Peer-to-Peer Energy Trading in a Local Energy Market Using Quantum Reinforcement Learning
Bibliographic record
Abstract
Recent years have witnessed a significant transformation in the sources of energy and its distribution among consumers and prosumers. Renewable energy sources (RES) such as photovoltaic have become particularly popular. However, RES requires a new approach to manage energy demand and supply by leveraging distributed energy generation. In this context, a useful paradigm is a peer-to-peer (P2P) energy marketplace, which is a trading mechanism that actively involves consumers and prosumers in trading electricity using state-of-the-art information and communication technologies. Accordingly, we focus on optimizing total energy trading costs by using quantum reinforcement learning (QRL) to ensure that local generation is first consumed by energy consumers within a local community. This is followed by a proposal of blockchain-based energy trading systems to facilitate decentralized, secured, and privacy-protected trading platforms for the trading participants. To this end, we use three optimization techniques - linear optimization techniques, reinforcement learning, and QRL - and analyze their performance. Simulation results show that the agents learning using quantum computing converge faster than those of the other two optimization techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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".