Although extreme events, mainly natural disasters and climate change-driven severe weather, are the result of naturally occurring processes, power system planners, regulators, and policy makers do not usually recognize them within network reliability standards. Instead, planners have historically designed the electric power infrastructure accounting for the so-called credible (or “average”) outages that usually represent single or (some kind of) simultaneous faults (e.g., faults on double circuits).
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Conditional Kernel Density Estimation Considering Autocorrelation for Renewable Energy Probabilistic Modeling
Renewable energy is essential for energy security and global warming mitigation. However, renewable power generation is uncertain due to volatile weather conditions and complex equipment operations. It is therefore important to understand and characterize the uncertainty in renewable power generation to improve operational efficiency. In this paper, we proposed a novel conditional density estimation method to model the distribution of power generation under various weather conditions. Compared with existing literature, our approach is especially useful for the purpose of short-term modeling, where the temporal dependence plays a more significant role.
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