Mobile energy hub planning for complex urban networks: A robust optimization approach

The electricity grid with a high penetration of renewable energy can enable travelers to travel free of emissions using state-of-the-art electric vehicles (EVs). Extensive electric vehicle demands at the peak times, and an increase in electricity consumption due to population growth, have led to higher utility infrastructure investments. Mobile energy hubs i.e. clustered EVs parked in a dedicated location, can be used as an innovative demand-side management solution to reduce long-term utility infrastructure investments. They can store and release electricity to the grid based on consumer demand. However, a scientific planning approach for grid integration has been overlooked. Accordingly, this study proposes a comprehensive framework required to plan and develop mobile energy hubs based on optimization of life cycle cost, access distance and parking duration considering the temporal variation of EV recharging demands. The results of the study show that the framework developed can minimize lifecycle costs, and improve infrastructure utilization by accounting for the interests of all stakeholders. The total cost with the proposed robust optimization model under uncertainties of 50% is lesser than the robust cost calculated from a scenario-based approach. Furthermore, the developed framework is useful for recharging infrastructure planners to devise the deployment schedules and attract investors based on the economic viability of the planned strategies. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study proposes a comprehensive framework based on optimization of life cycle cost, access distance, and parking duration considering the temporal variation of EV recharging demands to develop mobile energy hubs. The framework aims to minimize lifecycle costs, improve infrastructure utilization, and attract investors based on economic viability of planned strategies. The robust optimization model under uncertainties is shown to have lower total costs compared to the scenario-based approach.