Integration and control of grid‐scale battery energy storage systems: challenges and opportunities
Bibliographic record
Abstract
The current energy storage system technologies are undergoing a historic transformation to become more sustainable and dynamic. Beyond the traditional applications of battery energy storage systems (BESSs), they have also emerged as a promising solution for some major operational and planning challenges of modern power systems and microgrids, for example, enabling the integration of renewable energy sources by reducing their intermittency and improving the voltage, frequency, and reliability profiles of power grids. In other words, energy arbitrage, increased capacity of renewable energy resources, deferred investment in power grid components, reduced congestions, reduced carbon footprint, reduced losses, and stability stress the importance of conducting extensive and ground-breaking research and experimentation in the area of BESSs. This effort will maximize the benefits of using existing and novel BESS technologies in power systems. However, the success in the use of BESSs is driven by many technological developments and cost reductions. The current special issue enables a unique, dedicated opportunity to disseminate state-of-the-art research works in innovative aspects of BESSs from the technology and system points of view.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".