The rapid increase in highway tunnel mileage also signifies a gradual rise in operational costs, and the pressing issue of high electricity operation costs for highway tunnels urgently needs to be addressed. To reduce the electricity operation costs of highway tunnels and achieve energy conservation and emission reduction, it is essential to consider optimizing the energy structure under the "dual carbon" background. This involves exploring the application prospects of renewable energy supply systems in highway tunnels and establishing a wind-solar-storage complementary power generation system. Taking a 498 m long highway tunnel load as an example, an optimization based on an improved PSO (particle swarm optimization) algorithm was conducted. The goal was to minimize the full lifecycle costs of equipment construction and maintenance, with constraints on the power shortage load rate and storage capacity, specifically for the wind-solar-storage complementary system. The results show as follows. The improved discrete adaptive particle swarm algorithm obtained the optimal solution after the 20th iteration, while the standard particle swarm algorithm reached the optimal solution near the 300th iteration, indicating a stronger optimization capability of the discrete adaptive particle swarm algorithm. Compared to the standard particle swarm algorithm, the improved discrete adaptive particle swarm algorithm reduced the investment and usage costs of the wind, solar, and storage equipment by 578 300 yuan, approximately 17.37%. Compared to the annual electricity cost of the example tunnel, which is 515 000 yuan, the full lifecycle cost of the wind-solar-storage complementary system is 3 328 800 yuan. The investment cost will be recouped within 7 years, and the investment return rate of this wind-solar complementary system is 10.47%. Over the 20-year lifespan of the equipment, the wind-solar-storage complementary power generation system will save 6 971 200 yuan in electricity expenses.