Optimum planning of large distributed resources in a mesh connected system based on artificial neural networks
Bibliographic record
Abstract
This paper proposes a new approach to optimally determine the appropriate size and location of a dispatchable large Distributed Resource (DR) in a large mesh connected system. Inserting a DR in an already existing distribution system is an important issue at present, specifically under the deregulated electricity market. Determining the optimal siting and/or sizing of the DR is the key factor in determining the penetration level of the renewable energy sources. Various parameters; like losses, voltage profile, etc were investigated in the previous work. Beside the losses and the voltage profile, the proposed approach introduces another important parameter related to DR installation, which is the short circuit level representing the capacity of the already existing network protective devices. The proposed algorithm uses Artificial Neural Networks (ANN) to determine the appropriate weighting factors of each parameter included in the optimization problem. The selected parameters, i.e., the voltage level, the total system losses and the short circuit level are weighted in order to choose the optimal DR allocation and its corresponding sizing. The proposed technique has been tested on the IEEE 24-bus mesh connected test system. This test system is a large heavily loaded interconnected system. The main advantages of the proposed optimization technique are its simplicity, and applicability to other systems, i.e. radial and interconnected. Simulation results using the MATLAB simulation package are presented to validate the effectiveness of the proposed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".