基于不同类型的风力发电波动的电力系统运营成本的数据驱动模型
Jie Yan1, Shan Liu1, Yamin Yan1
1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China.
Journal of environmental management
|December 30, 2023
概括
本研究开发了一种新型模型,以准确计算由波动的风能影响的电力系统运营成本. 深度神经网络方法精确地将风力波动映射到成本上,改善电网管理并降低开支.
科学领域:
- 电力系统工程 电力系统工程
- 整合可再生能源的整合
- 计算经济学计算经济学
背景情况:
- 间歇性风能发电增加了电力系统的运营成本.
- 在复杂的风力波动下,准确量化这些成本是具有挑战性的.
研究的目的:
- 开发一个能适应各种风力发电波动的电力系统运营成本模型.
- 使用数据驱动方法精确地映射风能变化到电力系统和热单元的运营成本.
主要方法:
- 一个两层的集群策略来分类风力发电波动.
- 一个生产模拟模型,包括热电厂,储能和储备成本.
- 基于风的波动模式进行成本预测的深度神经网络.
主要成果:
- 该模型准确地模拟了电力系统的整体运营成本 (4%-18%的误差).
- 它准确地模拟了热电厂的运营成本 (3%-13%的误差).
- 深度神经网络有效地将风力波动映射到跨季节的运营成本.
结论:
- 拟议的数据驱动模型有效地解决了在风力发电透率高的情况下计算运营成本的挑战.
- 该方法提供了准确的成本估计,有助于电网管理和经济优化.
- 这种方法验证了深度学习在分析可再生能源整合的经济影响方面的有效性.
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