A Predictive Energy Management System Using Pre-Emptive Load Shedding for Islanded Photovoltaic Microgrids
Bibliographic record
Abstract
This paper presents an energy management system (EMS) for an islanded microgrid with photovoltaic generation and battery storage. The system uses a predictive approach to set operational schedules in order to minimize system-wide outages in the microgrid, specifically through pre-emptive load shedding. Four-times daily updated online weather forecasts are combined with the photovoltaic system model to predict energy production over a 48 h period. These predictions are used, along with load forecasts and a model of the energy storage system, to predict the state-of-charge and characterize potential upcoming outages. Outage mitigation actions using pre-emptive load shedding are then planned and executed to avoid outages or minimize the duration of unavoidable outages. The approach also features bounds on the battery state-of-charge to account for uncertainties in the estimate of the stored energy. The EMS has been implemented using an event-driven framework with TCP/IP communication, which is modular and extensible to more complex system configurations. The approach has been validated through simulations and experiments, which demonstrate its feasibility and potential, for the chosen test scenario, to reduce the outage duration by 87% to 100%.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".