Energy Yield in Photovoltaic Systems

Theristis, Marios; Venizelou, Venizelos; Makrides, George; Georghiou, George E.

doi:10.1016/b978-0-12-809921-6.00017-3

Cited by 26 publications

(17 citation statements)

References 79 publications

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“…The polycrystalline silicon (poly‐c‐Si) PV module was installed in an open‐field mounting structure at the GI OTF, and it is rated at 220 W p nominal power. The test PV system at the UCY OTF includes 5 poly‐c‐Si PV modules (rated at 205 W p ) that were connected in series to form a string of nominal power capacity 1.025 kW p at the input of a string inverter 28 . The PV system is installed in an open‐field mounting arrangement.…”

Section: Methodsmentioning

confidence: 99%

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Data processing and quality verification for improved photovoltaic performance and reliability analytics

Livera

Theristis

Koumpli³

et al. 2020

Progress in Photovoltaics

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Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in‐field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

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Section: Methodsmentioning

confidence: 99%

“…The test PV system at the UCY OTF includes 5 poly-c-Si PV modules (rated at 205 W p ) that were connected in series to form a string of nominal power capacity 1.025 kW p at the input of a string inverter. 28 The PV system is installed in an open-field mounting arrangement.…”

Section: Experimental Apparatusmentioning

confidence: 99%

Data processing and quality verification for improved photovoltaic performance and reliability analytics

Livera

Theristis

Koumpli³

et al. 2020

Progress in Photovoltaics

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“…As previously stated, after the clear-sky conditions were confirmed and the correlation windows were determined, the estimation difference values for each panel (ED 2 , ED 3 , and ED 4 ) were calculated, using expressions (4), (2), and (3), respectively. As stated in Section 3.2, linear regression analysis was used, as the parameter b, the slope of the model, was determined using expression (9). The input data for the linear regression analysis were time-series of estimated difference ED 3 values for time intervals t 1 = 12:30-12:40; t 2 = 12:40-12:50; t 3 = 12:50-13:00; t 4 = 13:00-13:10 for a period of ten consecutive days, only involving detected clear-sky condition.…”

Section: Detection Of Soiling Degradation Effectmentioning

confidence: 99%

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

Radovanović

Popović

2021

Electronics

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As photovoltaics technologies have emerged as one of the most promising renewable energy resources in urban environments, monitoring and maintaining of such systems have gained significance. In order to support reliable system operation during the projected in-field operation lifetime, effective strategies for identifying potential problems in photovoltaic systems operation are needed. In this paper, novel methods for the identification of degrading effects in the operation of neighboring photovoltaic systems are presented. The proposed methods are applicable for identifying panel aging properties, soiling effects, and the operation of photovoltaic systems under different shading scenarios. Since the proposed methods are based on the cross-correlation of the operation of neighboring systems, they are particularly suitable performance assessment in urban environments. The proposed identification methods are integrated according to the adopted fog computing model, providing a scalable solution capable of uniform integration into the distributed applications for monitoring and maintenance of photovoltaic systems in urban areas. The details regarding the implementation of the identification methods in the form of data processing services and service operation and dependencies are also provided in this paper. The identification methods, integration concept, and related service operation are verified through the presented case study.

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“…Although normalized to cover a 25-year lifetime, these patterns could represent early life degradation modes such as light and elevated induced degradation (LeTID) [39], light induced degradation [40], and Staebler-Wronski [41] effects. Such types of degradation exhibit various time scales from a number of hours to years [4,5,22].…”

Section: Soiling and Degradation Profilesmentioning

confidence: 99%

Photovoltaic cleaning frequency optimization under different degradation rate patterns

et al. 2020

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Dust accumulation significantly affects the performance of photovoltaic modules and its impact can be mitigated by various cleaning methods. Optimizing the cleaning frequency is therefore essential to minimize the soiling losses and, at the same time, the costs. However, the effectiveness of cleaning lowers with time because of the reduced energy yield due to degradation. Additionally, economic factors such as the escalation in electricity price and inflation can either compound or counterbalance the effect of degradation. The present study analyzes the impact of degradation, escalation in electricity price and inflation on cleaning frequency and proposes a methodology than can be applied to maximize the profits of soiling mitigation in any system worldwide. The energy performance and soiling losses of a 1 MW system installed in southern Spain were analyzed and integrated with theoretical linear and nonlinear degradation rate patterns. The Levelized Cost of Energy and Net Present Value were used as criteria to identify the optimum cleaning strategies. The results showed that the two metrics convey distinct cleaning recommendations, as they are influenced by different factors. For the given site, despite the degradation effects, the optimum cleaning frequency is found to increase with time of operation.

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Energy Yield in Photovoltaic Systems

Cited by 26 publications

References 79 publications

Data processing and quality verification for improved photovoltaic performance and reliability analytics

Data processing and quality verification for improved photovoltaic performance and reliability analytics

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

Photovoltaic cleaning frequency optimization under different degradation rate patterns

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