Study On Unit Commitment Of New Energy Base With Wind-thermal-bundled Power Transmitted By DC Islanded System

Due to the dual impacts of energy exhaustion and environmental problems,the development and utilization of clean renewable energy in large-scale such as wind is the inevitable choice to achieve renewable development.Several 10-GW wind power bases have already been built in China.From the actual operation of the wind power bases that have been officially put into operation at present,the problems of wind power consumption and distribution are facing tough challenges due to the relatively weak power grid structure and the mismatch between local load level and the construction scale of wind farms.The way of wind-thermal-bundled power transmitted by DC islanded system is an effective route to cover the shortage of exchange ability in new energy base.It is difficult for traditional unit commitment to deal with the uncertainties brought by large-scale wind power.Reasonably arranging unit commitment in the new energy base to ensure the safe and stable operation of the delivery side system and the smooth of transmission power has great significance.This paper is organized as follows:Firstly,the distribution characteristics of wind power forecast error both in power level and time series are analyzed,and a two-dimensional distribution fitting method of forecast errors based on time period-power level is proposed.Then,considering the reserve risk caused by the error of wind power forecast,a chance-constrained unit commitment model considering the uncertainty of wind power is established,and the influence of energy storage system on unit commitment is considered further.In order to improve the solution efficiency and accuracy,the model is linearized and the expression of chance constraints is transformed deterministically,so that the original problem is equivalent to a mixed integer linear programming problem,which can be solved by the optimal solver perfectly based on branch&bound algorithm.Finally,through setting different confidence levels and energy storage allocation capacity in the example,the sensitivity of the model is quantitatively analyzed from various angles,and the effectiveness of the proposed method and model is verified.