Electric utility portfolio risk simulation requires stochastically forecasting various time series data: power and gas prices, peak and off-peak loads, thermal, solar and wind generation, and other covariates, in different time granularities. All these together presents modeling issues of autocorrelation, linear and non-linear covariate relationships, non-normal distribution, outliers, seasonal and weekly shapes, heteroskedasticity, temporal disaggregation and dispatch optimization. As a practitioner, I’ll discuss how to organize and put together such a portfolio model from data scraping, simulation modeling, all the way to deployment through Shiny UI, while pointing out what worked what didn’t.
