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Residual load probabilistic forecast for reserve assessment: A real case study

TitoloResidual load probabilistic forecast for reserve assessment: A real case study
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2020
AutoriPierro, M., De Felice Matteo, Maggioni E., Moser D., Perotto A., Spada F., and Cornaro C.
RivistaRenewable Energy
Volume149
Paginazione508-522
ISSN09601481
Parole chiaveassessment method, climate conditions, demand analysis, Forecasting, forecasting method, Meteorological condition, Photovoltaic, Photovoltaic cells, photovoltaic system, power generation, Probabilistic approaches, Probabilistic forecasts, Probabilistic methods, probability, PV generation, Reserve assessment, Solar energy, solar power, Variable generations, Wind power
Abstract

Distributed generation from wind and solar acts on regional electric demand as a reduced consumption, giving rise to a “load shadowing effect”. The net load becomes much more difficult to predict due to its dependence on the meteorological conditions. As a consequence, the growing penetration of variable generation increases the imbalance between demand and scheduled supply (net load forecast) and the reserve margins (net load uncertainty). The aim of this work is to quantify the benefit of the use of advanced probabilistic approaches rather than a traditional time-series method to assess the day-ahead reserves. For this purpose, several methods for load and net load uncertainty assessment have been developed and applied to a real case study considering also future solar penetration scenarios. The results show that, when forecasting only the load both traditional and probabilistic methods exhibit similar accuracy. Instead, in the case of net load prediction, i.e. when solar power is present, the probabilistic forecast can effectively limit the reserve margin needed to arrange the imbalance between residual demand and supply. The developed probabilistic approach provides a notable reduction of the Following Reserve which increases with the solar penetration: from 32.5% to 68.3% at 7% and 45% of penetration. © 2019 Elsevier Ltd

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076710806&doi=10.1016%2fj.renene.2019.12.056&partnerID=40&md5=7ca44bc6376026f21aed2a8dc29773f0
DOI10.1016/j.renene.2019.12.056
Citation KeyPierro2020508