Bibliographic record
Abstract
The utilization of the wind to generate electrical energy is increasing rapidly throughout the world. By the end of 2009, the worldwide installed wind capacity reached 159,213 MW (World Wind Energy Report 2009). Wind turbine generators can be added and are being added in large grid connected electric power systems. Wind power, however, behaves quite differently than conventional electric power generating facilities due to its intermittent and diffuse nature. The incorporation of wind energy conversion system (WECS) in bulk electric system (BES) planning, therefore, requires distinctive and applicable modeling, data and method considerations to ensure BES reliability levels as wind power penetration levels increase. The objective of power system planning is to select the most economical and reliable plan in order to meet the expected future load growth at minimum cost and optimum reliability subject to economic and technical constraints. Reliability assessment, which consists of adequacy and security, is an important aspect of power system planning. A BES security assessment normally utilizes the traditional deterministic criterion known as the N-1 security criterion (North American Electric Reliability Council Planning Standards, 2007) in which the loss of any BES component (a contingency) will not result in system failure. The deterministic N-1 (D) planning criterion for BES has been used for many years and will continue to be a benchmark criterion The D planning criterion has attractive characteristics such as, simple implementation, straightforward understanding, assessment and judgment. The N-1 criterion has generally resulted in acceptable security levels, but in its basic simplest form does not provide an assessment of the actual system reliability as it does not incorporate the probabilistic nature of system behaviour and component failures. Probabilistic (P) approaches to BES reliability evaluation can respond to the significant factors that affect the reliability of a system. There is, however, considerable reluctance to use probabilistic techniques in many areas due to the difficulty in interpreting the resulting numerical indices. A survey conducted as part of an EPRI project indicated that many utilities had difficulty in interpreting the expected load curtailment indices as the existing models were based on adequacy analysis and in many cases did not consider realistic operating conditions. These concerns were expressed in response to the survey and are summarized in the project report (EPRI report, 1987). This difficulty can be alleviated by combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct www.intechopen.com
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".