Modeling Variability and Uncertainty of Photovoltaic Generation: A Hidden State Spatial Statistical Approach


In this paper, we have a tendency to construct, fit, and validate a hidden Markov model for predicting variability and uncertainty in generation from distributed (PV) systems. The model is distinctive in that it: one) predicts metrics that are directly connected to operational reserves, two) accounts for the consequences of stochastic volatility and geographic autocorrelation, and three) conditions on latent variables known as “volatility states.” We tend to work and validate the model using one-min resolution generation information from approximately a hundred PV systems within the California Central Valley or the Los Angeles coastal area, and condition the volatility state of every system at every time on 15-min resolution generation data from nearby PV systems (which are offered from over 600zero PV systems in our information set). We tend to notice that PV variability distributions are roughly Gaussian once conditioning on hidden states. We tend to additionally propose a method for simulating hidden states that ends up in a terribly smart higher bound for the likelihood of maximum events. So, the model can be used as a tool for planning further reserve capacity requirements to balance solar variability over massive and tiny spatial areas.

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