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Record W4407318174 · doi:10.1016/j.rser.2025.115416

Lyapunov-based real-time optimization method in microgrids: A comprehensive review

2025· review· en· W4407318174 on OpenAlexaff
Masoud Alilou, Amin Mohammadpour Shotorbani, Behnam Mohammadi‐Ivatloo

Bibliographic record

VenueRenewable and Sustainable Energy Reviews · 2025
Typereview
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLyapunov functionComputer scienceControl theory (sociology)Lyapunov optimizationMathematical optimizationLyapunov redesignLyapunov exponentMathematicsControl (management)Artificial intelligenceNonlinear systemPhysics

Abstract

fetched live from OpenAlex

An effective energy management system in a microgrid is of paramount importance, optimizing local energy utilization for diverse consumer needs. Prevalent strategies often rely on offline day-ahead or two-stage methods, assuming stable microgrid configurations or precise forecasts—a challenge in practical operations. A real-time energy management system approach struggles to achieve global optimal solutions, though inherently providing robustness against forecast uncertainties. A recent and promising approach is applying the Lyapunov optimization method, known for online optimization, to address challenges in real-time microgrid energy management systems. This paper provides a comprehensive exploration of Lyapunov-based real-time energy management systems in microgrids. We begin by elucidating the integration of the Lyapunov method into microgrid energy management. Categorizing pertinent research papers systematically, we differentiate parameters such as microgrid components with respect to real-time energy management systems, objective functions, and designs of the Lyapunov algorithm, covering establishment of virtual queues, drift-plus-penalty, and the control parameter roles of the algorithm. Integral to our investigation is a thorough assessment of the efficacy of the Lyapunov method in real-time microgrid energy management. The analysis highlights the efficacy of Lyapunov optimization in microgrid energy management system operation and underscores it as a solution to real-time energy management system challenges in microgrids, establishing its merits and applicability in scholarly and practical contexts. • Presenting a comprehensive review of Lyapunov-based online optimization in microgrids. • Analyzing and comparing the papers published on Lyapunov optimization (LO). • Elaborating the LO technique and formulating different energy management methods. • Comparing the effectiveness of the LO method with other energy management methods.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2025
Admission routes1
Has abstractyes

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