Optimizing Distribution Networks with Photovoltaic Unmanned Aerial Vehicles

The rapid integration of photovoltaic (PV) systems into modern power distribution networks poses significant operational challenges, particularly with regard to generation intermittency, voltage instability, and system reliability. This paper presents an advanced optimization model that 3 incorporates both stationary PV generators and Photovoltaic Unmanned Aerial Vehicles (PUAVs) to mitigate these issues. The proposed model utilizes a multi-objective framework based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III), optimizing key objectives such as total power loss minimization, renewable energy maximization, and system resilience enhancement. Here, renewable energy maximization specifically refers to increasing the proportion of photovoltaic energy generation that is effectively utilized to meet the total load demand, ensuring optimal integration of renewable resources within the system. The mathematical model comprises energy balance constraints, PUAV power transfer, voltage stability, and dynamic dispatch of PUAVs. The PUAVs, with an energy capacity of 50 kWh and a flight range of 100 km, are treated as mobile storage units capable of transferring up to 10 kW of energy between nodes in the distribution network. The optimization algorithm produces a Pareto-optimal set of solutions, balancing trade-offs between competing objectives. Simulation results, based on a modified IEEE 123-bus system, show a 25% reduction in power losses, a 30% increase in renewable energy utilization, and a significant improvement in 16 system reliability, particularly during peak demand periods. The model’s scalability and dynamic nature offer a robust solution to the challenges posed by intermittent renewable energy sources.