Negotiated Learning for Smart Grid Agents: Entity Selection based on Dynamic Partially Observable Features

An attractive approach to managing electricity demand in the Smart Grid relies on real-time pricing (RTP) tariffs, where customers are incentivized to quickly adapt to changes in the cost of supply. However, choosing amongst competitive RTP tariffs is difficult when tariff prices change rapidly. The problem is further complicated when we assume that the price changes for a tariff are published in real-time only to those customers who are currently subscribed to that tariff, thus making the prices partially observable. We present models and learning algorithms for autonomous agents that can address the tariff selection problem on behalf of customers. We introduce Negotiated Learning, a general algorithm that enables a self-interested sequential decision-making agent to periodically select amongst a variable set of entities (e.g., tariffs) by negotiating with other agents in the environment to gather information about dynamic partially observable entity features (e.g., tariff prices) that affect the entity selection decision. We also contribute a formulation of the tariff selection problem as a Negotiable Entity Selection Process, a novel representation. We support our contributions with intuitive justification and simulation experiments based on real data on an open Smart Grid simulation platform.