Results 191 to 200 of about 123,459 (289)

Multi‐Objective Low Carbon Energy Management of Integrated Energy Systems Considering Renewable Energy Sources and Water Response Programs

open access: yesIET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
This paper proposes a two‐layer, multi‐objective hybrid optimization model for low‐carbon planning in integrated energy systems. By considering uncertainty scenarios, the model simultaneously minimizes costs, greenhouse gas emissions and groundwater extraction.
Hamid Karimi, Hamid Reza Sezavar
wiley   +1 more source

Short‐Term Load Forecasting of Multi‐Energy in Integrated Energy System Based on Efficient Information Extracting Informer

open access: yesIET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
To enhance multi‐energy load forecasting accuracy crucial for Integrated Energy System (IES) planning and control, this study proposes the EI2 (Efficient Information Extracting Informer) model. This transformer‐based approach features a shared encoder, incorporates high‐dimensional MLP layers in encoder/decoder feed‐forward networks for deeper pattern ...
Tianlu Gao   +7 more
wiley   +1 more source

A Framework for Resilient Coordinated Planning of Distributed Energy Resources in a Multi‐Microgrid System

open access: yesIET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
This paper presents a mixed integer linear programming (MILP) model for coordinated placement and sizing of distributed energy resources (DERs) (including dispatchable and renewable DG units and energy storage units in a multi‐microgrid system). DERs’ investment and operating costs in normal and emergency conditions, as well as interrupted energy cost ...
Hossein Farzin, Ali Kamaie, Mehdi Monadi
wiley   +1 more source

Mean‐Guided Elite Selection Genetic Algorithm for Multi‐Objective Optimization of Operational Costs and Voltage Control in Grid‐Connected Microgrids

open access: yesIET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
This paper proposes a mean‐guided elite selection genetic algorithm (MGES‐GA) for bi‐objective optimisation of grid‐connected microgrids, minimising both operational costs and voltage deviation. MGES‐GA improves the selection process by considering both high‐performing and low‐performing individuals to balance exploration and exploitation.
Natasha Dimishkovska Krsteski   +1 more
wiley   +1 more source

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