A Computational Strategy to Solve Preventive Risk-Based Security-Constrained OPF

The benefit of risk-based (RB) security-constrained optimal power flow (SCOPF) model lies in its ability to improve the economic performance of a power system while enhancing the system's overall security level. However, the RB-SCOPF model is difficult to solve due to the following two characteristics: 1) the overload severity of a circuit changes with the loading condition on it, thus is hard to express with a deterministic function, and 2) the risk index is a function of the state variables in both normal and contingency states, which greatly increases the scale of optimization. To handle the first issue, a new expression of severity function is proposed so that it is possible to decompose the model into a SCOPF subproblem and a risk subproblem. To deal with the second issue, a nested Benders decomposition with multi-layer linear programming method is proposed. Illustrations use the ISO New England bulk system is provided to demonstrate the feasibility of the proposed method. Analysis is presented to demonstrate the merits of the RB-SCOPF over the traditional SCOPF model.