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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
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    References listed on IDEAS

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    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.
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