An Integrated Charging and Computation Scheduling of Electric Vehicles in Edge Computing System

The development of Electric Vehicles (EVs) in smart city infrastructure has ushered in new opportunities for vehicle automation but poses challenges to the electricity load (EL) and computational load (CL) on the smart grid. While existing studies utilized Fog/Edge computing as a decentralized solution to mitigate the EL and CL on the smart grid, these often overlook the idle computational resources of EVs. Therefore, Electric Vehicle Edge Computing (EVEC) leverages EVs as the extended computational resources of edge compute nodes called edge servers. Our proposed architecture strategically schedules EVs to targeted electric vehicle parking lots (EVPLs), to minimize the EL and CL of all EVPLs, EVs based on EV preference within the smart grid as a whole. To handle the EV scheduling, we develop a greedy-based algorithm to tackle the complexity of the optimization problem. While the greedy algorithm ensures computational efficiency and effective local optimization, it does not always guarantee global optimality. Upon EV arrival at an EVPL, we develop an algorithm based upon the convex-concave procedure (CCP) and derive the Karush-Kuhn-Tucker (KKT) conditions to determine the optimal devoted computational resources of EVs. Furthermore, our model introduces an incentive mechanism that encourages EVs as vehicular fog computing (VFC) nodes to fully devote their computational resources, while ensuring the profitability of the Edge Computing Orchestrator (ECO). Through comprehensive simulations, we demonstrate the effectiveness of our approach by completely utilizing the charging time of EVs for computation at EVPLs, which brings benefits to EVs, edge servers, and ECO by optimizing the devoted computational resources based upon the CL of edge servers.