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Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators

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  • Yan, Xingyu
  • Abbes, Dhaker
  • Francois, Bruno

Abstract

Setting an adequate operating power reserve (PR) to compensate unpredictable imbalances between generation and consumption is essential for power system security. Operating power reserve should be carefully sized but also ideally minimized and dispatched to reduce operation costs with a satisfying security level. Although several energy generation and load forecasting tools have been developed, decision-making methods are required to estimate the operating power reserve amount within its dispatch over generators during small time windows and with adaptive capabilities to markets, as new ancillary service markets. This paper proposes an uncertainty analysis method for power reserve quantification in an urban microgrid with a high penetration ratio of PV (photovoltaic) power. First, forecasting errors of PV production and load demand are estimated one day ahead by using artificial neural networks. Then two methods are proposed to calculate one day ahead the net demand error. The first perform a direct forecast of the error, the second one calculates it from the available PV power and load demand forecast errors. This remaining net error is analyzed with dedicated statistical and stochastic procedures. Hence, according to an accepted risk level, a method is proposed to calculate the required PR for each hour.

Suggested Citation

  • Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
  • Handle: RePEc:eee:renene:v:106:y:2017:i:c:p:288-297
    DOI: 10.1016/j.renene.2017.01.022
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    2. Wen, Xin & Abbes, Dhaker & Francois, Bruno, 2021. "Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 116-128.
    3. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    4. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    5. Zhang, Jing & Hu, Sijia & Zhang, Zhiwen & Li, Yong & Lin, Jinjie & Wu, Jinbo & Gong, Yusheng & He, Li, 2023. "An adaptive frequency regulation strategy with high renewable energy participating level for isolated microgrid," Renewable Energy, Elsevier, vol. 212(C), pages 683-698.
    6. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    7. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
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    9. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    10. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    11. e Silva, Danilo P. & Félix Salles, José L. & Fardin, Jussara F. & Rocha Pereira, Maxsuel M., 2020. "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data," Applied Energy, Elsevier, vol. 278(C).

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