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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: closed (31 January 2025) | Viewed by 6507

Special Issue Editors

Dr. Murilo E.C. Bento

E-Mail Website
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 policies can be found here.

Published Papers (5 papers)

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Research

Jump to: Review

25 pages, 375 KiB  
Article
On the Exact Formulation of the Optimal Phase-Balancing Problem in Three-Phase Unbalanced Networks: Two Alternative Mixed-Integer Nonlinear Programming Models
by Oscar Danilo Montoya, Brandon Cortés-Caicedo and Óscar David Florez-Cediel
Electricity 2025, 6(1), 9; https://doi.org/10.3390/electricity6010009 - 2 Mar 2025
Viewed by 208
Abstract
This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address the optimal phase-balancing problem in asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while the second uses a compact vector-based representation [...] Read more.
This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address the optimal phase-balancing problem in asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while the second uses a compact vector-based representation (V-MINLP). Both integrate the power flow equations through the current injection method, capturing the nonlinearities of Delta and Wye loads. These formulations, solved via an interior-point optimizer and the branch-and-cut method in the Julia software, ensure global optima and computational efficiency. Numerical validations on 8-, 25-, and 37-node feeders showed power loss reductions of 24.34%, 4.16%, and 19.26%, outperforming metaheuristic techniques and convex approximations. The M-MINLP model was 15.6 times faster in the 25-node grid and 2.5 times faster in the 37-node system when compared to the V-MINLP approach. The results demonstrate the robustness and scalability of the proposed methods, particularly in medium and large systems, where current techniques often fail to converge. These formulations advance the state of the art by combining exact mathematical modeling with efficient computation, offering precise, scalable, and practical tools for optimizing power distribution networks. The corresponding validations were performed using Julia (v1.10.2), JuMP (v1.21.1), and AmplNLWriter (v1.2.1). Full article
(This article belongs to the Special Issue Advances in Operation, Optimization, and Control of Smart Grids)
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Figure 1

Figure 1
<p>General implementation of the Julia-based methodology to solve the optimal phase-balancing problem in unbalanced three-phase distribution networks.</p> Full article ">Figure 2
<p>Single-line diagram of the 8-node test system.</p> Full article ">Figure 3
<p>Single-line diagram of the 25-node test system.</p> Full article ">Figure 4
<p>Single-line diagram of the 37-node test system.</p> Full article ">Figure 5
<p>Percentage of unbalance before and after the implementation of the best solution provided by the proposed methodology: (<b>a</b>) 8-node test system; (<b>b</b>) 25-node test system; (<b>c</b>) 37-node test system.</p> Full article ">Figure 6
<p>Voltage profiles for the 8-node test system: (<b>a</b>) Before phase-balancing. (<b>b</b>) After phase-balancing.</p> Full article ">Figure 7
<p>Voltage profiles for the 25-node test system: (<b>a</b>) before phase-balancing; (<b>b</b>) after phase-balancing.</p> Full article ">Figure 8
<p>Voltage profiles for the 37-node test system: (<b>a</b>) before phase-balancing; (<b>b</b>) after phase-balancing.</p> Full article ">
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 947
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The SOP configuration on a sample network.</p> Full article ">Figure 2
<p>The flowchart of the proposed method.</p> Full article ">Figure 3
<p>Interconnected multi-microgrids with switches (Case 1).</p> Full article ">Figure 4
<p>Interconnected multi-microgrids through SOPs (Case 2).</p> Full article ">Figure 5
<p>Voltage of grid under cases 1 and 2.</p> Full article ">Figure 6
<p>Diesel generator commitments.</p> Full article ">Figure 7
<p>Reactive power supplied by solar PV.</p> Full article ">Figure 8
<p>Active power transaction among microgrids by SOPs.</p> Full article ">Figure 9
<p>Reactive power-supplied SOPs.</p> Full article ">
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 964
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart diagram of the proposed power system contingency screening and ranking method integrated into a framework for near real-time application.</p> Full article ">Figure 2
<p>Flowchart of the algorithm for fast transient stability-based TSI evaluation.</p> Full article ">Figure 3
<p>Recursive process to solve power series coefficients source [<a href="#B21-electricity-05-00048" class="html-bibr">21</a>].</p> Full article ">Figure 4
<p>One-line diagram of (<b>a</b>) New England 39-bus system; (<b>b</b>) IEEE 9-bus system [<a href="#B24-electricity-05-00048" class="html-bibr">24</a>].</p> Full article ">Figure 5
<p>SI and AI plots of 39-bus test system.</p> Full article ">Figure 5 Cont.
<p>SI and AI plots of 39-bus test system.</p> Full article ">Figure 6
<p>SI and AI plots of 9-bus test system.</p> Full article ">Figure 6 Cont.
<p>SI and AI plots of 9-bus test system.</p> Full article ">Figure 7
<p>DTM- and AsDTM-based SI and AI error plots for 9-bus test system.</p> Full article ">Figure 7 Cont.
<p>DTM- and AsDTM-based SI and AI error plots for 9-bus test system.</p> Full article ">Figure 8
<p>DTM- and AsDTM-based SI and AI error plots for 39-bus test system plots.</p> Full article ">Figure 8 Cont.
<p>DTM- and AsDTM-based SI and AI error plots for 39-bus test system plots.</p> Full article ">Figure 9
<p>Elapsed time (<b>a</b>) for AsDTM-based TSI evaluation (<b>b</b>) for DTM-based TSI evaluation (<b>c</b>) for Rk4-based TSI evaluation, of 9-bus test system.</p> Full article ">Figure 10
<p>Elapsed time (<b>a</b>) for AsDTM-based TSI evaluation (<b>b</b>) for DTM-based TSI evaluation (<b>c</b>) for Rk4-based TSI evaluation, of 9-bus test system.</p> Full article ">
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 1346
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the proposed method for designing a PINN.</p> Full article ">Figure 2
<p>Histogram of distribution of operating conditions and load margins for the IEEE 68-bus system.</p> Full article ">Figure 3
<p>Histogram of distribution of operating conditions and load margins for the Brazilian interconnected power system.</p> Full article ">

Review

Jump to: Research

23 pages, 2121 KiB  
Review
General Approach to Electrical Microgrids: Optimization, Efficiency, and Reliability
by Ma. Del Carmen Toledo-Pérez, Rodolfo Amalio Vargas-Méndez, Abraham Claudio-Sánchez, Gloria Lilia Osorio-Gordillo, Luis Gerardo Vela-Valdés, Juan Ángel González-Flores and Omar Rodríguez-Benítez
Electricity 2025, 6(1), 12; https://doi.org/10.3390/electricity6010012 - 6 Mar 2025
Viewed by 149
Abstract
In this article, a comprehensive review of electrical microgrids is presented, emphasizing their increasing importance in the context of renewable energy integration. Microgrids, capable of operating in both grid-connected and standalone modes, offer significant potential for providing energy solutions to rural and remote [...] Read more.
In this article, a comprehensive review of electrical microgrids is presented, emphasizing their increasing importance in the context of renewable energy integration. Microgrids, capable of operating in both grid-connected and standalone modes, offer significant potential for providing energy solutions to rural and remote communities. However, the inclusion of diverse energy sources, energy storage systems (ESSs), and varying load demands introduces challenges in control and optimization. This review focuses on hybrid microgrids, analyzing their operational scenarios and exploring various optimization strategies and control approaches for efficient energy management. By synthesizing recent advancements and highlighting key trends, this article provides a detailed understanding of the current state and future directions in hybrid microgrid systems. Full article
(This article belongs to the Special Issue Advances in Operation, Optimization, and Control of Smart Grids)
►▼ Show Figures

Figure 1

Figure 1
<p>MG classifications.</p> Full article ">Figure 2
<p>Power converters in MGs.</p> Full article ">Figure 3
<p>Planning process of the DC microgrid [<a href="#B14-electricity-06-00012" class="html-bibr">14</a>].</p> Full article ">Figure 4
<p>General structure of a DC microgrid [<a href="#B19-electricity-06-00012" class="html-bibr">19</a>].</p> Full article ">Figure 5
<p>AC microgrid architecture.</p> Full article ">Figure 6
<p>AC-coupled hybrid MG [<a href="#B42-electricity-06-00012" class="html-bibr">42</a>].</p> Full article ">Figure 7
<p>Coupled HMG [<a href="#B48-electricity-06-00012" class="html-bibr">48</a>].</p> Full article ">Figure 8
<p>AC/DC-coupled hybrid MG [<a href="#B41-electricity-06-00012" class="html-bibr">41</a>].</p> Full article ">Figure 9
<p>Challenges of hybrid microgrids [<a href="#B56-electricity-06-00012" class="html-bibr">56</a>].</p> Full article ">Figure 10
<p>Classification of optimization techniques [<a href="#B61-electricity-06-00012" class="html-bibr">61</a>].</p> Full article ">
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