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Energies
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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 4231

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

Manuscript Submission Information

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Keywords

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

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Related Special Issue

Published Papers (5 papers)

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Research

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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 577
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)
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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
Viewed by 733
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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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 847
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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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 1 | Viewed by 637
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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Review

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19 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
Viewed by 774
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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