The Simultaneous Impacts of Seasonal Weather and Solar Conditions on PV Panels Electrical Characteristics

Farahmand, Mahsa Z.; Nazari, Mohammad Esmaeil; Shamlou, Sadegh; Shafie‐khah, Miadreza

doi:10.3390/en14040845

Cited by 22 publications

(13 citation statements)

References 28 publications

(29 reference statements)

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“…Generally, the power output from the solar PV power plant depends on solar irradiance and ambient temperature [68]. Higher solar irradiance usually leads to higher power output whereas higher ambient temperature causes lower power output.…”

Section: Solar Pvmentioning

confidence: 99%

“…Thus, rather than modeling the random variable itself, the noise is modeled instead. In addition, the generation model of solar PV in this study considers a correlation of two uncertainties of solar irradiance and ambient temperature in detail [68]. According to solar PV characteristics, the ambient temperature somehow correlates with solar irradiance.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation

2022

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In power system planning, the growth of renewable energy generation leads to several challenges including system reliability due to its intermittency and uncertainty. To quantify the relatively reliable capacity of this generation, capacity credit is usually adopted for long-term power system planning. This paper proposes an evaluation of the capacity credit of renewable energy generation using stochastic models for resource availability. Six renewable energy generation types including wind, solar PV, small hydro, biomass, biogas, and waste were considered. The proposed models are based on the stochastic process using the Wiener process and other probability distribution functions to explain the randomness of the intermittency. Moreover, for solar PV—the generation of which depends on two key random variables, namely irradiance and temperature—a copula function is used to model their joint probabilistic behavior. These proposed models are used to simulate power outputs of renewable energy generations and then determine the capacity credit which is defined as the capacity of conventional generation that can maintain a similar level of system reliability. The proposed method is tested with Thailand’s power system and the results show that the capacity credit depends on the time of day and the size of installed capacity of the considered renewable energy generation.

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Section: Solar Pvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation

2022

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“…Deep residual neural network [27] Mini-grid/large grid Online Acoustic wave method [28] Mini-grid/large grid Online Imaging technique [29] Mini-grid/large grid Offline Weather Inverse distance weighting [30] Home/mini-grid/large grid Online Data analytics [31] Home/mini-grid/large grid Online Theta-krill herd algorithm [32] Home/mini-grid/large grid Online Delamination Electric discharge channel [33] Mini-grid/large grid Online Thermal imaging [34] Mini/large grid Offline Aging test [35] Mini/large grid Offline Discoloration I-V characteristic analysis [36] Mini/large grid Offline Accelerated testing (AT) [37] Mini-grid/large grid Online Spectroscopic investigation [38] Mini-grid/large grid Offline…”

Section: Dust Accumulationmentioning

confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

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“…The impacts of real-world situations on the performance and efficiency of solar panels were investigated in [5]. In another study, a long-term reliability assessment approach was employed to study the implications of climate change on the structural level of PV systems [6]. The production of electricity from a PV system is affected by temperature and irradiance, and its P-V curve has a singular peak known as a maximum power point (MPP) [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions

et al. 2022

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In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algorithm (TSA), particle swarm optimization (PSO), grey wolf optimization (GWO), flower pollination algorithm (FPA), and perturb and observe (P&O). A quantitative and qualitative analysis was carried out based on three distinct partial shading conditions. It was observed that the proposed TSA-PSO technique had remarkable success in locating the maximum power point and had quick convergence at the global maximum power point. The presented TSA-PSO MPPT algorithm achieved a PV tracking efficiency of 97.64%. Furthermore, two nonparametric tests, Friedman ranking and Wilcoxon rank-sum, were also employed to validate the effectiveness of the proposed TSA-PSO MPPT method.

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The Simultaneous Impacts of Seasonal Weather and Solar Conditions on PV Panels Electrical Characteristics

Cited by 22 publications

References 28 publications

Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation

Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation

An Effective Evaluation on Fault Detection in Solar Panels

A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions

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