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• W2344990967 abstract "Deterministic models based on most likely forecasts can bring simplicity to the electricity power planning but do not explicitly consider uncertainties and risks which are always present on the electricity systems. Stochastic models can account for uncertain parameters that are critical to obtain a robust solution, requiring however higher modelling and computational effort. The aim of this work was to propose a methodology to identify major uncertainties presented in the electricity system and demonstrate their impact in the long-term electricity production mix, through scenario analysis. The case of an electricity system with high renewable contribution was used to demonstrate how renewables uncertainty can be included in long term planning, combining Monte Carlo Simulation with a deterministic optimization model. This case showed that the problem of including risk in electricity planning could be explored in short running time even for large real systems. The results indicate that high growth demand rate combined with climate uncertainty represent major sources of risk for the definition of robust optimal technology mixes for the future. This is particularly important for the case of electricity systems with high share of renewables as climate change can have a major role on the expected power output." @default.
• W2344990967 created "2016-06-24" @default.
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• W2344990967 date "2016-11-01" @default.
• W2344990967 modified "2023-10-11" @default.
• W2344990967 title "A methodology to incorporate risk and uncertainty in electricity power planning" @default.
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• W2344990967 doi "https://doi.org/10.1016/j.energy.2016.03.080" @default.
• W2344990967 hasPublicationYear "2016" @default.
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