Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
复制标题
基于库普曼的动态感知经济调度问题的可微预测控制
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
D. Vrabie
中科院分区：
文献类型：
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作者：
Ethan King;Ján Drgoňa;Aaron Tuor;S. Abhyankar;Craig Bakker;Arnab Bhattacharya;D. Vrabie
动态感知经济调度（DED）问题嵌入低级别的发电机动态和操作约束，使近实时调度的发电机组在电力网络中。与传统的经济调度（T-ED）相比，DED产生了更动态的监督控制策略，从而降低了整体发电成本。然而，纳入微分方程，支配系统的动力学，使DED的优化问题，是计算禁止解决。在这项工作中，我们提出了一种新的数据驱动的方法，基于可微规划，以有效地获得参数的解决方案的基础DED问题。特别是，我们采用最近提出的微分预测控制（DPC）离线学习的显式神经控制策略，使用确定的Koopman算子（KO）模型的电力系统动态。我们证明了高的解决方案的质量和五个数量级的计算时间节省的DPC方法在原来的在线优化为基础的DED方法上的9总线测试电网网络。
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that leads to reduced overall generation costs. However, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies using an identified Koopman operator (KO) model of the power system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original online optimization-based DED approach on a 9-bus test power grid network.