Optimization-Based Method for Aggregate Wind and Solar Capacity Estimation and Feeder Power Prediction
Why this work is in the frame
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Bibliographic record
Abstract
In 2050, clean deep electrification will require the connection of innumerable distributed energy resources (DERs) to power distribution utilities. The current utility practice of requiring complete DER information for operational applications, such as short-term feeder power prediction (FPP), will not be feasible in the future considering innumerable DERs. Unlike existing studies on DER hosting capacity estimation—which focus on determining the maximum DER capacity a system can integrate without grid reinforcements—this study addresses the future challenge of estimating the aggregate capacity (EAC) of connected DERs. Additionally, it aims to forecast the feeder power at the medium-voltage (MV) level. The proposed approach integrates advanced optimization techniques with deep neural network (DNN) models. An optimization method is introduced, where the first stage involves training basic solar and wind DNN models. In the next stages, EAC values for DERs are determined, and a load model is developed. Once the models for DERs and loads are trained and their aggregate capacities are determined, the framework enables real-time short-term FPP by utilizing weather and chronological data as inputs. The proposed model is tested on real utility data, demonstrating an average accuracy of 97.45% for EAC and 97.29% for FPP. A comparison with a direct FPP shows the superiority of the proposed sequential approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it