Enhancing Energy Sustainability in Intelligent Highways: An Optimization Approach for High-Entropy Energy Harvesting Devices Layouts

The optimization of energy harvesting device layouts along highways is crucial for advancing energy sustainability in intelligent transportation systems. However, previous studies commonly ignored the impact of environmental changes on the generating power of high-entropy energy harvesting devices, resulting in low energy utilization efficiency and energy wastage. This study introduces a model to optimize the layout of renewable energy facilities along highways, integrating three types of renewable energy sources: vibration, thermoelectric, and solar-raindrop energy. The model estimates the power generation capabilities of each device by accounting for various environmental factors, including traffic flow, temperature fluctuations, and rainfall. A genetic algorithm is designed to solve this model. A case study on Beijing's Jingxiong Highway demonstrates the model's effectiveness, achieving an average energy self-sufficiency ratio of 61.9% and an energy wastage ratio of 5.42%. These findings highlight the potential of our approach to promote sustainability on intelligent highways.