Social Networking and Consumer Preference Based Power Peak Reduction for Safe Smart Grid

Abstract

Efficient power peak reduction is a classic scheduling target to make smart grid more safe. To handle multiple energy consumers, energy management are usually built based on game theory. Despite their effectiveness, they do not consider consumer preferences, which are however important in developing salient scheduling frameworks. This work explores consumer preference based social networking in computing optimized schedules to facilitate the incorporation in energy management. We propose the consumer preference driven intelligent energy management technique for smart cities using game theoretic social tie. In our technique, social communities are constructed based on the preference of electricity usage. Community pricing strategy is adjusted during each time period through leveraging cooperative game theory. The simulation results demonstrate the effectiveness and efficiency of the proposed intelligent energy management technique.