Parameterizing fluctuations in solar photovoltaic generation using Hidden Markov Models

This paper parameterizes the distribution of fluctuations of generation from over 100 solar photovoltaic (PV) systems in the areas of San Jose, Los Angeles, and the Central Valley of California using a Hidden Markov Model (HMM) with Gaussian emissions. Emissions from the hidden states of the HMM, referred to as volatility states, have similar means and different variances, thus accounting for the high kurtosis of the general distribution. The resulting Gaussian shape of emissions from the HMM allows for simple prediction of the distribution shape of the sum of fluctuations from many systems. Geographic auto-correlation among fluctuations from neighboring systems is also assessed and is found to be highest for the volatility state with the second highest emission variance. Preliminary evidence shows that volatility states are dependent on cloud cover observations at a nearby ground station, indicating that distributions of these states may be predictable with commonly observed weather data.