Optimal Configuration of Energy Storage System Considering Uncertainty of Load and Wind Generation
Bibliographic record
Abstract
Energy storage systems are promising solutions to the mitigation of power fluctuations and the management of load demands in distribution networks. However, the uncertainty of load demands and wind generations increasingly seen in distribution networks may have a great impact on the configuration of ESS. To solve the problem, a novel optimal configuration method for energy storage system is proposed to reduce the influence of uncertainty of both load demands and WGs. The proposed method first reduce the uncertainty of load through a comprehensive demand response system based on time-of-use and incentive. Then, to predict the output of wind generations, we use the particle swarm optimization and backpropagation neural network to create a predictive model of the wind power. Then, an optimal configuration model is established to minimize the ESS investment cost and the network power loss reduction, subject to technical constraints such as ESS operational constraints and power balance constraint et al. An improved simulated annealing PSO algorithm is used to solve the optimization problem. Finally, the numerical studies on a modified IEEE 33-node distribution system show the advantages of the proposed methodology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".