探索适应性随机化正弦共弦算法在风能集成场景基础的电力系统优化中使用FACTS设备的有效性
Sunilkumar P Agrawal1, Pradeep Jangir2,3,4, Arpita5
1Department of Electrical Engineering, Government Engineering College, Gandhinagar, Gujarat, 382028, India.
Scientific reports
|February 27, 2025
概括
适应性随机化正弦共弦算法 (ARSCA) 优化灵活交流传输系统 (FACTS) 设备,以减少功耗和成本. 这种新方法为现代电网提供了更快的融合和更好的稳定性.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 电力系统 电力系统
背景情况:
- 现代电力系统面临着不断增长的需求和有限的基础设施带来的挑战.
- 高效的传输和响应对于电网稳定性和经济运行至关重要.
研究的目的:
- 引入自适应随机化正弦共弦算法 (ARSCA) 以实现FACTS设备的最佳放置和设置.
- 提高电力系统效率,降低运营成本,提高电压稳定性.
主要方法:
- 实施ARSCA以优化电istor控制的系列电容器 (TCSC),电istor控制的相位变换器 (TCPS) 和静态VAR补偿器 (SVC).
- 在动态负载场景下,在IEEE 30-总线系统上测试ARSCA.
- 与基准算法进行比较,比如正弦正弦算法 (SCA),改进的灰狼优化 (IGWO) 和鱼优化算法 (WOA).
主要成果:
- 阿尔斯卡将活动电力损失降至最低,为1.7655兆瓦.
- 实现了最低807.17美元/小时的发电成本,并将总系统成本降低到883.53美元/小时.
- 与其他算法相比,证明了更快的融合,一致的解决方案准确性,更好的电压稳定性和更好的反应功率管理.
结论:
- 阿尔斯卡为电力系统优化提供了一种高效,可扩展,成本效益和稳定的解决方案.
- 算法的强大的探索和利用机制是其性能的关键.
- 未来的研究应该探索ARSCA在更大的网络中的可扩展性和不确定性下的适应性.
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