IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model

Abstract

Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind–storage–electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind–storage–EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind–storage–EVs hybrid system.

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“…Te optimization results were compared with those of the ordinary Kriging method and the co-Kriging method, and the best solution was obtained [19]. Sun et al studied the construction method of a mixed substitution model based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging lattice substitution models for the multiparameter optimization problem involved in the pulse jet cleaning process of bag flters [20]. Liu et al proposed a new alternative model PC-GK-SBL, which combines polynomial chaotic expansion (PCE) and Gaussian kernel (GK), under the sparse Bayesian learning (SBL) framework, signifcantly improving computational efciency [21].…”