使用 Integrated Gradient 的电网成本归因与风险规避机组组合
Cost Attribution And Risk-Averse Unit Commitment In Power Grids Using Integrated Gradient
AI总结：
本文提出基于 Integrated Gradients 的成本归因算法，量化负荷与可再生能源预测偏差对系统成本的影响，并构建风险规避机组组合框架，在 RTS-GMLC 上验证可提升可靠性并降低成本。
摘要：
本文介绍了一种应对电力系统管理中不确定性及相关风险的新方法，重点关注负荷需求和可再生能源发电的预测值与实际值之间的差异。通过采用结合日前预测和实际值的经济调度（ED），我们得出两种不同的系统成本，揭示了由不确定性引发的财务风险。我们提出了一种受 Integrated Gradients（IG）方法启发的数值算法，用于将随机成分的贡献归因于系统成本差异。该方法最初为机器学习而开发，有助于理解各个输入特征对模型输出预测的影响。通过分配数值来表示波动性对运营成本的影响，我们的方法为电网管理提供了可操作的见解。作为一项应用，我们提出了一个风险规避机组组合框架，利用我们的成本归因算法来调整可再生能源发电机的容量，从而降低系统风险。在 RTS-GMLC 电网上的仿真结果证明了我们的方法在提高电网可靠性和降低运营成本方面的有效性。
英文摘要：
This paper introduces a novel approach to addressing uncertainty and associated risks in power system management, focusing on the discrepancies between forecasted and actual values of load demand and renewable power generation. By employing Economic Dispatch (ED) with both day-ahead forecasts and actual values, we derive two distinct system costs, revealing the financial risks stemming from uncertainty. We present a numerical algorithm inspired by the Integrated Gradients (IG) method to attribute the contribution of stochastic components to the difference in system costs. This method, originally developed for machine learning, facilitates the understanding of individual input features' impact on the model's output prediction. By assigning numeric values to represent the influence of variability on operational costs, our method provides actionable insights for grid management. As an application, we propose a risk-averse unit commitment framework, leveraging our cost attribution algorithm to adjust the capacity of renewable generators, thus mitigating system risk. Simulation results on the RTS-GMLC grid demonstrate the efficacy of our approach in improving grid reliability and reducing operational costs.
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