ORFLEX: Open Platform for Rapid Testing and Deploying Local Flexibility Markets
Bibliographic record
Abstract
Local Flexibility Markets (LFMs) play a crucial role in modern electrical grids as they can address grid congestion by efficiently exploiting demand-side flexibility through market-clearing algorithms and incentive signals. However, demo projects worldwide offer limited access to detailed information that enables a good understanding of market design, estimating computational requirements, and exploring different algorithms at multiple levels. Accordingly, This paper introduces ORFLEX, an open-source platform for rapid testing and deploying local flexibility markets. ORFLEX exhibits salient features, including scalability, agile deployment, and secure interactions by leveraging cloud and edge infrastructure and Functions as a Service (FaaS). The platform aims to provide a go-to guide for LFM implementation. The proposal was validated using a straightforward market with a hierarchical configuration involving a market operator, a spot market aggregator, and many flexumers. Experimental results revealed the systems’s ability to assist in large-scale deployments while increasing the number of participants on the demand side. The methodical simulation insights will help market designers and regulators to properly develop LFMs and analyze the impacts of different clearing and demand-side flexibility estimations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".