Optimal energy management for building microgrid with constrained renewable energy utilization
Bibliographic record
Abstract
In this paper, we consider the energy management for a building microgrid considering a probabilistic constraint on the renewable energy utilization. Facilitated by the microgrid technology with integrated renewable energy resources, we assume that the building microgrid can participate in the electricity market to efficiently utilize the renewable energy and reduce electricity cost. In this paper, we develop an optimal energy management framework for the building microgrid considering various building loads, renewable energy, storage facility, and a natural gas combined heat and power (CHP) system. In addition, we employ the chance constrained and two-stage stochastic programming approach in our design to ensure efficient utilization of the renewable energy and to capture various system uncertainties. The proposed solution addresses the risk that available renewable energy may not be fully utilized due to its intermittent nature. Extensive numerical results are presented to illustrate the effectiveness of our proposed design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".