Solar PV on mosque rooftops: Results from a pilot study in Saudi Arabia

Elshurafa, Amro M.; Alsubaie, Abdullah; Alabduljabbar, Ayman A.; Al-Hsaien, Shafi Ali

doi:10.1016/j.jobe.2019.100809

Cited by 55 publications

(35 citation statements)

References 54 publications

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“…The techno‐economic data of each component is necessary for effectively investigating the performance of each nominated HES. The economic and technical information related to the HES components is listed in Table 1 55 .…”

Section: Case Study and Simulation Resultsmentioning

confidence: 99%

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

et al. 2020

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Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply-demand-based optimization (SDO) is applied to the HES sizing problem based on long-term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big-bang-big-crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.

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Section: Case Study and Simulation Resultsmentioning

confidence: 99%

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

et al. 2020

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“…Source: (Salem & Kinab, 2015) The feasibility analysis of the implementation of a photovoltaic system involves the study regarding the capacity that the model must generate savings (or even profits) over the years, which is, the relationship between the cost of installation, maintenance and operation of the system that will generate energy with the costs that the user pays for the use of energy that is generated by others. The feasibility of the transition to the generation of photovoltaic solar energy must also take into account the infrastructure of each location, since it is possible that, although the financial return is good over the years, the area is not accessible for the implementation of the photovoltaic panels, bringing problems to the system as a whole and, consequently, to the local energy supply (Elshurafa, Alsubaie, Alabduljabbar, & Al-Hsaien, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…The understanding of the development and feasibility of products with green technology can influence business management and purchasing behavior (Silva et al, 2017). Some authors that study this type of subject are Lobaccaro et al (2016), Winther et al, (2018), Elshurafa et al, (2019). From this more detailed analysis, the contribution of this study becomes evident since the better understanding on the subject can make it possible to reduce the knowledge gaps about it as well as support the implementation of solar systems in certain locations.…”

Section: Introductionmentioning

confidence: 94%

Economic feasibility for photovoltaic solar energy projects: a systematic review

Concolato

Cunha

Afonso

2020

Rev. Prod. Desenvolv.

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Purpose: This paper presents a systematic literature review regarding economic feasibility studies and photovoltaic solar energy production. Methodology/Approach: To this end, publications from 2015 to 2019 were collected in two journal databases: Web of Science and Science Direct. In order to create a corpus more relevant to the aim of this research, a selection among the papers found was made respecting some established filters. Subsequently this refinement, the resulting corpus consisted of seventeen papers. Findings: A current matter discussed by the researchers was identified, regarding the study on the best photovoltaic system to be used for each user: whether batteries are the best option as a storage source, or the connection of the system to the network is the most adequate guarantee to meet demand. Research Limitation/implication: This review gives the present study a characteristic of bringing together what, within the mentioned period and in the searched bases, has been approached in different works that deal with the economic feasibility analysis regarding the photovoltaic solar energy production. Originality/Value of paper: can support the implementation of solar systems in peripheral locations where the technology is still little used.

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“…where C rep is the capital replacement cost and Y rep is the component lifetime. The technical and economic parameters of all HES components are required to assess the performance of the proposed HES which are listed in Table 2 [45].…”

Section: Proposed Configuration Of the Hybrid Energy Systemmentioning

confidence: 99%

Techno-Economic Optimization of Small-Scale Hybrid Energy Systems Using Manta Ray Foraging Optimizer

et al. 2020

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Hybrid energy systems (HESs) are becoming popular for electrifying remote and rural regions to overcome the conventional energy generation system shortcomings and attain favorable technical and economic benefits. An optimal sizing of an autonomous HES consisting of photovoltaic technology, wind turbine generator, battery bank, and diesel generator is achieved by employing a new soft computing/metaheuristic algorithm called manta ray foraging optimizer (MRFO). This optimization problem is implemented and solved by employing MRFO based on minimizing the annualized system cost (ASC) and enhancing the system reliability in order to supply an off-grid northern area in Saudi Arabia. The hourly wind speed, solar irradiance, and load behavior over one year are used in this optimization problem. As for result verification, the MRFO is compared with five other soft computing algorithms, which are particle swarm optimization (PSO), genetic algorithm (GA), grasshopper optimization algorithm (GOA), big-bang-big-crunch (BBBC) algorithm, and Harris hawks optimization (HHO). The findings showed that the MRFO algorithm converges faster than all other five soft computing algorithms followed by PSO, and GOA, respectively. In addition, MRFO, PSO, and GOA can follow the optimal global solution while the HHO, GA and BBBC may fall into the local solution and take a longer time to converge. The MRFO provided the optimum sizing of the HES at the lowest ASC (USD 104,324.1), followed by GOA (USD 104,347.7) and PSO (USD 104,342.2) for a 0% loss of power supply probability. These optimization findings confirmed the supremacy of the MRFO algorithm over the other five soft computing techniques in terms of global solution capture and the convergence time.

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Solar PV on mosque rooftops: Results from a pilot study in Saudi Arabia

Cited by 55 publications

References 54 publications

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Economic feasibility for photovoltaic solar energy projects: a systematic review

Techno-Economic Optimization of Small-Scale Hybrid Energy Systems Using Manta Ray Foraging Optimizer

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