Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty

Extending traditional deterministic economic dispatch to incorporate significant stochastic wind power is an important but challenging task in today's power system decision making. In this paper, this issue is formulated as a chance-constrained economic dispatch (CCED) problem. Usually, in the presence of non-Gaussian correlated random variables, both the objective function and constraints are difficult to handle. To address this issue, this paper provides a novel method dealing with non-Gaussian random variables. First, the Gaussian mixture model is adopted to represent the joint probability density function of power output for multiple wind farms. Then, analytical formulae are derived that can be used for fast computation of partial derivatives of the objective function and transformation of chance constraints into linear ones. Thereafter, the CCED can be solved as a deterministic linear convex optimization with a global optimal solution. The effectiveness and efficiency of the proposed methodology are validated via a case study with a modified IEEE 39-bus system.