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Record W4409786295 · doi:10.1016/j.esr.2025.101712

Penalty mechanism in transactive energy: A mechanism design approach for day-ahead markets

2025· article· en· W4409786295 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy Strategy Reviews · 2025
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsHydro-QuébecUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaFondation de l’UQTRHydro-QuébecUniversité du Québec à Trois-Rivières
KeywordsMechanism (biology)Transactive memoryMechanism designEnergy (signal processing)Computer scienceRisk analysis (engineering)EconomicsBusinessEnvironmental economicsMicroeconomicsMathematicsKnowledge management

Abstract

fetched live from OpenAlex

Ensuring incentive compatibility mechanisms to enforce market obligations is crucial in deploying a transactive energy system. While previous studies have reported adopting penalty mechanisms for market compliance, these studies did not generally analyse the incentive compatibility property of mechanism design. Neglecting this mechanism design property can lead to inefficient market outcomes and economic losses for system operators. This paper analyses self-enforcing policies to verify whether they comply with the incentive compatibility property in a one-shot market architecture. Additionally, it provides a comprehensive introduction to the phases of mechanism design – ex-ante , interim , and ex-post – and their relationship with key design principles: individual rationality, efficiency, budget balance, and incentive compatibility, highlighting expected outcomes at each phase. A case study demonstrates how a strategy-proof mechanism significantly influences individual rationality, efficiency, and budget balance, offering practical insights for improving decision-making frameworks in electricity markets. Moreover, the findings reveal that adopting a non-strategy-proof mechanism undermines the long-term viability of transactive energy systems. This work provides actionable recommendations for system operators and policymakers on implementing mechanisms that prevent strategic behaviour from agents. • Transactive energy offers new economic opportunities to customers. • Customers are encouraged to adopt agent-based technology for optimized negotiations. • Rational and intelligent agents may exploit misbehaviours for economic gain. • Agents’ misbehaviour must be prevented for a successful transactive energy deployment. • This work addresses fundamental concepts of mechanism design.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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