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Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation

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  • Zepter, Jan Martin
  • Weibezahn, Jens

Abstract

This paper investigates the impact of uncertain photovoltaic generation on unit commitment decisions for the German rolling planning procedure employing a large-scale stochastic unit commitment electricity market model (stELMOD). A novel approach to simulate a time-adaptive intra-day photovoltaic forecast, solely based on an exponential smoothing of deviations between realized and forecast values, is presented. Generation uncertainty is then incorporated by numerous multi-stage scenario trees that account for a decreasing forecast error over time. Results show that total system costs significantly increase when uncertainty of both wind and photovoltaic generation is included by a single forecast, with more frequent starting processes of flexible plants and rather inflexible power plants mainly deployed at part-load. Including the improvement of both wind and photovoltaic forecasts by a scenario tree of possible manifestations, the scheduling costs could be significantly reduced in representative weeks for spring and summer. In general, stochastic representations increase the need for congestion management as well as more frequent use of storage in the model, leading to a more realistic depiction of the markets.

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  • Zepter, Jan Martin & Weibezahn, Jens, 2019. "Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation," Applied Energy, Elsevier, vol. 243(C), pages 336-349.
    • Handle: RePEc:eee:appene:v:243:y:2019:i:c:p:336-349
    DOI: 10.1016/j.apenergy.2019.03.191
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    2. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    3. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    4. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
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    6. Mayer, Martin János, 2022. "Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy," Applied Energy, Elsevier, vol. 323(C).

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