Optimal dispatching of renewable energy‐based urban microgrids using a deep learning approach for electrical load and wind power forecasting
Bibliographic record
Abstract
Optimal load dispatching plays a vital role in improving the reliability and efficiency of renewable energy systems. This research presents a Mixed-Integer Linear Programming (MILP) approach for optimizing a power system's daily operational cost while increasing its resilience, including a wind turbine, battery, and conventional grid. Deep learning and statistical models along with a novel hybrid model, were developed and used to forecast the 3 days ahead load demand and wind power output. Testing these models shows that the proposed hybrid model could predict load with more accuracy than other models and it could reduce the root mean squared error by 22% to 44% for load forecasting and by 10.5% to 16.6% for wind speed prediction. The MILP model is applied for optimizing the load dispatch of an urban microgrid. The results of the dispatching model show that adding battery storage not only can bring down the grid-connected daily operational cost (from $8.4/day cost to $109.8/day income) and increase the resilience of the system by providing an off-grid mode, but also can extend its lifetime through minimization of degradation cost. The results also indicate that the degradation cost of batteries will contribute to a bigger portion of the operational costs in an off-grid mode in comparison to that of wind power curtailment cost. This research can inform effective and logical decisions for urban micro-grids and direct better integration and use of renewable energy systems in urban areas.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".