Agent-Based Energy Consumption Scheduling for Smart Grids: An Auction-Theoretic Approach

The future smart grid would help to benefit both the users and the electricity providing companies from smart pricing techniques. In addition, smart pricing can be used to achieve social objectives and would in turn fluctuate wholesale market into demand side. Collecting abundant information regarding the users electricity consumption pattern is a challenging task for utility providing companies. That is, users may not be willing to expose their indigenous information without any incentive. In this paper an Optimal Energy Consumption Scheduling (OECS) mechanism is proposed to tackle this problem. An agent-based forecasting method is designed, which is capable of predicting energy consumption of each consumer with a lead-time of one hour. This forecasting is exploited to estimate the cost of buying required amount of energy from multiple suppliers. Consequently, based on the estimated required energy and cost, an auction mechanism is proposed to optimize the energy traded between consumers and multiple suppliers within a smart grid. The objectives include increased efficiency and cost reduction of electricity usage by the end users. The results and properties of the proposed OECS mechanism are studied, and it is shown that the auction technique is budget balanced for distribution of electrical energy among consumers from diverse renewable generation resources. Extensive numerical simulations are also conducted to show and prove the beneficial properties of OECS mechanism.