Alleviating post-contingency congestion risk of wind integrated systems with dynamic line ratings

Abstract

One of the factors hindering the large scale integration of wind power is the post contingency congestion of a network due to limited availability of network capacity and auxiliary constraints. Under such conditions, the network operators can potentially request a curtailment of wind farm output if the remedial strategies fail. The paper investigates this problem in detail and proposes a mathematical framework to capture the post contingency spare capacity of network assets that is required to limit the wind curtailment. The proposed approach incorporates stochastic variation in asset thermal rating; models network congestion, and quantifies the risk of congestion using an extended version of conic-quadratic programming based optimization. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The results suggest that the wind utilization can be maximized if the networks are operated 30-50% less than the nominal rating of the assets.