Research on Collaborative Optimisation of Smart Inspection and Charging Networks for Distribution Network UAVs Based on Digital Twins

ABSTRACT

Addressing the issues of limited endurance and load capacity in unmanned aerial vehicle (UAV) inspection of distribution networks, the lack of dynamic adaptability in charging networks and insufficient multi‐objective collaborative optimisation, this paper proposes a four‐dimensional collaborative system architecture consisting of ‘digital twin‐UAV‐charging facility‐communication network’. It clarifies the essential differences between digital twins and traditional simulation/data‐driven platforms and constructs a full‐element, high‐fidelity, closed‐loop iterative digital twin operation mechanism. This architecture achieves precise replication and trend prediction of inspection scenarios, equipment status and grid operation through full‐element modelling of digital twins, real‐time synchronisation between virtual and real worlds, and dynamic deduction. On this basis, a multi‐objective optimisation model is constructed with the goals of minimising the total life cycle cost of the charging network, minimising the waiting time for UAV charging, and minimising the operational fluctuations in the distribution network. A dynamic weight coefficient is introduced to coordinate the objectives and achieve balance. An innovative approach is proposed to integrate digital twin deduction with an improved nondominated sorting genetic algorithm (DT‐NSGA‐II). This method enhances solution efficiency and convergence accuracy through heuristic population initialisation driven by twin data, adaptive genetic operations involving virtual–real interaction, and population optimisation strategies based on closed‐loop feedback. Additionally, the computational complexity and convergence properties of the algorithm are analysed. A digital twin simulation verification is conducted using a 120‐km ² complex terrain distribution network in East China as a case study, and cross‐validation is performed with actual distribution network operation and maintenance data. The results show that the robustness and adaptability of the proposed method are improved by 40% (quantified based on multi‐objective optimisation standard evaluation metrics) compared to traditional methods in complex scenarios. This provides an engineering paradigm for the precise configuration and dynamic optimisation of UAV intelligent inspection and charging networks in distribution networks.