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Overcapacity in European power systems: Analysis and robust optimization approach

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  • Moret, Stefano
  • Babonneau, Frédéric
  • Bierlaire, Michel
  • Maréchal, François

Abstract

The planning of national power systems is traditionally based on long-term forecasts of electricity demand and fuel prices. However, over such long time horizons (20–50 years), these forecasts often prove to be inaccurate. As an example, wrong projections of low natural gas prices and high electricity demand in Europe in the early 2000s stimulated massive investments in electricity generation, which led to the current situation of overcapacity in the European electricity market. In this work, we first present the issue of overcapacity in Europe and discuss its causes; in particular, we highlight the relationship between overcapacity and errors in forecasts of electricity demand and fuel prices. Then, we apply a novel robust optimization framework to a real-world strategic energy planning problem, and compare it to the standard, deterministic decision-making approach. The results show that the robust investment strategies are up to 58% less likely to require future modifications leading to overcapacity. Overall, this suggests that considering uncertainties in the long-term planning process can reduce the risk of generating overcapacity in national power systems.

Suggested Citation

  • Moret, Stefano & Babonneau, Frédéric & Bierlaire, Michel & Maréchal, François, 2020. "Overcapacity in European power systems: Analysis and robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919316575
    DOI: 10.1016/j.apenergy.2019.113970
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    Cited by:

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    2. Jun Liu & Yu Qian & Huihong Chang & Jeffrey Yi-Lin Forrest, 2022. "The Impact of Technology Innovation on Enterprise Capacity Utilization—Evidence from China’s Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    3. Annika Gillich & Kai Hufendiek, 2022. "Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model," Energies, MDPI, vol. 15(12), pages 1-31, June.
    4. Borasio, M. & Moret, S., 2022. "Deep decarbonisation of regional energy systems: A novel modelling approach and its application to the Italian energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Guevara, Esnil & Babonneau, Fréderic & Homem-de-Mello, Tito & Moret, Stefano, 2020. "A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty," Applied Energy, Elsevier, vol. 271(C).
    6. Wang, Zhan-ao & Zheng, Chengsi, 2022. "Is technological innovation the cure for overcapacity? Exploring mediating and moderating mechanisms," Journal of Business Research, Elsevier, vol. 147(C), pages 348-361.
    7. Abdin, Adam F. & Caunhye, Aakil & Zio, Enrico & Cardin, Michel-Alexandre, 2022. "Optimizing generation expansion planning with operational uncertainty: A multistage adaptive robust approach," Applied Energy, Elsevier, vol. 306(PA).
    8. Zhang, Weike & Meng, Jia & Tian, Xiaoli, 2020. "Does de-capacity policy enhance the total factor productivity of China's coal companies? A Regression Discontinuity design," Resources Policy, Elsevier, vol. 68(C).

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