Open Energy Market Strategies in Microgrids: A Stackelberg Game Approach Based on a Hybrid Multiobjective Evolutionary Algorithm
Bibliographic record
Abstract
The emergence of microsources holds promise to reduce the carbon emissions and exploit more renewables in order to meet the worldwide growing electrical energy demands. However, there exist several challenges, such as optimizing the tradeoff between the use of renewable and nonrenewable energy sources, to leverage affordable electric power while minimizing carbon emissions. Game theoretic approaches have been widely used in various scientific domains and have recently also increasingly been used in smart grids, whereby evolutionary paradigms have been widely deployed as a popular heuristic search method to solve and optimize complex real-life scientific problems. A promising approach is the development of such evolutionary algorithms and game theoretic approaches in the context of open energy markets. In this paper, we develop an analytic model of a multileader and multifollower Stackelberg game approach and propose a bi-level hybrid multiobjective evolutionary algorithm to find optimal strategies that maximize the profit of utilities, and minimize carbon emissions in an open energy market among interconnected microsources.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".