Energy Management in a Network of Grid-Connected Microgrids/Nanogrids Using Compromise Programming
Bibliographic record
Abstract
A new multi-objective optimization model is proposed for efficient integration of a group of microgrids/nanogrids with local energy storage devices into the power grid. In this model, the individual microgrids/nanogrids can exchange power locally among each other as well as with the external electricity grid. A pricing regime is introduced in which differences in the local and grid buy and sell time-of-use prices of electricity incentivize local inter-microgrid/nanogrid exchanges of power over power exchange with the grid. A novel formulation of a multiple-objective constrained optimization is presented for solving the microgrids/nanogrids energy management problem under the proposed electricity pricing regime. This approach is based on minimization of l1or l2distances of the microgrids/nanogrids cost vector to a utopia point in the solution space. Components of the utopia point are defined as the minimum cost achievable by the corresponding microgrid/nanogrid when it always uses the favorable local buy/sell prices. The proposed optimization models are in the form of convex linear/quadratic programs without any binary or integer variables for l1/l2norms. Results of numerical simulations with on-line rolling horizon optimization of the storage device power flow decisions demonstrate the effectiveness of the proposed methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".