energies-logo

Journal Browser

Journal Browser

Intelligent Control and Simulation of Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 9970
Submit your paper and select the Journal “Energies” and the Special Issue “Intelligent Control and Simulation of Power Systems” via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the guest editor or the journal editor ([email protected]) for any queries.

Special Issue Editors


E-Mail Website
Guest Editor
Electric Power System Department, Melentiev Energy Systems Institute SB RAS, 664033 Irkutsk, Russia
Interests: modeling of power systems; intelligent control; operation and dynamics performance of large power grids; emergency protection and control of power grids; reliability and security of energy systems; power industry restructuring

E-Mail Website
Guest Editor
Electric Power System Department, Melentiev Energy Systems Institute SB RAS, 664033 Irkutsk, Russia
Interests: power grids; intelligent systems; microgrids; machine learning; autonomous systems; power systems flexibility; emergency control; modelling; renewable energy; digital twins

Special Issue Information

Dear Colleagues,

The problems of managing the operation of modern electric power systems of recent decades can be compared with the stages of growth of a child. Energy systems are becoming more autonomous, intelligent, and require less human involvement, provided that their reliability should remain high and efficiency should increase. In other words, there is an evolution of the traditional power system in the direction of a full-fledged intelligent grid, which has such properties as continuous self-monitoring of the state and self-healing of network components, deep penetration of renewable energy sources, the participation of active consumers, increased physical/cyber security and flexibility, etc. Such an intelligent transformation of power systems is largely due to the introduction of new information technologies (artificial intelligence, machine learning), the improvement of information collection and processing systems (PMU, WAMS/WAPS technologies, intelligent SCADA/EMS/DMS systems), as well as the emergence of new grid structures (hybrid AC-DC networks, integrated systems, microgrids) and adaptive control and regulation devices. The above-mentioned electric power system transformation will require a competitive approach of available principles and methods for modeling such systems, for studying their main properties, and for justifying their development and control of their operation.

Prof. Nikolai Voropai
Dr. Nikita Tomin
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. 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

  • intelligent control
  • power system
  • simulation
  • flexibility
  • autonomous systems
  • emergency control
  • hybrid networks
  • artificial intelligence
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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

Research

Jump to: Review

14 pages, 2629 KiB  
Article
Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning
by Nikita Tomin, Nikolai Voropai, Victor Kurbatsky and Christian Rehtanz
Energies 2021, 14(24), 8270; https://doi.org/10.3390/en14248270 - 8 Dec 2021
Cited by 11 | Viewed by 2870
Abstract
The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in [...] Read more.
The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction. Full article
(This article belongs to the Special Issue Intelligent Control and Simulation of Power Systems)
Show Figures

Figure 1

Review

Jump to: Research

25 pages, 4052 KiB  
Review
Dynamic Modeling of HVDC for Power System Stability Assessment: A Review, Issues, and Recommendations
by Tarek Abedin, M. Shahadat Hossain Lipu, Mahammad A. Hannan, Pin Jern Ker, Safwan A. Rahman, Chong Tak Yaw, Sieh K. Tiong and Kashem M. Muttaqi
Energies 2021, 14(16), 4829; https://doi.org/10.3390/en14164829 - 8 Aug 2021
Cited by 31 | Viewed by 6336
Abstract
High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and [...] Read more.
High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system. Full article
(This article belongs to the Special Issue Intelligent Control and Simulation of Power Systems)
Show Figures

Figure 1

Back to TopTop