Flexibility-Oriented Scheduling of Smart Energy Hubs Considering Integrated Demand Response Programs
Bibliographic record
Abstract
The transition towards a sustainable energy system requires the integration of renewable energy sources (RESs) and the adoption of innovative energy management frameworks to handle RES variability and uncertainty. The evolution of novel energy management technologies has made it possible to design and operate integrated energy systems (IESs) that couple various energy carriers such as electricity, heat, and gas. The emergence of energy hubs (EHs) presents opportunities for enhanced efficiency, reliability, and flexibility in energy supply and demand. However, optimizing operation schedules in EHs incorporates non-renewables, renewable sources, and energy storage devices becomes increasingly complex as they strive to meet diverse customer demands. This paper introduces a stochastic model for EH operation integrating electrical, thermal, and cooling storage with an electric vehicle parking lot (EVPL). In addition to uncertainty modeling of demands, PV, and EVPL outputs, the model incorporates an integrated demand response program (IDRP) and seasonal load patterns. It's formulated as a linear model solved using the CPLEX solver in GAMS. Results show integrating a shiftable IDRP reduces operational costs by 6% and validates that ESS discharges during peak hours enhance system flexibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".