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Energies
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Smart Grids: Operation, Planning, and Management II
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Smart Grids: Operation, Planning, and Management II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (12 June 2023) | Viewed by 1592

Special Issue Editor

Dr. Mahmoud Ghofrani

E-Mail Website
Guest Editor
Associate Professor, Division of Engineering and Mathematics, School of STEM, University of Washington, Bothell, WA 98011-8246, USA
Interests: power system operation and planning; smart grids; power market; renewable energy systems; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical grids worldwide are undergoing a major transition towards smart grids to increase their efficiency, reliability, sustainability and security. Distributed energy resources including energy storage systems, electric vehicles (EVs) with vehicle-to-grid (V2G) capabilities, and renewable energy generation have had significant penetration over recent years. In addition, advancements in communication technologies and smart metering devices along with increasing applications of data analysis and artificial intelligence (AI) methods in energy systems have facilitated the transition.

While much effort is devoted to smart grid studies, there is a pressing need to develop and innovate frameworks for optimal operation, planning and management of smart grids. Such frameworks recognize the synergies among the smart grid technologies and optimize their interactions to benefit the smart grid and its energy entities.

This Special Issue invites researchers from academia and industry to bring together innovative developments, challenges and solutions in the field of smart grids’ operation, planning and management. Potential topics include, but are not limited to:

  • Novel optimization algorithms for operating and planning smart electric grids;
  • Micro-grids and state-of-the-art methods for their optimization and control; Innovative energy management strategies and practices for efficient use of distributed energy sources in smart grids;
  • Smart grids: Technologies, management, big data analytics, communications, security and privacy;
  • Artificial intelligence and machine learning;
  • Reliability analysis of smart grids;
  • Economics of smart energy systems and technologies;
  • EVs and transportation electrification.

Dr. Mahmoud Ghofrani
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • novel optimization algorithms for operating and planning smart electric grids
  • micro-grids and state-of-the-art methods for their optimization and control
  • innovative energy management strategies and practices for efficient use of distributed energy sources in smart grids
  • smart grids: technologies, management, big data analytics, communications, security and privacy
  • artificial intelligence and machine learning
  • reliability analysis of smart grids
  • economics of smart energy systems and technologies
  • EVs and transportation electrification

Published Papers (1 paper)

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Research

25 pages, 10544 KiB  
Article
Frequency Stabilization in an Interconnected Micro-Grid Using Smell Agent Optimization Algorithm-Tuned Classical Controllers Considering Electric Vehicles and Wind Turbines
by Shreya Vishnoi, Srete Nikolovski, More Raju, Mukesh Kumar Kirar, Ankur Singh Rana and Pawan Kumar
Energies 2023, 16(6), 2913; https://doi.org/10.3390/en16062913 - 22 Mar 2023
Cited by 5 | Viewed by 1312
Abstract
In micro-grids (MGs), renewable energy resources (RESs) supply a major portion of the consumer demand. The intermittent nature of these RESs and the stochastic characteristics of the loads cause a frequency stabilization issue in MGs. Owing to this, in the present manuscript, the [...] Read more.
In micro-grids (MGs), renewable energy resources (RESs) supply a major portion of the consumer demand. The intermittent nature of these RESs and the stochastic characteristics of the loads cause a frequency stabilization issue in MGs. Owing to this, in the present manuscript, the authors try to uncover the frequency stabilization/regulation issue (FRI) in a two-area MG system comprising wind turbines (WTs), an aqua-electrolyzer, a fuel cell, a bio-gas plant, a bio-diesel plant, diesel generation (DG), ship DG, electric vehicles and their energy storage devices, flywheels, and batteries in each control area. With these sources, the assessment of the FRI is carried out using different classical controllers, namely, the integral (I), proportional plus I (PI), and PI plus derivative (PID) controllers. The gain values of these I, PI, and PID controllers are tuned using the recently proposed smell agent optimization (SAO) algorithm. The simulation studies reveal the outstanding performance of the later controller compared with the former ones in view of the minimum settling period and peak amplitude deviations (overshoots and undershoots). The SAO algorithm shows superior convergence behavior when tested against particle swarm optimization and the firefly algorithm. The SAO-PID controller effectively performs in continuously changing and increased demand situations. The SAO-PID controller designed in nominal conditions was found to be insensitive to wide deviations in load demands and WT time constants. Full article
(This article belongs to the Special Issue Smart Grids: Operation, Planning, and Management II)
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