Socio-Economic Oriented Microgrid Energy Management System with Islanding Capability during Adverse Weather Conditions
Bibliographic record
Abstract
This paper presents a resilient-economic Microgrid Energy Management System (M-EMS). The main idea is to increase microgrid resilience through dynamic adjustments to the generation reserve and Demand Response Program (DRP) by considering weather conditions and islanding probability. In this regard, a multi-objective optimization problem is proposed considering Microgrid System Operator (MSO) and Distributed Energy Resources Owner (DERO) cost functions. In order to increase the readiness of the microgrid against load shedding during islanded mode, values of Expected Energy Not Supplied (EENS) and load shedding are estimated by considering dynamic changes in demand level, generation of DGs, Energy Storage System (ESS), and failure rate of branches. Accordingly, resources scheduling and DRP are modified to minimize total operational costs, including the cost of EENS and load shedding, by modelling the effect of adverse weather conditions on the failure rate. Several resilience measurement indices are used for evaluating the proposed method. The profits from decreased EENS/load shedding are split between the MSO and the DERO by selecting optimal Pareto solution. The proposed method is studied in IEEE 33-bus benchmark system, and simulation results indicate that the proposed method effectively increases the benefits of all microgrid actors and resilience indices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".