Affinely Adjustable Stochastic Economic Dispatch with Chance Constraints for Transmission Congestion

The increasing renewable power penetration lead to a strong uncertainty on the power transmission in the power system. To consider the uncertainty on the transmission congestion, chance-constraint based methods are widely employed, which put a confidence level for each transmission line. However, the renewable power uncertainty on different transmission lines usually has strong correlation so it's less meaningful for the separate chance-constraint strategy. In this paper, the uncertainty on the transmission congestion is considered based on a joint chance-constraint for all transmission line and a Big-M method is proposed to convert the model to be a mixed integer linear programming (MILP) that could be solved by off-the-shelf commercial solver. A modified affinely adjustable approach is proposed to consider the system reserve for each conventional power plant, which employed the scheduled renewable power as the based point. Finally, numerical studies in the IEEE 118-bus system are presented to demonstrate the merits of the proposed method.