Energy Trading in the Smart Grid: A Distributed Game-Theoretic Approach
Bibliographic record
Abstract
In this paper, we propose both a centralized solution and a distributed game-theoretic approach to solve the problem of energy trading in the smart grid. In particular, we consider a number of geographically distributed consumers that need to buy energy from the neighboring users who have a surplus stored energy to meet their local demands. While buyers minimize their energy bill by buying energy at prices lower than that of the grid, sellers can make pro?t by selling the extra stored energy, which could have been acquired from affordable renewable resources. Buying energy from neighboring users also helps in reducing the stress on the main grid, which decreases electricity prices and protects the environment. We ?rst formulate the energy trading problem as a centralized optimization problem. Then, we propose a distributed solution based on the game theory that requires each user to merely apply its best response strategy to the current state of the system. Buyers play the game by deciding how much energy to be bought from each seller in order to minimize their energy bill taking into account different constraints tied to the grid infrastructure. Extensive simulations con?rm the effectiveness of the proposed approach in terms of energy bill savings, convergence in acceptable times, and fairness to its users.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".