Locational Marginal Carbon Emission of Power Grids Approach: Optimal Scheduling of Recycling Electricity/Heat Rural Supply System Based on Waste Feedstock
Bibliographic record
Abstract
To improve the power supply ability, heat supply ability, and waste recovery rate, a recycling electricity/heat rural supply system with waste feedstock is established. The energy supply system generates electricity/heat from biomass energy produced by wastes, which is also coupled to distributed renewable energy. The optimal scheduling of the established rural system will improve energy efficiency and cause emission reduction. Firstly, the waste recovery process is presented, and the architecture of the energy supply system is designed for the 100% absorption of renewable energy in rural areas. A carbon accounting model based on the locational marginal carbon emission factor is introduced, which considers the power exchange with the bulk power system and the carbon emission of biomass. Secondly, the optimal scheduling model for the recycling energy supply system is proposed to minimize both the total cost of energy supply and carbon emission, based on the constraints of energy balancing of electricity and heat, net carbon emissions, waste supply, etc. Finally, the IEEE 15-node system and PG&E 69-node system are employed for verification purposes. The proposed model contributes to 100% absorption of renewable energy and the efficient utilization of waste through the optimal cooperation of the waste supply, biomass power generation, and biomass heat, thereby supporting the achievement of zero carbon.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".