Layout Optimization of Distributed Photovoltaic Facilities on Expressway Based on Improved Differential Evolution for Multi-objective Optimization Algorithm

Reasonable photovoltaic-energy utilization and facility-layout optimization are crucial for the low-carbon construction of expressways and the approach of net-zero emission targets. From the perspective of energy self-consistency and based on the lowest economic cost as the objective of layout optimization, a photovoltaic layout optimization model was constructed by considering the selection and construction costs, location-distance cost, operation and maintenance costs, and additional energy gain. The layout area, density setting, layout number, and inclination azimuth of photovoltaic facilities were restricted. To avoid uneven spatial distribution and missing solutions, a control-parameter adjustment strategy, an elite-selection double-variation strategy, and a dynamic crowding-distance ranking strategy were introduced, and improved differential evolution for multiobjective optimization (IDEMO) was proposed to solve the layout scheme. Based on the geographic information and road network data of the G40 Shanghai-Shaanxi Expressway (Shaanxi section) and its surrounding areas, the layout scheme and power-generation effect of photovoltaic facilities on the roof of the service area and road slope area are discussed. The results show that roof area, solar-radiation intensity, slope location, and facility-inclination orientation are key factors that determine the location layout of expressway photovoltaic facilities. When the inclination was set to 18°-21° and the slope was oriented south, the energy conversion and power-generation effects were the best. Moreover, the performance of the IDEMO algorithm was compared with that of the standard differential evolution for multiobjective optimization algorithm, non-dominated sorting genetic algorithm, particlc swarm optimization algorithm, chaos cat swarm optimization algorithm, and tabu search algorithm. Results of comparative analysis show that the IDEMO algorithm offers better searching ability and convergence accuracy under each benchmark function, as well as obtains the global optimal solution more easily than the other algorithms. Moreover, it offers better optimization efficiency, optimization credibility, and overall optimization performance. The proposed method can provide a theoretical basis and reference for the low-carbon construction of expressways and the approach of zero-carbon targets. © 2024 Chang'an University. All rights reserved.