Data-driven adjustable robust Day-ahead economic dispatch strategy considering uncertainties of wind power generation and electric vehicles

Abstract

Considering the intermittent features of wind power generation and electric vehicles, it is important for microgrid to formulate available dispatch strategy while ensuring the system economy. A two-stage data-driven adjustable robust optimization (ARO) model is presented in this paper to realize an optimal day-ahead economic dispatch strategy for the microgrid considering the uncertainties of wind power generation and electric vehicles. In the optimization model, an imprecise-Dirichlet-model-based nonparametric ambiguity set is constructed without any presumption about the probability distributions of the uncertainties. Further, a polyhedron uncertainty set obtained from the ambiguity set is driven by historical data, and this becomes narrower with an increase in the amount of the historical data used to construct the ambiguity set. The data-driven ARO problem is converted into a traditional two-stage ARO model with the obtained polyhedron uncertainty set. Further, this can be decomposed into a mixed integer linear programming (MILP) master problem and a sub problem with a max–min structure. Then, duality theory, Big-M method, and column and constraint generation (C&CG) algorithm are applied to achieve the optimized day-ahead economic dispatch strategy for the microgrid. Case studies illustrate the solution robustness, economic benefit, flexible adjustment capability and uncertainty depiction ability of the presented method.