Autonomous drone charging station planning through solar energy harnessing for zero-emission operations

Abstract

• Solar energy from building envelopes can extend UAVs coverage and reliability in last-mile delivery applications. • This integration can omit GHG emissions in parcel delivery while improving building energy efficiency. • A multi-objective optimization model is proposed that maximizes UAV coverage and minimizes the total cost of energy and decarbonization. • A flexible energy use model is applied to a digital twin to generate minimum-energy 3D trajectories. • RI-SHOT multi-objective optimization algorithm is used to solve all the delivery demands. • GHG emissions for the entire UAV charging network are omitted compared to a grid-connected operation. The introduction of Unmanned Arial Vehicles (UAVs) in smart city operations is considered a sustainable technological solution due to the promised significant greenhouse gas emission reductions. This study developed an integrated multi-objective charging infrastructure coverage optimization model that integrates UAV-based operations with solar energy harnessing from building envelopes. This model maximizes UAVs’ coverage range and minimizes the total cost of energy and decarbonization. The model is based on a flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory. We also developed an origin-destination (O-D) demand geo-referenced in a digital-twin model to replicate real-world operation. Overall, 12,532 simulated daily missions in a large-sized city are modelled. The results show that the proposed system can eliminate GHG emissions. Furthermore, the system can significantly reduce the decarbonization price through associated savings and excess generated electricity. The proposed approach demonstrates avenues to advance smart cities and capitalize on renewable energy.