Using Reinforcement Learning to Make Smart Energy Storage Source in Microgrid

Subject Areas : International Journal of Smart Electrical Engineering

Sadegh  Etemad ¹ (School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.)
Nasser  Mozayani ² (School of Computer Engineering, , Iran University of Science and Technology, Tehran, Iran.)

Received: 2016-01-23 Accepted : 2016-01-23 Published : 2015-04-01

Keywords: Microgrid, Rreinforcement learning, Renewabl energy, Battery, Q_learning, Intelligence agent,

Abstract :

The use of renewable energy in power generation and sudden changes in load and fault in power transmission lines may cause a voltage drop in the system and challenge the reliability of the system. One way to compensate the changing nature of renewable energies in the short term without the need to disconnect loads or turn on other plants, is the use of renewable energy storage. The use of energy storage improved electrical stability, power quality and improve the peak power load. In this paper, we have used the reinforcement learning to present an optimal method for charge and discharge the consumer battery. In this way the uncertainty of production due to the random nature of wind energy is improved. Simulation results indicate not only the use of renewable energy and battery is successfully enhanced but also the cost of annual payments and peak consumption times is reduced.