We present a decomposition algorithm to perform simultaneous scheduling and control decisions in concentrated solar power (CSP) systems. Our algorithm is motivated by the need to determine optimal market participation strategies at multiple timescales. The decomposition scheme uses physical insights to create surrogate linear models that are embedded within a mixed‐integer linear scheduling layer to perform discrete (operational mode) decisions. The schedules are then validated for physical feasibility in a dynamic optimization layer that uses a continuous full‐resolution CSP model. The dynamic optimization layer updates the physical variables of the surrogate models to refine schedules. We demonstrate that performing this procedure recursively provides high‐quality solutions of the simultaneous scheduling and control problem. We exploit these capabilities to analyze different market participation strategies and to explore the influence of key design variables on revenue. Our results also indicate that using scheduling algorithms that neglect detailed dynamics significantly decreases market revenues. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2408–2417, 2018

中文翻译：

---

一种同时调度和控制CSP系统的分解算法

我们提出一种分解算法，以在集中太阳能（CSP）系统中执行同时调度和控制决策。我们的算法受到在多个时间范围内确定最佳市场参与策略的需求的激励。分解方案利用物理洞察力来创建代理线性模型，该模型嵌入在混合整数线性调度层中以执行离散（操作模式）决策。然后在使用连续全分辨率CSP模型的动态优化层中对计划的物理可行性进行验证。动态优化层更新代理模型的物理变量以细化计划。我们证明，递归执行此过程可为同时调度和控制问题提供高质量的解决方案。我们利用这些功能来分析不同的市场参与策略，并探索关键设计变量对收入的影响。我们的结果还表明，使用忽略详细动态的调度算法会大大减少市场收入。©2018美国化学工程师学会AIChE J，64：2408-2417，2018