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Record W4402477129 · doi:10.11159/icert24.114

Energy and Economic Comparison Between Traditional APV and Concentrating APV-CPV Agrivoltaic Systems

2024· article· en· W4402477129 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2024
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy (signal processing)Environmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

In recent years, agrivoltaics has taken on a primary role as an innovative technology in the agricultural and energy sectors [1].Thanks to its synergistic approach, it allows the production of electricity and cultivation on the same land, significantly reducing conflicts over land use [2].Traditional solutions are based on the use of opaque photovoltaic panels that generate partial shading on the underlying ground.According to some studies, this aspect could compromise the optimal growth of crops.[3].However, recent innovations have been made in the field of agrivoltaics such as the use of concentrated photovoltaic systems (APV-CPV) [4].This technology splits solar radiation to take advantage both of direct sunlight for an efficient electricity generation and of diffuse light for crop benefits [5].In this study, a detailed comparison was conducted between the installation of a traditional agrivoltaic system (APV) and a concentrating agrivoltaic system (APV-CPV) using Fresnel lens technology, which allows the separation of direct and diffuse radiation [6].An agricultural user from southern Italy specialized in tomato cultivation was taken into consideration.Thanks to the predictions derived from an Artificial Neural Network (ANN) model, in both cases the effects of shading on water demand (ET0) and crop growth rate (CGR) were evaluated [7].The model predicts a greater reduction in water demand compared to the open field in the case of the APV, equal to 38%, and a more pronounced decrease in the growth rate of 54%, due to the increase in shading.Conversely, in the APV-CPV case, thanks to the Fresnel lenses which partially allow the passage of solar radiation, a smaller reduction in water demand, 22%, and a more modest decrease in the growth rate, 33%, are expected.These aspects influenced the trend of cash flows.Through an energy analysis, it has been highlighted that in both cases, the electrical output of the system meets the energy demands of the user for several months, resulting in significant savings and revenue from selling surplus energy.This enables diversification of income sources for farmers.The economic analysis has allowed the calculation of the main indicators in both cases, including the Net Present Value (NPV) and the Discounted Payback Period (DPBP).In particular, a DPBP of 6 years has been obtained for the traditional agrivoltaic system (APV), and 9 years for the concentration agrivoltaic system (APV-CPV).Considering the presence of incentives, in both cases greater economic benefits and significantly shorter amortization times of the investment could be obtained.Furthermore, the analyzes were conducted on the same cultivation area.Future developments include a feasibility study with equal performance in terms of yield in order to highlight the advantages of APV-CPV systems in achieving greater productivity, thanks to their partial transparency and smaller size.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.211
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it