基于气象分类的多微电网的低碳和经济调度战略,以应对风力发电的不确定性
Yang Liu1,2, Xueling Li1, Yamei Liu1,2
1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了多微电网系统的可调节的强大优化模型,以管理风力发电的不确定性. 这种方法提高了风力发电的准确性,成本效益,并减少了碳排放.
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
- 优化理论 优化理论
背景情况:
- 减少碳排放对于现代电力系统至关重要.
- 风力发电整合面临着由于其不确定性的挑战.
- 多微电网系统 (MMGS) 为风力发电部署提供了一个潜在的解决方案.
研究的目的:
- 为解决风电不确定性的MMGS制定最佳的调度策略.
- 提高风力发电描述的准确性,减少碳排放.
- 为MMGS运行提供一个可调节的强大优化 (ARO) 模型.
主要方法:
- 使用MRMR和CURE聚类进行气象学分类,用于风格识别.
- 条件生成对抗网络 (CGAN) 用于风力发电数据丰富和模糊性集构建.
- 可调节的强大优化 (ARO) 框架,采用两阶段合作调度模型和阶段化碳交易.
主要成果:
- 在MMGS中提高风力发电描述准确性和成本效益.
- 显著减少系统碳排放.
- 使用ADMM和C&CG算法实现的分散解决方案.
结论:
- 拟议的ARO模型有效地解决了MMGS中的风能不确定性.
- 该模型提高了运营效率和环境可持续性.
- 未来的工作重点是提高解决方案算法的效率.
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