IET Generation, Transmission & Distribution is a fully open access and influential journal for the best research in the field. We empower the discussion and publication of current practice and future developments in electric power generation, transmission and distribution which is highly read and cited worldwide.


Read our latest Virtual Collection, Selected Papers from the IET-Wiley Symposium on Renewable Energy, published jointly with IET Renewable Power Generation.


New Special Issue Editor

We are pleased to announce that Matti Lehtonen, Subject Editor, will now be representing the journal as its Special Issue Editor. The Special Issue Editor is responsible for overseeing the IET Generation, Transmission & Distribution special issue programme, ensuring that the best high-impact special issues are being published in the journal.

Matti Lehtonen received the master’s and Licentiate degrees in electrical engineering from the Helsinki University of Technology, in 1984 and 1989, respectively, and the Doctor of Technology degree from the Tampere University of Technology, in 1992. From 1987 to 2003, he was with VTT Energy, Espoo, Finland. Since 1999, he has been a Professor with the Helsinki University of Technology (now Aalto University), where he is currently the Head of the Power Systems and High Voltage Engineering. His main research interests include power system planning and asset management, power system protection, earth fault problems, harmonics related issues, and applications of information technology in power systems.


 

 

Articles

Open access

Multienergy load forecasting model for integrated energy systems based on coupling auxiliary sequences and multitask learning

  •  18 April 2024

Graphical Abstract

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Multienergy load forecasting (MELF) with high accuracy is crucial for the economic operation and optimal dispatch of the integrated energy system (IES). Within such systems, electrical, heat, and cold loads may exhibit complex and highly coupled relationships. The accuracy of MELF can be improved by exploiting the coupling of multienergy loads. To address this issue, the authors propose a novel framework named coupling auxiliary transformer that leverages coupling auxiliary forecasting and multitask learning to improve MELF in IES.

Open access

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

  •  18 April 2024

Graphical Abstract

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The optimal allocation of medium voltage to low voltage (MV/LV) substations in risk-neutral (RN) and risk-averse (RA) frameworks is investigated. The load uncertainty is modelled by information gap decision theory (IGDT) technique. Also, this paper compares particle swarm optimization (PSO) and crow search algorithm (CSA) performances on the transformer allocation problem.

Open access

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

  •  16 April 2024

Graphical Abstract

Description unavailable

To achieve accurate and efficient short-term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN-GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. The overall framework of short-term load forecasting based on CNN-GRU-Attention network is proposed, and the coupling relationship between each stage is revealed.

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