Guest Editorial: Advanced and Innovative Control Technologies for Grid-Resilience-Enhancing Energy Storage Systems
Bibliographic record
Abstract
Along with the higher penetration of renewables, more frequent natural disasters/disturbing events, and increased level of interconnection between different industrial processes, higher-level flexibility and adaptability are demanded to address the intermittence, rising costs, and high uncertainty and vulnerability in energy supply and utilization. Energy storage systems are bound to play an ever-growing crucial role in future smart grid systems withhigh power quality and resilience requirements. As the key drivers of a carbon-neutral and smart society, they are also essential to electrified transportation, industrial cyber-physical systems, and residential communities. This dispensability has been witnessed by the rapid growth of global energy storage deployments and emerging business paradigms, especially with the proliferation of high-density power batteries and highly efficient power electronics over the past years. This operational vision for enhancing grid resilience motivates the in-depth investigation of advanced and innovative control technologies for energy storage. The microscopic, component- and system-level optimization and management of energy storage systems and their interplay with other industrial systems are critical to the security andlongevity of system deployment. This endeavor on control system development can also be further promoted by incorporating the emerging advancements in data science and artificial intelligence (AI), which have been increasinglyexplored in the existing body of knowledge.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".