Integration of machine learning with economic energy scheduling
Bibliographic record
Abstract
The aim of economic load dispatch (ELD) is to deliver required electrical power for a specified period at the lowest possible generation cost using available generating units (GUs). It is imperative to lower the generation costs in order to reduce the consumer costs and to generate adequate revenue from large capital investments in the power sector. There are several optimization algorithms (OAs) to solve this issue. In this study, a new method that combines machine learning (ML) with an OA is used to come up with a high-precision, best solution for ELD issues in the quickest time possible. The ’Lagrange Multiplier’ (LM) method is used as the OA, while the ’Decision Tree’ (DT) algorithm is used as the ML algorithm . ML algorithms require data to train themselves. A data generation algorithm (DGA) is used to generate data considering constraints such as the power balance constraint, transmission loss (TL), generating capacity, and prohibited operating zones (POZs). The DGA is based on the LM method with constraint handling techniques. Without considering ramp rate limits (RRLs), the optimal load sharing data is generated over the whole power capacity range of the committed GUs. The power capacity ranges from the sum of the minimum power capacity to the maximum power capacity of the committed GUs. This range is divided into several discrete data points with a step size of 0.01. Optimal load sharing among the GUs has been calculated for each of the data points using DGA. Then the DT model was trained with the generated data that could have been used further to predict the load sharing among the GUs. To impose RRLs, we have developed a search method using the trained DT model. We have validated our proposed method through three case studies: Case 1: 6 GUs with a 1263 MW power demand; Case 2: 15 GUs with a 2630 MW power demand; and Case 3: 140 GUs with a 49342 MW power demand. Finally, the optimal solution for all the case studies using the proposed method was compared with the existing methods. The proposed method was found to be better than the existing methods in terms of time, precision, and cost. This opens up a new way to help with the ELD issue by combining ML with OA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".