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| Title | Energy Loss Reduction In Power Distribution Systems Through Intelligent Power Management Of Electric Uavs |
|---|---|
| ID_Doc | 23300 |
| Authors | Shirvani M.H.; Hafezi Y.; Esmailifar S.M. |
| Year | 2024 |
| Published | Proceeding of 8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024 |
| DOI | http://dx.doi.org/10.1109/SCIoT62588.2024.10570123 |
| Abstract | The proliferating utilization of electric drones across diverse operational domains necessitates frequent recharging of these aerial vehicles, imposing a substantial burden on the power distribution grid. This surge in demand exacerbates peak consumption levels within the distribution network, consequently leading to increased energy losses. Hence, there is an imperative need for a strategic management plan aimed at mitigating energy loss within the network. This study proposes an intelligent power management system tailored for efficiently handling the energy requirements of a large fleet of electric drones. Leveraging a mixed-integer nonlinear programming (MINLP) methodology, the proposed system optimizes the distribution of loads within the network. The framework posits drones as distributed capacitors, thus enhancing the network's efficiency. Additionally, the study investigates the distribution network's load profile under three distinct conditions. Firstly, it analyzes the network's behavior under typical operating conditions, devoid of an extensive drone presence. Subsequently, it examines the impact of drone charging on network load without optimization. Finally, by optimizing power exchange between the distribution network and drones while considering battery lifespan constraints, the study demonstrates a reduction in peak load consumption and associated energy losses within the power distribution network. © 2024 IEEE. |
| Author Keywords | Electric UAVs; Energy loss reduction; Intelligent power management; Smart city |
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