# Cost Attribution And Risk-Averse Unit Commitment In Power Grids Using Integrated Gradient

arXiv: https://arxiv.org/abs/2408.04830 · HTML: https://mlsanity.com/paper/2408.04830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes](https://mlsanity.com/paper/2409.16308/) (2024)

## Abstract (arXiv)

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.