通过基于深度学习的场景分析,优化光伏在电网管理中的集成
Zhiming Gu1,2, Bo Li3,4, Guipeng Zhang1,2
1Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming, 650217, China.
本研究介绍了一种使用深度学习的双相优化模型,用于将光伏 (PV) 系统集成到电网中. 人工智能增强的框架提高了电网稳定性和效率,降低了成本和排放.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 将光伏 (PV) 系统集成到电网中,由于太阳能的间歇性而存在挑战.
- 现有的电网管理策略往往难以适应波动的能源生产和消费模式.
研究的目的:
- 开发一个强大的优化模型,以无地将光伏集成到电网中.
- 通过使用人工智能来提高电网稳定性,效率和经济/环境性能.
主要方法:
- 开发了一种采用深度学习技术的双相优化模型.
- 使用生成对抗网络 (GAN) 来模拟各种能源生产-消费场景.
- 一个实时自适应控制框架利用合成数据进行动态操作调整.
主要成果:
- 在能源管理方面达到高达96%的效率.
- 降低了20%的能源开支和30%的碳排放.
- 将年度运营停机时间减少50% (从120小时减少到60小时).
结论:
- 人工智能增强的框架加强了对可再生能源间歇性的电网弹性.
- 数据驱动的优化和预测分析支持可持续向绿色能源过渡.
- 该模型为改善能源系统性能提供了主动决策.
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