Reliability Assessment of Distribution Systems With Microgrids Using Discrete-Time Markov Chains
Why this work is in the frame
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Bibliographic record
Abstract
Microgrids can improve the reliability and resiliency of modern distribution systems. The stochasticity of local non-dispatchable distributed energy resources (NDDERs), combined with the time-dependency of battery energy storage systems (BESSs) and load shedding strategies (LSSs), complicates the reliability assessment of distribution networks embedded with microgrids. In this work, we propose a minimal cut-set method using a discrete-time Markov chain to perform the time-series adequacy assessment. Our method offers an alternative to sequential Monte Carlo simulations (SMCSs) to account for the stochasticity of NDDERs and the time-dependency of BESSs and LSSs. Case studies on modified IEEE-RTBS Bus2 and IEEE 123-Test Feeder systems assess the accuracy of the method when compared with SMCSs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it