在多维不确定性下,用于PV-微电网协调调度的量子灵感强大的优化
Yunxiao Bai1, Yu Sui2, Xiaoyu Deng2
1Guangdong Power Grid Co., Ltd., CSG, Guangzhou, 510080, China. baiyunxiao23@163.com.
Scientific reports
|August 12, 2025
概括
本研究介绍了光伏-微电网的量子灵感强大优化 (QRO) 框架. 它通过动态适应天气和需求等不确定性来提高能源调度的弹性,并降低成本.
科学领域:
- 能源系统工程 能源系统工程
- 优化理论 优化理论
- 人工智能的人工智能
背景情况:
- 集成光伏 (PV) 生产和储能的农村微电网面临运营挑战,原因是光伏产量,需求和市场价格的高度不确定性.
- 传统的优化方法与这些系统固有的动态,多维的不确定性作斗争.
- 现有的方法缺乏适应实时干扰和不断变化的操作反的能力.
研究的目的:
- 开发一种新的量子灵感强大优化 (QRO) 框架,用于协调PV-H2微电网调度.
- 在动态和不确定的条件下提高微电网的弹性和运营效率.
- 为下一代基于的微电网运营提供可扩展和适应的解决方案.
主要方法:
- 一个量子灵感强大的优化 (QRO) 框架,将分布式强大的优化与强化学习相结合.
- 根据运营反演变的适应性不确定性集,增强对网络攻击和电网中断的弹性.
- 深度Q学习和政策梯度方法,用于在非静止环境中不断改进调度策略.
主要成果:
- 在5MW的PV-H2微电网案例研究中,QRO框架在全年的时间里证明了实际的有效性.
- 运营成本降低了9.3%,在不利条件下 (例如,电网故障,网络攻击) 弹性得分提高了20%以上.
- 与经典优化相比,融合速度增加了42%,突出显示了计算效率和实时学习可行性.
结论:
- 该QRO框架提供了一个可扩展和适应性的解决方案,用于PV-H2微电网中的弹性能源调度.
- 量子启发型建模,分布强度和强化学习的整合解决了传统方法的局限性.
- 该研究验证了先进的人工智能和优化技术对于强大的微电网运营的实际可行性.
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