A Deep Learning Based Multiobjective Optimization for the Planning of Resilience Oriented Microgrids in Active Distribution System
Bibliographic record
Abstract
When facing severe weather events, a distribution system may suffer from the loss or failure of one or more of its components, the so-called N-K contingencies. Nevertheless, taking advantage of the system’s isolate switches and the increasing availability of distributed energy resources (DERs), a distribution system may be clustered into microgrids able to withstand such contingencies with minimal power interruption. In this perspective, this work proposes a novel bilevel optimization framework for planning microgrids in active distribution systems under a resilience-oriented perspective. For this, first, the outer level optimization features a multi-objective problem seeking to optimally allocate DERs and isolate switches in the distribution network while balancing the competing objectives of cost, resilience, and environmental impact. Next, the inner level handles the optimization problem pertaining to the optimal operation of the microgrids that can be created by harnessing local DERs and isolate switches allocated in the outer level. Further, given the proposed approach resilience-oriented focus, the developed framework employes deep learning models based on deep neural network (DNN) architectures trained using Bayesian Regularization Backpropagation (BRB) technique. This strategy allows for avoiding the modeling simplifications typically employed to alleviate the computational burden that can otherwise jeopardize planning solutions’ feasibility. Thus, enabling the accurate consideration of microgrids’ operational behavior, including hierarchal controls and the stochastic nature of loads, generation, and weather-induced line failures, especially critical aspects under resilience-oriented planning. Simulation case studies are developed to demonstrate the effectiveness of the developed planning framework.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".