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Record W7117261912 · doi:10.5267/j.ijiec.2025.11.002

Multi-level interactive self-balancing optimization strategy of source-grid-load-storage considering cluster security constraints

2025· article· W7117261912 on OpenAlexvenueno aff
Yongqi Dai, Jialong Zhou, Shangqiu Shi, Lue Sun, Jingshuai Pang

Bibliographic record

VenueInternational Journal of Industrial Engineering Computations · 2025
Typearticle
Language
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsRobustness (evolution)Renewable energyElectric power systemScheduling (production processes)Energy securityConstraint (computer-aided design)Energy supplyVoltageRobust optimization

Abstract

fetched live from OpenAlex

As the global energy structure undergoes transformation, large-scale access to renewable energy presents the power system with unprecedented dynamic balance challenges. Traditional centralized power supply architecture is challenging to adapt to complex scenarios where high proportions of new energy, high-density power electronic devices, and diversified load demands are intertwined. There is an urgent need to build a new balancing mechanism for collaborative interaction between source, grid, load and storage. Aiming at the scientific problem of deep integration of cluster security constraints and multi-level interaction, this study proposes an integrated optimization strategy. By quantitatively characterizing the CIA (Confidentiality, Integrity, Availability) triple security criterion, it establishes three types of constraint models, including extreme weather equipment current carrying capacity correction coefficient, node health index and adjustment instruction convergence time threshold. Experimental verification demonstrates that this strategy effectively controls the system frequency deviation within 0.010 Hz and stabilizes the voltage deviation to below 1.50% during the 16-period scheduling cycle. At the same time, it improves energy utilization efficiency to 92%, with clean energy accounting for 61%. Carbon emissions were reduced to 10,200 tons, and pollutant emissions were reduced to 5,100 tons. The direct trust of the high-precision recommendation module in the system is positively correlated with its precision trust value, and the source-grid-load-storage (SGLS) samples exhibit significant differences in aggregation characteristics under different feature representation methods. In addition, the short-circuit current capacity of the system is stable at 31 kA, and the transient stability index reaches 0.94, which verifies the robustness of the strategy under extreme working conditions. By analyzing the dynamic influence of the ψ parameter on the detection results of each cluster, the effectiveness of the security constraint embedding method is further confirmed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.258
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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