Because of the diversity of microsources and operation modes in a microgrid

secondary frequency control faces great challenges from the uncertainty of system parameters of the microgrid. To solve the problem

a Qlearning intelligent algorithm integrated in the hierarchical multiagent model is proposed. Firstly

by the proposed method

the power to be regulated

which is called the microgrid regulation error (MRE)

is dynamically calculated in the secondary control for realtime operation. Secondly

the generation schedule of distributed generators and batteries is modified in realtime with the MRE by the fuzzy theory and particle swarm optimization method by taking both economy and environmental benefits into consideration. Finally

a Qlearning algorithm based multiagent hybrid energy management system for the microgrid simulation platform in terms of clientserver frame is established in C++ Builder. Simulation results have verified that by the proposed QLearning method

secondary intelligent and adaptive frequency control is realized under the condition of variable structure and parameters in the microgrid. This work is supported by National Basic Research Program of China (973 Program) (No. 2009CB219708)

National Natural Science Foundation of China (No. 51077033)

Specialized Research Fund for the Doctoral Program of Higher Education (No. 201301111110005)

and the Introduction of Innovative Team of Guangdong Province Project (No. 2011N015).