Quantifying renewables reliability risk in modern and future electricity grids

Arvind Shrivats, Ronnie Sircar and Xinshuo Yang

Abstract

We propose and implement a methodology to quantify, allocate and account for the risk introduced to electricity production from the unpredictable intermittency of renewable resources such and wind and solar. Incorporating this stochasticity into grid risk management is viewed by the industry (which has remained almost entirely tethered to a deterministic viewpoint, and in particular to weather forecasts) as increasingly crucial, given the aim of greater renewables penetration to reduce dependence on carbon-emitting fuels. Our methodology involves feeding Monte Carlo simulations of solar generation, wind generation and demand into grid optimization software that emulates the performance and costs of the Texas electricity grid. This outputs a distribution of running costs, from which we can numerically extract a measure of system (grid) risk. The more challenging part is to allocate this risk back (top down) to the individual renewable assets in order to assign them a reliability cost. This adapts existing approaches for the risk allocation problem related to Shapley values but is computationally intensive. We show results, project to potential future grids and propose a way to incorporate the reliability costs back into the day-a-head bid curve and thereby reoptimize unit commitment and economic dispatch of assets while taking into account the probabilistic nature of supply from renewables.