Big Data Analytics for Modelling the Impact of Wind Power Generation on Competitive Electricity Market Prices
Bibliographic record
Abstract
There is vast potential value being stored in the massive amounts of data being produced each day in an electricity market. However, most of the value is not being realized due to the challenges in efficiently and intelligently processing and analyzing the large volumes data. These are the challenges of Big Data. Modern power systems are producing Big Data and better understanding it can lead to many business advantages. In this paper a data-driven approach is proposed for analyzing the effect of wind generation on the wholesale electricity price. It is a question of interest to know how electricity price is affected with higher level of wind penetration in a market. A model representing the quantitative effect of wind generation on electricity price would offer useful information to different sectors of electricity market from generators to consumers. Method is applied to the market of Alberta as a case study. The massive database is made based on the available public data from Alberta Electric System Operators (AESO). The impact of each MWh wind generation on the price of electricity is assessed. Results show that increased wind generation reduces wholesale market prices by a small, but economically-important amount. This impact is not constant and depends on the operating condition of the electricity market.
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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) | 0.000 | 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)
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".