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Intelligent Control in Energy Systems Ⅱ
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Intelligent Control in Energy Systems Ⅱ

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 18615

Special Issue Editor

Prof. Dr. Anastasios Dounis

E-Mail Website
Guest Editor
Faculty of Engineering, University of West Attica, 12243 Athens, Greece
Interests: computational intelligence and evolutionary computation; fuzzy systems; fuzzy control and modelling; fuzzy cognitive maps and petri nets in decision support systems; intelligent control; time series prediction; automation systems in renewable energy resources; intelligent energy management systems and smart buildings; design and management of autonomous smart micro grids; power electronics in photovoltaic systems; control electrochromic devices; modelling and control of reverse osmosis desalination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue "Intelligent Control in Energy Systems Ⅱ" is a continuation of the previous successful Special Issue "Intelligent Control in Energy Systems".

Energy systems (ES) are a complex and constantly evolving research area. Because energy systems are multi-layered and distributed, there is a growing interest in integrating heterogeneous entities (energy sources, energy storage, micro-grids, grid networks, buildings, electrical vehicles, etc.) into distribution systems. The challenge in handling the vast volume of information is the requirement the use of modern efficient management control strategies such as intelligent control technologies.

Intelligent control (IC) describes a class of control techniques that use various artificial intelligence techniques such as neural network control, Bayesian control, fuzzy logic control, neuro-fuzzy control, evolutionary computation, machine learning and intelligent agents. IC systems are very useful when no mathematical model is available a priori. IC is inspired by the intelligence and genetics of living beings.

IC, communications infrastructure and wireless networking play an important role in a smart grid network in achieving reliable, efficient, secure, distribution, cost-effective generation and consumption. IC on energy storage devices provide reliability and economic impacts on the energy systems.

Buildings consume a large portion of the world’s energy and they are a source of greenhouse gas emissions. The concept of sustainable and zero energy buildings is emerging as an important area for the smart micro-grid initiative. In addition, effective energy management is becoming more feasible using the innovative smart micro-grid technologies and IC. These changes have resulted an environment of high complexity, uncertainty and imprecision. The IC can play a remarkable and vital role in handling a significant part of this high uncertainty and nonlinearity by providing new smart solutions for a more efficient and reliable operation of ESs.

This Special Issue is focused on to bring together innovative developments and synergies in the fields of intelligent control and energy systems.

Potential topics include, but are not limited to:

  • Energy management and IC in energy micro-grids;
  • ESs modeling and IC;
  • IC and optimization for zero energy buildings;
  • Evolutionary control in ESs;
  • IC in hybrid ESs of isolated areas;
  • Fuzzy logic control in ESs;
  • Intelligent multiagent control systems in ESs;
  • Artificial neural networks for control in ESs;
  • IC of holonic ESs;
  • IC in energy storage systems;
  • IC in sustainable smart ESs;
  • Fault diagnosis and IC in ESs;
  • Chaos control in ESs;
  • Bayesian control in renewable energy systems;
  • Neuro-fuzzy control in ESs;
  • Machine learning in ESs;
  • IC in distributed electrical energy generation system;
  • IC in smart grid network;
  • IC and ESs stability;
  • IC and demand side forecasting in ESs;
  • IC and uncertainty analysis of ESs.

Prof. Dr. Anastasios Dounis
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. 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

  • Intelligent control
  • Evolutionary control
  • Fuzzy logic control
  • Energy systems
  • Machine learning
  • Artificial neural network
  • Intelligent energy management systems
  • Intelligent buildings
  • Micro-grids
  • Energy storage systems
  • Smart grid network

Published Papers (6 papers)

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Research

23 pages, 11282 KiB  
Article
Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms
by Kostas Bavarinos, Anastasios Dounis and Panagiotis Kofinas
Energies 2021, 14(2), 335; https://0-doi-org.brum.beds.ac.uk/10.3390/en14020335 - 09 Jan 2021
Cited by 12 | Viewed by 1840
Abstract
In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required [...] Read more.
In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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17 pages, 5291 KiB  
Article
Assessment Indexes for Converter P-Q Control Coupling
by Panagis N. Vovos, Ioannis D. Bouloumpasis and Konstantinos G. Georgakas
Energies 2020, 13(5), 1144; https://0-doi-org.brum.beds.ac.uk/10.3390/en13051144 - 03 Mar 2020
Viewed by 1921
Abstract
This work presents a concise methodology for the calculation of assessment indexes regarding the coupling between active and reactive power control observed on distribution level converters. First, the reader is introduced to the concept of power coupling; when, where and how it appears [...] Read more.
This work presents a concise methodology for the calculation of assessment indexes regarding the coupling between active and reactive power control observed on distribution level converters. First, the reader is introduced to the concept of power coupling; when, where and how it appears in power control of converters. A brief summary of the theory and formulation behind it is also included, together with relevant literature. Then, the methodology for the assessment of active and reactive power control performance of any grid-connected converter is presented. The impact of small control disturbances during a testing procedure is monitored, analyzed and converted to meaningful indexes, so that the type and level of coupling is quantified without putting the converter or the grid at risk. The efficiency of the methodology to assess the type and level of coupling is verified experimentally. This is done by assessing several power control approaches with different level of decoupling efficiency on the same power converter connected to a distribution grid. While the assessment is performed with safe, minimal disturbances, its exceptional accuracy is later confirmed by the level and type of coupling observed during significant power step changes. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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12 pages, 6120 KiB  
Article
Improved Position Control for an EGR Valve System with Low Control Frequency
by Hyeong-Jin Kim, Yung-Deug Son and Jang-Mok Kim
Energies 2020, 13(2), 457; https://0-doi-org.brum.beds.ac.uk/10.3390/en13020457 - 17 Jan 2020
Cited by 1 | Viewed by 3527
Abstract
An exhaust gas recirculation (EGR) valve position control system requires fast response without overshoot, but the low control frequency limits control bandwidth and results in poor position response. A novel EGR valve position control scheme is proposed to improve the position response at [...] Read more.
An exhaust gas recirculation (EGR) valve position control system requires fast response without overshoot, but the low control frequency limits control bandwidth and results in poor position response. A novel EGR valve position control scheme is proposed to improve the position response at low control frequency. This is based on the feedforward controller, but the feedforward control loop is implemented without the position pattern generator or derivative. The proposed method estimates the acceleration command through the relationship between the position controller output, the speed command and the speed limiter, and compensates the cascaded proportional-proportional integral (P-PI) controller. In this method, many operations are not required and noise due to derivative is not generated. This method can improve the position response without much computation and derivative noise at the low control frequency. Experimental results are presented to verify the feasibility of the proposed position control algorithm. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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15 pages, 10365 KiB  
Article
A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR
by Bo Hu, Jiaxi Li, Shuang Li and Jie Yang
Energies 2019, 12(19), 3739; https://0-doi-org.brum.beds.ac.uk/10.3390/en12193739 - 30 Sep 2019
Cited by 8 | Viewed by 2834
Abstract
Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors’ best knowledge, there seem to be few [...] Read more.
Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors’ best knowledge, there seem to be few studies that apply the latest DRL algorithms on real-world powertrain control problems. If there are any, the requirement of classical model-free DRL algorithms typically for a large number of random exploration in order to realize good control performance makes it almost impossible to implement directly on a real plant. Unlike most of the other DRL studies, whose control strategies can only be trained in a simulation environment—especially when a control strategy has to be learned from scratch—in this study, a hybrid end-to-end control strategy combining one of the latest DRL approaches—i.e., a dueling deep Q-network and traditional Proportion Integration Differentiation (PID) controller—is built, assuming no fidelity simulation model exists. Taking the boost control of a diesel engine with a variable geometry turbocharger (VGT) and cooled (exhaust gas recirculation) EGR as an example, under the common driving cycle, the integral absolute error (IAE) values with the proposed algorithm are improved by 20.66% and 9.7% respectively for the control performance and generality index, compared with a fine-tuned PID benchmark. In addition, the proposed method can also improve system adaptiveness by adding another redundant control module. This makes it attractive to real plant control problems whose simulation models do not exist, and whose environment may change over time. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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23 pages, 4121 KiB  
Article
HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response
by Nikolaos Kampelis, Nikolaos Sifakis, Dionysia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori and Cristina Cristalli
Energies 2019, 12(11), 2177; https://0-doi-org.brum.beds.ac.uk/10.3390/en12112177 - 07 Jun 2019
Cited by 32 | Viewed by 4479
Abstract
Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration [...] Read more.
Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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21 pages, 2942 KiB  
Article
A Novel Approach to Arcing Faults Characterization Using Multivariable Analysis and Support Vector Machine
by John Morales, Eduardo Muñoz, Eduardo Orduña and Gina Idarraga-Ospina
Energies 2019, 12(11), 2126; https://0-doi-org.brum.beds.ac.uk/10.3390/en12112126 - 03 Jun 2019
Cited by 7 | Viewed by 3364
Abstract
Based on the Institute of Electrical and Electronics Engineers (IEEE) Standard C37.104-2012 Power Systems Relaying Committee report, topics related to auto-reclosing in transmission lines have been considered as an imperative benefit for electric power systems. An important issue in reclosing, when performed correctly, [...] Read more.
Based on the Institute of Electrical and Electronics Engineers (IEEE) Standard C37.104-2012 Power Systems Relaying Committee report, topics related to auto-reclosing in transmission lines have been considered as an imperative benefit for electric power systems. An important issue in reclosing, when performed correctly, is identifying the fault type, i.e., permanent or temporary, which keeps the faulted transmission line in service as long as possible. In this paper, a multivariable analysis was used to classify signals as permanent and temporary faults. Thus, by using a simple convolution process among the mother functions called eigenvectors and the fault signals from a single end, a dimensionality reduction was determined. In this manner, the feature classifier based on the support vector machine was used for acceptably classifying fault types. The algorithm was tested in different fault scenarios that considered several distances along the transmission line and representation of first and second arcs simulated in the alternative transients program ATP software. Therefore, the main contribution of the analysis performed in this paper is to propose a novel algorithm to discriminate permanent and temporary faults based on the behavior of the faulted phase voltage after single-phase opening of the circuit breakers. Several simulations let the authors conclude that the proposed algorithm is effective and reliable. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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