融合多维度多目标约束的集中式光伏电站迭代排布优化设计方法
An iterative layout optimization design method for centralized photovoltaic power stations that incorporates multi-dimensional and multi-objective constraints
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摘要: 针对复杂地形集中式光伏电站排布设计多约束深度耦合、单值寻优局限的问题,提出融合多维度多目标约束的迭代排布优化设计方法。构建硬性边界、可变调控与目标驱动三层融合约束体系,引入全参数网格遍历与帕累托非支配排序策略,基于地形分析与发电量仿真进行参数枚举及多目标筛选。结果表明,优化后容量最大方案达79.8 MWp,较初始提升16.5%;发电量最高方案首年发电量达11270万kWh,提升12.0%;平准化度电成本最低方案降至0.283元/kWh,降低10.2%。结果表明该方法可有效揭示三层约束间的耦合路径,为复杂地形下集中式光伏电站排布设计的容量发电量与度电成本多目标协同决策提供优化框架与量化依据。Abstract: To address the limitations of multi-constraint deep coupling and single-value optimization in the layout design of centralized photovoltaic power stations in complex terrains, an iterative layout optimization design method integrating multi-dimensional and multi-objective constraints is proposed. A three-layer integrated constraint system combining hard boundaries, variable regulation, and goal-driven strategies was constructed. The full-parameter grid traversal and Pareto non-dominated sorting strategies are introduced. Parameter enumeration and multi-objective screening were carried out based on terrain analysis and power generation simulation. The results show that the optimized maximum capacity scheme reaches 79.8 MWp, an increase of 16.5% compared to the initial design; the highest power generation scheme has a first-year power generation of 112.7 million kWh, an increase of 12.0%; and the lowest levelized cost of electricity scheme drops to 0.283 yuan/kWh, a reduction of 10.2%. The results indicate that this method can effectively reveal the coupling paths among the three layers of constraints, providing an optimization framework and quantitative basis for the multi-objective collaborative decision-making of capacity and power generation cost in the layout design of centralized photovoltaic power stations in complex terrains.
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