Coordinated Optimal Dispatch of Energy Storage in a Network of Grid-Connected Microgrids
Bibliographic record
Abstract
A method is proposed for coordinated optimal dispatch of storage units in a group of grid-connected microgrids with storage and renewable energy assets to minimize the electricity costs. The method allows the microgrids to share these resources and collectively interact with the grid as one customer. A multiobjective optimization problem is formulated to obtain optimal storage charge/discharge activities using a forecast of the microgrids net electricity demands within a rolling horizon control framework. The solution to this problem also produces a virtual decomposition of the microgrids net power into local and grid components for the purpose of computing their share of electricity cost. The multiple-objective optimization is converted to a single-objective optimization by adding up the costs of the individual microgrids. An equivalent linear program free of binary/integer variables is derived from the original nonlinear optimization model, which can be effectively solved using existing solvers. Results of numerical simulations with real demand and renewable generation data are presented. They demonstrate that the coordinated optimal dispatch of the energy storage devices with the possibility of local energy transactions can significantly reduce the microgrids electricity costs compared to the cases in which they interact with the utility grid on an individual basis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".