论文标题
严重中断期间,在主动分配系统上的馈线微电网管理
Feeder Microgrid Management on an Active Distribution System during a Severe Outage
论文作者
论文摘要
在恶劣天气事件发生后大规模中断的分配系统上形成一个微电网已成为一种可行的解决方案,以提高分配水平的弹性。当配电系统具有高水平的分布式PV时,此选项变得更具吸引力。但是,此类馈线级微电网的管理有许多挑战,例如可以快速部署在馈线上的有限资源,以及对分配系统的实时监控和控制有限。由于未经监控和控制,有效使用分布式PV也是具有挑战性的。为了应对这些挑战,本文提出了一种2阶段的分层能源管理计划,以安全地操作这些馈线水平。该方案的第一阶段解决了一个顺序滚动优化问题,以最佳安排主要资源(例如移动柴油发电机和电池存储单元)。第二阶段为主要资源采用了调度方案,以将阶段1的设定点更接近实时调整。该提出的方案具有独特的功能,可以确保该方案在高度变化的操作条件下具有鲁棒性,并且系统可观察性有限:(i)创新的PV预测误差调整和动态储备调整方案,以处理PV功率输出的极端不确定性,以及(ii)智能燃料管理方案,以确保资源在恢复期的多个日期中使用最佳的资源。提出的算法在样本系统上使用实时数据进行了测试。结果表明,通过有效利用所有资源并正确考虑到具有挑战性的操作条件,提出的方案在最大程度地提高了负载服务方面表现良好。
Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system. Effective use of the distributed PV is also challenging as they are not monitored and controlled. To handle these challenges, the paper proposes a 2-stage hierarchical energy management scheme to securely operate these feeder level micorgrids. The first stage of the scheme solves a sequential rolling optimization problem to optimally schedule the main resources (such as a mobile diesel generator and battery storage unit). The second stage adopts a dispatching scheme for the main resources to adjust the stage-1 set-points closer to real- time. The proposed scheme has unique features to assure that the scheme is robust under highly varying operating conditions with limited system observability: (i) an innovative PV forecast error adjustment and a dynamic reserve adjustment scheme to handle the extreme uncertainty on PV power output, and (ii) an intelligent fuel management scheme to assure that the resources are utilized optimally over the multiple days of the restoration period. The proposed algorithm is tested on sample system with real-time data. The results show that the proposed scheme performs well in maximizing service to loads by effective use of all the resources and by properly taking into account the challenging operating conditions.