对具有混合风能-太阳能-气体储存系统的VPP的最大可调度容量评估
Chunyu Zhang1, Dongxu Li2, Chaojie Li3
1Zhejiang Provincial Key Laboratory of Island Green Energy and New Materials, Taizhou University, Zhejiang, 318000, China. chuzhcn@tzc.edu.cn.
Scientific reports
|December 5, 2025
概括
这项研究为评估虚拟发电厂 (VPP) 容量提供了一个新的框架. 它有效地管理可再生能源的变化,确保可靠和经济的电网运行.
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
- 随机优化 随机优化的优化
背景情况:
- 可再生能源的变化给虚拟发电厂 (VPP) 能力评估带来了挑战.
- 准确评估最大可调度容量对于电网稳定性和经济可行性至关重要.
研究的目的:
- 开发一个基于场景的框架来评估VPP的最大可调度容量.
- 为应对风能,太阳能,天然气和储存资源的综合变化所带来的挑战.
- 确保在不确定性条件下对VPP进行可靠和经济可行的容量规划.
主要方法:
- 混合深度学习模型 (AGCN,CNN,LSTM) 用于预测风能和太阳能.
- 概率集和场景减少以捕捉源负载动态.
- 多场景随机优化和二进制搜索以确定容量.
主要成果:
- 拟议的框架准确地预测了可再生能源发电和模型负载的不确定性.
- 统计缩小场景有效地代表了复杂的时间变化和需求相关性.
- 该方法可靠地在各种不确定性条件下确定最大可调度容量.
结论:
- 场景驱动的框架提高了VPP能力规划的可靠性和经济可行性.
- 混合深度学习和随机优化方法有效地管理可再生能源的整合.
- 这种方法提供了一个强大的解决方案,可以在能源市场的不确定性中优化VPP运营.
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