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In geographical areas where direct solar irradiation levels are relatively high, concentrated solar energy systems are one of the most promising green energy technologies. Dish-Stirling systems are those that achieve the highest levels of solar-to-electric conversion efficiency, and yet they are still among the least common commercially available technologies. This paper focuses on a strategy aimed at promoting greater diffusion of dish-Stirling systems, which involves optimizing the size of the collector aperture area based on the hourly frequency distributions of beam irradiance and defining a new incentive scheme with a feed-in tariff that is variable with the installed costs of the technology. To this purpose, a new numerical model was defined and calibrated on the experimental data collected for an existing dish-Stirling plant located in Palermo (Italy). Hourly-based simulations were carried out to assess the energy performance of 6 different system configurations located on 7 sites in the central Mediterranean area using two different solar databases: Meteonorm and PVGIS. A new simplified calculation approach was also developed to simulate the dish-Stirling energy production from the hourly frequency histograms of the beam irradiance. The results reveal that an optimised dish-Stirling system can produce 70–87 MWhe/year in locations with direct irradiation varying between 2000 and 2500 kWh/(m2·year). The proposed incentive scheme would guarantee a payback time for investment in this technology of about ten years and the effect of economies of scale could lead, over the years, to a levelized cost of energy similar to that of other concentrating power systems.
A. Buscemi; S. Guarino; G. Ciulla; V. Lo Brano. A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance. Applied Energy 2021, 303, 117681 .
AMA StyleA. Buscemi, S. Guarino, G. Ciulla, V. Lo Brano. A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance. Applied Energy. 2021; 303 ():117681.
Chicago/Turabian StyleA. Buscemi; S. Guarino; G. Ciulla; V. Lo Brano. 2021. "A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance." Applied Energy 303, no. : 117681.
The application of Building Automation and Control (BAC) systems has many advantages. One of these is the reduction of the end-user electricity consumption and, if applied to lighting systems, the achievement of well-acknowledged benefits from daylight, such as productivity, health, visual comfort and well-being. Concerning the first aspect, the international Standard EN 15232 proposes the so-called BAC Factors (BF) method to assess the impact of BAC systems on the final energy consumption. The method provides a simplified estimation of the energy savings due to automation in buildings and questions arise on its applicability in some situations. For this reason, the authors have carried out an experimental study aiming at comparing the energy savings calculated using the simplified BAC factor method with those evaluated with a measurement campaign on a laboratory setup. In particular, the BF are evaluated for an office and a residential environment, using sets of data measured in two cases study in South Italy by testing two lighting control systems in different end-uses (residential and office). The comparison between the sets of data shows the limits of the simplified BAC factor method.
Marina Bonomolo; Gaetano Zizzo; Simone Ferrari; Marco Beccali; Stefania Guarino. Empirical BAC factors method application to two real case studies in South Italy. Energy 2021, 236, 121498 .
AMA StyleMarina Bonomolo, Gaetano Zizzo, Simone Ferrari, Marco Beccali, Stefania Guarino. Empirical BAC factors method application to two real case studies in South Italy. Energy. 2021; 236 ():121498.
Chicago/Turabian StyleMarina Bonomolo; Gaetano Zizzo; Simone Ferrari; Marco Beccali; Stefania Guarino. 2021. "Empirical BAC factors method application to two real case studies in South Italy." Energy 236, no. : 121498.
Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pixels, greyscaling, thresholding, discrete wavelet transform, and Sobel Feldman and box blur filtering. These techniques allow the classification of thermographic images of differen quality and acquired using different equipments, without specific protocols. Several tests with different parameters and overfitting reduction techniques were carried out to assess the performance of the neural networks: images acquired by unmanned aerial vehicles and ground-based operators were compared for the network performance and for the time required to execute the thermographic inspection. Our tool is based on a convolutional neural network that allows to immediately recognize a failure in a PV panel reaching a very high accuracy. Considering a dataset of 1000 images that refer to different acquisition protocols, it was reached an accuracy of 99% for a convolutional neural network with 30 min of computational time on Low Mid-Range CPU. While a dataset of 200 sectioned images, the same tool achieved 90% accuracy with a multi-layer perceptron architecture and 100% accuracy for a convolutional neural network. The proposed methodology offers an open alternative and a valid tool that improves the resolution of image classification for remote failure detection problems and that can be used in any scientific sector.
D. Manno; G. Cipriani; G. Ciulla; V. Di Dio; S. Guarino; V. Lo Brano. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management 2021, 241, 114315 .
AMA StyleD. Manno, G. Cipriani, G. Ciulla, V. Di Dio, S. Guarino, V. Lo Brano. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management. 2021; 241 ():114315.
Chicago/Turabian StyleD. Manno; G. Cipriani; G. Ciulla; V. Di Dio; S. Guarino; V. Lo Brano. 2021. "Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images." Energy Conversion and Management 241, no. : 114315.
Energy consumed for air conditioning in residential and tertiary sectors accounts for a large share of global use. To reduce the environmental impacts burdening the covering of such demands, the adoption of renewable energy technologies is increasing. In this regard, this paper evaluates the energy and environmental benefits achievable by integrating a dish-Stirling concentrator into energy systems used for meeting the air conditioning demand of an office building. Two typical reference energy plants are assumed: (i) a natural gas boiler for heating purposes and air-cooled chillers for the cooling periods, and (ii) a reversible heat pump for both heating and cooling. For both systems, a dish-Stirling concentrator is assumed to operate first in electric-mode and then in a cogenerative-mode. Detailed models are adopted for plant components and implemented in the TRNSYS environment. Results show that when the concentrator is operating in electric-mode the electricity purchased from the grid decreases by about 72% for the first plant, and 65% for the second plant. Similar reductions are obtained for CO2 emissions. Even better performance may be achieved in the case of the cogenerative-mode. In the first plant, the decrease in natural gas consumption is about 85%. In the second plant, 66.7% is the percentage increase in avoided electricity purchase. The integration of the dish-Stirling system allows promising energy-saving and reduction in CO2 emissions. However, both a reduction in capital cost and financial support are needed to encourage the diffusion of this technology.
Stefania Guarino; Pietro Catrini; Alessandro Buscemi; Valerio Lo Brano; Antonio Piacentino. Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector. Energies 2021, 14, 1163 .
AMA StyleStefania Guarino, Pietro Catrini, Alessandro Buscemi, Valerio Lo Brano, Antonio Piacentino. Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector. Energies. 2021; 14 (4):1163.
Chicago/Turabian StyleStefania Guarino; Pietro Catrini; Alessandro Buscemi; Valerio Lo Brano; Antonio Piacentino. 2021. "Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector." Energies 14, no. 4: 1163.
This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.
Giovanni Cipriani; Antonino D’Amico; Stefania Guarino; Donatella Manno; Marzia Traverso; Vincenzo Di Dio. Convolutional Neural Network for Dust and Hotspot Classification in PV Modules. Energies 2020, 13, 6357 .
AMA StyleGiovanni Cipriani, Antonino D’Amico, Stefania Guarino, Donatella Manno, Marzia Traverso, Vincenzo Di Dio. Convolutional Neural Network for Dust and Hotspot Classification in PV Modules. Energies. 2020; 13 (23):6357.
Chicago/Turabian StyleGiovanni Cipriani; Antonino D’Amico; Stefania Guarino; Donatella Manno; Marzia Traverso; Vincenzo Di Dio. 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules." Energies 13, no. 23: 6357.
In the future, renewable energy sources will increasingly represent an efficient energy source capable of meeting the demands of residential and industrial buildings avoiding the emissions of greenhouse gases into the atmosphere. In this paper, a heat and electric power cogeneration plant implementing a field of dish-Stirling collectors, a seasonal geothermal storage and a system of water-to-water heat pumps is proposed for the first time. The cogeneration plant has been designed both to supply thermal energy to the heating system of Building 9 of the Department of Engineering in Palermo and to produce electricity. The operation of the plant has been tested by means of hourly-based numerical simulations that have been carried out using a numerical model implemented with Transient System Simulation Tool. The experimental data of a pilot dish-Stirling collector, located in the same area, has been used to carefully calibrate the numerical model. Using energy and economic performance indicators, it was possible to select the best configurations among 1440 analysed cases. Results of simulations show that with the best plant configuration, it is possible to cover 97% of the building's annual thermal loads with energy produced by the solar system. The remaining 64% of electrical energy produced by the electric engines is free to be used for other applications. Financial analyses have shown that market penetration of this type of plant would need a strong support through incentives.
Stefania Guarino; Alessandro Buscemi; Giuseppina Ciulla; Marina Bonomolo; Valerio Lo Brano. A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis. Energy Conversion and Management 2020, 222, 113228 .
AMA StyleStefania Guarino, Alessandro Buscemi, Giuseppina Ciulla, Marina Bonomolo, Valerio Lo Brano. A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis. Energy Conversion and Management. 2020; 222 ():113228.
Chicago/Turabian StyleStefania Guarino; Alessandro Buscemi; Giuseppina Ciulla; Marina Bonomolo; Valerio Lo Brano. 2020. "A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis." Energy Conversion and Management 222, no. : 113228.