The valuation of a natural gas-fired power plant with multiple turbines using clean spark spread and weather options
Bibliographic record
Abstract
Assessing the value of a power plant is an important issue for plant owners and prospective buyers. In a deregulated market, an owner has the option to operate the plant when the revenue from selling the electricity is higher than the cost of operating the plant. This option is known as the spark spread option. Under emission restrictions, when the carbon cost is deducted from the spark spread, the option is named as the clean spark spread option. This thesis presents an analysis on the spark spread and clean spark spread option based valuation methods for a power plant with multiple gas turbines having different input–output characteristics, emission rates, and capacities. Electricity, natural gas and carbon allowance prices are assumed to follow mean–reverting processes. Results demonstrate that CO2 allowance cost reduces the expected plant value, while the flexibility of switching among turbines adds value to the power plant. Weather also affects the power plant operation. This thesis also presents a valuation model for a power plant integrating spark spread and weather options. A cooler winter drawing more electricity could generate a higher payoff for the plant owner. A warmer winter, however, could lead to a lower payoff. An owner holding a long position in a temperature–based put option could exercise the option when the winter is milder. The exercise is triggered by the drop of heating degree days below a strike degree day. The number of weather contracts to buy is determined by minimizing the variance of the total payoff. Pricing of the weather option is calculated based on the mean–reverting behavior of temperature. Results demonstrate that the integrating weather option along with spark spread option adds value to the downward spark spread option based valuation of the plant in a warmer winter. A comparison of temperature modeling approaches with an aim to pricing weather option is also investigated. Regime–switching models generated from a combination of different underlying processes are utilized to determine the expected heating and cooling degree days. Weather option prices are then calculated based on a range of strike heating degree days.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".