Special Issue "Computational Intelligence Applications in Smart Grid Optimization"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Smart Grids and Microgrids".

Deadline for manuscript submissions: 20 May 2021.

Special Issue Editors

Dr. Fernando Lezama
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Guest Editor
GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Interests: computational intelligence; energy resource management; energy systems simulation; evolutionary computation; local energy markets; multi-agent systems; smart grids
Special Issues and Collections in MDPI journals
Dr. Joao Soares
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Guest Editor
GECAD - Knowledge Engineering and Decision Support Research Center - Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Interests: energy resource management; energy systems simulation; electric vehicles; meta-heuristic optimization; smart grid; swarm intelligence
Special Issues and Collections in MDPI journals
Dr. Tobias Rodemann
Website
Guest Editor
Honda Research Institute Europe GmbH, 63073 Offenbach am Main, Germany
Interests: energy management; many-objective optimization; digital twin; evolutionary computation

Special Issue Information

Dear Colleagues,

Evolutionary computation (EC)—a set of optimization algorithms mostly inspired by biological and evolutionary processes—is arguably one of the most successful branches of computational intelligence (CI) used by practitioners all over the world in all areas of engineering.

On the other hand, smart grids are intelligent electrical networks enabling bidirectional power flows and communication between energy sources (traditional, distributed, and renewables) and smart devices (loads, storage, smart appliances). While smart grids promise benefits for users and operators (e.g., the enhance of features such as flexibility, reliability, sustainability, efficiency, etc.), their evolution into a complex socioeconomic environment—requiring a great deal of analysis and planning—is pushing the application of accepted deterministic solutions to its limits. In some cases, these solutions are not suitable for dealing with issues related to high-dimensionality, lack of information, noisy and corrupt data, and real-time requirements, among numerous other real-world considerations.

Thus, EC embracing algorithms that are tolerant to imprecision, uncertainty, and approximation can play a key role as an efficient tool to deal with the challenging scenario encountered in many smart grid applications. This Special Issue aims to address and disseminate the state-of-the-art research and development in the application of evolutionary computational in smart grids.

Dr. Fernando Lezama
Dr. Joao Soares
Prof. Dr. Zita Vale
Dr. Tobias Rodemann
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.

Published Papers (3 papers)

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Research

Open AccessArticle
Hybrid Energy Systems Sizing for the Colombian Context: A Genetic Algorithm and Particle Swarm Optimization Approach
Energies 2020, 13(21), 5648; https://doi.org/10.3390/en13215648 - 28 Oct 2020
Abstract
The use of fossil resources for electricity production is one of the primary reasons for increasing greenhouse emissions and is a non-renewable resource. Therefore, the electricity generation by wind and solar resources have had greater applicability in recent years. Hybrid Renewable Energy Systems [...] Read more.
The use of fossil resources for electricity production is one of the primary reasons for increasing greenhouse emissions and is a non-renewable resource. Therefore, the electricity generation by wind and solar resources have had greater applicability in recent years. Hybrid Renewable Energy Systems (HRES) integrates renewable sources and storage systems, increasing the reliability of generators. For the sizing of HRES, Artificial Intelligence (AI) methods such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) stand out. This article presents the sizing of an HRES for the Colombian context, taking into account the energy consumption by three typical demands, four types of wind turbines, three types of solar panels, and a storage system for the system configuration. Two optimization approaches were set-up with both optimization strategies (i.e., GA and PSO). The first one implies the minimization of the Loss Power Supply Probability (LPSP). In contrast, the second one concerns adding the Total Annual Cost (TAC) or the Levelized Cost of Energy (LCOE) to the objective function. Results obtained show that HRES can supply the energy demand, where the PSO method gives configurations that are more adjusted to the considered electricity demands. Full article
(This article belongs to the Special Issue Computational Intelligence Applications in Smart Grid Optimization)
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Open AccessFeature PaperArticle
Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms
Energies 2020, 13(10), 2466; https://doi.org/10.3390/en13102466 - 14 May 2020
Cited by 3
Abstract
Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy [...] Read more.
Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem. Full article
(This article belongs to the Special Issue Computational Intelligence Applications in Smart Grid Optimization)
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Open AccessArticle
A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids
Energies 2020, 13(7), 1706; https://doi.org/10.3390/en13071706 - 03 Apr 2020
Cited by 2
Abstract
The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be [...] Read more.
The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency. Full article
(This article belongs to the Special Issue Computational Intelligence Applications in Smart Grid Optimization)
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