In order to reduce the output uncertainty of wind and photovoltaic power generation and make the wind-solar-battery hybrid power system schedule

the outputs of wind and photovoltaic power generation are processed into certainty variables and fuzzy error sum. An energy storage model is built for wind-solar-battery hybrid power system

in which the maximum matching degree between the planning output curve and the scheduling output curve

the most smoothness of charge and discharge power in energy storage battery and the most optimal sustainable work ability of the energy storage battery are taken as objectives using the dependent-chance goal programming method. By combining fuzzy simulation

neural networks with improved particle swarm optimization algorithm

a new hybrid intelligent algorithm is proposed to solve the model

and its recently day schedule is given. Finally

numerical examples verify the effectiveness of the proposed method in solving high-dimensional uncertain programming problems with fast convergence speed and satisfactory global and local searching ability. This work is supported by Fundamental Research Funds for the Central Universities (No. 12MS106).