Demand–Response Games for Peer-to-Peer Energy Trading With the Hyperledger Blockchain
Why this work is in the frame
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Bibliographic record
Abstract
In smart grids, the large-scale integration of distributed renewable energy resources has enabled the provisioning of alternative sources of supply. Peer-to-peer (P2P) energy trading among local households is becoming an emerging technique that benefits both energy prosumers and operators. Since conventional energy supply is still needed to help fill the gap between local demand and supply when the local solar generation is not sufficient, demand–response management will keep playing an important role in the future P2P energy market. Blockchain and smart contract technology has gained increasing attention in P2P trading for its secure operation. The performance of blockchain-based P2P energy trading still remains to be improved, in terms of latency and cost of computation resources. This article studies the challenges of demand–response management in P2P energy trading and proposes a blockchain-empowered energy trading system for a community-based P2P market. The proposed demand–response mechanism is developed using two noncooperative games, in which dynamic pricing is applied for suppliers. The proposed energy trading system is prototyped on a cluster network, with a coordinator running as a smart contract in a Hyperledger blockchain. We implemented both on-chain and off-chain processing modes to study the system performance. The results from experiments with our prototype indicate that our proposed demand–response games have a great effect on reducing the net peak load, and at the same time, the off-chain processing mode provides lower latency and overhead compared to the on-chain mode while still keeping the same system integrity as the on-chain mode.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it