A Stochastic Model for Power Prices in the Alberta Electricity Market
Bibliographic record
Abstract
While energy companies commit to provide the required amount of electricity to meet the power consumers’ demand, several factors can affect the demand for electricity and consequently, affect the electricity pricing. Example of these factors are weather conditions, cost of power generation, and government tax policies. Therefore, energy companies must deal with the problem of hedging load and price risk. Power prices typically exhibit some characteristics that are crucial to be considered in modelling power prices. In Alberta, periodicity, mean-reversion, and sudden power price spikes are the most common characteristics of power prices that can be explained by changes in supply and demand for electricity. The other significant feature of power prices in Alberta is the strong link between power prices and fuel prices. Therefore, it is important to obtain a power price model which shows the stochastic dynamics of fuel prices and energy demand (i.e., load) in Alberta. For this purpose, we propose a power price model considering the strong link between power price and load which facilitates the energy companies’ hedging purposes. We use the structural model for power price modelling in which the power spot price is assumed to be a parametric function of the influential factors, such as, fuel prices and load, and the dynamic features of power prices are specified by the stochastic processes of these influential factors. Incorporating these factors in the power price model allows to capture the significant features of power prices. This model also provides the opportunity of finding a closed form formula for power forward prices.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".