Comprehensive platform for distribution transactive energy markets
Bibliographic record
Abstract
Abstract Reducing the cost of distributed energy resources (DERs) such as renewables, storage, electric vehicles and smart loads is driving their increased connection to distribution systems. Extracting maximum benefits from DERs require liberalising distribution systems by allowing: (1) a distribution transactive energy market (DTEM) operated by a local distribution operator (LDO) and (2) peer‐to‐peer (P2P), peer‐to‐LDO (P2LDO) and Transmission‐to‐LDO (T2LDO) type transactions. A DTEM will bring several benefits such as: (1) enhanced economic opportunity for DERs, making them more profitable and (2) increased social welfare benefiting both buyers and sellers. To achieve this objective, we develop a comprehensive three‐phase DTEM platform that provides maximum economic opportunities for DERs and maximises social welfare that benefits all market participants, while considering P2P, P2LDO and T2LDO transactions, for both energy and ancillary services. Interaction between bulk electricity market independent system operator (ISO) and LDO controlled DTEM is presented. The DTEM model is implemented as a practical mixed‐integer linear programming formulation that includes a network reconfiguration feature. The DTEM model is studied on three‐phase 5‐bus and 34‐bus systems, demonstrating its effectiveness to settle energy and ancillary service transactions, while obtaining distribution locational marginal prices. Results show that P2P transactions, when allowed, increase social welfare and increases profitability of DERs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".