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Electricity
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Advances in Operation, Optimization, and Control of Smart Grids
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Advances in Operation, Optimization, and Control of Smart Grids

A special issue of Electricity (ISSN 2673-4826).

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4090

Special Issue Editors

Dr. Murilo E.C. Bento

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Guest Editor
Department of Electrical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro 21941909, Brazil
Interests: power system stability; smart grids; dynamic security assessment; artificial intelligence
Dr. Hugo Morais

E-Mail Website
Guest Editor
INESC-ID, Department of Electrical and Computer Engineering, Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: smart grids; electricity markets; energy resource management; distributed power generation; smart power grids; battery-powered vehicles; distribution networks; electric vehicle charging; power distribution economics; power distribution operational planning; power system management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The expansion and advancements of smart grids occur largely due to the benefits they can provide for the operation, optimization and control of large power systems. The integration of renewable energy sources, the need for resource optimization and the uncertainties in the operation of smart grids enhance the motivation and need for the development of tools capable of benefiting the proper operation of smart grids.

In this context, this Special Issue aims to present and disseminate the most recent advances related to techniques for the operation, optimization, and control of smart grids.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Methods for improving smart grid operation;
  • Power system optimization;
  • Control techniques for improving the dynamic performance of smart grids;
  • Applications of artificial intelligence and machine learning in the operation, optimization and control of smart grids.

Dr. Murilo E.C. Bento
Dr. Hugo Morais
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. Electricity is an international peer-reviewed open access quarterly 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 1000 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
  • power system optimization
  • power system operation
  • power system control
  • artificial intelligence
  • machine learning

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

Published Papers (3 papers)

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Research

14 pages, 3110 KiB  
Article
Utilizing Soft Open Points for Effective Voltage Management in Multi-Microgrid Distribution Systems
by Ali Azizivahed, Khalil Gholami, Ali Arefi, Mohammad Taufiqul Arif and Md Enamul Haque
Electricity 2024, 5(4), 1008-1021; https://doi.org/10.3390/electricity5040051 - 6 Dec 2024
Viewed by 440
Abstract
To enhance stability and reliability, multi-microgrid systems have been developed as replacements for conventional distribution networks. Traditionally, switches have been used to interconnect these microgrids, but this approach often results in uncoordinated power sharing, leading to economic inefficiencies and technical challenges such as [...] Read more.
To enhance stability and reliability, multi-microgrid systems have been developed as replacements for conventional distribution networks. Traditionally, switches have been used to interconnect these microgrids, but this approach often results in uncoordinated power sharing, leading to economic inefficiencies and technical challenges such as voltage fluctuations, delay in response, etc. This research, in turn, introduces a novel multi-microgrid system that utilizes advanced electronic devices known as soft open points (SOPs) to enable effective voltage management and controllable power sharing between microgrids while also providing reactive power support. To account for uncertainties in the system, the two-point estimate method (2PEM) is applied. Simulation results on an IEEE 33-bus network with high renewable energy penetration reveal that the proposed SOP-based system significantly outperforms the traditional switch-based method, with a minimum voltage level of 0.98 p.u., compared to 0.93 p.u. in the conventional approach. These findings demonstrate the advantages of using SOPs for voltage management in forming multi-microgrid systems. Full article
(This article belongs to the Special Issue Advances in Operation, Optimization, and Control of Smart Grids)
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25 pages, 5497 KiB  
Article
Transient Stability-Based Fast Power System Contingency Screening and Ranking
by Teshome Lindi Kumissa and Fekadu Shewarega
Electricity 2024, 5(4), 947-971; https://doi.org/10.3390/electricity5040048 - 25 Nov 2024
Viewed by 556
Abstract
Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power [...] Read more.
Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power systems dynamic security assessment tools are required. One of the core elements of near real-time dynamic security assessment tools is contingency screening and ranking. Most of the commercially available tools screen and rank contingencies by using the traditional numerical integration or Transient Energy Functions (TEFs) or hybrid methods. The traditional numerical integration method is accurate but computationally intensive and has a slow assessment speed which makes it difficult to identify any insecure contingency before it happens. Despite the TEF method of transient stability analysis being relatively fast, it develops less accurate results due to models simplification and assumptions. This paper introduces transient stability based on fast and robust contingency screening and ranking using an Adaptive step-size Differential Transformation (AsDTM) method. Based on the most current snapshot from Supervisory Control and Data Accusation (SCADA) data, the proposed method triggers AsDTM-based transient stability simulation for each credible contingency and evaluates Transient Stability Indices (TSI) as the normalized weighted sum of squares of errors derived from state variables and complex bus voltages at every simulation time step. Finally, contingencies are ranked based on these TSI and the worst contingency is identified for the next detail assessment. The method is tested on IEEE 9 bus and 39 bus test systems. Test results reveal that the proposed method is faster, robust, and can be used in near real-time dynamic security assessment sessions. Full article
(This article belongs to the Special Issue Advances in Operation, Optimization, and Control of Smart Grids)
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19 pages, 340 KiB  
Article
Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement
by Murilo Eduardo Casteroba Bento
Electricity 2024, 5(4), 785-803; https://doi.org/10.3390/electricity5040039 - 31 Oct 2024
Viewed by 770
Abstract
The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to [...] Read more.
The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction. Full article
(This article belongs to the Special Issue Advances in Operation, Optimization, and Control of Smart Grids)
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