在分布式能源资源可变性下的微电网控制的数据驱动优化
Akhilesh Mathur1, Ruchi Kumari1, V P Meena2,3
1Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India.
Scientific reports
|May 11, 2024
概括
这项研究优化了混合微电网运行,使用基于优先级的成本函数和灰狼优化 (GWO). 该方法有效地管理成本效益的不确定性,可靠的能源在电网连接和独立模式.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 优化技术 优化技术
背景情况:
- 智能电网受益于可再生能源的整合,以提高弹性和清洁电力.
- 可再生能源的间歇性需要微电网的先进调度策略.
- 用光伏 (PV),风能和可控制分布式发电机 (CDG) 优化混合系统对于连接到电网和独立运营至关重要.
研究的目的:
- 为混合微电网开发基于优先级的成本优化功能.
- 将间歇性参数的不确定性纳入调度方法.
- 通过先进的优化来最大限度地降低微电网的整体运营成本.
主要方法:
- 开发了一个基于优先级的成本优化功能,包括运营,排放,电池,电网能源交换和减负成本.
- 蒙特卡洛模拟为不确定的参数生成了多个场景,然后使用k-means集群缩小了这些场景.
- 使用灰狼优化 (GWO) 算法来最小化开发的成本函数,并在CIGRE测试网络上验证.
主要成果:
- 在各种场景下,GWO算法在最大限度地降低微电网的成本函数方面表现出有效性.
- 分配给子目标的不同优先级产生了不同的操作结果,展示了拟议方法的灵活性.
- 在优化微电网的成本方面,GWO方法证明优于Jaya和粒子优化 (PSO) 算法.
结论:
- 提出的基于优先级的成本优化战略有效地管理混合微电网的不确定性.
- 灰狼优化算法为优化微电网运行提供了强大而高效的方法.
- 该方法确保在连接到电网的和独立的微电网配置中提供可靠和成本效益的能源供应.
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