适应性非参数核密度估计用于电动汽车的低频负载减排和可再生能源的不确定性
Feng Renhai1, Wajid Khan1, Afshan Tariq1
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Scientific reports
|April 3, 2025
概括
这项研究引入了适应性低频负载减排 (AUFLS) 的新框架,该框架可在具有高风能和电动汽车 (EV) 集成的电力系统中显著减少50%以上的负载减排. 这种方法提高了电网稳定性和对可再生能源变化的弹性.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 整合可再生能源的整合
背景情况:
- 风力发电等可变可再生能源的日益集成对电网稳定性构成挑战.
- 传统的低频负载释放 (AUFLS) 方法与现代电网的动态性质作斗争,特别是电动汽车 (EV) 的参与.
- 保持电网频率在安全范围内对于可靠的电力系统运行至关重要.
研究的目的:
- 开发一个强大的优化框架适应性低频负载减排 (AUFLS).
- 加强AUFLS的能力,以管理来自高风力透率和动态电动汽车充电的不确定性.
- 为了提高电力系统的稳定性和弹性,面对可再生能源的变化.
主要方法:
- 提出了一种新的双层强大的优化框架,包括适应性非参数内核密度估计 (AAKDE) 用于风力发电预测.
- 引入了一种战略性电动汽车排队机制,根据实时充电状态和充电行为优先排放.
- 集成了一个强化学习模型,用于实时调整AUFLS决策,以优化频率稳定.
主要成果:
- 在升级的IEEE 39总线测试系统上进行的模拟表明,与传统的AUFLS相比,负载减排要求减少了50%以上.
- 拟议的方法有效地在高可再生能源变化和电动汽车集成场景下在安全运行范围内保持系统频率.
- 在增强电网稳定性和应对动态电网条件的弹性方面取得了卓越的性能.
结论:
- 新的双层强大优化框架显著提高了高可再生能源透率和电动汽车参与度的电网的AUFLS性能.
- 适应性非参数方法和智能电动汽车管理是开发更智能,更有弹性电力系统的关键.
- 这项研究为先进的AUFLS战略提供了途径,以确保在不断变化的能源景观中确保电网稳定性.
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