Integrating renewable and nonrenewable energies in power plant planning
Bibliographic record
Abstract
Abstract: Problem statement: Electricity can be generated from different type of technologies such as fossil and non-fossil power plants. Among these technologies, coal-fired power plants have been a major route for electricity generation. Recently, environmental constraints were imposed over the coal power plant operations in order to reduce their emissions. Besides, renewable energy power plants such as hydroelectric, wind, solar and geothermal have emerged with a potential of low impact on the environment. Approach: In this study, coal-fired power plants with a mix of low emission power plants were analyzed from the viewpoint of coal power plant emission reductions while supplying electricity demand. Electricity capacity expansion was also included within the problem to insure sufficient electricity supply in circumstances of emission reduction constraints. Results: Pollutants such as Nitrogen Oxides (NOx), Sulfur Oxides (SOx) and mercury (Hg) were assumed to be the target compounds. A discrete mathematical programming model was formulated to give an assessment about the coal-fired power plant operations in an electricity generation network. Different scenarios of increased electricity demand and emission reduction targets were applied on Ontario Power Generation (OPG) network to give an illustration of the proposed model. Conclusion: The case study results show the significant impact of combining renewable energy or zero emission technologies on the optimal operation of a network that combines coal-fired power plants. Key words: Coal power plants, renewable energy power plants, multi-pollutant emissions, optimal
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".