Multi-Agent Hierarchical Fuzzy Reinforcement Learning for Cooperative-Competitive Peer-to-Peer Energy Trading With Privacy Preservation
Bibliographic record
Abstract
The growing integration of distributed energy resources and advancements in communication and information technology have necessitated the development of smart grids with advanced demand response capabilities. This paper proposes a hierarchical reinforcement learning framework integrating intelligent home energy management and a peer-to-peer energy trading community. The low-level policy optimizes home energy management by leveraging a fuzzy actor-critic reinforcement learning algorithm with a centralized-training-decentralized-execution structure. It regulates energy consumption based on Time-of-Use tariffs while considering user dissatisfaction levels. Then, the low-level policy communicates surplus or deficit energy to the high-level policy. The high-level policy employs the fuzzy actor-critic reinforcement learning algorithm under the decentralized-training-decentralized-execution structure, with value decomposition networks to generate a privacy-preserving cooperative-competitive strategy for pricing in a dynamic continuous double auction market. While optimizing individual agent benefits, the high-level policy also fosters cooperation to enhance energy trade within the community. The simulations using the real-world data demonstrate the effectiveness of the low-level policy in managing the energy consumption and determining the required/excessive energy in the market. The comparisons with the purely cooperative and purely competitive markets represent the superiority of the proposed approach in terms of increased load transactions, buyers' cost savings, and sellers' revenue.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".