In the last few years we have observed the deregulation in electricity markets and an increasing interest in price dynamics has been developed especially to consider all stylized facts shown by spot prices. Only few papers have considered the Italian Electricity Spot market since it has been deregulated recently. Therefore, this contribution is an investigation with emphasis on price dynamics accounting for technologies, market concentration, congestions and volumes. We aim to understand how these four variables affect zonal prices since these ones combine to bring about the single national price (prezzo unico d'acquisto, PUN). Hence, understanding its features is important for drawing policy indications referred to production planning and selection of generation sources, pricing and risk-hedging problems, monitoring of market power positions and finally to motivate investment strategies in new power plants and grid interconnections. Implementing Reg-ARFIMA-GARCH models, we assess the forecasting performance of selected models showing that they perform better when these factors are considered. © 2012 Elsevier B.V.
Forecasting Italian electricity zonal prices with exogenous variables / Gianfreda, A.; Grossi, L.. - In: ENERGY ECONOMICS. - ISSN 0140-9883. - 34:6(2012), pp. 2228-2239. [10.1016/j.eneco.2012.06.024]
Forecasting Italian electricity zonal prices with exogenous variables
Grossi L.
2012-01-01
Abstract
In the last few years we have observed the deregulation in electricity markets and an increasing interest in price dynamics has been developed especially to consider all stylized facts shown by spot prices. Only few papers have considered the Italian Electricity Spot market since it has been deregulated recently. Therefore, this contribution is an investigation with emphasis on price dynamics accounting for technologies, market concentration, congestions and volumes. We aim to understand how these four variables affect zonal prices since these ones combine to bring about the single national price (prezzo unico d'acquisto, PUN). Hence, understanding its features is important for drawing policy indications referred to production planning and selection of generation sources, pricing and risk-hedging problems, monitoring of market power positions and finally to motivate investment strategies in new power plants and grid interconnections. Implementing Reg-ARFIMA-GARCH models, we assess the forecasting performance of selected models showing that they perform better when these factors are considered. © 2012 Elsevier B.V.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.