Ekaterina Abramova, Derek Bunn
arXiv 9 Mar 2019 · Statistics — Applications
arXiv:1903.06668 · PDF · DOI · OpenAlex · Extracted main text
This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage facility into the day-ahead auctions for wholesale electricity. The four latent moments of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the mean, variance, skewness and kurtosis of the densities to respond hourly to such factors as weather and demand forecasts. The best specification for each spread is selected based on the Pinball Loss function, following the closed form analytical solutions of the cumulative density functions. Those analytical properties also allow the calculation of risk associated with the spread arbitrages. From these spread densities, the optimal daily operation of a battery storage facility is determined.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gianfreda, A. and Bunn, D. W (2017) A stochastic latent moment model for electricity price formation self | 0.737 | 3 | 2 | 100% |
| 2 | Boogert, A. and De Jong, C (2011) Gas storage valuation using a multifactor price process | 0.405 | 1 | 1 | 100% |
| 3 | Denholm, P., O'Connell, M., Brinkman, G., and Jorgenson, J (2015) Overgeneration from solar energy in california. a field guide to the duck chart | 0.405 | 1 | 1 | 100% |
| 4 | Diebold, F. X. and Mariano, R. S (2002) Comparing predictive accuracy | 0.405 | 1 | 1 | 100% |
| 5 | Garcia-Martos, C., Rodrguez, J., and Sanchez, M (2012) Forecasting electricity prices by extracting dynamic common factors: application to the iberian market | 0.405 | 1 | 1 | 100% |
| 6 | Harvey, D., Leybourne, S., and Newbold, P (1997) Testing the equality of prediction mean squared errors | 0.405 | 1 | 1 | 100% |
| 7 | Karakatsani, N. V. and Bunn, D. W (2010) Fundamental and behavioural drivers of electricity price volatility self | 0.405 | 1 | 1 | 100% |
| 8 | Nowotarski, J. and Weron, R (2015) Computing electricity spot price prediction intervals using quantile regression and forecast averaging | 0.405 | 1 | 1 | 100% |
| 9 | Nowotarski, J. and Weron, R (2017) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.405 | 1 | 1 | 100% |
| 10 | Rigby, R. A. and Stasinopoulos, M. D (1996) Mean and dispersion additive models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 19 scored citations.