Complementary EV Scheduling in Peer-to-Peer Energy Markets

Abstract

The adoption of electric vehicles (EVs) is accelerating and is expected to continue growing alongside the decentralization of energy markets. This paper presents an EV charging algorithm tailored for decentralized peer-to-peer (P2P) transactive energy systems and evaluates its cost minimization performance against traditional methods. To enhance computational efficiency, the nonlinear charging characteristics of EVs are linearized using segmentation techniques. The study incorporates multiple EV types and real-world parking lot data to reflect realistic scenarios. Vehicle-to-Grid (V2G) functionality is also explored, with the problem formulation simplified by eliminating binary variables, converting it from a mixed-integer to a continuous optimization problem. Different case studies are conducted to test the effectiveness of the proposed algorithm. The simulation results demonstrate a 16% reduction in total charging costs, which is 4.5% better than the nearest alternative. Additionally, the proposed approach consistently operates within a 10-minute runtime constraint for real-time applications, achieving runtimes under 10% of those observed in dynamic models, which often exceed the limit on typical weekdays. The V2G scenario yielded only a 1% cost benefit over the Grid-to-Vehicle (G2V) case, indicating that meaningful gains from V2G schemes depend heavily on user profiles and market pricing conditions.