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Computational Intelligence in Electrical Systems
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Computational Intelligence in Electrical Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 14528

Special Issue Editors

Prof. Dr. Massimo Panella

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Interests: deep learning; computational intelligence; smart sensor networks; quantum computing
Special Issues, Collections and Topics in MDPI journals
Dr. Antonello Rosato

E-Mail Website
Co-Guest Editor
Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Interests: machine learning techniques for time series analysis and forecasting; distributed learning algorithms; neural and fuzzy neural models for ICT and industrial applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rodolfo Araneo

E-Mail Website
Co-Guest Editor
Department of Astronautical, Electrical and Energetic Engineering, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Interests: electromagnetic compatibility; energy harvesting; graphene electrodynamics; numerical and analytical techniques for modeling high-speed printed circuit boards; shielding; transmission lines; periodic structures; devices based on graphene
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical systems play a central role in the energy transition from fossil fuels to renewables. Toward this end, it is essential that final prosumers can collectively cooperate in the management of distributed energy resources (DERs), to share energy and assets. Distributed resources in an energy community can be geographically near, sharing a smart microgrid conceived as a set of renewable energy sources (RESs), loads, energy storage systems (ESSs), and electric vehicles (EVs).

In this scenario, a crucial role is played by data-driven modeling techniques based on the paradigm of machine learning and, more generally, of computational intelligence in synergy with ICT technologies that help to share information across complex infrastructures. In fact, many control, decision, and optimization problems for electrical systems should be handled with real-time constraints while they involve a large amount of data in very complex operation frameworks. Consequently, such tasks should be solved using distributed learning techniques, as they cannot be handled by a centralized authority (i.e., for privacy concerns, networking reliability, etc.), nor can they be carried out efficiently by human operators.

This Special Issue is intended to bring forth advances in the use of computational intelligence tools (shallow and deep neural networks, fuzzy systems, evolutionary computation, etc.), in connection with statistical machine learning and signal processing techniques, for the solution of real-world problems related to electrical systems. Special attention should be paid to the distributed contexts of smart grid, RES, ESS, and EV infrastructures, as well as to the energy/power aspects in ICT technologies and the related applications as, for instance, hungry data centers, green computing and green networking, EMC/EMI, energy harvesting, low-power micro/nano/optoelectronic systems, and so forth. Strategic tasks to be considered are pattern analysis, data regression and classification, optimization and control, decision-making, time series forecasting.

Prof. Dr. Massimo Panella
Dr. Antonello Rosato
Prof. Dr. Rodolfo Araneo
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. 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 2600 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, Microgrids, and Virtual Power Plants
  • Distributed Energy Resources
  • Renewable Energy Sources
  • Energy Storage Systems
  • Electric Vehicles
  • Green Computing and Green Networking
  • Energy Harvesting
  • Low-power ICT Systems
  • Neural Networks
  • Fuzzy Systems
  • Evolutionary Computation
  • Deep Learning
  • Classification and Clustering
  • Data Regression
  • Optimization and Control
  • Time Series Forecasting

Published Papers (8 papers)

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Research

25 pages, 18022 KiB  
Article
Optimal Electric Vehicle Parking Lot Energy Supply Based on Mixed-Integer Linear Programming
by Damir Jakus, Josip Vasilj and Danijel Jolevski
Energies 2023, 16(23), 7793; https://0-doi-org.brum.beds.ac.uk/10.3390/en16237793 - 27 Nov 2023
Viewed by 776
Abstract
E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions [...] Read more.
E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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22 pages, 2492 KiB  
Article
Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence
by Marinko Barukčić, Goran Kurtović, Tin Benšić and Vedrana Jerković Štil
Energies 2023, 16(22), 7567; https://0-doi-org.brum.beds.ac.uk/10.3390/en16227567 - 14 Nov 2023
Viewed by 662
Abstract
The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the [...] Read more.
The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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14 pages, 611 KiB  
Article
Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach
by Antonella R. Finamore, Vito Calderaro, Vincenzo Galdi, Giuseppe Graber, Lucio Ippolito and Gaspare Conio
Energies 2023, 16(22), 7522; https://0-doi-org.brum.beds.ac.uk/10.3390/en16227522 - 10 Nov 2023
Cited by 2 | Viewed by 756
Abstract
This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis [...] Read more.
This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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29 pages, 23445 KiB  
Article
Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations
by Gülsah Erdogan and Wiem Fekih Hassen
Energies 2023, 16(18), 6656; https://0-doi-org.brum.beds.ac.uk/10.3390/en16186656 - 16 Sep 2023
Cited by 1 | Viewed by 1536
Abstract
The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge [...] Read more.
The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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34 pages, 14410 KiB  
Article
Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles
by Marat Sadykov, Sam Haines, Mark Broadmeadow, Geoff Walker and David William Holmes
Energies 2023, 16(4), 1628; https://0-doi-org.brum.beds.ac.uk/10.3390/en16041628 - 06 Feb 2023
Cited by 2 | Viewed by 1703
Abstract
This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine [...] Read more.
This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources as possible on openly available battery data. The results highlighted an average percentage accuracy of 96.56% for the correct SoC estimation and several drawbacks of the overall implementation, and we propose potential solutions for further improvement. Every implemented model had a similar drawback, which was the poor capturing of the middle area of charge, applying a higher weight to the voltage than the current. The combination of these techniques into a single custom model could result in a better-suited model, further improving the accuracy. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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37 pages, 17210 KiB  
Article
Challenges and Perspectives of Smart Grid Systems in Islands: A Real Case Study
by Federico Succetti, Antonello Rosato, Rodolfo Araneo, Gianfranco Di Lorenzo and Massimo Panella
Energies 2023, 16(2), 583; https://0-doi-org.brum.beds.ac.uk/10.3390/en16020583 - 04 Jan 2023
Cited by 5 | Viewed by 1430
Abstract
Islands are facing significant challenges in meeting their energy needs in a sustainable, affordable, and reliable way. Traditionally, the primary source of electricity on the islands has been imported diesel fuel, with high financial costs for most utilities. In this context, even replacing [...] Read more.
Islands are facing significant challenges in meeting their energy needs in a sustainable, affordable, and reliable way. Traditionally, the primary source of electricity on the islands has been imported diesel fuel, with high financial costs for most utilities. In this context, even replacing part of the traditional production with renewable energy source can reduce costs and improve the quality of life of islanders. However, integrating large amounts of renewable energy production into existing grids introduces many concerns regarding feasibility, economic analysis, and technical implementation. From this point of view, machine learning and deep learning techniques are efficient tools to mitigate these problems. Their potential results are beneficial considering isolated grids of small islands which are not connected to the national grid. In this paper, a study of the Italian island of Ponza is carried out. The isolation leads to several challenges, such as the high cost related to the transport, installation, and maintenance of renewable energy sources in a small area with several constraints and their intermittent power production, which requires the use of storage systems for dispatching purposes. The proposed study aims to identify future developments of the electricity grid by considering the deployment of both renewable energy sources and energy storage systems. Furthermore, future scenarios are depicted through the use of autoregressive and deep learning techniques to give an idea about the economic costs of both energy demand and supply. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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18 pages, 3348 KiB  
Article
2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series
by Antonello Rosato, Rodolfo Araneo, Amedeo Andreotti, Federico Succetti and Massimo Panella
Energies 2021, 14(9), 2392; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092392 - 23 Apr 2021
Cited by 11 | Viewed by 2749
Abstract
Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies [...] Read more.
Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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35 pages, 504 KiB  
Article
Application of the Vortex Search Algorithm to the Phase-Balancing Problem in Distribution Systems
by Brandon Cortés-Caicedo, Laura Sofía Avellaneda-Gómez, Oscar Danilo Montoya, Lazaro Alvarado-Barrios and Harold R. Chamorro
Energies 2021, 14(5), 1282; https://0-doi-org.brum.beds.ac.uk/10.3390/en14051282 - 26 Feb 2021
Cited by 26 | Viewed by 3319
Abstract
This article discusses the problem of minimizing power loss in unbalanced distribution systems through phase-balancing. This problem is represented by a mixed-integer nonlinear-programming mathematical model, which is solved by applying a discretely encoded Vortex Search Algorithm (DVSA). The numerical results of simulations performed [...] Read more.
This article discusses the problem of minimizing power loss in unbalanced distribution systems through phase-balancing. This problem is represented by a mixed-integer nonlinear-programming mathematical model, which is solved by applying a discretely encoded Vortex Search Algorithm (DVSA). The numerical results of simulations performed in IEEE 8-, 25-, and 37-node test systems demonstrate the applicability of the proposed methodology when compared with the classical Cuh & Beasley genetic algorithm. In addition, the computation times required by the algorithm to find the optimal solution are in the order of seconds, which makes the proposed DVSA a robust, reliable, and efficient tool. All computational implementations have been developed in the MATLAB® programming environment, and all the results have been evaluated in DigSILENT© software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Full article
(This article belongs to the Special Issue Computational Intelligence in Electrical Systems)
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