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Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 19888

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Special Issue Editors

Dr. Tariq Kamal

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Guest Editor

School of Technology and Innovations, Electrical Engineering, University of Vaasa, 65200 Vaasa, Finland
Interests: adaptive control; artificial intelligence; hybrid power system (solar photovoltaic, wind, fuel cells, hydro including microgrid design, power management and power market) distributed generation; smart grid applications; power electronics and control
Special Issues, Collections and Topics in MDPI journals

Dr. Syed Zulqadar Hassan

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Guest Editor

Faculty of Engineering & Architecture, University of Sialkot, Sialkot, Pakistan
Interests: intelligent control; artificial Intelligence; renewable energy sources; solar photovoltaic; wind; fuel cell; microgrid control

Special Issue Information

Dear Colleagues,

To achieve net-zero carbon emissions worldwide by 2050, energy transition from traditional power generation towards clean, flexible, and affordable renewable power generation is needed instantly. However, there are many technical issues in deploying renewable power generation. The highly variable, non-controllable, and stochastic nature of renewable energy generation creates power quality, stability concerns, and inconsistency in power systems. Using proper control and optimization methods would yield superior power quality and stability within the standard parameters imposed by the power industry and energy market. This Special Issue aims to disseminate state-of-the-art research and contribute to the applications of control and optimization paradigms in renewable energy systems. The application topics of interest include, but are not limited to:

Advanced control methods (fuzzy, artificial intelligence, neuro-fuzzy control, sliding-mode control, backstepping control, adaptive and predictive control, data-driven control, etc.);
Optimization methods (swarm optimization, genetic algorithms, Newton–Raphson, bacterial foraging optimization, simulated annealing, linear programming, hybrid optimization methods, etc.);
Other methods (any improved control and/or optimization method dealing with renewable energy systems.

Dr. Tariq Kamal
Dr. Syed Zulqadar Hassan
Guest Editors

Manuscript Submission Information

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Keywords

advanced/optimization methods
renewable energy
microgrid control
hybrid power systems
distributed generation
solar photovoltaics
wind power
fuel cells
hydroelectric power
energy storage systems.

Published Papers (11 papers)

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Editorial

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4 pages, 182 KiB

Open AccessEditorial

Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”

by Tariq Kamal and Syed Zulqadar Hassan

Energies 2023, 16(22), 7551; https://doi.org/10.3390/en16227551 - 13 Nov 2023

Viewed by 576

Abstract

The increasing environmental damage caused by adversarial factors, a growing need for energy, the continued reliance on fossil fuels, which comes with rising costs, and the global push for net-zero emissions targets have drawn significant focus on the global promotion of renewable energy [...] Read more.

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

Research

Jump to: Editorial

18 pages, 13816 KiB

Open AccessArticle

Structural Performance-Based Design Optimisation of a Secondary Mirror for a Concentrated Solar Power (CSP) Plant

by Lucio Pinello, Massimo Fossati, Marco Giglio, Francesco Cadini, Carla Bevilacqua, Mario Cilento, Fulvio Bassetti and Raffaello Magaldi

Energies 2023, 16(16), 6000; https://doi.org/10.3390/en16166000 - 16 Aug 2023

Cited by 2 | Viewed by 1294

Abstract

Concentrated Solar Power (CSP) plants use mirrors to reflect and concentrate sunlight onto a receiver, to heat a fluid and store thermal energy, at high temperature and energy density, to produce dispatchable heat and/or electricity. The secondary mirror is a critical component in the optical system of certain Solar Power Tower plants (SPT), as it redirects the concentrated sunlight from the primary mirror onto the receiver, which can be arranged at ground level. In this study, we propose a design optimisation for the secondary mirror of a CSP plant. The design optimisation method consists of two steps. The first step involves the use of the finite element simulation software Abaqus 2022 to analyse the structural performance of the secondary mirror under thermal loads and wind. The second step consists of the use of simulation results to identify the combination of design parameters and best performances, with respect to both design constraints and structural safety. This is carried out by developing an algorithm that selects those configurations which satisfy the constraints by using safety coefficients. The proposed optimisation method is applied to the design of a potential configuration of a secondary mirror for the beam-down of the CSP Magaldi STEM^® technology, although the methodology can be extended to other components of CSP plants, such as primary mirrors and receivers, to further enhance the structural performance of these systems. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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14 pages, 3792 KiB

Open AccessArticle

Exploring the Commercialization of Smart Rural Energy in Times of Energy Supply Chain Disruptions

by Hokey Min

Energies 2023, 16(14), 5364; https://doi.org/10.3390/en16145364 - 14 Jul 2023

Cited by 2 | Viewed by 764

Abstract

The lingering COVID-19 pandemic and ongoing war between Russia and Ukraine have wreaked havoc on the global oil supply chain. The current disruption of the oil supply chain and the rapidly growing energy demand created unprecedented oil shortages and raised the oil price beyond the affordable level. As worldwide oil price hikes continue, there is an urgent need for developing alternative energy sources, such as smart rural energy. Despite its enormous potential as a viable alternative to traditional fossil fuel-based energy sources, smart rural energy has never been fully utilized in society. The limited use of smart rural energy may be related to its lack of commercialization, which could have created more eco-friendly and cost-efficient alternative energy sources. This paper assesses the eco-friendliness and cost-efficiency of smart rural energy sources such as solar, wind, biomass, and hydropower for the first time. This paper is also one of the first studies that intends to develop viable strategic plans for commercializing smart rural energy using strategy maps, which subsequently helps increase public awareness of renewable energy by creating visual communication tools that convey the benefits of smart rural energy commercialization to multiple stakeholders, including government entities, business communities, and energy consumers. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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30 pages, 12613 KiB

Open AccessEditor’s ChoiceArticle

Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment

by Boris V. Malozyomov, Nikita V. Martyushev, Elena V. Voitovich, Roman V. Kononenko, Vladimir Yu. Konyukhov, Vadim Tynchenko, Viktor Alekseevich Kukartsev and Yadviga Aleksandrovna Tynchenko

Energies 2023, 16(13), 5046; https://doi.org/10.3390/en16135046 - 29 Jun 2023

Cited by 13 | Viewed by 1480

Abstract

Autonomous power systems serving remote areas with weather stations with small settlements are characterized by a fairly high cost of generating electricity and the purchase and delivery of fuel. In addition, diesel power plants require regular maintenance, have a relatively short service life during continuous operation and produce a large amount of emissions into the environment. This article discusses various methods of placing solar panels in the space for the autonomous power supply of weather station equipment. The principles of these methods are described and their advantages and disadvantages are outlined. The optimal algorithms of functioning for photomodules are described and their comparison regarding the main, significant parameters is carried out. The choice of the most effective algorithm for use at a weather station is made. The effective positioning of solar panels is also calculated, and positioning conditions are determined depending on the territorial location and various environmental conditions. Simulation of the power supply system of a weather station consisting of solar panels, batteries and inverters is performed. As a result, a practical example of the application of the method of selecting the optimal composition of equipment for a hybrid power system of a weather station territorially located in Siberia with different configurations of equipment is considered. In numerical terms, it was possible to reduce the cost of power equipment operation by more than 60% with a fairly low payback period of 5.5 years and an increased reliability of the power system, which is very important for autonomous power systems of northern weather stations. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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21 pages, 3982 KiB

Open AccessEditor’s ChoiceArticle

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

by M. Usman Saleem, Mustafa Shakir, M. Rehan Usman, M. Hamza Tahir Bajwa, Noman Shabbir, Payam Shams Ghahfarokhi and Kamran Daniel

Energies 2023, 16(12), 4835; https://doi.org/10.3390/en16124835 - 20 Jun 2023

Cited by 19 | Viewed by 4102

Abstract

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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19 pages, 6527 KiB

Open AccessArticle

Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load

by Zikuo Dai, Kejian Shi, Yidong Zhu, Xinyu Zhang and Yanhong Luo

Energies 2023, 16(11), 4432; https://doi.org/10.3390/en16114432 - 31 May 2023

Cited by 1 | Viewed by 1074

Abstract

In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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15 pages, 1196 KiB

Open AccessFeature PaperArticle

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

by Jiawei Zhang, Rongquan Zhang, Yanfeng Zhao, Jing Qiu, Siqi Bu, Yuxiang Zhu and Gangqiang Li

Energies 2023, 16(10), 4237; https://doi.org/10.3390/en16104237 - 22 May 2023

Cited by 4 | Viewed by 1098

Abstract

Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power system, accurate wind power forecasting is an essential technical tool of great value to ensure safe, stable, and efficient power generation. Therefore, in this paper, a hybrid intelligent model based on isolated forest, wavelet transform, categorical boosting, and quantile regression is proposed for deterministic and probabilistic wind power prediction. First, isolated forest is used to pre-process the original wind power data and detect anomalous data points in the power sequence. Then, the pre-processed original power sequence is decomposed into sub-frequency signals with better profiles by wavelet transform, and the nonlinear features of each sub-frequency are extracted by categorical boosting. Finally, a quantile-regression-based wind power probabilistic predictor is developed to evaluate uncertainty with different confidence levels. Moreover, the proposed hybrid intelligent model is extensively validated on real wind power data. Numerical results show that the proposed model achieves competitive performance compared to benchmark methods. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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21 pages, 2746 KiB

Open AccessArticle

The Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays of the IEEE Bus System

by Yuheng Wang, Kashif Habib, Abdul Wadood and Shahbaz Khan

Energies 2023, 16(9), 3726; https://doi.org/10.3390/en16093726 - 26 Apr 2023

Cited by 5 | Viewed by 1271

Abstract

The hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays (DOPR) of the IEEE bus system proposes a new method for coordinating directional overcurrent protection relays in power systems. The method combines the hybrid particle swarm optimization (HPSO) algorithm and a heuristic PSO algorithm to find the minimum total operating time of the directional overcurrent protection relays with speed and accuracy. The proposed method is tested on the IEEE 4-bus, 6-bus, and 8-bus systems, and the results are compared with those obtained using traditional coordination methods. The collected findings suggest that the proposed method may produce better coordination and faster operation of DOPRs than the previous methods, with an increase of up to 74.9% above the traditional technique. The hybridization of the PSO algorithm and heuristic PSO algorithm offers a promising approach to optimize power system protection. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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22 pages, 1655 KiB

Open AccessArticle

RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning

by Silvio Simani, Saverio Farsoni and Paolo Castaldi

Energies 2023, 16(9), 3644; https://doi.org/10.3390/en16093644 - 24 Apr 2023

Cited by 4 | Viewed by 1767 | Retraction

Abstract

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine’s operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross–turbine transfer learning can improve the accuracy of fault detection models in turbines with scarce data from supervisory control and data acquisition systems. In particular, it highlights that combining the knowledge from turbines with scarce data and turbines with plentiful data enables earlier detection of faults than prior art methods. In this way, the reuse and the knowledge transfer across wind turbines allows us to overcome this lack of data, thus enabling accurate fault detection in wind turbines. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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16 pages, 2672 KiB

Open AccessArticle

Optimization of Conductive Fins to Minimize UO₂ Fuel Temperature and Radial Temperature Gradient

by Kyle M. Paaren, Pavel Medvedev and Robert Mariani

Energies 2023, 16(6), 2785; https://doi.org/10.3390/en16062785 - 17 Mar 2023

Cited by 2 | Viewed by 1141

Abstract

To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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26 pages, 12787 KiB

Open AccessArticle

Experimental Study of an Inverter Control for Reactive Power Compensation in a Grid-Connected Solar Photovoltaic System Using Sliding Mode Control

by Manuel Flota-Bañuelos, María Espinosa-Trujillo, José Cruz-Chan and Tariq Kamal

Energies 2023, 16(2), 853; https://doi.org/10.3390/en16020853 - 11 Jan 2023

Cited by 8 | Viewed by 2300

Abstract

In photovoltaic (PV) systems, inverters have an essential role in providing an energy supply to meet the demand with power quality. Inverters inject energy into the grid considering that a renewable source is available; however, during intermittent periods or in the absence of power generation, the inverter remains inactive, which decreases the performance of the PV system. One way to increase the operation of inverters is to operate them as Volt-Amps Reactive (VAR) compensators to generate reactive power in the absence of renewable sources. The paper presents the development of a control scheme that allows the PV system’s inverter to improve the power factor in the electrical system with or without PV power generation. The proposed control is based on using a sliding mode controller (SMC) current control loop and PI-based voltage control loop. The control scheme is developed in MATLAB/SIMULINK, and for real evaluation, a PV prototype is implemented. The control strategy efficiency is confirmed by the obtained results. The control scheme increases the practical utility of PV systems. Additionally, it improves the power factor in all cases during the injection of active power to the grid operating under intermittent conditions and/or in the absence of power generation. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)

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