Research On Day-ahead Scheduling Model Of Wind Farms Integrated Power System Considering Source-side And Load-side Uncertainties

In recent years,the increasing depletion of traditional fossil energy and the continuous development of smart grid construction result in the promotion of wind power and other renewable energy sources,and provided technical support for the participation of flexible loads in power grid dispatch.However,with the growth of installed capacity of wind power and the exploration of flexible load potential,the uncertainties of wind power output and flexible load response increase the volatility of power grid supply and demand,which poses severe challenges to the safe and stable operation of the system.This paper deeply analyzes the uncertain characteristics of the source-load side in the wind power system.The double uncertainties theory is used to model the wind power forecast error and flexible load response uncertainty.A multi-objective optimization scheduling model for wind power system based on fuzzy stochastic constraints is constructed.The main achievements are as follows:(1)Considering the uncertainty of wind power forecasting error,a day-ahead scheduling model with wind power system considering the source side uncertainty is proposed,and kernel function is utilized to fit the wind power output.Firstly,a wind power output model based on kernel function fitting is constructed by analyzing the results of parameter distribution and kernel function fitting to wind power forecasting errors.Secondly,taking into account the influence of wind power uncertainty on power balance,standby system,and line flow,a random chance constraint is introduced.Then,a day-ahead dispatching model in wind power system with the goal of minimizing the operating cost is established.The result of the simulation shows that the stochastic chance constraint model achieves better performance comparing to the conservative definite constraint model,which improves the economic benefits of the system based on the acceptable security risks.(2)Based on the detailed analysis of the price-type and incentive-type load response characteristics,a power system optimization scheduling model considering the uncertainty of demand response is proposed.Firstly,this paper adopts fuzzy mathematics theory to establish the transfer uncertainty model based on consumer psychology for price-type demand response,and establishes the response uncertainty model based on fuzzy parameters for incentive type.Secondly,considering the influence of demand response uncertainty on power balance,standby system,and line flow,a fuzzy opportunity constraint is introduced.Finally,taking the minimization of the total cost as the objective function,a day-ahead scheduling model of power system considering the uncertainty of demand response is proposed.The simulation result shows that the demand response participation in the scheduling can partially substitute the power generation side resources,which is beneficial to the economic operation of the conventional units and improve the economic benefits of the system.(3)Considering the uncertainty of wind power output and load response,aiming at the lowest cost of economic operation and carbon emissions,a multi-objective optimization scheduling model of power system based on fuzzy stochastic chance constraints is constructed.Firstly,After analyzing the characteristics of multi-objective problems and common solving methods,this paper selects the method of maximum membership degree to transform economic and environmental multi-objective optimization problems into singleobjective optimization problems with the largest membership function.Secondly,considering the fuzzy random variables in the model,fuzzy random chance constraints are introduced to improve the deterministic constraints in some traditional scheduling models.Finally,the corresponding equivalence classes are proposed for the fuzzy stochastic chance constrained model,and the dual uncertainty problem is transformed into the deterministic problem solving.The result of the simulation shows that the multi-objective optimal scheduling considering economic efficiency and low carbon is more in line with the expected result of the practitioner from the respective of the overall system.