A Review of Optimization Strategies for Energy Management in Microgrids
Bibliographic record
Abstract
Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the critical need for sustainable energy solutions. Microgrids (MGs) provide practical applications for renewable energy, reducing reliance on fossil fuels and mitigating ecological impacts. However, renewable energy poses reliability challenges due to its intermittency, primarily influenced by weather conditions. Additionally, fluctuations in fuel prices and the management of multiple devices contribute to the increasing complexity of MGs and the necessity to address a range of objectives. These factors make the optimization of Energy Management Strategies (EMSs) essential and necessary. This study contributes to the field by categorizing the main aspects of MGs and optimization EMS, analyzing the impacts of weather on MG performance, and evaluating their effectiveness in handling multi-objective optimization and data considerations. Furthermore, it examines the pros and cons of different methodologies, offering a thorough overview of current trends and recommendations. This study serves as a foundational resource for future research aimed at refining optimization EMS by identifying research gaps, thereby informing researchers, practitioners, and policymakers.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".