages 1-17, September.

    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:19:p:4801-:d:1485658. 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.