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

Zhiming Gu, Tingzhe Pan, Bo Li, Xin Jin, Yaohua Liao, Junhao Feng, Shi Su and Xiaoxin Liu ()
Additional contact information
Zhiming Gu: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Tingzhe Pan: Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
Bo Li: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Xin Jin: Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
Yaohua Liao: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Junhao Feng: Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510000, China
Shi Su: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Xiaoxin Liu: China Southern Power Grid Industrial Investment Group Co., Ltd., Guangzhou 510000, China

Energies, 2024, vol. 17, issue 19, 1-15

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.

Keywords: deep learning; energy management; generative adversarial networks (GANs); grid stability; photovoltaic systems; renewable energy integration; robust optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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