Abstract:

Photovoltaic aprays are affected by external environmental factors， and local shading effects limit the system's power generation efficiency. To address this issue， this paper proposes an optimization control method that utilizes Logistics chaotic sequence initialization and adaptive weighting to construct an adaptive search aquila optimizer. This algorithm can dynamically track the maximum power point of the photovoltaic array， thus achieving optimal control of traffic self consistent energy system. Experimental results under different lighting conditions on a simulation platform demonstrate that the algorithm proposed in this paper has better tracking accuracy and speed compared to perturbation observation methods， particle swarm algorithms， and traditional aquila optimizer， and is less likely to get stuck in local optimal solutions. The proposed method has a certain reference value for improving the power generation efficiency of photovoltaic power generation systems and reducing operational costs in transportation.

Key words: transportation engineering, traffic self consistent energy, photovoltaic optimization control, local shadow, aquila optimizer