Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy

Giovanni Brusco, Alessandro Burgio, Daniele Menniti, Anna Pinnarelli, Nicola Sorrentino and Pasquale Vizza
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Giovanni Brusco: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Alessandro Burgio: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Daniele Menniti: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Anna Pinnarelli: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Nicola Sorrentino: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Pasquale Vizza: Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy

Energies, 2017, vol. 10, issue 11, 1-17

Abstract: In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispatching electric power systems. Although this type of source cannot be programmed, their production can be predicted using soft computing techniques that consider weather forecasts, reducing the imbalance costs paid to the transmission system operator (TSO). The problem is mainly that the forecasting procedures used by the TSO, distribution system operator (DSO) or large producers and they are too expensive, as they use complex algorithms and detailed meteorological data that have to be bought, this can represent an excessive charge for small-scale producers, such as prosumers. In this paper, a cheap photovoltaic (PV) production forecasting method, in terms of reduced computational effort, free-available meteorological data and implementation is discussed, and the economic results regarding the imbalance costs due to the utilization of this method are analyzed. The economic analysis is carried out considering several factors, such as the month, the day type, and the accuracy of the forecasting method. The user can utilize the implemented method to know and reduce the imbalance costs, by adopting particular load management strategies.

Keywords: building integrated PV forecasting; neural network; solar power; imbalance costs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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