Distributed learning strategies for collaborative agents in adaptive decentralized power systems

For regenerative electric power the traditional top-down and long-term power management is obsolete, due to the wide dispersion and high unpredictability of wind and solar based power facilities. In the R&D DEZENT(1) project we developed a multi-level bottom-up solution where autonomous software agents negotiate available energy quantities and needs on behalf of consumers and producer groups. We operate within very short time intervals of assumedly constant demand and supply, in our case 0.5 sec (switching delay for a light bulb). We prove security against a relevant variety of malicious attacks. In this paper the main contribution is to make the negotiation strategies themselves adaptive across periods. We adapted a Reinforcement Learning approach for defining and discussing learning strategies for collaborative autonomous agents that are clearly superior to previous (static) procedures. We report briefly on extensive comparative simulation.