Simulation of Self-Consumption in Small Photovoltaic Panel Energy Application: A Case Study in Estonia

2021 ◽  
Keyword(s):  
Photovoltaic Panel ◽  
Energy Application

scholarly journals Photovoltaic Cleaning Optimization: A Simplified Theoretical Approach for Air to Water Generator (AWG) System Employment

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4271
Author(s):  
Lucia Cattani ◽  
Paolo Cattani ◽  
Anna Magrini
Keyword(s):  
Energy Loss ◽  
Energy Production ◽  
New Technologies ◽  
Optimization Procedure ◽  
System Size ◽  
Design Tool ◽  
Semiempirical Model ◽  
Photovoltaic Panel ◽  
Water Amount

Photovoltaic panel efficiency can be heavily affected by soiling, due to dust and other airborne particles, which can determine up to 50% of energy production loss. Generally, it is possible to reduce that impact by means of periodic cleaning, and one of the most efficient cleaning solutions is the use of demineralized water. As pauperization of traditional water sources is increasing, new technologies have been developed to obtain the needed water amount. Water extracted from the air using air to water generator (AWG) technology appears to be particularly suitable for panel cleaning, but its effective employment presents issues related to model selection, determining system size, and energy efficiency. To overcome such issues, the authors proposed a method to choose an AWG system for panel cleaning and to determine its size accordingly, based on a cleaning time optimization procedure and tailored to AWG peculiarities, with an aim to maximize energy production. In order to determine the energy loss due to soiling, a simplified semiempirical model (i.e., the DIrt method) was developed as well. The methodology, which also allows for energy saving due to an optimal cleaning frequency, was applied to a case study. The results show that the choice of the most suitable AWG model could prevent 83% of energy loss related to soling. These methods are the first example of a design tool for panel cleaning planning involving AWG technology.

Life cycle cost analysis: A case study of hydrogen energy application on the Orkney Islands

2019 ◽  
Vol 44 (19) ◽  
pp. 9517-9528 ◽  
Author(s):  
Guangling Zhao ◽  
Eva Ravn Nielsen ◽  
Enrique Troncoso ◽  
Kris Hyde ◽  
Jesús Simón Romeo ◽  
...  
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Hydrogen Energy ◽  
Life Cycle Cost Analysis ◽  
Orkney Islands ◽  
Energy Application

scholarly journals Thermographic and electrical characterization of a photovoltaic panel under partial shading conditions: a case study

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 93
Author(s):  
Giovanni Bucci ◽  
Fabrizio Ciancetta ◽  
Edoardo Fiorucci ◽  
Antonio Delle Femine
Keyword(s):  
Electrical Characterization ◽  
Testing Procedure ◽  
Pv System ◽  
Partial Shading ◽  
Photovoltaic Panel ◽  
Pv Module ◽  
Pv Modules ◽  
Consequent Reduction

<p class="Abstract">Shading is one of the most critical factors that produces a reduction in power in photovoltaic (PV) modules. The main causes of shading are related to cloud cover; local specificity; natural characteristics; building and other civil works; and the presence of the PV system itself. A reduction in overall radiation produces a consequent reduction in electric power. Another more problematic effect is associated with the partial shading of the PV modules. The shaded cell behaves as a load, dissipating energy and increasing its temperature. This effect can involve irreversible changes to the PV module, with a decrease in performance that can even cause the destruction of the shaded cell.</p><p>The main aim of this work is the development of a testing procedure for the performance evaluation of commercial PV modules in the presence of partial shading on one cell. Tests were carried out using thermographic and electric measurements and by varying the shading levels according to IEC standards. Shading up to total darkening is achieved by means of a number of filters that reduce the direct solar irradiance.</p><p>As a case study, a complete characterisation of a 180 Wp polycrystalline PV module was performed according to the proposed testing procedure, showing that high temperatures can be measured on the shaded PV module surface even if only 50 % of the surface of one cell of the PV module is darkened.</p>

Maximizing Energy Harvesting of Photovoltaic Panel Full Tracking Optimization

2019 ◽  
Author(s):  
Ahmed Y. Abdelmaksoud ◽  
Hesham A. Hegazi ◽  
Mohamed S. El Morsi ◽  
Sayed M. Metwalli
Keyword(s):  
Performance Optimization ◽  
Fossil Fuels ◽  
Optimization Technique ◽  
Attractive Alternative ◽  
Photovoltaic Panel ◽  
Meta Model ◽  
The Cost ◽  
Photovoltaic Solar Cells ◽  
The City

Abstract The wide spread use of solar energy and photovoltaic solar cells attracts researchers to work hard for the objective of improving their performance to be a viable attractive alternative to fossil fuels. This work presents the optimization of the output power of a photovoltaic solar cell to harvest maximum power. The power is optimized based on PVP full tracking of the sun and the effect of the tilt, the azimuth, and the incidence angles on its performance. A meta-model is utilized as a tool in the optimization technique. A case study which develops 2nd, 3rd, and 4th order hyper-surface equations that depend on day and hour to get the optimum tilt angle and optimum azimuth angle for the city of Hurghada in Egypt. The power produced from the polynomial meta-model algorithm is higher by 25.5% over the fixed tilt and azimuth angles. The developed equations from the meta-model optimization technique can be used in tracking control systems to get optimum tilt and optimum azimuth angles at any hour and at any day. By comparing the metamodel and the lengthy performance optimization technique, the error is around 1.1%. Comparison has also been made between the cost of the fixed and full tracking photovoltaic systems.

Case Study

2013 ◽  
Vol 2 (4) ◽  
pp. 25-37 ◽  
Author(s):  
Aydin Tabrizi ◽  
Paola Sanguinetti
Keyword(s):  
Renewable Energy ◽  
Performance Enhancement ◽  
Energy Modeling ◽  
Information Modeling ◽  
Weather Data ◽  
Building Performance ◽  
Net Energy ◽  
Photovoltaic Panel ◽  
Building Information

This case study focuses on the operational performance of a Leadership in Energy & Environmental Design (LEED)-rated building with the application of Building Information Modeling (BIM) to evaluate its capacity to achieve Zero Net Energy (ZNE). Retrofit options for renewable energy implementation are examined in conjunction with scenarios of building operation. In this study, two different BIM processes have been conducted for the energy modeling: object-oriented geometric information modeling (e.g., envelope, doors, windows, walls, zones, etc.) with a BIM tool and energy modeling (e.g., materials, heat resistance, location, weather data, renewables, etc.) with an energy simulation tool. The simulation model is compared to the real building performance and alternative renewable energy scenarios are evaluated. The results are used to make recommendations for the optimization of building performance and consideration of energy-efficient strategies for building performance enhancement. The research points to discontinuities between photovoltaic panel degradation over time and the LEED credit.

Design considerations for photovoltaic panel arrays made from aluminium: a case study

2019 ◽  
Vol 13 (1) ◽  
pp. 52-60
Author(s):  
Foivos Bardopoulos ◽  
George Papagiannopoulos ◽  
Nikos Pnevmatikos
Keyword(s):  
Photovoltaic Panel ◽  
Design Considerations

scholarly journals Evaluation of the renewable energy application in Mediterranean hotels: Case study the Balearic Islansd` hotels

2006 ◽  
Vol 1 (04) ◽  
pp. 106-110 ◽  
Author(s):  
A. Moiá-Pol ◽  
Michalis Karagiorgas ◽  
V. Martínez-Moll ◽  
R. Pujol ◽  
Carles Riba-Romeva
Keyword(s):  
Renewable Energy ◽  
Energy Application

An Approach for Revealed Consumer Preferences for Technology Products: A Case Study of Residential Solar Panels

2012 ◽  
Author(s):  
Heidi Q. Chen ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Machine Learning ◽  
Consumer Preferences ◽  
Solar Panels ◽  
Learning Methods ◽  
Photovoltaic Panel ◽  
Machine Learning Methods ◽  
Technology Products ◽  
Technical Specifications ◽  
S Curve

Consumer preferences can serve as an effective basis for determining key product attributes necessary for market success, allowing firms to optimally allocate time and resources toward the development of these critical attributes. However, identification of consumer preferences can be challenging, particularly for technology-push products that are still early on in the technology diffusion S-curve, which need an additional push to appeal to the early majority. This paper presents a method for revealing preferences from actual market data and technical specifications. The approach is explored using three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression applied on the residential photovoltaic panel industry in California, USA. Residential solar photovoltaic installation data over a period of 5 years from 2007–2011 obtained from the California Solar Initiative is analyzed, and 3 critical attributes are extracted from a pool of 34 technical attributes obtained from panel specification sheets. The work shows that machine learning methods, when used carefully, can be an inexpensive and effective method of revealing consumer preferences and guiding design priorities.

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