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Planning, Operation and Control of Power Grids with Penetration of Renewable Energy Sources

This special issue belongs to the section “A1: Smart Grids and Microgrids“.

Special Issue Information

Dear Colleagues,

The power system transformation to sustainable energy is required in order to mitigate their negative impact on the natural environment and human health. However, the transformation itself is a challenging task and requires overcoming multiple obstacles. The complexity of the process is increasing due to the highly variable and weather-driven nature of renewable energy sources. Such high stochasticity on both the supply and demand sides drastically changes the operating rules and conditions under which the power system is managed, controlled, and developed.

This Special Issue seeks papers in the areas of distribution system optimization, planning operation and control, with an emphasis on solving real-time or near-real-time research problems. This also includes the power system operation and control and modelling, and numerical techniques for power flow and optimal power flow studies. Example applications include, but are not limited to, power grids, smart grids, transmission and distribution systems, Virtual Power Plants (VPPs), and Artificial Intelligence (AI) and forecasting techniques for distribution system operation, planning and control. Topics of interest for publication include, but are not restricted to, the following:

  • Distribution system and smart grids optimization, planning and control;
  • Active management of distribution systems and smart grids;
  • Operation and management of smart grids/microgrids;
  • The role of Transmission System Operator (TSO)/ Distribution System Operator (DSO) interactions in both permanent and dynamic power system regimes;
  • Applications of Big data Analytics techniques in power system and smart grid operation, planning and control;
  • Applications of Internet of Things (IoT) in power system and smart grid operation and planning;
  • Applications of Artificial Intelligence (AI) in power system and smart grid planning, operation and control.

Dr. Geev Mokryani
Prof. Yim-Fun Hu
Prof. Raed A. Abd-Alhameed
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Power system transformation and grid integration
  • Distribution system operator
  • Microgrids, Virtual Power Plants, and Aggregators
  • Smart Grid Technologies and Infrastructures
  • Optimization and Computational Intelligence
  • Uncertainty and Risk Management Methods
  • Big Data Analytics and Industrial Informatics for smart grids
  • Internet of Things (IoT) for smart grids
  • Artificial Intelligence and Machine learning for smart grids

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Energies - ISSN 1996-1073