Research on the Optimal Scheduling Model of Polymorphic Energy on Highways Considering Source Load Uncertainty

With the increasingly severe global climate change, reducing carbon emissions has become an urgent task. In order to achieve energy transformation and the development goals of “carbon peaking and carbon neutrality”, the application of polymorphic energy in the field of highways is increasingly valued.This article will explore in depth the construction technology of the polymorphic energy application scenario set and energy efficiency optimization operation solutions in the field of highways, aiming to provide more economical and sustainable solutions for the future energy system and the integrated development of transportation and energy.Firstly, four factors including wind turbines, photovoltaics, batteries, and loads are considered. The wind turbine power generation, photovoltaic power generation, and load energy consumption models of the highway's polymorphic energy system are systematically analyzed, and corresponding scene sets are constructed. The Latin hypercube sampling method is used for scene generation, and the Kantorovich distance is considered for scene reduction, providing more effective raw data for subsequent energy efficiency optimization.Secondly, to overcome the problem of traditional particle swarm optimization algorithms easily falling into local optima due to uneven solution space distribution, the adaptive chaos particle swarm optimization algorithm (ACPSO) is adopted to optimize the operation of energy system, providing a more economical and sustainable solution for the application of polymorphic energy on highways, thereby improving system efficiency and stability. Finally, simulation experiments will be conducted based on the actual meteorological data of the Xuelin service area.The experimental results show that the scene generation and reduction method used in this article can effectively construct a set of energy consumption scenarios for system power generation. The ACPSO algorithm used in this article can avoid some of the drawbacks of traditional PSO algorithms, and has better performance. It can effectively optimize energy efficiency for polymorphic energy systems. The research results are of great significance for promoting the integration of transportation and energy development and promoting energy transformation.