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A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles

Author

Listed:
  • Amedeo Buonanno

    (Department of Energy Technologies and Renewable Energy Sources, ENEA, 80055 Portici, NA, Italy)

  • Martina Caliano

    (Department of Energy Technologies and Renewable Energy Sources, ENEA, 80055 Portici, NA, Italy)

  • Marialaura Di Somma

    (Department of Energy Technologies and Renewable Energy Sources, ENEA, 80055 Portici, NA, Italy)

  • Giorgio Graditi

    (Department of Energy Technologies and Renewable Energy Sources, ENEA, 00123 Rome, Italy)

  • Maria Valenti

    (Department of Energy Technologies and Renewable Energy Sources, ENEA, 80055 Portici, NA, Italy)

Abstract

Despite their positive effects on the decarbonization of energy systems, renewable energy sources can dramatically influence the short-term scheduling of distributed energy resources (DER) in smart grids due to their intermittent and non-programmable nature. Renewables’ uncertainties need to be properly considered in order to avoid DER operation strategies that may deviate from the optimal ones. This paper presents a comprehensive tool for the scenario generation of solar irradiance profiles by using historical data for a specific location. The tool is particularly useful for creating scenarios in the context of the stochastic operation optimization of DER systems. Making use of the Roulette Wheel mechanism for generating an initial set of scenarios, the tool applies a reduction process based on the Fast-Forward method, which allows the preservation of the most representative ones while reducing the computational efforts in the next potential stochastic optimization phase. From the application of the proposed tool to a numerical case study, it emerged that plausible scenarios are generated for solar irradiance profiles to be used as input for DER stochastic optimization purposes. Moreover, the high flexibility of the proposed tool allows the estimation of the behavior of the stochastic operation optimization of DER in the presence of more fluctuating but plausible solar irradiance patterns. A sensitivity analysis has also been carried out to evaluate the impact of key parameters, such as the number of regions, a metric, and a specific parameter used for the outlier removal process on the generated solar irradiance profiles, by showing their influence on their smoothness and variability. The results of this analysis are found to be particularly suitable to guide users in the definition of scenarios with specific characteristics.

Suggested Citation

  • Amedeo Buonanno & Martina Caliano & Marialaura Di Somma & Giorgio Graditi & Maria Valenti, 2022. "A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles," Energies, MDPI, vol. 15(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8830-:d:981676
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    References listed on IDEAS

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    1. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
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    6. Marialaura Di Somma & Amedeo Buonanno & Martina Caliano & Giorgio Graditi & Giorgio Piazza & Stefano Bracco & Federico Delfino, 2022. "Stochastic Operation Optimization of the Smart Savona Campus as an Integrated Local Energy Community Considering Energy Costs and Carbon Emissions," Energies, MDPI, vol. 15(22), pages 1-27, November.
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    Cited by:

    1. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
    2. Daniel Fernández Valderrama & Juan Ignacio Guerrero Alonso & Carlos León de Mora & Michela Robba, 2024. "Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems," Energies, MDPI, vol. 17(21), pages 1-14, October.
    3. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    4. Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.

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