Arrangement optimization based on shadow adaptation for strip PV arrays along electrified railways corridors

Abstract

Arranging photovoltaic (PV) modules on the surface of embankment slopes along railway corridors can effectively harness PV energy from idle land along the corridor, which is crucial for promoting the green and low-carbon development of railways. Owing to the vast length and wide span of railway corridors, PV modules are typically arranged as strip photovoltaic (SPV) arrays in a linear layout. Furthermore, the complex climatic and sunlight conditions along long-distance railway corridors result in significant SPV mismatch losses. These unique challenges, which are absent in conventional PV power stations, render conventional array connections and optimization methods unsuitable for SPV arrays. Hence, this study proposes a general approach for modeling the output characteristics of arbitrary PV arrays as well as a new parameter, i.e., shadow adaptation, to quantify the adaptability of PV arrays under different shading conditions. Based on two key optimization objectives for SPV arrays — shadow adaptability and total cable length — this study proposes, for the first time, a layout optimization algorithm for SPV arrays, i.e., the shadow adaptation genetic algorithm (SAGA), which employs a genetic approach to optimize the SPV array arrangement. Simulation results show that an SPV array layout optimized by the SAGA achieves a maximum increase in output power by 17.08% and a 4.30% improvement in shadow adaptation compared with the conventional total-cross-tied configuration under various typical shading conditions.