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Special Issue "Intelligent Control in Energy 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 2019

Special Issue Editor

Guest Editor
Prof. Dr. Anastasios Dounis

Department of Industrial Design and Production Engineering, University of West Attica, University Campus 2, P. Ralli & Thivon 250, 12244, Egaleo-Athens, Greece
Website | E-Mail
Interests: computational intelligence; intelligent control; intelligent buildings; renewable energy polygeneration; smart microgrids

Special Issue Information

Dear Colleagues,

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 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 monthly 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 1600 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 (8 papers)

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Research

Open AccessArticle Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings
Energies 2018, 11(9), 2427; https://doi.org/10.3390/en11092427
Received: 24 August 2018 / Revised: 6 September 2018 / Accepted: 6 September 2018 / Published: 13 September 2018
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Abstract
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure
[...] Read more.
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method
Energies 2018, 11(9), 2404; https://doi.org/10.3390/en11092404
Received: 25 August 2018 / Revised: 6 September 2018 / Accepted: 7 September 2018 / Published: 11 September 2018
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Abstract
The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit
[...] Read more.
The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit test was performed on an SCB10-1000/10 dry-type transformer, and the vibration signal at the surface was measured. Based on actual experimental conditions, a vibration-simulation model of the transformer was established using COMSOL Multiphysics software. A multiphysics coupling simulation of the circuit, magnetic field, and solid mechanics of the transformer was performed on this model. The simulation results were compared with measured data to verify the validity of the simulation model. The simulation model for a transformer operating under normal conditions was then used to develop simulation models of transformer-winding looseness, winding deformation, and winding-insulation failure, and the winding fault vibration characteristics were analyzed. The results provide a basis for detecting and analyzing the mechanical state of transformer windings. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor
Energies 2018, 11(9), 2212; https://doi.org/10.3390/en11092212
Received: 15 July 2018 / Revised: 19 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
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Abstract
A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology
[...] Read more.
A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology and working principle of the CSPM-HEV, and analyzes its operating mode and corresponding energy flow laws. On this basis, the relationship about the power loss of the vehicle, the CSPM transmission ratio iCSPM and the CSPM-HEV power distribution coefficient f1 were derived. According to the optimal combination of (iCSPM, f1), the engine power and speed which minimize the power loss of the vehicle, were calculated, thus realizing the instantaneous optimal control of the vehicle. In addition, in order to improve the instantaneously optimized control processing speed, a neural network controller was established. The drive axle demand power, speed and battery State of Charge (SOC), were taken as input variables. Then, the engine power and speed were taken as output variables. The simulation results show that the average speed of the instantaneous optimization strategy after BP neural network optimization is increased by 98.1%, the control effect is significant, and it has high application value. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Low Cost Position Controller for Exhaust Gas Recirculation Valve System
Energies 2018, 11(8), 2171; https://doi.org/10.3390/en11082171
Received: 30 July 2018 / Revised: 17 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
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Abstract
This paper proposes a position control method for a low-cost exhaust gas recirculation (EGR) valve system for automotive applications. Generally, position control systems used in automotive applications have many restrictions, such as cost and space. The mechanical structure of the actuator causes high
[...] Read more.
This paper proposes a position control method for a low-cost exhaust gas recirculation (EGR) valve system for automotive applications. Generally, position control systems used in automotive applications have many restrictions, such as cost and space. The mechanical structure of the actuator causes high friction and large differences between static friction and coulomb friction. When this large friction difference occurs, the position control vibrates when the controller uses a conventional linear controller such as the P or PI controller. In this paper, we introduce an inexpensive position control method that can be applied under the high-difference-friction mechanical systems. The proposed method is verified through the use of experiments by comparing it with the results obtained when using a conventional control system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid
Energies 2018, 11(7), 1852; https://doi.org/10.3390/en11071852
Received: 11 June 2018 / Revised: 6 July 2018 / Accepted: 9 July 2018 / Published: 16 July 2018
Cited by 1 | PDF Full-text (576 KB) | HTML Full-text | XML Full-text
Abstract
Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we
[...] Read more.
Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Decision Tree-Based Preventive Control Applications to Enhance Fault Ride Through Capability of Doubly-Fed Induction Generator in Power Systems
Energies 2018, 11(7), 1760; https://doi.org/10.3390/en11071760
Received: 2 June 2018 / Revised: 29 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
Cited by 1 | PDF Full-text (6090 KB) | HTML Full-text | XML Full-text
Abstract
The development of a preventive control methodology to increase the capacity of voltage sag recovery (Fault Ride Through Capability (FRTC)) of a doubly-fed induction generator (DFIG) connected in an electrical network is presented. This methodology, which is based on the decision trees (DT)
[...] Read more.
The development of a preventive control methodology to increase the capacity of voltage sag recovery (Fault Ride Through Capability (FRTC)) of a doubly-fed induction generator (DFIG) connected in an electrical network is presented. This methodology, which is based on the decision trees (DT) technique, assists with monitoring and support for security and preventive control, ensuring that wind systems remain connected to the power system even after the occurrence of disturbances in the electric system. Based on offline studies, DT discovers inherent attributes of the FRTC scenario related to electrical system behavior and provides a quick prediction model for real-time applications. From the obtained results, it is possible to check that the DFIG is contributing to a system’s operation security from the availability of power dispatch and participation in the voltage control. It is also noted that the use of DT, in addition to classifying the system’s operational state with good accuracy, also significantly facilitates the operator´s task, by directing him to monitor the most critical variables of the monitored operation state for a given system’s topological configuration. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems
Energies 2018, 11(7), 1686; https://doi.org/10.3390/en11071686
Received: 28 May 2018 / Revised: 24 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
In this paper, the secondary load frequency controller of the power systems with renewable energies is investigated by taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies, and the power unbalance caused between the supply side
[...] Read more.
In this paper, the secondary load frequency controller of the power systems with renewable energies is investigated by taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies, and the power unbalance caused between the supply side and demand side. For this, the μ-synthesis robust approach based on structure singular value is researched to design the load frequency controller. In the proposed control scheme, in order to improve the power system stability, an ultracapacitor is introduced to the system to rapidly respond to any power changes. Firstly, the load frequency control model with uncertainties is established, and then, the robust controller is designed based on μ-synthesis theory. Furthermore, a novel method using integrated system performance indexes is proposed to select the weighting function during controller design process, and solved by a differential evolution algorithm. Finally, the controller robust stability and robust performance are verified via the calculation results, and the system dynamic performance is tested via numerical simulation. The results show the proposed method greatly improved the load frequency stability of a micro-grid power system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell
Energies 2018, 11(7), 1669; https://doi.org/10.3390/en11071669
Received: 8 June 2018 / Revised: 18 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
Cited by 1 | PDF Full-text (2462 KB) | HTML Full-text | XML Full-text
Abstract
Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it
[...] Read more.
Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it describes self-discharge phenomenon caused by the internal short circuit clearly. However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal voltage of the pack, leading to difficulty in estimating accurate internal short circuit resistance. In this paper, estimating the resistance with the whole terminal voltages and the load currents of the pack, a detection method for the soft internal short circuit in the pack is proposed. Open circuit voltage of a faulted cell in the pack is extracted to reflect the self-discharge phenomenon obviously; this process yields accurate estimates of the resistance. The proposed method is verified with various soft short conditions in both simulations and experiments. The error of estimated resistance does not exceed 31.2% in the experiment, thereby enabling the battery management system to detect the internal short circuit early. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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