Smart grids: A comprehensive survey of challenges, industry applications, and future trends
Bibliographic record
Abstract
With the increasing energy demands of the 21st century, there is a clear need for developing a more sustainable method of energy generation, distribution, and transmission. Modern power grid infrastructures are currently managing these aspects, though their outdated configuration results in rigid and inefficient operation. The emerging technology of Smart Grids offers innovative solutions to these issues, utilizing advanced communication and computation structures. Through the integration of a bidirectional power and information flow, smart systems, and renewable energy sources, Smart Grids are the next generation of power grids, enabling cooperativity, automation, and efficiency. Even on small scales, the proposed benefits of the Smart Grid are substantial in maintaining sustainable energy use with growing demands. In this survey, we provide a comprehensive overview of Smart Grid technology, specifically focusing on the challenges presented by cybersecurity, interoperability, and renewable energy integration. These aspects were determined to be the most prevalent issues facing the advancement of Smart Grids, specifically for global application. We discuss these challenges thoroughly, determining the difficulties they induce and proposed solutions presented in literature. As such, this survey is intended to be a reference for other researchers, providing state-of-the-art approaches to solving these problems, as well as offering insights on ongoing issues and future endeavors. Additionally, we will highlight the current state of Smart Grid implementation through an analysis of programs and research being conducted by academic institutions, industry, and government.
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.
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 | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".