# Reducing Congestion-Induced Renewable Curtailment with Corrective Network Reconfiguration in Day-Ahead Scheduling

arXiv: https://arxiv.org/abs/2002.01090 · HTML: https://mlsanity.com/paper/2002.01090/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

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

- [Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations](https://mlsanity.com/paper/2208.06742/) (2022)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [Machine Learning Assisted Approach for Security-Constrained Unit Commitment](https://mlsanity.com/paper/2111.09824/) (2021)

## Abstract (arXiv)

Renewable energy sources (RES) has gained a lot of interest recently. The limited transmission capacity serving RES often leads to network congestion since they are located in remote favorable locations. As a result, when poorly scheduled, the intermittent nature of RES may result in high curtailments of the free resource. Currently, grid operators utilize a static network when performing day-ahead scheduling and ignore transmission flexibility. This paper explores the possibility of utilizing network reconfiguration as a corrective action to reduce the transmission congestion and thereby the reduction of RES curtailments in day-ahead scheduling. To facilitate the RES integration in the grid, a stochastic N-1 security-constrained unit-commitment with corrective network reconfiguration (SSCUC-CNR) is modelled. SSCUC-CNR model is studied on a modified IEEE 24-bus system with RES. The simulation results demonstrate that CNR not only leads to a lower cost solution by reducing network congestion but also facilitates RES integration by reducing congestion-induced curtailments in high penetration cases. Emission studies demonstrate that more green generators are committed resulting in reduced carbon emissions when CNR is implemented.