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Renewable Generation and Network Congestion: an Empirical Analysis of the Italian Power Market

Faddy Ardian, Silvia Concettini, and Anna Creti

Year: 2018
Volume: Volume 39
Number: Special Issue 2
DOI: 10.5547/01956574.39.SI2.fard
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Abstract:
This article empirically investigates the impact of renewable production on congestion using a unique database on the Italian Power Market, where zonal pricing is implemented. We estimate two econometric models: a multinomial logit model, to assess whether renewables increase the occurrence of congestion, and a two stage least squares (2SLS) model to evaluate the impact of wind and photovoltaics on congestion costs. Our analysis suggests that larger renewable supply in importing regions decreases the probability of congestion compared to the no congestion case, while the reverse occurs when renewable production is located in an exporting region. The 2SLS estimations reveal that the same mechanisms explain the level of congestion costs. Our results also highlight that the magnitude of the congestion effects, both in terms of probability and costs, is very sensitive to the location of the historical efficient production, mainly hydro power, and to the geographical configuration of the transmission network.



Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy

Carlo Fezzi and Luca Mosetti

Year: 2020
Volume: Volume 41
Number: Number 4
DOI: 10.5547/01956574.41.4.cfez
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Abstract:
Short-term electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the literature does not provide guidelines on how to select the optimal size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets (Nord Pool and IPEX) with a selection of eleven different forecasting models. Incidentally, our empirical application reveals that simple models, such as a simple linear regression (SLR) with only two parameters, can perform unexpectedly well if estimated on extremely short samples. Surprisingly, in the Nord Pool, such SLR is the best performing model in 13 out 24 trading periods.





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