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Record W4254187592 · doi:10.5383/ijtee.10.02.004

Financial Risk and market Effects for the Congestion Costs of the electricity System in Italy

2015· article· en· W4254187592 on OpenAlexvenueno aff
Enrico Maria Mosconi, Stefano Poponi, Cecilia Silvestri

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsElectricity marketElectricityBusinessElectric power systemGridMicroeconomicsEnvironmental economicsIndustrial organizationFinanceEconomicsPower (physics)

Abstract

fetched live from OpenAlex

The physical and structural constraints of the electricity grid economically and financially affect the operators imposing on them a set of measures designed to contain the problem of electric congestions. The Congestion costs occur in the electricity market when the power flows, equivalent to a projection mapping of energy injection and withdrawal, established by the “electricity stock exchange” and by “bi-lateral contracts”, are incompatible with the transfer capacity guaranteed by the power grid, in required security conditions. Transfer restrictions in the national and transboundary transmission grid can potentially determine the market segmentation in different geographical areas, preventing the producers to compete freely to satisfy the overall demand in those areas. As a result of power congestions, those producers that have reduced limitations in the power transfer are obviously favored, also if in the presence of high marginal production costs and prices, leading to the constitution of pools of producers that de facto control the local market. Through a critical survey of the situation of the Italian electricity market the paper aims to describe and discuss the problem of power congestions costs, the electricity price formation and the congestion risk management.

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.

How this classification was reachedexpand

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.181

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 head0.002
GPT teacher head0.156
Teacher spread0.154 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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