Game-Theoretic Market-Driven Smart Home Scheduling Considering Energy Balancing

Abstract

In a smart community infrastructure that consists of multiple smart homes, smart controllers schedule various home appliances to balance energy consumption and reduce electricity bills of customers. In this paper, the impact of the smart home scheduling to the electricity market is analyzed with a new smart-home-aware bi-level market model. In this model, the customers schedule home appliances for bill reduction at the community level, whereas aggregators minimize the energy purchasing expense from utilities at the market level, both of which consider the smart home scheduling impacts. A game-theoretic algorithm is proposed to solve this formulation that handles the bidirectional influence between both levels. Comparing with the electricity market without smart home scheduling, our proposed infrastructure balances the energy load through reducing the peak-to-average ratio by up to 35.9%, whereas the average customer bill is reduced by up to 34.3%.