Providing robust and low-cost edge computing in smart grid: An energy harvesting based task scheduling and resource management framework

Recently, one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components (such as monitoring systems for renewable energy power stations). To solve the problem, we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid. First, we formulate an energy consumption minimization problem with regard to task offloading, time switching, and resource allocation for mobile devices, which can be decoupled and transformed into a typical knapsack problem. Then, solutions are derived by two different algorithms. Furthermore, we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems. Finally, we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies, number of energy storage units, and renewable energy utilization. The simulation results show the efficiency and superiority of our proposed framework.