Penalty mechanism in transactive energy: A mechanism design approach for day-ahead markets
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".