Optimal Transmission Grid Assets Maneuvering for Improving Voltage Profile in RES Scenarios

The deep penetration of renewable energy sources (RES) in power systems is increasingly influencing electricity markets and the physical operations of transmission networks. Besides the environmental and climate benefits, RES volatility and aleatory behaviour, combined with the consequent difficulties in control, might lead to voltage issues, which could be particularly critical when the grid lacks reactive power support. In this context, transmission system operators are called upon to make decisions about the use of available grid assets, such as compensating shunt devices and tap-changing transformers. In addition, transmission line switching can be adopted to provide further reactive power control resource. However, it is often difficult to determine which network assets should be enabled within the operation time requirements. Therefore, in this paper, an Optimal Reactive Power Flow tool for operators is proposed, including both shunt devices and transmission line switching, as well as the adjustment of transformers’ tap settings. The optimization problem is solved by resorting to a genetic algorithm to account for network model nonlinearities. In order to improve inclusiveness and replicability, the whole tool was developed exploiting open-source libraries only. Numerical tests are carried out on a modified IEEE 118-Bus Test System.

Authors

Institutions

Publication Details

Published
2024-11-27
DOI
https://doi.org/10.1109/ihtc61819.2024.10855048
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimal Transmission Grid Assets Maneuvering for Improving Voltage Profile in RES Scenarios

Sérgio Bruno, Massimo La Scala, Mohammad Rajabi Nasab, Roberto Cometa et al.
Power Systems and Technologies
article

Optimal Transmission Grid Assets Maneuvering for Improving Voltage Profile in RES Scenarios

Sérgio Bruno, Massimo La Scala, Mohammad Rajabi Nasab, Roberto Cometa, Angelo Velini
article en

Abstract

The deep penetration of renewable energy sources (RES) in power systems is increasingly influencing electricity markets and the physical operations of transmission networks. Besides the environmental and climate benefits, RES volatility and aleatory behaviour, combined with the consequent difficulties in control, might lead to voltage issues, which could be particularly critical when the grid lacks reactive power support. In this context, transmission system operators are called upon to make decisions about the use of available grid assets, such as compensating shunt devices and tap-changing transformers. In addition, transmission line switching can be adopted to provide further reactive power control resource. However, it is often difficult to determine which network assets should be enabled within the operation time requirements. Therefore, in this paper, an Optimal Reactive Power Flow tool for operators is proposed, including both shunt devices and transmission line switching, as well as the adjustment of transformers’ tap settings. The optimization problem is solved by resorting to a genetic algorithm to account for network model nonlinearities. In order to improve inclusiveness and replicability, the whole tool was developed exploiting open-source libraries only. Numerical tests are carried out on a modified IEEE 118-Bus Test System.

Polytechnic University of Bari (IT)
Affordable and clean energy
Openalex Percentile: Top 76%
Power Systems and Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.