Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: EmpiricalConsensus signal: none
- Teacher disagreement score
- 0.872
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.199 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
In this paper, we study an optimal day-ahead price-based power scheduling problem for a community-scale microgrid (MG). The proposed optimization framework aims to balance between maximizing the expected benefit of the MG in the deregulated electricity market and minimizing the MG operation cost considering users' thermal comfort requirements and other system constraints. The power scheduling and bidding problem is formulated as a two-stage stochastic program where various system uncertainties are captured by using the Monte Carlo simulation approach. Our formulation is novel in that it can exploit the thermal dynamic characteristics of buildings to compensate for the variable and intermittent nature of renewable energy resources and enables us to achieve desirable tradeoffs for different conflicting design objectives. Extensive numerical results are presented to demonstrate the great benefits in exploiting the building thermal dynamics and the flexibility of the proposed scheduling method in achieving different practical design tradeoffs. We also investigate the impacts of different design and system parameters on the curtailment of renewable energy resources and the optimal expected profit of the MG.
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.
The record
- Venue
- IEEE Transactions on Smart Grid
- Topic
- Smart Grid Energy Management
- Field
- Engineering
- Canadian institutions
- Institut National de la Recherche ScientifiqueUniversité du Québec à Montréal
- Funders
- not available
- Keywords
- BiddingMicrogridRenewable energyComputer scienceMathematical optimizationExploitScheduling (production processes)Electric power systemStochastic programmingDemand responseStochastic optimizationFlexibility (engineering)ElectricityEngineeringPower (physics)EconomicsMicroeconomics
- Has abstract in OpenAlex
- yes