Title :
Cooperative reinforcement learning algorithm to distributed power system based on Multi-Agent
Author :
Gao, La-Mei ; Zeng, Jun ; Wu, Jie ; Li, Min
Author_Institution :
Coll. of Electr. Power, South China Univ. of Technol., Guangzhou, China
Abstract :
With the development of renewable energy technology, the distributed wind-PV power system has a wider application. This paper proposes a distributed wind- PV power system based on Multi-Agent, whose main character is energy management, and describes the multi-agent cooperative reinforcement learning process using the joint action learning pattern as the cooperative strategy. The experiment of a distributed wind-PV power system shows the efficiency.
Keywords :
distributed power generation; energy management systems; learning (artificial intelligence); multi-agent systems; photovoltaic power systems; wind power plants; cooperative reinforcement learning algorithm; distributed power system; distributed wind-PV power system; energy management; multiagent systems; renewable energy technology; Educational institutions; Energy management; Learning; Medical services; Power electronics; Power system protection; Power system security; Power systems; Renewable energy resources; Wind; Q-learning; distributed power; joint action learning; multi-agent; reinforcement learning;
Conference_Titel :
Power Electronics Systems and Applications, 2009. PESA 2009. 3rd International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-3845-7
