基于遗传算法的塔式光热电站功率优化

Power Optimization of Tower-Type Solar Thermal Power Plants Based on Genetic Algorithm

摘要: 近年来，清洁能源的重视推动了太阳能光伏的广泛应用。定日镜场作为光热发电的关键设施，极大影响光照效率与能量产出。本研究聚焦塔式光热电站功率最大化，通过角度分析创建了光学效率与输出功率模型，并应用遗传算法求解，揭示了功率与定日镜尺寸的关联及吸收塔位置的影响。首要利用太阳高度角等参数建立模型，结合光线追踪法优化光学效率，计算得出了高效的数据指标。其次，在确认吸收塔中心位置提升整体输出后，构建了以最大功率为目标的优化模型，并借助遗传算法有效降低了计算复杂度，找出了最大输出功率对应的最优定日镜参数。最后，在新约束条件下形成了更全面的优化模型，求解得到了最大年均输出功率。

Abstract: In recent years, the heightened emphasis on clean energy has propelled the widespread application of solar photovoltaics. Heliostat fields, as pivotal components in concentrated solar power generation, significantly influence light efficiency and energy yield. This study focuses on maximizing the power output of tower-type CSP plants by developing models for optical efficiency and output power through angular analysis, employing genetic algorithms for solving and elucidating the relationship between power output, heliostat dimensions, and the impact of receiver tower positioning. Initially, a model is established using parameters such as solar altitude angles, complemented by ray tracing methodology to optimize optical efficiency, resulting in high-performance data metrics. Subsequently, after affirming the receiver tower’s central placement for optimized overall output, a single-objective optimization model aimed at maximum power output is constructed, with the complexity of calculations effectively mitigated by genetic algorithms, unearthing the optimal heliostat parameters corresponding to the highest output power. Lastly, under additional constraints, a more comprehensive optimization model is derived, leading to the resolution of the maximum annual average output power.