Gu Zhiming, Li Bo, Zhang Guipeng, Li Bo
Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming, 650217, China.
Yunnan Key Laboratory of Green Energy, Electric Power Measurement Yunnan Key Laboratory of Green Energy, Electric Power Measurement, Digitalization, Control and Protection, Kunming, 650217, China.
Sci Rep. 2025 Apr 28;15(1):14851. doi: 10.1038/s41598-025-98724-3.
Addressing the challenges of integrating photovoltaic (PV) systems into power grids, this research develops a dual-phase optimization model incorporating deep learning techniques. Given the fluctuating nature of solar energy, the study employs Generative Adversarial Networks (GANs) to simulate diverse and high-resolution energy generation-consumption patterns. These synthetic scenarios are subsequently utilized within a real-time adaptive control framework, allowing for dynamic adjustments in operational strategies that enhance both efficiency and grid stability. By leveraging this approach, the model has demonstrated substantial improvements in economic and environmental performance, achieving up to 96% efficiency while reducing energy expenses by 20%, lowering carbon emissions by 30%, and cutting annual operational downtime by half (from 120 to 60 h). Through a scenario-driven predictive analysis, this framework provides data-driven optimization for energy systems, strengthening their resilience against renewable energy intermittency. Furthermore, the integration of AI-enhanced forecasting techniques ensures proactive decision-making, supporting a sustainable transition toward greener energy solutions.
为应对将光伏（PV）系统集成到电网中的挑战，本研究开发了一个结合深度学习技术的双阶段优化模型。鉴于太阳能的波动特性，该研究采用生成对抗网络（GAN）来模拟多样且高分辨率的能源生成-消耗模式。这些合成场景随后被用于实时自适应控制框架中，从而能够对运行策略进行动态调整，提高效率并增强电网稳定性。通过采用这种方法，该模型在经济和环境绩效方面展现出显著提升，实现了高达96%的效率，同时将能源费用降低了20%，碳排放量降低了30%，并将年度运行停机时间减半（从120小时降至60小时）。通过情景驱动的预测分析，该框架为能源系统提供数据驱动的优化，增强其对可再生能源间歇性的抵御能力。此外，人工智能增强的预测技术的集成确保了主动决策，支持向更绿色能源解决方案的可持续转型。
