A Multi-Objective Energy and Environmental Systems Planning Model: Management of Uncertainties and Risks for Shanxi Province, China
Bibliographic record
Abstract
In this study, a fuzzy chance-constrained fractional programming (FCFP) approach is developed to help tackle various uncertainties involved in electric power systems (EPSs) management. The FCFP approach is capable of solving ratio optimization decision problems in power systems associated with random and fuzzy information by chance-constrained programming (CCP) method, fuzzy measure programming, fractional programming (FP) into a general framework. It can tackle inexact information expressed as fuzzy set and probability distributions, comprehensively reflect the decision maker’s pessimistic and optimistic preferences, and balance dual objectives of system economy and sustainability. To demonstrate its applicability, FCFP approach is then applied to a case study of Shanxi Province, a typical coal-heavy electricity region in China. The results indicate that the FCFP approach reveals uncertain interactions among the decision maker’s preferences and various random variables. Reasonable solutions have been generated for Shanxi EPS management practices, which can provide strategies in mitigating pollutant emissions, reducing system costs, and promoting coalbed methane as an alternative energy source for coal-fired and plays an essential role in Shanxi’s municipal planning. The solutions will help decision makers generate alternatives in the event of the reducing coal-fired power generation and could be applicable in other coal-heavy electricity regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".