Energy Routing Protocols for Energy Internet: A Review on Multi-Agent Systems, Metaheuristics, and Artificial Intelligence Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Energy Internet (EI) is transforming power networks by integrating Smart Grids (SGs), Distributed Energy Sources (DESs), and advanced communication and data technologies. This transformation increases complexity, as energy transmission evolves into a multi-source, multi-path, and multi-load system, with Peer-to-Peer (P2P) energy trading markets and Energy Routers as central drivers. As power networks grow and become more decentralized, the need for efficient and adaptive power routing protocols has become crucial to ensure their reliable and scalable management. This review focuses on energy routing strategies using multi-Agent architectures, Artificial Intelligence, and Metaheuristic optimization techniques. These approaches are well-suited to support the transformation of power networks into more distributed, dynamic, and complex systems. Spanning research from 2018 to 2024, this paper consolidates diverse studies, filling a critical gap by providing a comprehensive overview of power routing solutions for the evolving EI. It highlights key methodologies, limitations, and future research directions, offering a valuable reference for researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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