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Technologies
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Solar Thermal Power Generation Technology
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Solar Thermal Power Generation Technology

A Special Issue of Technologies (ISSN 2227-7080) belonging to the section "Environmental Technology".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 10763

Editors

Dr. Jing Li

E-Mail Website
Guest Editor
School of Engineering and Technology, University of Hull, Hull HU67RX, UK
Interests: heat pump; solar thermal conversion; concentrated solar power; thermal storage; organic Rankine cycle; refrigeration cycle
Special Issues, Collections and Topics in MDPI journals
Dr. Pengcheng Li

E-Mail Website
Guest Editor
Department of New Energy Science and Engineering, Hefei University of Technology, Hefei 230009, China
Interests: thermodynamic cycle; solar thermal power generation; carnot battery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to capture the latest research in advanced heat collection, heat storage, and thermodynamic cycles for solar thermal power generation technology and heat batteries. Topics include, but are not limited to, the following:

  • Concentrating solar power (including the design and optimization of novel concentrators, concentrator materials, and tracking and control systems for concentrators);
  • Heat collection and storage (including the design and optimization of novel heat collection technology, heat storage materials, and the design and optimization of thermal storage system);
  • Advanced solar heat pump cycles and their integration with storage units (including sensible heat, latent heat, and thermochemical heat);
  • Solar heat-to-power conversion technology (including the supercritical CO2 cycle, organic Rankine cycle, transcritical CO2 cycle, Stirling cycle, Steam Rankine cycle, Kalina cycle, and Cascade cycle);
  • Solar multi-generation system (including combined heat and power systems, advanced cooling cycle, and triple-generation system);
  • System integration and optimization (including integrated solar combined cycle, solar thermal desalination, solar thermal for industrial process heat, grid integration, and forecasting and control);
  • Cost analysis and economic assessment (including the levelized cost of electricity and life cycle assessment);
  • Emerging concepts and technologies (including space-based solar power and thermophotovoltaics).

Articles may describe innovative concepts, numerical simulations, experimental studies, or reviews of state-of-the-art solar thermal power generation technology.

Dr. Jing Li
Dr. Pengcheng Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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 1800 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

  • concentrating solar power
  • heat collection and storage
  • solar multi-generation
  • solar heat-to-power conversion
  • system integration and optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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0 pages, 6463 KB  
Article
A Techno-Economic Analysis of a Direct Vapour Generation Solar Cascade Organic Rankine Cycle for Efficient Cogeneration
by Xiao Ren, Zhaodong Tuo, Jing Li, Zhiying Zhang, Xiaolei Mou and Liang Gong
Technologies 2026, 14(9), 532; https://doi.org/10.3390/technologies14090532 - 28 Aug 2026
Viewed by 202
Abstract
Concentrated solar power systems can provide dispatchable renewable energy, but their application in distributed cogeneration is constrained by high costs and thermal losses associated with indirect heat transfer. This study proposes a direct vapour generation cascade organic Rankine cycle (DVG-CORC) for combined heat [...] Read more.
Concentrated solar power systems can provide dispatchable renewable energy, but their application in distributed cogeneration is constrained by high costs and thermal losses associated with indirect heat transfer. This study proposes a direct vapour generation cascade organic Rankine cycle (DVG-CORC) for combined heat and power production. A biphenyl–diphenyl oxide (BDO) mixture is used as both the solar-collection fluid and the high-temperature cycle working fluid, while thermal storage and four operating modes are incorporated to accommodate variations in solar irradiance and enable continuous operation. Thermodynamic and economic models are developed to evaluate system performance under different evaporation and condensation temperatures. At an evaporation temperature of 400 °C, the maximum thermal efficiencies are 37.67%, 35.38%, and 33.11% at condensation temperatures of 60 °C, 80 °C, and 100 °C, respectively. At a condensation temperature of 60 °C, the cogeneration system generates an estimated annual revenue of USD 929,637, which is USD 284,269 higher than that of the power-generation-only configuration operating at a condensation temperature of 30 °C. These results demonstrate that direct vapour generation, cascade energy utilization, and heat recovery can improve the thermodynamic and economic performance of distributed solar cogeneration systems. Full article
(This article belongs to the Special Issue Solar Thermal Power Generation Technology)
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16 pages, 884 KB  
Article
An Improved Deep Learning Framework for In Situ Detection of Geometric Keypoints of Heliostats in Concentrated Solar Power Plants
by Fen Xu and Hongyu Miao
Technologies 2026, 14(7), 424; https://doi.org/10.3390/technologies14070424 - 11 Jul 2026
Viewed by 413
Abstract
In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be [...] Read more.
In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be off from sun-tracking during the calibration process. This paper presents a deep learning-based framework for in situ detection of geometric keypoints of the heliostat surface. The proposed framework is built upon YOLOv8-Pose but integrates a high-resolution P2 feature branch to recover fine-grained spatial details that are otherwise lost in deep semantic layers. Further, a geometry-consistency loss is introduced to regularize the predicted quadrilateral, enforcing strict structural integrity under dynamically changing illumination. An experimental study on a real-world heliostat image dataset shows that the proposed framework achieves an end-to-end inference speed of 25.14 FPS. The mean end-point error (EPE) of detected keypoints is around 1.22 pixels, while the stringent mAP@0.5:0.95 metric reaches 0.9823. The keypoint detection framework could be integrated with an in-field heliostat control system for further improvement of the working efficiency of heliostats in a large-scale CSP plant in future. Full article
(This article belongs to the Special Issue Solar Thermal Power Generation Technology)
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36 pages, 1335 KB  
Article
Analysis of Concentrated Solar Power Potential in the Photovoltaic Competitive Landscape
by Mladen Bošnjaković
Technologies 2025, 13(12), 554; https://doi.org/10.3390/technologies13120554 - 27 Nov 2025
Cited by 6 | Viewed by 5538
Abstract
Concentrated Solar Power (CSP) technology offers significant potential for stable and dispatchable renewable electricity generation through integration with thermal energy storage. However, adoption remains limited due to high capital costs, technical complexity, and market competition from photovoltaic (PV) systems. This review systematically synthesises [...] Read more.
Concentrated Solar Power (CSP) technology offers significant potential for stable and dispatchable renewable electricity generation through integration with thermal energy storage. However, adoption remains limited due to high capital costs, technical complexity, and market competition from photovoltaic (PV) systems. This review systematically synthesises recent literature on CSP and applies a hybrid SWOT–Analytic Hierarchy Process (AHP) methodology to quantitatively evaluate key internal and external factors influencing CSP deployment. The analysis identifies major strengths such as high-capacity factors and grid stability enabled by thermal storage, as well as weaknesses including high initial investment and site requirements. Opportunities stem from technological innovation, supportive policy frameworks, and potential for local job creation, while threats include rapid cost reductions in PV systems, water scarcity, and market and regulatory uncertainties. The integrated SWOT–AHP approach provides a robust decision-making framework and strategic insights for stakeholders seeking to promote CSP technology in diverse market contexts. The findings underscore the importance of tailored policy support and targeted investment to overcome barriers and realise CSP’s full potential within the renewable energy landscape. Full article
(This article belongs to the Special Issue Solar Thermal Power Generation Technology)
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Review

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44 pages, 5202 KB  
Review
Impact of Dust Deposition on Photovoltaic Systems and Mitigation Strategies
by Mohammad Reza Maghami
Technologies 2026, 14(1), 15; https://doi.org/10.3390/technologies14010015 - 24 Dec 2025
Cited by 11 | Viewed by 3657
Abstract
Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV [...] Read more.
Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV performance, covering its optical, thermal, and electrical impacts. Findings from global literature indicate that dust-induced efficiency losses typically range from 10% to 70%, depending on particle characteristics, environmental conditions, and surface orientation. Experimental and modeled I–V and P–V characteristics further reveal significant declines in current and power output as soiling levels increase. Through an extensive literature assessment, this paper identifies Machine Learning (ML)-based approaches as emerging and highly effective techniques for dust detection and mitigation. Recent studies demonstrate the integration of image processing, drone-assisted monitoring, and convolutional neural networks (CNNs) to enable automated, real-time soiling assessment. These intelligent methods outperform conventional manual and time-based cleaning strategies in accuracy, scalability, and cost efficiency. By synthesizing current research trends, this review highlights the growing role of ML and data-driven technologies in enhancing PV system reliability, informing predictive maintenance, and supporting sustainable solar energy generation. Full article
(This article belongs to the Special Issue Solar Thermal Power Generation Technology)
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