A new approach to manage the connection queue

Abstract The connection queue has seen unparalleled growth over the last 24 months. With the record number of connection agreement applications representing a pivotal time for system development as we drive investment that will enable us to transition into an electrified future. The authors look to address challenges posed to the connection queue through congestion management‐based techniques. Conducting a top down approach, the authors first visualise the state of the queue. Observing highest growth in pure storage based applications distributed close to traditional load centres, followed by wind projects along the East Coast and Northern Scotland. Supporting close analysis is performed on the representative B6 boundary. The authors compare methodologies, validate a process for the attribution of congestion cost and quantify the value of investment given by unit power changes. Finding that most significant value of investment in storage lies in the Southeast of England and the North of Scotland. This reflects the identified current state of constraint and most efficient current utilisation of available capacity. These approaches have been aligned with current system thinking and progress towards a new industry data sharing standard, forming strategy by which we look to transition the energy system and country into a decarbonised future.

Authors

Institutions

Publication Details

Journal
IET Smart Grid
Published
2024-12-01
DOI
https://doi.org/10.1049/stg2.12200
Citations
1
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A new approach to manage the connection queue

Lewis Dale, Furong Li, Nicolas Ostler
1 citations
IET Smart Grid
Electric Power System Optimization
0.21
article

A new approach to manage the connection queue

Lewis Dale, Furong Li, Nicolas Ostler
article en
1 citations

Abstract

Abstract The connection queue has seen unparalleled growth over the last 24 months. With the record number of connection agreement applications representing a pivotal time for system development as we drive investment that will enable us to transition into an electrified future. The authors look to address challenges posed to the connection queue through congestion management‐based techniques. Conducting a top down approach, the authors first visualise the state of the queue. Observing highest growth in pure storage based applications distributed close to traditional load centres, followed by wind projects along the East Coast and Northern Scotland. Supporting close analysis is performed on the representative B6 boundary. The authors compare methodologies, validate a process for the attribution of congestion cost and quantify the value of investment given by unit power changes. Finding that most significant value of investment in storage lies in the Southeast of England and the North of Scotland. This reflects the identified current state of constraint and most efficient current utilisation of available capacity. These approaches have been aligned with current system thinking and progress towards a new industry data sharing standard, forming strategy by which we look to transition the energy system and country into a decarbonised future.

IET Smart GridVol. 7(6)
University of Bath (GB)
University of Bath
Affordable and clean energy
Openalex Percentile: Top 45%
Electric Power System Optimization
0.21
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.

A new approach to manage the connection queue — Lewis Dale, Furong Li, et al. · IET Smart Grid (2024) | TGRS Research Map | TGRS