ORFEUS
Abstract
We describe part of a project, sponsored by ARPA-E, to quantify, allocate and account for the risk introduced to electricity grid due to the unpredictable reliability of production from renewables. Incorporating this stochasticity into grid risk management is viewed by the industry (which has remained almost entirely tethered to a deterministic viewpoint an in particular to weather forecasts) as increasingly crucial, as we aim for greater renewables penetration to reduce dependence on carbon-emitting fuels. Our methodology involves feeding Monte Carlo simulations of solar, wind and load into a 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 to assign them a reliability cost. This adapts exisiting approaches for the risk allocation problem related to Shapley values, but is computationally intense. We show results, project to potential future grids, and propose a way to incorporate the reliability costs back into the day ahead bid curve and thereby re-optimize unit commitment and economic dispatch of assets taking into (some) account the stochasticity. This work is in collaboration with a team listed here: orfeus.princeton.edu and this part in particular with Michal Grzadkowski and Arvind Shrivats (both Princeton).
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