Mechanism Design for Aggregated Demand Prediction in the Smart Gri

This paper presents a novel scoring rule-based mechanism that encourages agents to produce costly estimates of future events and truthfully report them to a centre when the budget for payments to the agents is itself determined by their reports. This is applied to a model of aggregated demand prediction within a microgrid where, given estimates of future consumptions, an aggregator must optimally purchase electricity for a set of homes, each represented by self-interested, rational home agents. This in turn reduces the need for costly standby generation within the grid. The aggregator has prior information about the amount each home will consume, and determines the amount to pay each agent based on savings resulting from using the agents’ reported information, over its own prior information. Agents use sensory information regarding their property and its occupants to generate these estimates, which they transmit to the aggregator using smart grid technology. The proposed mechanism is dominant strategy incentive compatible and empirical evaluation shows that it encourages agents to exert effort in producing precise estimates. We show that the mechanism is ex ante individually rational for the aggregator, and that it outperforms a simpler mechanism whereby savings are distributed evenly.