E-Bus-Based Standby Energy Sharing for EVs in the Event of Large-Scale Grid Outage

Electric vehicles (EVs), which have become one of the most important commuting vehicles in the world, heavily depend on the support of robust and efficient grids. Many researchers focus on optimizing the charging of EVs by the grid. However, they overlook the emergency charging when the large-scale grid fails. Inspired by the DC-2-DC technology, EVs can achieve vehicle-2-vehicle (V2V) energy sharing between each other, which inspires us to study leveraging the significant number of electric buses (e-buses) as effective and cost-efficient standby energy to charge low-power EVs when a large-scale grid outage occurs, whereas the feasibility, scheduling algorithm, and performance of e-bus-based V2V energy sharing remain open questions. With this in mind, we first reveal that it is feasible for e-buses to serve as effective standby power for low-power EVs, after careful analysis of real-world data. Then, we conduct a data-driven estimation of the energy consumption to determine the volume of the V2V shared energy for large-scale EVs. Accordingly, we formulate the e-bus-based V2V energy sharing problem, whose two objectives are to maximize the total number of the sufficiently charged EVs and to minimize the distance of these EVs moving to the e-bus for charging. The first objective is to charge more low-power EVs with the limited energy volume of the e-buses and the second objective is to reduce the range anxiety of the low-power EV drivers. Observing that existing algorithm is time-consuming or inefficient, which cannot be applied for the problem instance with a large number of e-buses and EVs, we design a low-complexity approximation algorithm for the problem. Both theoretical analysis and comprehensive data-driven evaluation demonstrate that the proposed algorithm, 1) significantly optimizes the optimization objectives by 23.6% and 15.8% on average, and up to 34.8% and 34.0% in extreme cases, compared with the existing approximation algorithm and 2) achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> execution speed improvement compared to the linear programming (LP)-based algorithm and evolutionary learning (EL) algorithms. The results demonstrate that the proposed algorithm achieves a better overall performance and makes it a promising solution for large-scale problem instances.