# Probabilistic Forecast of Real-Time LMP and Network Congestion

arXiv: https://arxiv.org/abs/1503.06171 · HTML: https://mlsanity.com/paper/1503.06171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2015.

## Built on by (most cited first, 3 of 3)

- [Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation](https://mlsanity.com/paper/2301.03766/) (2023)
- [Generative Probabilistic Time Series Forecasting and Applications in Grid Operations](https://mlsanity.com/paper/2402.13870/) (2024)
- [Unsupervised Congestion Status Identification Using LMP Data](https://mlsanity.com/paper/2411.10058/) (2024)

## Abstract (arXiv)

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation cost associated with multiparametric programs offline, the online computation cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.