MétaCan
Menu
Back to cohort
Record W4401631385 · doi:10.1016/j.egyr.2024.08.019

Navigating challenges in large-scale renewable energy storage: Barriers, solutions, and innovations

2024· article· en· W4401631385 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Reports · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEnergy storageRenewable energyPumped-storage hydroelectricityEnergy engineeringIntermittent energy sourceProcess engineeringFlexibility (engineering)Grid energy storageComputer scienceEngineeringDistributed generationElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

The accelerated growth in renewable energy systems offers resolutions for reaching clean and sustainable energy production. Electrical Energy Systems (ESS) present indispensable tools with diverse applications to satisfy intermittent characteristics of renewable energies and enable the transition to clean energy production. The applied methodology to assess and review the hybridization concept summarizes the employments of the technical evaluations in the mutual resolutions between the energy production and consumption sectors while the high-efficiency EES has been considered as a synchronizer. Optimized smart grids and microgrids benefit from EES, making energy systems more efficient and reliable. The rise of electric vehicles as an eco-friendly transportation solution also depends on EES to overcome energy storage challenges. The novel aim of this work lies in the elaboration of the large-scale EES for storing and harvesting energy for effective peak-shaving purposes. This multidisciplinary review put a higher emphasis on the essence of the interconnection between different industrial subdivisions for environmentally benign, high-efficiency, and economical process reflections. Mechanical energy storage, thermomechanical energy storage, thermal energy storage, chemical energy storage, electrical energy storage, and electrochemical energy storage are the involved concepts in this study. These divisions collectively form a comprehensive strategy for optimizing energy utilization. RE sites increasingly utilize energy storage systems to enhance system flexibility, grid stability, and power supply reliability. Whether the primary energy source is solar, wind, geothermal, hydroelectric, or oceanic, EES provides the critical ability to store and manage energy efficiently. • Hybrid Energy Storage Systems - A strategic approach to overcome renewable energy challenges. • Challenges Hinder ESS Adoption - Economic constraints, industry acceptance, technology, safety, and regulatory barriers. • Public Attitudes Matter - Influence energy storage adoption and widespread use. • Enhancing renewables through EES-driven energy management scenarios.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.269
Teacher spread0.249 · 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