Wind power is a kind of new

clean

renewable and widely used energy. Utilizing wind power in an industrial microgrid is of great significance in energy conservation and emission reduction. This paper researches on an industrial microgrid with wind power

establishes a multi-stage model with self-generation scheduling optimation for an industrial microgrid

which aims at minimizing the energy costs through adjusting the production and power generation schedule properly based on robust optimization

and then utilizes Benders decomposition algorithm to solve the model. Numerical tests on the model are given with data of an industrial microgrid

and it is confirmed that the fluctuation of wind power and load is smoothed by the model effectively and the electric energy costs are decreased. Additionally

the model is proved to perform better than the deterministic optimal model under the worst condition

and the balance of conservatism and optimality can be adjusted by different uncertainty prices according to the requirement. This work is supported by National Natural Science Foundation of China (No. 61221063

No. U1301254

No. 61174146

No. 61304212

No. 61473218) and National High Technology Research and Development Program of China (863 Program) (No. 2012AA011003).