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<p>The increasing penetration of nonprogrammable renewable energy sources requires powerful supporting tools, mostly based on Monte Carlo simulations, for long term power system planning, in order to assure its reliability and sustainability. This study presents the SPOPSI (Stochastic Power Profile Simulator) procedure, which generates an arbitrary number of different year-long hourly series of wind and photovoltaic power that can feed probabilistic tools at regional or market zone scale. Focusing on Italian power system, SPOPSI, at first, analyses historical power data and divides them in their stochastic and weather-dependent components, then produces new power series that preserve the statistical behavior and intrinsic variability of the data and accounts for the variability due to meteorological variables affecting the power generation. SPOPSI was trained and validated on both historical power and meteorological data, i.e. temperature, wind speed and short-wave radiation. Then, SPOPSI generates power series consistent with climate projections of the same weather variables derived from 10 Euro-CORDEX models at <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40866_2025_263_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">\(\sim 12\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>∼</mo> <mn>12</mn> </mrow> </math></EquationSource> </InlineEquation> km spatial resolution under the three emission pathways RCP2.6, RCP4.5 and RCP8.5, corresponding to increasing global warming scenarios. The climate model data were first preprocessed through bias correction techniques to become consistent with historical meteorological series. The results of 100 SPOPSI series for the years 2050 and 2100 are discussed. Since the SPOPSI series are independent of installed capacity and generated on regional scale, the result is that SPOPSI is a computational time saving procedure, versatile and easily extendable to other countries. In addition, compared with other state-of-art tools, it is based not only on past climatology, but also on climate model projections and it has virtually no limitation in the number of generated series.</p>

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Stochastic Renewable Power Simulations Depending on Bias-corrected Climate Projections

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Gaia Ceresa,
  • Arianna Trevisiol,
  • Marco Raffaele Rapizza

摘要

The increasing penetration of nonprogrammable renewable energy sources requires powerful supporting tools, mostly based on Monte Carlo simulations, for long term power system planning, in order to assure its reliability and sustainability. This study presents the SPOPSI (Stochastic Power Profile Simulator) procedure, which generates an arbitrary number of different year-long hourly series of wind and photovoltaic power that can feed probabilistic tools at regional or market zone scale. Focusing on Italian power system, SPOPSI, at first, analyses historical power data and divides them in their stochastic and weather-dependent components, then produces new power series that preserve the statistical behavior and intrinsic variability of the data and accounts for the variability due to meteorological variables affecting the power generation. SPOPSI was trained and validated on both historical power and meteorological data, i.e. temperature, wind speed and short-wave radiation. Then, SPOPSI generates power series consistent with climate projections of the same weather variables derived from 10 Euro-CORDEX models at \(\sim 12\) ∼ 12 km spatial resolution under the three emission pathways RCP2.6, RCP4.5 and RCP8.5, corresponding to increasing global warming scenarios. The climate model data were first preprocessed through bias correction techniques to become consistent with historical meteorological series. The results of 100 SPOPSI series for the years 2050 and 2100 are discussed. Since the SPOPSI series are independent of installed capacity and generated on regional scale, the result is that SPOPSI is a computational time saving procedure, versatile and easily extendable to other countries. In addition, compared with other state-of-art tools, it is based not only on past climatology, but also on climate model projections and it has virtually no limitation in the number of generated series.