Abstract
Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids’ operating security. Therefore, it is significant to provide the accurate ranges of power grids’ variables, which can be used by the operators to guarantee the power grid’s operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods.
Highlights
Wind power belongs to sustainable and clean energy sources, which play a vital role in reducing environment pollution and addressing the energy crisis
Wind power outputs are quite difficult to predict because they are derived from wind speeds, which always vary irregularly and greatly [1]
If we assume a nonlinear function as ẑ = f (ε 1, ε 2, · · ·, ε p ), its linear affine arithmetic form can be expressed as f a ( ε 1, ε 2, · · ·, ε p ) = ε 0 + z1 ε 1 + z2 ε 2 + · · · + z p ε p + z k ε k, (22)
Summary
Wind power belongs to sustainable and clean energy sources, which play a vital role in reducing environment pollution and addressing the energy crisis. This information, is always crude since the parameters of random variables’ distribution functions are inaccurate, and the probabilistic power flow methods always underestimate power flow results due to their inherent limited sampling space To overcome this problem, the interval power flow approach uses intervals, whose bound information is acquired, to model the uncertainties, building the interval power flow model and obtaining the conventional ranges of the power flow variables. To solve the IPFWF model, the AA-based method and the OSM are modified for considering the models of wind farms, and thereby employed to acquire results of the interval power flow variables. To accomplish this target, in the present paper, we conducted the relevant work listed below.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
