Power Systems Stability in Smart Grid Era

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 1436

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Engineering and Architectural Science, Ryerson Cairo Campus – UofCanada, Cairo 11835, Egypt
Interests: power system analysis and optimization; energy market models; energy storage; renewable resources

E-Mail Website
Guest Editor
Center for Urban Energy, Faculty of Engineering and Architectural Science, Toronto Metropolitan University (Formerly Ryerson), Toronto, ON M5B 2K3, Canada
Interests: power system analysis and optimization

E-Mail Website
Guest Editor
1. Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
2. Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
Interests: power systems; energy storage; electrical vehicles; renewable energy; smart grids; applied machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many power system operators around the globe have devoted extensive efforts to reduce their gas emissions and ensure their commitment toward climate change. The adoption of modern smart grid technologies, energy storage, and renewable energy resources (RERs) is among the most effective solutions for achieving a green and sustainable energy system. However, the high penetration of these non-dispatchable RERs imposes many challenges, such as stability concerns around power systems. Indeed, while many research works have attempted to address the power system stability challenges in the last few years, many questions remain unanswered, such as how power system stability can be maintained within these smart grid technologies considering both steady-state and transient stability, as well as voltage and frequency stability. In this context, novel approaches for stability enhancement should be developed for power systems in the era of smart grids. Potential topics include but are not limited to the following:

  • Data-driven and machine learning approaches for predicting smart grids stability;
  • Steady-state stability enhancement of smart grids;
  • Transient/dynamic stability enhancement of smart grids;
  • Voltage and frequency stability assessment techniques for smart grids;
  • Optimal planning and operation methods of smart grid considering its stability.

Dr. Amr Mohamed
Dr. Bala Venkatesh
Dr. Karar Mahmoud
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 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. Electronics 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 2400 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

  • smart grids
  • renewable resources
  • transient
  • dynamic
  • stability
  • voltage stability
  • frequency stability
  • machine learning
  • data-driven models

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 781 KiB  
Article
A DRL-Based Load Shedding Strategy Considering Communication Delay for Mitigating Power Grid Cascading Failure
by Yongjing Wei, Anqi Tian, Yingjie Jiang, Wenjian Zhang, Liqiang Ma, Liang Ma, Chao Sun and Jian Sun
Electronics 2023, 12(14), 3024; https://doi.org/10.3390/electronics12143024 - 10 Jul 2023
Cited by 1 | Viewed by 1008
Abstract
Successive failures in power transmission lines can cause cascading failures in the power grid, which may eventually affect large parts of the power grid and even cause the power grid system to go down. Collecting and transmitting primary equipment information and issuing load-shedding [...] Read more.
Successive failures in power transmission lines can cause cascading failures in the power grid, which may eventually affect large parts of the power grid and even cause the power grid system to go down. Collecting and transmitting primary equipment information and issuing load-shedding action commands in the power grid depend on the power communication network. With the help of the power communication network, we can better observe the situation of the power grid in real time and provide a guarantee for the regular working of the power grid. However, the communication network also has the problem of communication delay causing latency in load-shedding action. On the premise of preserving the key physical properties and operational characteristics of the power grid, this paper uses the IEEE 14 and 30 bus systems as examples to establish a direct current (DC) power flow simulation environment. We establish a communication network model based on the power grid topology and the corresponding communication channels. For the problem of cascading failures occurring in the power grid after transmission line failures, a load-shedding strategy using soft actor-critic (SAC) based on deep reinforcement learning (DRL) was developed to effectively mitigate cascading failures in the power grid while considering the impact of communication delay. The corresponding communication delay is obtained by calculating the shortest communication path using the Dijkstra algorithm. The simulation verifies the feasibility and effectiveness of the SAC algorithm to mitigate cascading failures. The trained network can decide on actions and give commands quickly when a specific initial failure is encountered, reducing the scale of cascading failures. Full article
(This article belongs to the Special Issue Power Systems Stability in Smart Grid Era)
Show Figures

Figure 1

Back to TopTop