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.
Highlights
The increasing shortage of traditional energy and the increasing environmental pollution have prompted people to pay more and more attention to renewable energy
T =1 where πt denotes the on-grid price of the wind–storage–Electric vehicles (EVs) hybrid system at time t; Ptω is the wind power at time t; Ptdis and Ptch denote the discharging and charging ability of energy storage system at time t, respectively; ∆t represents the time interval, which usually is 1 h; and CtDR represents the demand response costs at the time when EVs participate in the joint scheduling of the wind–storage hybrid system, shown as follows, CtDR = πtev
In the Information gap decision theory (IGDT) robust scheduling model, under an evasive attitude towards the threat caused by uncertainty, the expected profit that the decision maker can accept is the minimum benefit of the wind–storage–EVs hybrid system α, T
Summary
The increasing shortage of traditional energy and the increasing environmental pollution have prompted people to pay more and more attention to renewable energy. Under the premise that the wind energy obeys the normal distribution [13], the Monte Carlo method [14] and Stochastic dynamic programming model [15] are used to obtain the wind power forecasting value On this basis, the joint scheduling model of the wind–storage hybrid system is established, and the optimal scheduling plan in spot market is developed. (2) IGDT theory is an optimization method that determines the range of variable fluctuations based on the degree of threat acceptance of uncertainty by decision makers It does not require the probability distribution of uncertain variables, but models the deviation between predicted and actual values. It has been widely used to deal with the uncertainty of load recovery and the uncertainty of electricity price
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