A novel day-ahead and real-time model of transmission congestion management using uncertainties prioritizing

Congestion in transmission lines will not be tolerable except for a short time as it may cause power outages and losses. In this paper, through multistage congestion management (CM), a conceptual framework for transmission CM is presented. At first, by the contingency analysis, uncertainties are prioritized based on their impacts on the congestion. Those with severe impacts (in congestion) are taken into account in the day ahead, while those with minor impacts are considered in the real time. It is because of the fact that all uncertainties do not happen in reality and considering all of them in the day ahead imposes high additional costs. In other words, the minor effect uncertainties are neglected in day ahead and are postponed to real time. Hence, even if they happen, they will be overcome by remedial measures with no significant concern. Then at each time interval (for both day ahead and real time), using the sensitivity analysis a congestion control zone is formed for each congested line. Finally, through the intelligent implementation of the nonlinear demand response programs (DRPs) and an alternative load shedding model, the congestion is alleviated. Results prove the effectiveness of the proposed methodology.