Optimal selection of energy storage system in distribution networks with distributed generation considering operation model by neural networks
Bibliographic record
Abstract
This thesis presents a methodology for optimal determination of type, bar, and capacity of Battery Energy Storage Systems (BESS) in distribution systems with distributed generation (DG) where the battery optimal operation is approximated by an input/output model created with neural networks. A genetic algorithm selects the storage by a fitness function defined with the annual operation costs of the distribution system, the voltage limits, and batteries costs. The model allows to compare different types of batteries technologies, considering its technical and economical characteristics. Lifetime of the battery is based on the depth of discharge (DOD) impact to the life cycle. The database for the input/output model is obtained by a Monte Carlo simulation of the optimal daily operation of the battery for a representative sample from a yearly real data. This approach allows to consider the stochastic behavior of the distributed generation, the load and the energy prices. The daily operation of the battery is optimized by a nonlinear optimization model, considering a load flow by OpenDSS proprietary software from the Electric Power System Research Institute (EPRI). The neural network was based on the Group Method of Data Handling (GMDH). The neural network implementation allows to reduce the yearly simulation time, where the possible selection alternatives are chosen by the genetic algorithm. This methodology is tested in a distribution system of 33 nodes, and the generation, demand, and prices curves are taken from data of the Independent Electricity System Operator IESO relative to the Canadian distribution system, considering solar and wind as renewable sources. The studied case shows a good approximation of the neural network with the obtained data for the daily load flow and allows to identify the critic cases of the systems, as bar location not allowed and probability of risk of the results. The results compare the use of the batteries in the distribution network, reducing losses and operational costs along the day in the system and selecting the best type. Also, the storage systems can reduce the final energy cost of the system (limited by the proposed constraints) and the loses, with the possibility to determine the best alternative.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".