Abstract
In this paper, we describe an adaptive trading agent which can sell and buy electric power effectively in a locally produced and consumed electric energy network. Recently, to reduce CO2 emissions, and to overcome the problem that fossil fuels will run out one day, we are required to use renewable energies. However, traditional centralized electric network are not suitable to renewable energies. Distributed electric network, such as a micro grid and an ECONET is considered to be an alternative choice. In our model, trading agents manage a storage battery in a minimal cluster, which is a node of local cluster in ECONET. The agent learns a trading strategy by maximizing future cumulative reward based on reinforcement learning method. We build autonomous trading agents based on the natural actor-critic method. Several experiments show that the adaptive trading agents can reduce useless energy consumption and a deficiency in most cases.
