Social-Aware Learning-Based Online Energy Scheduling for 5G Integrated Smart Distribution Power Grid

Abstract

A 5G integrated smart distribution power grid brings a new paradigm shift to realize base station (BS) operation cost reduction, efficient renewable energy utilization, and stable energy supply. However, energy scheduling still faces some major challenges, such as coupling between energy sharing and energy trading, dimensionality curse, and intertwinement of social network attributes and BS load. To tackle these challenges, we propose a social-aware learning-based online energy scheduling (SNES) algorithm, which minimizes BS operation cost minimization under the constraints of energy supply stability. SNES leverages a deep neural network (DNN) to learn the action-state value of energy scheduling and intelligently adjusts purchased, sold, and shared energy based on only casual information. Moreover, SNES achieves social awareness by approximating the nonlinear interconnection between energy scheduling and quality of service (QoS) requirements of social network services. Simulation results verify the superior performance of SNES compared with state-of-the-art energy scheduling algorithms.