Optimal Economic Dispatch and Risk Management of Thermal Power Plants in Deregulated Markets
Bibliographic record
Abstract
This paper presents a methodology for the valuation, optimization, market, margin and credit risk management of gas-fired power plants and associated tolling contracts. Term structure models for the power and gas forward curves are employed to facilitate hedging and risk adjustment and for improved forecasting of short-term prices. The model for the power forward curve is capable of reproducing the important phenomena often observed in power markets, including spot price spikes and spike clustering, negative prices, and the empirically observed volatility term structures of power and gas forward prices as well as the correlation term structure between these forward curves. The method solves the stochastic dynamic optimization problem that arises from the inclusion of the various operational constraints of gas-fired power plants including minimum uptime and downtime requirements, ramp rate restrictions and costs, variable output and efficiency rates, and minimum generation levels. The model involves the solution of a system of partial differential equations (PDEs), which are solved using the radial basis function (RBF) method. At each time step and operational configuration the model produces an analytic function (RBF expansion) for the value of the power plant as a function of the independent risk factors. These functions can be used for determining optimal operating strategies and can be differentiated analytically to obtain the relevant hedging statistics for the dynamic management of market risk. In addition, these value functions facilitate the calculation of the credit value adjustment (CVA) and potential future exposure (PFE) measurement of tolling contracts. The analytic differentiability of these value functions also facilitates the pricing and risk management of commodity contingent revolvers (CCRs), credit vehicles used to manage margin requirements that result from hedging market risk on an exchange.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".