Dealing with uncertainty in the smart grid: A learning game approach

We model the smart grid as a decentralized and hierarchical network, made up of three categories of agents: suppliers, generators and captive consumers organized in microgrids. To optimize their decisions concerning prices and traded power, agents need to forecast the demand of the microgrids and the fluctuating renewable productions. The biases resulting from the decentralized learning could create imbalances between demand and supply leading to penalties for suppliers and for generators. We analytically determine prices that provide generators with a guarantee to avoid such penalties, transferring risk to the suppliers. Additionally, we prove that collaborative learning, through coalitions of suppliers among which information is shared, minimizes the sum of their average risk. Simulations run for a large sample of parameter combinations, using external and internal regret minimization, show that the convergence of collaborative learning strategies is clearly faster than that resulting from individual learning. Finally, we analyze the suppliers’ incentives to organize in a grand coalition versus multiple coalitions, and the tightness of the learning algorithm’s theoretical bounds.