针对居民住宅屋顶形态多样不规则的特点,以及常规光伏阵列布局普遍依赖人工排布或穷举搜索等布局方法,布局效率低且难以实现最优配置的问题。该文提出一种住宅屋顶光伏阵列优化方法,以平准化度电成本（LCOE）最小化为目标,构建顾及组件自遮阳效应和建筑构件限制的优化模型。利用遗传粒子群算法（GA-PSO）来确定不规则平屋顶上光伏阵列的最佳布局方案。最后,通过若干居民住宅典型不规则平屋顶为研究对象进行光伏布局优化研究。结果表明,提出的光伏阵列布局约束优化方法通过精确分析面板布局的设计参数,为屋顶光伏系统在复杂环境下的合理空间配置提供参考建议,显著提升光伏系统性能,并降低成本,达到成本与效益的最佳平衡,最优的平准化度电成本（LCOE）为3.86元/（kW·h）。
Abstract
Given the diverse and irregular shapes of residential rooftops, and the common reliance on manual layout or exhaustive search methods for conventional photovoltaic array configurations—which suffer from low efficiency and difficulty in achieving optimal arrangements—this paper proposes an optimization method for residential rooftop PV arrays. Aiming to minimize the levelized cost of energy (LCOE), it constructs an optimization model that accounts for both the self-shading effects of modules and architectural constraints. PV array configuration on irregular flat roofs is optimized using the Genetic Particle Swarm Algorithm (GA-PSO). Lastly, a number of typical irregular flat roofs of residential dwellings are used as research objects in the PV layout optimization study. As a result of precisely analyzing the panel layout’s design parameters, the PV array layout constraint optimization method presented in this paper offers reference suggestions for the appropriate spatial configuration of rooftop PV systems in complex environments. It also significantly increases the PV system’s performance and lowers its cost, achieving the ideal balance between cost and benefit. The optimal levelized cost of electricity, or LCOE, is 3.86 CNY/（kW·h）.
关键词
太阳能 /
建筑物 /
优化 /
屋顶分布式光伏 /
遗传粒子群算法 /
平准化度电成本
Key words
solar energy /
buildings /
optimization /
distributed rooftop photovoltaics /
genetic algorithm-particle swarm optimization algorithm /
levelized cost of electricity
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