Deep reinforcement learning for joint dispatch of battery storage and gas turbines in renewable-powered microgrids
Bibliographic record
Abstract
This study introduces a novel deep reinforcement learning (DRL) framework for the joint dispatch of Gas Turbines (GTs) and Battery Energy Storage Systems (BESs) in microgrids that face the variability of renewable energy sources and demands. BESs can store surplus renewable energy for nearly instantaneous use, while GTs offer sustained energy output over longer periods, offering complementary benefits. Previous studies often oversimplified GT operations, neglecting critical factors such as ramp-up times and increased degradation from frequent starts. This research addresses these gaps by proposing an advanced modeling framework that accurately captures the dynamic interaction between GTs and BESs, including GT ramp-up times and maintenance costs associated with operational cycles. Through extensive case studies involving diverse microgrid configurations, we demonstrate that DRL effectively learns dispatch policies directly from historical data, outperforming traditional optimization techniques. Deploying DRL to our framework yields more realistic dispatch policies, reducing GT maintenance costs by avoiding frequent starts. The proposed framework has significant potential to improve energy management strategies and to streamline the planning of hybrid energy systems. To encourage further research, we have released our codebase to the public, enabling the scientific community to build upon our findings. • Developed novel framework for joint GT-BES dispatch in renewable-powered microgrids. • Accurately modeled GT and BES operational dynamics and cost implications. • Compared DRL against traditional methods for GT and BES energy dispatch. • Publicly shared codebase and hyperparameters to promote reproducibility. • Demonstrated DRL’s potential in enhancing the planning of renewable power systems.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Deep reinforcement learning for microgrid dispatch; engineering.
It develops and evaluates an energy-management algorithm, while code sharing is incidental to the scientific object.
Engineering application of deep reinforcement learning to microgrid energy dispatch.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".