Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms
Bibliographic record
Abstract
This paper deals with application of meta-heuristic algorithms to resolve the problem of combined economic emission dispatch (CEED) with peak load management for a mediumsized power system in an efficient manner. The objective is to optimize the fuel cost of generation simultaneously minimizing the environmental pollution caused by fossil fuel based power generating units working at their peak limits. In this paper combined problem of minimizing fuel cost and emission of flue gases (NOx, CO2, SO2), is solved using Cuckoo search (CS) and Grasshopper optimization algorithm (GOA) via composite function of all four objectives with help of weight ratios and price penalty factors. Demand Side Management (DSM) measure is also applied at least expensive areas to manage the peak load condition at generating units. The problem is implemented on IEEE 30-bus system with 6 generator units. The simulation results of the CS algorithm for CEED with and without DSM have been compared with the results of GOA algorithm. The compound results obtained by CS algorithm for the problem of CEED with DSM validated its potential.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".