Performance evaluation and feasibility analysis of 10 kWp PV system for residential buildings in Saudi Arabia

2022 ◽  
Vol 51 ◽  
pp. 101920
Author(s):  
Awsan Mohammed ◽  
Ahmed Ghaithan ◽  
Ahmad Al-Hanbali ◽  
Ahmed M. Attia ◽  
Haitham Saleh ◽  
...  
Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6759
Author(s):  
Mohamed Mohana ◽  
Abdelaziz Salah Saidi ◽  
Salem Alelyani ◽  
Mohammed J. Alshayeb ◽  
Suhail Basha ◽  
...  

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.


2019 ◽  
Vol 8 (1) ◽  
pp. 34-52 ◽  
Author(s):  
M. Asif ◽  
Mohammad A. Hassanain ◽  
Kh Md Nahiduzzaman ◽  
Haitham Sawalha

Purpose The Kingdom of Saudi Arabia (KSA) is facing a rapid growth in energy demand mainly because of factors like burgeoning population, economic growth, modernization and infrastructure development. It is estimated that between 2000 and 2017 the power consumption has increased from 120 to 315 TWh. The building sector has an important role in this respect as it accounts for around 80 percent of the total electricity consumption. The situation is imposing significant energy, environmental and economic challenges for the country. To tackle these problems and curtail its dependence on oil-based energy infrastructure, KSA is aiming to develop 9.5 GW of renewable energy projects by 2030. The campus of the King Fahd University of Petroleum and Minerals (KFUPM) has been considered as a case study. In the wake of recently announced net-metering policy, the purpose of this paper is to investigate the prospects of rooftop application of PV in buildings. ArcGIS and PVsyst software have been used to determine the rooftop area and undertake PV system modeling respectively. Performance of PV system has been investigated for both horizontal and tilted installations. The study also investigates the economic feasibility of the PV application with the help of various economic parameters such as benefit cost ratio, simple payback period (SPP) and equity payback periods. An environmental analysis has also been carried out with the help of RETScreen software to determine the savings in greenhouse gas emissions as a result of PV system. Design/methodology/approach This study examines the buildings of the university campus for utilizable rooftop areas for PV application. Various types of structural, architectural and utilities-related features affecting the use of building roofs for PV have been investigated to determine the corrected area. To optimize the performance of the PV system as well as space utilization, modeling has been carried out for both horizontal and tilted applications of panels. Detailed economic and environmental assessments of the rooftop PV systems have also been investigated in detail. Modern software tools such as PVsyst, ArcGIS and RETScreen have also been used for system design calculations. Findings Saudi Arabia is embarking on a massive solar energy program as it plans to have over 200 GW of installed capacity by 2030. With solar energy being the most abundant of the available renewable resource for the country, PV is going to be one of the main technologies in achieving the set targets. The country has, however, unlike global trends, traditionally overlooked the small-scale and building-related application of solar PV, focusing mainly on larger projects. This study explores the prospects of utilization of solar PV on building roofs. Building rooftops are constrained in terms of PV application owing to wide ranging obstacles that can be classified into five types – structural, services, accessibility, maintenance and others. The total building rooftop area in the study zone, calculated through ArcGIS has been found to be 857,408 m2 of which 352,244 m2 is being used as car parking and hence is not available for PV application. The available roof area, 505,165 m2 is further hampered by construction and utilities related features including staircases, HVAC systems, skylights, water tanks and satellite dish antennas. Taking into account the relevant obstructive features, the net rooftop area covered by PV panels has been found to be in the range 25–41 percent depending upon the building typology, with residential buildings offering the least. To optimize both the system efficiency and space utilization, PV modeling has been carried out with the help of PVsyst software for both the tilted and horizontal installations. In terms of output, PV panels with tilt angle of 24° have been found to be 9 percent more efficient compared to the horizontally installed ones. Modeling results provide a net annual output 37,750 and 46,050 MWh from 21.44 and 28.51 MW of tilted and horizontal application of PV panels, sufficient to respectively meet 16 and 20 percent of the total campus electricity requirements. Findings of the economic analysis reveal the average SPP for horizontal and tilted applications of the PV to be 9.2 and 8.4 years, respectively. The benefit cost ratio for different types of buildings for horizontal and tilted application has been found to be ranging between 0.89 and 2.08 and 0.83 and 2.15, respectively. As electricity tariff in Saudi Arabia has been increased this year by as much as 45 percent and there are plans to remove $54bn of subsidy by 2020, the cost effectiveness of PV systems will be greatly helped. Application of PV in buildings can significantly improve their environmental performance as the findings of this study reveal that the annual greenhouse gas emission in the KFUPM campus can be reduced by as much as 40,199 tons carbon dioxide equivalent. Originality/value The PV application on building roof especially from economic perspective is an area which has not been addressed thus far. Khan et al. (2017) studied the power generation potential for PV application on residential buildings in KSA. Asif (2016) also investigated power output potential of PV system in different types of buildings. Dehwas et al. (2018) adopted a detailed approach to determine utilizability of PV on residential building roofs. None of these studies have covered the economics of PV systems. This study attempts to address the gap and contribute to the scholarship on the subject. It targets to determine the power output from different types of building in an urban environment by taking into account building roof conditions. It also provides detailed economic assessment of PV systems. Subsequent environmental savings are also calculated.


Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3805
Author(s):  
Nasser Alqahtani ◽  
Nazmiye Balta-Ozkan

The economic and social development of the Kingdom of Saudi Arabia (KSA) has led to a rapid increase in the consumption of electricity, with the residential sector consuming approximately 50% of total electricity production. The KSA depends largely on non-renewable energy resources, and the government has produced Saudi Vision 2030. This plan aims to lessen the country’s reliance on fossil fuels and reduce associated problems such as air pollution. Saudi Vision 2030 combines renewable energy and new building designs so that, for example, the planned city of Neom will be net zero energy. This study addresses how best to reduce Neom’s reliance on the national grid through rooftop photovoltaic generation in residential buildings. The study develops a techno-economic model of rooftop PV with battery storage suitable for existing residential building types likely to be built in Neom city (villas, traditional houses, and apartments), and assesses the optimal PV size, battery storage capacity, and optimal orientation of the PV panels. The study used HOMER Pro to compute the Net Present Cost, Levelized Cost of Energy, orientation of PV panels, and optimum PV system size. The optimal size of PV system is 14.0 kW for the villa, 11.1 kW for the traditional dwelling, and 10.3 kW for the apartment, each with a single battery of capacity 12 kWh.


Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1618
Author(s):  
Mohanasundaram Anthony ◽  
Valsalal Prasad ◽  
Raju Kannadasan ◽  
Saad Mekhilef ◽  
Mohammed H. Alsharif ◽  
...  

This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.


2021 ◽  
Vol 19 ◽  
pp. 205-210
Author(s):  
Milan Belik ◽  

This project focuses on optimisation of energy accumulation for various types of distributed renewable energy sources. The main goal is to prepare charging – discharging strategy depending on actual power consumption and prediction of consumption and production of utilised renewable energy sources for future period. The simulation is based on real long term data measured on photovoltaic system, wind power station and meteo station between 2004 – 2021. The data from meteo station serve as the input for the simulation and prediction of the future production while the data from PV system and wind turbine are used either as actual production or as a verification of the predicted values. Various parameters are used for trimming of the optimisation process. Influence of the charging strategy, discharging strategy, values and shape of the demand from the grid and prices is described on typical examples of the simulations. The main goal is to prepare and verify the system in real conditions with real load chart and real consumption defined by the model building with integrated renewable energy sources. The system can be later used in general installations on commercial or residential buildings.


2021 ◽  
Author(s):  
Jyoti Joshi ◽  
Anurag Kumar Swami ◽  
Somesh Bhattacharya ◽  
Vibhu Jately ◽  
Brian Azzopardi

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