In recent years

with the introduction of wind power forecast errors assessment policies

energy storage systems are widely used in engineering as an effective method to improve the forecast errors. Estimating wind power forecast errors effectively can not only provide a reference for the scheduling and operation of wind power integration systems

but also guide the reasonable configuration of energy storage system for wind farm. Hence

it is necessary to estimate the wind power forecast errors. Aiming at two common wind power forecast error estimation methods

that is

the Gaussian mixture distribution fitting method and the eigenvalue extraction method

the principle and process of these two methods are introduced in detail. Moreover

these two methods are verified by the actual wind farm data

and the estimation intervals of two methods are compared according to the impact on energy storage capacity configuration. In order to give consideration to the accuracy and validity of error estimation interval and guide the configuration of energy storage system effectively

based on Gaussian mixture model (GMM) and combined with Markov model (MM)

an MM-GMM based optimized forecast error interval estimation algorithm is proposed and verified