Convex relaxation algorithm for chance constrained wind farm capacity assessment

Large-scale wind power penetration brings significant technical challenges to the power system operation. For the convenience of planning and construction, the beforehand study of wind farm capacity assessment is necessary. Considering the uncertainty of wind power output, the chance constrained capacity assessment model is built to determine the maximum penetration level for all wind farms. Convex relaxation of chance constrained optimization with the random variable described accurately by gaussian mixture model (GMM) is implemented and finally, the assessment model is converted into a convex optimization problem, which is handled efficiently. With a pre-defined confidence level, the effectiveness of the proposed method is demonstrated in numerical test on a modified IEEE-24 node system.