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Algorithms
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Convex Optimization Methods and Metaheuristic Algorithms for Power Systems Planning,...
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Convex Optimization Methods and Metaheuristic Algorithms for Power Systems Planning, Operation and Control

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 2533

Special Issue Editor

Dr. Oscar Danilo Montoya

E-Mail Website
Guest Editor
Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
Interests: distribution system planning; renewable energy resources integration; energy storage devices and their applications; nonlinear control in power systems; distribution grids and microgrids; convex and combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in renewable energy and energy storage technologies have allowed us to fulfill the sustainability development goals regarding clean energy generation and decarbonization worldwide. The effective integration of these distributed energy storage and generation technologies is challenging because multiple regulatory, technical, economic, and environmental barriers must be supported. In the case of Power Systems, even if distributed energy resources are mature technologies with well-known advantages (efficiency and voltage profile improvements, reduction in thermal generation, and energy production cost reductions, among others), the complexity of the electrical systems makes the large-scale integration of renewables and storage technologies a complex task, from mathematical modeling to physical implementations. To contribute to the sustainable development of electricity, this Special Issue is focused on proposing new mathematical models, solution methodologies, and optimization techniques to study the effect of the massive integration of renewable energy and storage technologies in electrical networks from low- to high-voltage applications.

Dr. Oscar Danilo Montoya
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • renewable energy integration
  • energy management systems
  • energy storage technologies
  • mathematical modeling and optimization in power systems
  • semidefinite programming
  • second-order cone programming
  • convex optimization methods for power system applications
  • heuristic and metaheuristic optimization methods
  • distribution system planning
  • nonlinear control applications in power conversion
  • control design for power electronic converters
  • smart grids and microgrids
  • AC, DC, and hybrid electrical networks
  • graph theory and its applications in electrical systems
  • greenhouse gas emission reduction
  • voltage and frequency control
  • efficient operation algorithms for renewable energy applications
  • perspectives in the decarbonization of the electrical sector

Published Papers (3 papers)

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Research

62 pages, 1846 KiB  
Article
Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms
by Nikolaos P. Theodorakatos, Rohit Babu, Christos A. Theodoridis and Angelos P. Moschoudis
Algorithms 2024, 17(5), 191; https://doi.org/10.3390/a17050191 - 30 Apr 2024
Viewed by 288
Abstract
Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally [...] Read more.
Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids. Full article
(This article belongs to the Special Issue Convex Optimization Methods and Metaheuristic Algorithms for Power Systems Planning, Operation and Control)
17 pages, 2631 KiB  
Communication
Minimizing Voltage Ripple of a DC Microgrid via a Particle-Swarm-Optimization-Based Fuzzy Controller
by Hussein Zolfaghari, Hossein Karimi, Amin Ramezani and Mohammadreza Davoodi
Algorithms 2024, 17(4), 140; https://doi.org/10.3390/a17040140 - 28 Mar 2024
Viewed by 597
Abstract
DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the [...] Read more.
DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the power supply, and the stochastic load represents customer demand, whereas the ballast load includes a load to safeguard the boost circuits against the overvoltage in no-load periods. The stabilizer integrates components such as electrical vehicle batteries for energy storage and controlling long-time ripples, supercapacitors for controlling transient ripples, and an over-voltage discharge mechanism to prevent overcharging in the storage. To optimize the charging and discharging for batteries and supercapacitors, a multi-objective cost function is defined, consisting of two parts—one for ripple minimization and the other for reducing battery usage. The battery charge and discharge are considered in the objective function to limit its usage during transient periods, providing a mechanism to rely on the supercapacitor and protect the battery. Particle swarm optimization is employed to fine-tune the fuzzy membership function. Various operational scenarios are designed to showcase the DC microgrid’s functionality under different conditions, including scenarios where production exceeds and falls below consumption. The study demonstrates the improved performance and efficiency achieved by integrating a PSO-based fuzzy controller to minimize voltage ripple in a DC microgrid and reduce battery wear. Results indicate a 42% enhancement in the integral of absolute error of battery current with our proposed PSO-based fuzzy controller compared to a conventional fuzzy controller and a 78% improvement compared to a PI controller. This translates to a respective reduction in battery activity by 42% and 78%. Full article
(This article belongs to the Special Issue Convex Optimization Methods and Metaheuristic Algorithms for Power Systems Planning, Operation and Control)
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19 pages, 355 KiB  
Article
Optimal Integration of Flexible Alternating Current Transmission Systems in Electrical Distribution Grids Using a Mixed-Integer Convex Model
by Walter Gil-González, Oscar Danilo Montoya and César Leonardo Trujillo-Rodríguez
Algorithms 2023, 16(9), 420; https://doi.org/10.3390/a16090420 - 02 Sep 2023
Cited by 1 | Viewed by 1140
Abstract
This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the [...] Read more.
This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the expected annual operating costs of the network (i.e., energy losses and FACTS purchasing costs). The constraints of this problem include power equilibrium equalities, voltage regulation bounds, and device capacities, among others. Due to the power equilibrium constraints per node and period, the MINLP model is a non-convex optimization problem. To transform the exact MINLP model into a mixed-integer convex one, the approximation of the product between two variables in the complex domain is relaxed through its hyperbolic equivalent, which generates a set of convex cones. The main advantage of the proposed mixed-integer convex model is that it ensures the global optimum of the problem, even when considering objective multiplexes. Numerical simulations in the IEEE 33-, 69-, and 85-bus grids demonstrate the effectiveness and robustness of FACTS integration via the proposed convex approach in comparison with the exact solution of the MINLP model in the GAMS software as well as with combinatorial optimization algorithms (i.e., the black widow optimizer and the vortex search algorithm). All simulations were carried out in MATLAB with Yalmip optimization and the Gurobi and Mosek solvers. The simulation results show that, for a fixed operation of the FACTS devices (i.e., a VAR compensator) during the day, the annual operating costs are reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively, while for the operation variable, the reductions are by 14.24%, 15.79%, and 30.31%, respectively. Full article
(This article belongs to the Special Issue Convex Optimization Methods and Metaheuristic Algorithms for Power Systems Planning, Operation and Control)
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