Special Issue "Heuristic Optimization Techniques Applied to Power Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editors

Prof. Dr. José L. Bernal-Agustín
Website
Guest Editor
Electrical Engineering Department, University of Zaragoza. Calle María de Luna, 3. 50018 Zaragoza, Spain
Interests: evolutionary computation applications to engineering; renewable energy; distribution power system; energy management; electric markets
Special Issues and Collections in MDPI journals
Prof. Dr. Rodolfo Dufo-López
Website
Guest Editor
Electrical Engineering. Department, University of Zaragoza. Calle María de Luna, 3. 50018 Zaragoza, Spain
Interests: renewable energy; electricity storage; advanced batteries models; net metering; energy management; optimization algorithms
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The word heuristic comes from the Greek (heurískein) and means to find or to discover. Heuristic techniques find solutions that make it possible to solve problems that could hardly be solved with other techniques, such as linear and nonlinear programming, integer programming, and dynamic programming. Many heuristic techniques have been developed. Some of them have been inspired by nature, such as evolutionary algorithms. Others use different methodologies in order to discover good solutions in an efficient way. They have been successfully applied to solve a wide range of real‐world and complex engineering problems during the last few decades. One characteristic of these techniques is that they do not ensure that the solutions they provide are the best possible and that the necessary computational time is reasonable.

The topics of interest in this Special Issue include heuristic techniques applied to any field related to power systems, such as energy generation, distribution, and transmission networks; smart grids; energy storage; renewable energy integration; electric vehicles; electricity markets; electricity demands; etc.

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

  • Genetic algorithms;
  • Evolution strategies;
  • Evolutionary programming;
  • Differential evolution;
  • Particle swarm optimization;
  • Ant colony algorithm;
  • Tabu Search;
  • Simulated annealing;
  • Pareto multi-objective optimization;
  • Pattern search.

Prof. Dr. José L. Bernal-Agustín
Prof. Dr. Rodolfo Dufo-López
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 papers will be 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 1800 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

  • heuristic techniques
  • power systems
  • optimization
  • management
  • operation.

Published Papers (6 papers)

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Research

Open AccessArticle
Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm
Energies 2020, 13(7), 1544; https://doi.org/10.3390/en13071544 - 26 Mar 2020
Abstract
This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often [...] Read more.
This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time. Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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Open AccessArticle
Time Domain Particle Swarm Optimization of PI Controllers for Bidirectional VSC HVDC Light System
Energies 2020, 13(4), 866; https://doi.org/10.3390/en13040866 - 17 Feb 2020
Abstract
This paper proposes a novel technique to tune the PI controllers of a bidirectional HVDC light system by embedding particle swarm optimization directly in the Simulink model in the design procedure. The HVDC light system comprises of a rectifier station, a DC link, [...] Read more.
This paper proposes a novel technique to tune the PI controllers of a bidirectional HVDC light system by embedding particle swarm optimization directly in the Simulink model in the design procedure. The HVDC light system comprises of a rectifier station, a DC link, and an inverter station. Each converter station requires four PI controllers to be tuned in the decoupled d-q vector control scheme, and with the bidirectional HVDC system, the required PI controllers are doubled. Tuning these many controllers using conventional methods is a challenging task, especially if the parameters of the converter stations are different. A novel approach to tune the PI controllers for a bidirectional HVDC system using the time-domain performance indices is presented in this paper. The time-domain performance indices are optimized using the particle swarm optimization (PSO) algorithm. The results of the proposed tuning method show that the proposed method not only gives superior results but also is less cumbersome to tune compared to conventional methods like modulus optimum (MO). Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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Open AccessArticle
Heuristic Optimization of Virtual Inertia Control in Grid-Connected Wind Energy Conversion Systems for Frequency Support in a Restructured Environment
Energies 2020, 13(3), 564; https://doi.org/10.3390/en13030564 - 24 Jan 2020
Abstract
In the work reported in this paper, a novel application of the artificial bee colony algorithm is used to implement a virtual inertia control strategy for grid-connected wind energy conversion systems. The proposed control strategy introduces a new heuristic optimization technique that uses [...] Read more.
In the work reported in this paper, a novel application of the artificial bee colony algorithm is used to implement a virtual inertia control strategy for grid-connected wind energy conversion systems. The proposed control strategy introduces a new heuristic optimization technique that uses the artificial bee colony (ABC) algorithm to calculate the optimal gain value of an additional derivative control loop added to the control scheme of the machine side converter in a wind energy system to enable wind farms to participate in frequency control as specified by recent grid codes. This helps to minimize the frequency deviations, reduce active power deviation in the system, and increase the penetration level of wind energy in power systems. The study was performed in a restructured power system environment. The proposed control scheme and its robustness were evaluated using load–frequency analysis for three real-life transaction scenarios that can occur in an interconnected open-energy market and the validation was carried out using eigenvalue analysis. The results in this study show that the optimal gain of the proposed controller reduces the frequency deviations and improves stability and overall performance of the system. Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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Open AccessArticle
Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm
Energies 2019, 12(21), 4115; https://doi.org/10.3390/en12214115 - 28 Oct 2019
Abstract
Distributed, generation-based micro-grids are increasingly being used in the build-up of the modern power system. However, the protection of these micro-grids has many challenges. One of the important challenges is the coordination of directional overcurrent (DOC) relays. The optimization of the coordination of [...] Read more.
Distributed, generation-based micro-grids are increasingly being used in the build-up of the modern power system. However, the protection of these micro-grids has many challenges. One of the important challenges is the coordination of directional overcurrent (DOC) relays. The optimization of the coordination of DOC relays is considered a nonlinear programming problem with pre-defined constrains. In this paper, the problem of the optimal coordination of DOC relays is solved using a multi-verse optimization (MVO) algorithm which is inspired from cosmology science. The proposed algorithm is tested by applying it to Institute of Electrical and Electronics Engineers (IEEE) 3 bus and IEEE 9 bus networks. The performance of the proposed algorithm is compared with the particle swarm optimization (PSO) algorithm when applied to both networks. All results show that the performance of the MVO algorithm is better than PSO in terms of its reduction of both the overall operating time (OT) of DOC relays and the computational burden of the computer solving the optimization problem. Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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Open AccessArticle
Optimized Surge Arrester Allocation Based on Genetic Algorithm and ATP Simulation in Electric Distribution Systems
Energies 2019, 12(21), 4110; https://doi.org/10.3390/en12214110 - 28 Oct 2019
Abstract
The efficient protection of electric power distribution networks against lightning discharges is a crucial problem for distribution electric utilities. To solve this problem, the great challenge is to find a solution for the installation of surge arresters at specific points in the electrical [...] Read more.
The efficient protection of electric power distribution networks against lightning discharges is a crucial problem for distribution electric utilities. To solve this problem, the great challenge is to find a solution for the installation of surge arresters at specific points in the electrical grid and in a sufficient quantity that can ensure an adequate level of equipment protection and be within the utility’s budget. As a solution to this problem of using ATP (Alternative Transient Program), this paper presents a methodology for optimized surge arrester allocation based on genetic algorithm (GA), with a fitness function that maximizes the number of protected equipment according to the financial availability for investment in surge arresters. As ATP may demand too much processing time when running large distribution grids, an innovative procedure is implemented to obtain an overvoltage severity description of the grid and select only the most critical electric nodes for the incidence of lightning discharges, in the GA allocation procedure. The results obtained for the IEEE-123 bus electric feeder indicate a great reduction of flashover occurrence, thus increasing the equipment protection level. Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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Open AccessArticle
Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm
Energies 2019, 12(16), 3149; https://doi.org/10.3390/en12163149 - 16 Aug 2019
Cited by 2
Abstract
Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too [...] Read more.
Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms. Full article
(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)
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