The wide range of models needed to support the various short-term operations for electricity generation demonstrates the importance of accurate specifications for the uncertainty in market prices. This is becoming increasingly challenging, since electricity hourly price densities exhibit a variety of shapes, with their characteristic features changing substantially within the day and over time, and the influx of renewable power, wind and solar in particular, has amplified these effects. A general-purpose, analytically tractable representation of the stochastic price formation process would have considerable value for operations control and trading, but existing empirical approaches for the application of standard density functions are unsatisfactory. We develop a general four parameter stochastic model for hourly prices, in which the four moments of the density function are dynamically estimated as latent state variables and furthermore modelled as functions of several plausible exogenous drivers. This provides a transparent and credible model that is suffciently flexible to capture the shape-shifting effects, particularly with respect to the wind and solar output variations causing dynamic switches in the upside and downside risks. Extensive testing on German wholesale price data, benchmarked against quantile regression and other models in out-of-sample backtesting, validated the approach and its analytical appeal.
A Stochastic Latent Moment Model for Electricity Price Formation / Gianfreda, Angelica; Bunn, Derek. - In: OPERATIONS RESEARCH. - ISSN 1526-5463. - 66:5(2018), pp. 1189-1456.
|Data di pubblicazione:||2018|
|Titolo:||A Stochastic Latent Moment Model for Electricity Price Formation|
|Autore/i:||Gianfreda, Angelica; Bunn, Derek|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1287/opre.2018.1733|
|Codice identificativo ISI:||WOS:000446179500001|
|Codice identificativo Scopus:||2-s2.0-85057784166|
|Citazione:||A Stochastic Latent Moment Model for Electricity Price Formation / Gianfreda, Angelica; Bunn, Derek. - In: OPERATIONS RESEARCH. - ISSN 1526-5463. - 66:5(2018), pp. 1189-1456.|
|Tipologia||Articolo su rivista|
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