In view of the problem that the computing time for solving a security-constrained stochastic dynamic economic dispatch(DED)model of large-scale power system is too long

a method using approximate dynamic programming algorithm is proposed to quickly solve security-constrained stochastic DED problem of power system without pumped storage plants. A virtual storage model(VSM)for the stochastic DED problem is established. In this model

system's positive spinning reserve capacity of current time interval is taken as storage variable

then the system's state transition equation of relations between adjacent time intervals is established

including the security constraints of transmission lines and sections. Based on wind farms' power output forecast curves

error scenarios are generated by the Latin hypercube sampling method. In order to train the value functions of each time interval by scanning the error scenarios one by one

the quadratic programming model of each period should be solved successively. After getting the convergence value functions

the final generation dispatch scheme will be obtained by solving the optimal dispatch model with the forecast scenario. This method has realized the decoupling of each scenario and each time interval of the stochastic DED model by decomposing a large-scale of optimization problems into a series of small-scale optimization problems

effectively increasing the computing speed of solving the stochastic DED model of large-scale power system. Test results on a provincial power system demonstrate the feasibility and effectiveness of the proposed method by comparison with the scenario method and robust optimal dispatch method. This work is supported by National Basic Research Program of China(973 Program)(No. 2013CB228205)

National Natural Science Foundation of China(No. 51207056)and Guangdong Provincial Natural Science Foundation of China(No. 2015A030313233).