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Heuristic Optimization Techniques Applied to Power Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 28239

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Prof. Dr. José L. Bernal-Agustín

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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
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Prof. Dr. Rodolfo Dufo-López

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Department of Electrical Engineering, 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
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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

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Keywords

heuristic techniques
power systems
optimization
management
operation.

Published Papers (10 papers)

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Research

21 pages, 407 KiB

Open AccessArticle

Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm

by Walter Gil-González, Oscar Danilo Montoya, Arul Rajagopalan, Luis Fernando Grisales-Noreña and Jesus C. Hernández

Energies 2020, 13(18), 4914; https://doi.org/10.3390/en13184914 - 19 Sep 2020

Cited by 37 | Viewed by 2707

Abstract

This paper deals with the problem of the optimal selection of capacitor banks in electrical AC distribution systems for minimizing the costs of energy losses during a year of operation through a discrete version of the vortex search algorithm (DVSA). This algorithm works with a hypersphere with a variable radius defined by an exponential function where a Gaussian distribution is used to generate a set of candidate solutions uniformly distributed around the center of this hypersphere. This center corresponds to the best solution obtained at the iteration t, which is initialized at the center of the solution space at the iterative search beginning. The main advantage of combining the exponential function with the Gaussian distribution is the correct balance between the exploration and exploitation of the solution space, which allows reaching the global optimal solution of the optimization problem with a low standard deviation, i.e., guaranteeing repeatability at each simulation. Two classical distribution networks composed of 33 and 69 nodes were used to validate the proposed DVSA algorithm. They demonstrated that the DVSA improves numerical reports found in specialized literature regarding the optimal selection and location of fixed-step capacitor banks with a low computational burden. All the simulations were carried out in MATLAB software. Full article

(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)

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22 pages, 4229 KiB

Open AccessArticle

Heterogenic Autotuner for Electro-Pneumatic Single-Action Actuators

by Michał Bartyś

Energies 2020, 13(18), 4604; https://doi.org/10.3390/en13184604 - 04 Sep 2020

Viewed by 1998

Abstract

The main concept is to design the novel autotuner in a way that it will introduce benefits that arise from the effect of the fusion of the quantitative and qualitative knowledge gained from identification experiments, long-time expertise, and theoretical findings. The novelty of this approach is in the manner in which the expert heuristic knowledge is used for the development of an easy-to-use and time-efficient tuning process. In the proposed approach, the positioner simply learns, mimics, and follows up the tuning process that is performed by an experienced human operator. The major strength of this approach is that all parameters of positioner

P I D

controller can be estimated by only identifying one single parameter that is the effective time constant of the pneumatic actuator. The elaborated autotuning algorithm is experimentally examined with different commercially available pneumatic actuators and control valves. The obtained results demonstrate that the proposed autotuning approach exhibits good performance, usability, and robustness. This should be considered as particularly relevant in the processes of installing, commissioning, and servicing single-action final control elements. Full article

(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)

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16 pages, 1788 KiB

Open AccessArticle

An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem

by Chun-Yao Lee and Maickel Tuegeh

Energies 2020, 13(14), 3721; https://doi.org/10.3390/en13143721 - 20 Jul 2020

Cited by 10 | Viewed by 2286

Abstract

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems. Full article

(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)

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26 pages, 862 KiB

Open AccessArticle

A Reservoir Operation Policy Using Inter-Basin Water Transfer for Maximizing Hydroelectric Benefits in Brazil

by Anderson Passos de Aragão, Patrícia Teixeira Leite Asano and Ricardo de Andrade Lira Rabêlo

Energies 2020, 13(10), 2564; https://doi.org/10.3390/en13102564 - 18 May 2020

Cited by 4 | Viewed by 2066

Abstract

The Hydrothermal Coordination problem consists of determining an operation policy for hydroelectric and thermoelectric plants within a given planning horizon. In systems with a predominance of hydraulic generation, the operation policy to be adopted should specify the operation of hydroelectric plants, so that hydroelectric resources are used economically and reliably. This work proposes the implementation of reservoir operation rules, using inter-basin water transfer through an optimization model based on Network Flow and Particle Swarm Optimization (PSO). The proposed algorithm aims to obtain an optimized operation policy of power generation reservoirs and consequently to maximize the hydroelectric benefits of the hydrothermal generation system, to reduce the use of thermoelectric plants, the importation and/or energy deficit and to reduce the cost associated with meeting the demand and reduce CO₂ emissions from combustion of fossil fuels used by thermoelectric plants. In order to illustrate the efficiency and effectiveness of the proposed approach, it was evaluated by optimizing two case studies using a system with four hydroelectric plants. The first case study does not consider transfer and water and the second case study uses water transfer between rivers. The obtained results illustrate that the proposed model allowed to maximize the hydroelectric resources of a hydrothermal generation system with economy and reliability. Full article

(This article belongs to the Special Issue Heuristic Optimization Techniques Applied to Power Systems)

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21 pages, 2840 KiB

Open AccessArticle

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

by Damir Jakus, Rade Čađenović, Josip Vasilj and Petar Sarajčev

Energies 2020, 13(7), 1544; https://doi.org/10.3390/en13071544 - 26 Mar 2020

Cited by 45 | Viewed by 3902

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 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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15 pages, 2154 KiB

Open AccessArticle

Time Domain Particle Swarm Optimization of PI Controllers for Bidirectional VSC HVDC Light System

by Syed F Faisal, Abdul R Beig and Sunil Thomas

Energies 2020, 13(4), 866; https://doi.org/10.3390/en13040866 - 17 Feb 2020

Cited by 16 | Viewed by 2129

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, 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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28 pages, 921 KiB

Open AccessArticle

Heuristic Optimization of Virtual Inertia Control in Grid-Connected Wind Energy Conversion Systems for Frequency Support in a Restructured Environment

by Anuoluwapo Oluwatobiloba Aluko, David George Dorrell, Rudiren Pillay Carpanen and Evan E. Ojo

Energies 2020, 13(3), 564; https://doi.org/10.3390/en13030564 - 24 Jan 2020

Cited by 23 | Viewed by 2780

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 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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16 pages, 11173 KiB

Open AccessArticle

Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm

by Mahmoud Abdelsalam and Hatem Y. Diab

Energies 2019, 12(21), 4115; https://doi.org/10.3390/en12214115 - 28 Oct 2019

Cited by 5 | Viewed by 2421

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 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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15 pages, 1195 KiB

Open AccessArticle

Optimized Surge Arrester Allocation Based on Genetic Algorithm and ATP Simulation in Electric Distribution Systems

by Gabriel Vianna Soares Rocha, Raphael Pablo de Souza Barradas, João Rodrigo Silva Muniz, Ubiratan Holanda Bezerra, Igor Meireles de Araújo, Daniel de Souza Avelar da Costa, Abner Cardoso da Silva, Marcus Vinícius Alves Nunes and Jucileno Silva e Silva

Energies 2019, 12(21), 4110; https://doi.org/10.3390/en12214110 - 28 Oct 2019

Cited by 9 | Viewed by 2860

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 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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13 pages, 1328 KiB

Open AccessArticle

Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm

by Julian Garcia-Guarin, Diego Rodriguez, David Alvarez, Sergio Rivera, Camilo Cortes, Alejandra Guzman, Arturo Bretas, Julio Romero Aguero and Newton Bretas

Energies 2019, 12(16), 3149; https://doi.org/10.3390/en12163149 - 16 Aug 2019

Cited by 28 | Viewed by 3927

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 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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