Risk Analysis of Transactive Energy Retail Markets
Bibliographic record
Abstract
New grid management schemes have created exciting opportunities for end customers to maximize their utility by becoming active participants. In particular, Transactive Energy Systems (TES) allow customers to cooperate and negotiate in energy markets, increasing social welfare. These interactions also reduce demand-side uncertainties and simplify grid balancing at different levels. In TES, it is beneficial to employ forward contracts because they establish conditions for future energy supply, allowing grid maintainers to plan a cost-effective operation. Thus, end customers interact in local retail markets in advance to agree on service conditions that fulfill their needs. This paper presents a comprehensive analysis of the risks involved in those forward contracts with the aim of providing valuable information to participants. The TES environment modifies the typical risks of electricity contracts due to the information exchange in the negotiation and execution stages. Indeed, reliable data and realistic forecasting assumptions become a primary concern for each participant since they constitute the main threat of contract defaulting. Risk management strategies are presented in bow-tie and Ishikawa diagrams to elicit the decisions for market participants. Case study results demonstrate that forecasting errors impact the conditional value at risk of the contracts, in proportion to the demand uncertainty.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".