Joint Stochastic Model for Electric Load, Solar and Wind Power at Asset Level and Monte Carlo Scenario GenerationRen\'e Carmona \& Xinshuo Yang

TL;DR

This paper introduces a graphical model for joint stochastic simulation of wind power and electricity demand deviations, capturing dependencies for improved Monte Carlo scenario generation in power systems.

Contribution

It develops an extended graphical LASSO approach to identify temporal and geographical dependencies in deviations, enhancing scenario modeling accuracy.

Findings

The model captures realistic dependencies aligned with asset locations.

Heavy-tailed deviations are effectively handled in the modeling process.

Dependencies identified are consistent with physical asset and zone locations.

Abstract

For the purpose of Monte Carlo scenario generation, we propose a graphical model for the joint distribution of wind power and electricity demand in a given region. To conform with the practice in the electric power industry, we assume that point forecasts are provided exogenously, and concentrate on the modeling of the deviations from these forecasts instead of modeling the actual quantities of interest. We find that the marginal distributions of these deviations can have heavy tails, feature which we need to handle before fitting a graphical Gaussian model to the data. We estimate covariance and precision matrices using an extension of the graphical LASSO procedure which allows us to identify temporal and geographical (conditional) dependencies in the form of separate dependence graphs. We implement our algorithm on data made available by NREL, and we confirm that the dependencies identified by the algorithm are consistent with the relative locations of the production assets and the geographical load zones over which the data were collected.