通过智能能源终端,通过智能能源终端进行潜水弧的数据驱动辅助实时最佳控制策略,考虑大规模的可再生能源利用
Bowen Zheng1, Mingming Pan1, Qixin Liu2
1China Electric Power Research Institute Co., Ltd, Beijing, 100192, China.
Scientific reports
|March 6, 2024
概括
本研究介绍了潜水弧 (SAF) 系统的实时优化模型,集成可再生能源 (PV和风能) 来降低成本并提高当地能源使用. 该模型提高了电网稳定性,并减少了可再生能源的限制.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能在能源中的作用
- 工业过程优化 工业过程优化
背景情况:
- 将可再生能源 (光伏和风能) 与潜弧炉 (SAF) 等工业负载相结合,由于间歇性和价格波动,存在重大挑战.
- 智能能源终端需要先进的管理策略,以有效地利用当地可再生能源发电,并参与动态的电力市场.
- 在波动的能源价格和可再生能源的可用性下,优化SAF运营对于经济可行性和电网稳定性至关重要.
研究的目的:
- 开发和验证与可再生能源集成的SAF系统的数据驱动,实时优化模型.
- 加强当地可再生能源的利用,并提高智能能源终端内的SAF运营的经济绩效.
- 解决与可再生能源发电和电力市场价格相关的动态不确定性.
主要方法:
- 一个两阶段的方法:可再生能源和市场价格的数据驱动预测,然后是SAF调度的实时优化模型.
- 利用深度神经网络,修改为动态不确定性预测,以建模SAF操作阻力.
- 集成智能能源终端,需求端设备和负载管理系统,以提高可再生资源利用率.
主要成果:
- 拟议的优化模式有效降低了运营成本,并增加了本地可再生能源发电的利用率.
- 根据不满系数设置,SAF系统的利能力在每间隔111美元至416美元之间.
- 实现了总计每日损失回收1,906.84美元,证明了显著的经济效益和改进的能源管理.
结论:
- 数据驱动的优化模型有效地管理了与可再生能源集成的SAF系统,从而带来了巨大的经济优势.
- 该方法通过减少可再生能源的限制和提高能源管理的经济效率,有助于电网稳定.
- 通过整合不满系数来证明该模型的适应性,突出了其在现实能源市场的实际应用性.
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