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IET Smart Grid is a fully open access journal presenting pioneering research results spanning multiple disciplines such as power electronics, power and energy, control, communications, and computing sciences. We aim to pave the way for implementing more efficient, reliable, and secure power systems.
Read our latest Virtual Collection, Enabling High Performance and Resilience Power Grid: Decarbonization, Digitization, Automation and Beyond (IET APSCOM 2022).
Articles
Using assurance frameworks to manage the risks and uncertainties of an energy sector digital spine
-  5 December 2024
Graphical Abstract
The digital spine can serve as the energy system's connective tissue, enabling secure data sharing and enhancing visibility across all energy stakeholders. Significant risks accompany this shift, including data quality and security, organisational readiness, and the complexity and scalability of the digital infrastructure. This report will discuss these risks and suggest using independent assurance frameworks to promote trust and acceptance of the digital spine.
Intelligent islanding detection in smart microgrids using variance autocorrelation function‐based modal current envelope
-  5 December 2024
Graphical Abstract
This paper proposes and demonstrates an efficient and accurate approach to islanding detection based on the Variance Autocorrelation Function of a Modal Current Envelope (VAMCE) technique. The VAMCE methodology is better suited for islanding detection because of its response to current sensitivity under islanding scenarios but not under normal conditions. The proposed solution is not only more accurate but also much faster compared to other methods. The proposed approach can identify normal and islanded situations in just 0.4 s.
Open data for modelling the impacts of electric vehicles on UK distribution networks: Opportunities for a digital spine
-  29 November 2024
Graphical Abstract
This paper reviews the availability and utility of open data for modelling the impacts of electric vehicles (EVs) on the UK distribution network, identifying key challenges such as inconsistent data availability and formatting, and geographic discrepancies between datasets. By analysing EV charger connection data from two UK distribution network operators and comparing it to public charger locations, the study highlights the potential for a digital spine to enhance data standardisation and improve EV-grid integration models.
Survey of machine learning methods for detecting false data injection attacks in power systems
- IET Smart Grid
-  581-595
-  6 October 2020
Battery swapping station for electric vehicles: opportunities and challenges
- IET Smart Grid
-  280-286
-  19 May 2020
Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions
- IET Smart Grid
-  141-154
-  1 March 2019
Protection in DC microgrids: a comparative review
- IET Smart Grid
-  66-75
-  10 September 2018
Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation
- IET Smart Grid
-  86-102
-  20 November 2022