Direct Electricity Trading in Smart Grid: A Coalitional Game Analysis
Bibliographic record
Abstract
Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.
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.
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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".