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Renewable Energy System Technologies: 2nd Edition
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Renewable Energy System Technologies: 2nd Edition

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: 10 April 2025 | Viewed by 8818

Special Issue Editor

Prof. Dr. Tek-Tjing Lie

E-Mail Website
Guest Editor
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: AI applications to power systems; power system control and operation; smart grids; renewable energy resources; energy management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy resources, such as solar photovoltaic (PV) and wind turbine generation, are completely dependent on nature (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, their outputs are stochastic in nature, and are required to develop and apply new technologies to overcome intermittency issues as well as Big Data in real time.

Integrated system modelling methods and concepts are needed to study the self-organization, complexity, emergent properties, and dynamical behavior of complex systems for their holistic understanding, management, and development based primarily on neural networks, fuzzy and soft systems/fuzzy cognitive maps, network modelling, and mathematics. Other advanced applications in the computational early detection of mastitis and computer-based decision support systems for complex systems are also needed. Due to the scale of the network and the amount of data that needs to be digitized, new technologies such as techniques in data mining and AI approaches are needed to analyze and predict the behavior of these complex systems.

Prof. Dr. Tek-Tjing Lie
Guest Editor

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Keywords

  • big data
  • solar PV
  • wind turbine generation
  • intermittent
  • real time

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Related Special Issue

Published Papers (8 papers)

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19 pages, 8944 KiB  
Article
Fault Detection and Protection Strategy for Multi-Terminal HVDC Grids Using Wavelet Analysis
by Jashandeep Kaur, Manilka Jayasooriya, Muhammad Naveed Iqbal, Kamran Daniel, Noman Shabbir and Kristjan Peterson
Energies 2025, 18(5), 1147; https://doi.org/10.3390/en18051147 - 26 Feb 2025
Viewed by 249
Abstract
The growing demand for electricity, integration of renewable energy sources, and recent advances in power electronics have driven the development of HVDC systems. Multi-terminal HVDC (MTDC) grids, enabled by Voltage Source Converters (VSCs), provide increased operational flexibility, including the ability to reverse power [...] Read more.
The growing demand for electricity, integration of renewable energy sources, and recent advances in power electronics have driven the development of HVDC systems. Multi-terminal HVDC (MTDC) grids, enabled by Voltage Source Converters (VSCs), provide increased operational flexibility, including the ability to reverse power flow and independently control both active and reactive power. However, fault propagation in DC grids occurs more rapidly, potentially leading to significant damage within milliseconds. Unlike AC systems, HVDC systems lack natural zero-crossing points, making fault isolation more complex. This paper presents the implementation of a wavelet-based protection algorithm to detect faults in a four-terminal VSC-HVDC grid, modelled in MATLAB and SIMULINK. The study considers several fault scenarios, including two internal DC pole-to-ground faults, an external DC fault in the load branch, and an external AC fault outside the protected area. The discrete wavelet transform, using Symlet decomposition, is applied to classify faults based on the wavelet entropy and sharp voltage and current signal variations. The algorithm processes the decomposition coefficients to differentiate between internal and external faults, triggering appropriate relay actions. Key factors influencing the algorithm’s performance include system complexity, fault location, and threshold settings. The suggested algorithm’s reliability and suitability are demonstrated by the real-time implementation. The results confirmed the precise fault detection, with fault currents aligning with the values in offline models. The internal faults exhibit more entropy than external faults. Results demonstrate the algorithm’s effectiveness in detecting faults rapidly and accurately. These outcomes confirm the algorithm’s suitability for a real-time environment. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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Figure 1

Figure 1
<p>Single line diagram of the LCC HVDC system.</p> Full article ">Figure 2
<p>Structure of VSC-HVDC system.</p> Full article ">Figure 3
<p>Two-level wavelet decomposition trees.</p> Full article ">Figure 4
<p>Fault detection and protection flow.</p> Full article ">Figure 5
<p>Simulation of four terminal VSC-HVDC systems.</p> Full article ">Figure 6
<p>DC current for fault F1.</p> Full article ">Figure 7
<p>Voltage for internal fault F1.</p> Full article ">Figure 8
<p>Detailed coefficients for F1.</p> Full article ">Figure 9
<p>DC current for fault F2.</p> Full article ">Figure 10
<p>Voltage for internal fault F2.</p> Full article ">Figure 11
<p>Detailed coefficients for fault F2.</p> Full article ">Figure 12
<p>Detailed coefficients for fault F3.</p> Full article ">Figure 13
<p>Detailed coefficients for fault F4.</p> Full article ">Figure 14
<p>Current for DC fault F3.</p> Full article ">Figure 15
<p>DC current for fault F4.</p> Full article ">Figure 16
<p>Voltage for external fault F3.</p> Full article ">Figure 17
<p>Voltage for internal fault F4.</p> Full article ">Figure 18
<p>OpComm blocks for output.</p> Full article ">Figure 19
<p>Real-time model for four terminal grid.</p> Full article ">Figure 20
<p>Current for DC fault F1.</p> Full article ">Figure 21
<p>Current for DC fault F2.</p> Full article ">Figure 22
<p>Current for AC fault F3.</p> Full article ">Figure 23
<p>Current for DC fault F4.</p> Full article ">
18 pages, 1262 KiB  
Article
Evaluation of Technical Aspects of Solar Photovoltaic (PV) Power Installations on Farmland
by Lorenzo Sabino, Rafiq Asghar, Fabio Crescimbini and Francesco Riganti Fulginei
Energies 2025, 18(2), 317; https://doi.org/10.3390/en18020317 - 13 Jan 2025
Viewed by 471
Abstract
This research evaluates the technical and economic aspects of solar photovoltaic (PV) power installations on farmland, utilizing a simulation model in MATLAB to forecast annual system output based on nominal power and meteorological data. This study compares various configurations, including single-sided versus double-sided [...] Read more.
This research evaluates the technical and economic aspects of solar photovoltaic (PV) power installations on farmland, utilizing a simulation model in MATLAB to forecast annual system output based on nominal power and meteorological data. This study compares various configurations, including single-sided versus double-sided modules and fixed versus tracker structures, to determine their efficiency, losses, and economic viability. The findings indicate that, while theoretically superior technologies may offer better production rates, their economic feasibility varies significantly depending on specific project conditions. The main conclusions drawn from this research emphasize that land-based PV systems present a promising solution for sustainable energy generation. By addressing challenges such as solar energy intermittency and the need for supportive infrastructure, this study highlights the potential for these systems to significantly contribute to reducing greenhouse gas emissions and enhancing energy resilience. This analysis underscores the importance of optimizing configurations to maximize both technical performance and economic returns, ultimately supporting a transition towards a more sustainable energy future. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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Figure 1

Figure 1
<p>Flowchart for the model implemented in MATLAB.</p> Full article ">Figure 2
<p>Global horizontal radiation [kWh/m<sup>2</sup>] PVGIS weather database.</p> Full article ">Figure 3
<p>Temperature [°C] PVGIS weather database.</p> Full article ">Figure 4
<p>Total investment based on GCR for single-sided modules. F = fixed structure, T = tracker structure.</p> Full article ">Figure 5
<p>Total investment based on GCR for double-sided modules. F = fixed structure, T = tracker structure.</p> Full article ">Figure 6
<p>NPV as a function of GCR for single-sided modules. F = fixed structure, T = tracker structure.</p> Full article ">Figure 7
<p>NPV as a function of GCR for double-sided modules. F = fixed structure, T = tracker structure.</p> Full article ">Figure 8
<p>Monthly energy production with fixed single-sided module.</p> Full article ">Figure 9
<p>Monthly energy production with tracker single-sided module.</p> Full article ">Figure 10
<p>Monthly energy production with fixed double-sided module.</p> Full article ">Figure 11
<p>Monthly energy production with tracker double-sided module.</p> Full article ">Figure 12
<p>Final yield of single-sided modules as the GCR varies. F = fixed structure, T = with tracker.</p> Full article ">Figure 13
<p>Final yield of double-sided modules as the GCR varies. F = fixed structure, T = with tracker.</p> Full article ">Figure 14
<p>Annual energy produced by single-sided modules as the GCR varies. F = fixed structure, T = with tracker.</p> Full article ">Figure 15
<p>Annual energy produced by double-sided modules as the GCR varies. F = fixed structure, T = with tracker.</p> Full article ">
40 pages, 7137 KiB  
Article
Heterojunction Technology vs. Passivated Emitter and Rear Contact Photovoltaic Panels: Evaluating Efficiency and Profitability Under Challenging Summer Conditions in Lisbon Using Extensive Field Data
by André Sapina and Paulo Branco
Energies 2025, 18(1), 114; https://doi.org/10.3390/en18010114 - 30 Dec 2024
Viewed by 1075
Abstract
Renewable energy is essential for reducing fossil fuel dependence and achieving carbon neutrality by 2050. This study compares the widely used passivated emitter and rear contact (PERC) cells with advanced heterojunction technology (HJT) cells. Conducted in Lisbon during August 2022, this research evaluates [...] Read more.
Renewable energy is essential for reducing fossil fuel dependence and achieving carbon neutrality by 2050. This study compares the widely used passivated emitter and rear contact (PERC) cells with advanced heterojunction technology (HJT) cells. Conducted in Lisbon during August 2022, this research evaluates the energy yield of PV installations over 400 W under challenging summer conditions. HJT cells, which combine monocrystalline silicon and amorphous layers, showed a 1.88% higher efficiency and a 3% to 6% increase in energy yield compared to PERC cells. This study also examines the effects of irradiance and temperature on performance using experiment field data. HJT modules are ideal for limited space or power constraints, offering long-term profitability, while PERC modules are more cost-effective for budget-limited projects. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
►▼ Show Figures

Figure 1

Figure 1
<p>Rear surface passivation scheme illustrating the passivation layer located at the rear surface of the solar cell to reduce recombination losses and improve overall performance [<a href="#B8-energies-18-00114" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>PERC cell scheme: 1—“<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>g</mi> </mrow> </semantics></math>” composes the front electric contact; 2—“<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>x</mi> </msub> </mrow> </semantics></math>” (silicon nitride) composes the anti-reflective coating. 3—“<math display="inline"><semantics> <mrow> <mi>A</mi> <msub> <mi>l</mi> <mn>2</mn> </msub> <msub> <mi>O</mi> <mn>3</mn> </msub> </mrow> </semantics></math>” (aluminum oxide) forms the rear surface dielectric passivation layer; and the “<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>l</mi> </mrow> </semantics></math>” states a back surface increasing the electric field value to reduce the recombination.</p> Full article ">Figure 3
<p>Efficiency decrease due to the light-induced degradation (LID).</p> Full article ">Figure 4
<p>Cross-sectional view of SHJn (n-type and p-type silicon heterojunction). Both show an amorphous silicon layer (a-Si:H(i), i = p, n); a thin layer for surface passivation (a-Si:H(n)) for an n-type amorphous silicon layer [<a href="#B12-energies-18-00114" class="html-bibr">12</a>].</p> Full article ">Figure 5
<p>Busbar and SmartWire Connection Technology (SWCT): this decreases the amount of silver compared to busbar technology, also increasing the efficiency since it allows for reducing the shading effect and the series resistance of the cell. However, despite its higher initial costs, a reduction in silver usage can lead to lower costs [<a href="#B15-energies-18-00114" class="html-bibr">15</a>].</p> Full article ">Figure 6
<p>Electrical parameters taken from the datasheet of the PERC panel employed in field experiments.</p> Full article ">Figure 7
<p>Electrical parameters from the datasheet of the HJT panel N.1 employed in field experiments.</p> Full article ">Figure 8
<p>Electrical parameters from the datasheet of the HJT panel N.2 employed in field experiments.</p> Full article ">Figure 9
<p>HJT and PERC modules displaced horizontally at the roof.</p> Full article ">Figure 10
<p>Expanded roof view of the HJT and PERC modules and their cable connections to our laboratory.</p> Full article ">Figure 11
<p>Laboratory photo showing, with a red line, the path of the cables from the roof to the HJT and PERC modules.</p> Full article ">Figure 12
<p>Photo of the system used to analyze the voltage and electric current signals from the HJT and PERC panels. Above the oscilloscope image, the electrical resistance connected to each panel, HJT or PERC, is shown.</p> Full article ">Figure 13
<p>Picture showing the oscilloscope signals of current, voltage and power for one experiment.</p> Full article ">Figure 14
<p>Illustration of the horizontal irradiance acquisition taken on the roof and near the two modules periodically.</p> Full article ">Figure 15
<p>Picture of the thermographic camera used to measure and visualize the temperature distribution through the PERC and HJT modules.</p> Full article ">Figure 16
<p>August 18th, between 08:30 a.m. and 02:30 p.m.: evolution of the HJT and PERC efficiencies during this day. Also shown are the average temperatures recorded for each module.</p> Full article ">Figure 17
<p>August 18th, between 08:30 a.m. and 02:30 p.m.: evolution of irradiance to show its effects on the modules’ temperatures and efficiency.</p> Full article ">Figure 18
<p>August 19th, between 09:15 and 14:30: evolution of the HJT and PERC efficiencies during this day. Also shown are the average temperatures recorded for each module.</p> Full article ">Figure 19
<p>August 19th, between 09:15 and 14:30: evolution of irradiance to show its effects on the modules’ temperatures and efficiency.</p> Full article ">Figure 20
<p>August 29th, between 09:45 and 15:00: evolution of the HJT and PERC efficiencies during this day. Also shown are the average temperatures recorded for each module.</p> Full article ">Figure 21
<p>August 29th, between 09:45 and 15:00: evolution of irradiance to show its effects on the modules’ temperatures and efficiency.</p> Full article ">Figure 22
<p>Module efficiency and module temperature for August 29th.</p> Full article ">Figure 23
<p>Irradiance, module efficiency and module temperature for August 29th.</p> Full article ">Figure 24
<p>Evolution of how temperature variations impact the performance and efficiency of HJT and PERC solar modules during our field tests.</p> Full article ">Figure 25
<p>Evolution of how irradiance magnitude impacts the efficiency of HJT and PERC solar modules.</p> Full article ">Figure 26
<p>Evolution of how temperature impacts the efficiency of HJT and PERC solar modules.</p> Full article ">Figure 27
<p>Investigating how variations in irradiance levels affect the efficiency differences between HJT and PERC modules.</p> Full article ">Figure 28
<p>Monthly irradiation during the year 2020.</p> Full article ">Figure 29
<p>Energy output per month of one HJT module and one PERC module.</p> Full article ">Figure 30
<p>Average power delivered by the HJT and the PERC module for each month.</p> Full article ">Figure 31
<p>Showing how inverter efficiency changes with respect to the power input. Case for a fixed power installation of 4.5 kW.</p> Full article ">Figure 32
<p>Energy produced by HJT and PERC system for a year. Case for a fixed power installation of 4.5 kW.</p> Full article ">Figure 33
<p>Annual revenue of HJT and PERC system accounting for module degradation for 30 years. Case for a fixed power installation of 4.5 kW.</p> Full article ">Figure 34
<p>Cumulative profits of HJT and PERC systems throughout 30 years—4.5 kW fixed power installation.</p> Full article ">Figure 35
<p>Cumulative profits of HJT and PERC systems throughout 30 years—3.5 kW fixed power installation.</p> Full article ">Figure 36
<p>Cumulative profits of HJT and PERC systems for 30 years—2.5 kW fixed power installation.</p> Full article ">Figure 37
<p>Cumulative profits of HJT and PERC systems over 30 years: EUR 5000 installation.</p> Full article ">Figure 38
<p>Cumulative profits of HJT and PERC systems over 30 years: EUR 10,000 installation.</p> Full article ">Figure 39
<p>Cumulative profits of HJT and PERC systems over 30 years: EUR 10.000 installation with module replacements in years 13 and 25.</p> Full article ">Figure 40
<p>Cumulative profits of HJT and PERC systems over 30 years: EUR 5.000 installation with module replacements in years 13 and 25.</p> Full article ">
20 pages, 4395 KiB  
Article
Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study
by Ailton M. Tavares, Ricardo Conceição, Francisco M. Lopes and Hugo G. Silva
Energies 2024, 17(21), 5490; https://doi.org/10.3390/en17215490 - 2 Nov 2024
Viewed by 907
Abstract
The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at [...] Read more.
The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
►▼ Show Figures

Figure 1

Figure 1
<p>Temporal evolution of DNI (blue dots) and GHI (yellow dots) annual irradiation from 2003 to 2019 at four reference IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots show the data inset.</p> Full article ">Figure 1 Cont.
<p>Temporal evolution of DNI (blue dots) and GHI (yellow dots) annual irradiation from 2003 to 2019 at four reference IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots show the data inset.</p> Full article ">Figure 2
<p>Temporal evolution of the capacity factor of CSP (blue dots) and PV (yellow dots) plants from 2003 to 2019 at four IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots shows the data inset.</p> Full article ">Figure 2 Cont.
<p>Temporal evolution of the capacity factor of CSP (blue dots) and PV (yellow dots) plants from 2003 to 2019 at four IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots shows the data inset.</p> Full article ">Figure 3
<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p> Full article ">Figure 3 Cont.
<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p> Full article ">Figure 3 Cont.
<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p> Full article ">Figure 4
<p>Correlation between annual irradiation and CF of the solar power plants. The CF<sub>PV</sub> is correlated with the GHI irradiation (blue dots) and the CF<sub>CSP</sub> is correlated with the DNI irradiation (yellow dots). The trends are shown by solid lines.</p> Full article ">Figure 5
<p>Comparison of variability in annual irradiation: (<b>a</b>) GHI, (<b>b</b>) DNI.</p> Full article ">Figure 6
<p>Comparison of capacity factor variability for mainland Portugal: (<b>a</b>) PV, (<b>b</b>) CSP.</p> Full article ">
35 pages, 2143 KiB  
Article
A Holistic Multi-Criteria Assessment of Solar Energy Utilization on Urban Surfaces
by Hassan Gholami
Energies 2024, 17(21), 5328; https://doi.org/10.3390/en17215328 - 26 Oct 2024
Cited by 2 | Viewed by 1408
Abstract
Urban surfaces such as rooftops, facades, and infrastructure offer significant potential for solar energy integration, contributing to energy efficiency and sustainability in cities. This article introduces an advanced multi-criteria assessment (MCA) framework designed to evaluate the suitability of various urban surfaces for solar [...] Read more.
Urban surfaces such as rooftops, facades, and infrastructure offer significant potential for solar energy integration, contributing to energy efficiency and sustainability in cities. This article introduces an advanced multi-criteria assessment (MCA) framework designed to evaluate the suitability of various urban surfaces for solar energy deployment. The framework extends beyond traditional economic, environmental, and technological factors to include social, political, legal, health and safety, cultural, and psychological dimensions, providing a comprehensive evaluation of photovoltaic (PV) applications in urban contexts. By synthesizing existing literature and applying this holistic MCA framework, this research offers valuable insights for urban planners, architects, and policymakers, enabling strategic optimization of solar energy integration in urban environments. The findings underscore the importance of sustainable urban development and climate resilience, highlighting key factors influencing solar technology deployment and proposing actionable recommendations to address existing challenges. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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Figure 1

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<p>Sustainable and resilient city components.</p> Full article ">Figure 2
<p>Internal and external stakeholders.</p> Full article ">Figure 3
<p>Decision alternatives for utilization of solar energy on urban surfaces.</p> Full article ">Figure 4
<p>MCA criteria and tools.</p> Full article ">
15 pages, 3250 KiB  
Article
Design of Solar-Powered Cooling Systems Using Concentrating Photovoltaic/Thermal Systems for Residential Applications
by Fadi Ghaith, Taabish Siddiqui and Mutasim Nour
Energies 2024, 17(18), 4558; https://doi.org/10.3390/en17184558 - 11 Sep 2024
Viewed by 1320
Abstract
This paper addresses the potential of integrating a concentrating photovoltaic thermal (CPV/T) system with an absorption chiller for the purpose of space cooling in residential buildings in the United Arab Emirates (UAE). The proposed system consists of a low concentrating photovoltaic thermal (CPV/T) [...] Read more.
This paper addresses the potential of integrating a concentrating photovoltaic thermal (CPV/T) system with an absorption chiller for the purpose of space cooling in residential buildings in the United Arab Emirates (UAE). The proposed system consists of a low concentrating photovoltaic thermal (CPV/T) collector that utilizes mono-crystalline silicon photovoltaic (PV) cells integrated with a single-effect absorption chiller. The integrated system was modeled using the Transient System Simulation (TRNSYS v17) software. The obtained model was implemented in a case study represented by a villa situated in Abu Dhabi having a peak cooling load of 366 kW. The hybrid system was proposed to have a contribution of 60% renewable energy and 40% conventional nonrenewable energy. A feasibility study was carried out that demonstrated that the system could save approximately 670,700 kWh annually and reduce carbon dioxide emissions by 461 tons per year. The reduction in carbon dioxide emissions is equivalent of removing approximately 98 cars off the road. The payback period for the system was estimated to be 3.12 years. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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Figure 1

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<p>System schematic.</p> Full article ">Figure 2
<p>Working principle of single-effect absorption.</p> Full article ">Figure 3
<p>Schematic of the CPV/T collector.</p> Full article ">Figure 4
<p>Information flow diagram.</p> Full article ">Figure 5
<p>Irradiance vs. time.</p> Full article ">Figure 6
<p>Collector outlet temperatures vs. time.</p> Full article ">Figure 7
<p>Transient analysis of auxiliary heater: (<b>a</b>) generator inlet temperature versus time, (<b>b</b>) auxiliary heating power requirement versus time.</p> Full article ">Figure 7 Cont.
<p>Transient analysis of auxiliary heater: (<b>a</b>) generator inlet temperature versus time, (<b>b</b>) auxiliary heating power requirement versus time.</p> Full article ">Figure 8
<p>Power generated vs. time.</p> Full article ">Figure 9
<p>Useful heat gain versus mass flow rate obtained in this study compared to the literature [<a href="#B24-energies-17-04558" class="html-bibr">24</a>].</p> Full article ">Figure 10
<p>Comparison of costs between the proposed system against the conventional cooling system.</p> Full article ">Figure 11
<p>Comparison of annual CO<sub>2</sub> emissions of the proposed CPV/T against the conventional system.</p> Full article ">
20 pages, 2989 KiB  
Article
Enhanced Microgrid Control through Genetic Predictive Control: Integrating Genetic Algorithms with Model Predictive Control for Improved Non-Linearity and Non-Convexity Handling
by Muhammed Cavus and Adib Allahham
Energies 2024, 17(17), 4458; https://doi.org/10.3390/en17174458 - 5 Sep 2024
Cited by 7 | Viewed by 1099
Abstract
Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational [...] Read more.
Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA’s USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA’s 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC’s potential as a versatile, efficient, and environmentally beneficial tool for power generation systems. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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<p>Power flow representation in the MG.</p> Full article ">Figure 2
<p>Flowchart of practical implementation steps for GPC.</p> Full article ">Figure 3
<p>Mutation–Random Selection: (<b>a</b>) cost and (<b>b</b>) emissions comparisons of MPC, GA, and GPC methods.</p> Full article ">Figure 4
<p>Mutation–Elitism: (<b>a</b>) cost and (<b>b</b>) emissions comparisons of MPC, GA, and GPC methods.</p> Full article ">Figure 5
<p>Crossover–Random Selection: (<b>a</b>) cost and (<b>b</b>) emissions comparisons of MPC, GA, and GPC methods.</p> Full article ">Figure 6
<p>Crossover–Elitism: (<b>a</b>) cost and (<b>b</b>) emissions comparisons of MPC, GA, and GPC methods.</p> Full article ">

Review

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20 pages, 2562 KiB  
Review
A Comprehensive Review of Hybrid State Estimation in Power Systems: Challenges, Opportunities and Prospects
by Leila Kamyabi, Tek Tjing Lie, Samaneh Madanian and Sarah Marshall
Energies 2024, 17(19), 4806; https://doi.org/10.3390/en17194806 - 25 Sep 2024
Cited by 1 | Viewed by 1306
Abstract
Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of [...] Read more.
Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid’s state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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<p>SE function in a power system [<a href="#B18-energies-17-04806" class="html-bibr">18</a>].</p> Full article ">Figure 2
<p>Proposed categorization of SE methods.</p> Full article ">Figure 3
<p>None-overlapping areas [<a href="#B7-energies-17-04806" class="html-bibr">7</a>].</p> Full article ">Figure 4
<p>Border-bus overlapping areas [<a href="#B7-energies-17-04806" class="html-bibr">7</a>].</p> Full article ">Figure 5
<p>Mid-point virtual bus overlapping areas [<a href="#B7-energies-17-04806" class="html-bibr">7</a>].</p> Full article ">Figure 6
<p>Tie-line overlapping areas [<a href="#B7-energies-17-04806" class="html-bibr">7</a>].</p> Full article ">Figure 7
<p>Deep overlapping areas [<a href="#B7-energies-17-04806" class="html-bibr">7</a>].</p> Full article ">Figure 8
<p>Communication architectures of fully distributed and hierarchical DSE techniques [<a href="#B73-energies-17-04806" class="html-bibr">73</a>].</p> Full article ">Figure 9
<p>PMU and SCADA measurements update [<a href="#B13-energies-17-04806" class="html-bibr">13</a>].</p> Full article ">Figure 10
<p>Diagram of sequential measurement–state fusion [<a href="#B14-energies-17-04806" class="html-bibr">14</a>].</p> Full article ">Figure 11
<p>Diagram of parallel state fusion [<a href="#B14-energies-17-04806" class="html-bibr">14</a>].</p> Full article ">Figure 12
<p>Diagram of integrated hybrid state estimator [<a href="#B14-energies-17-04806" class="html-bibr">14</a>].</p> Full article ">
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