  Power grid operations increasingly interact with environmental systems and
human systems such as transportation, agriculture, the economy, and financial
markets. Our objective is to discuss the modelling gaps and opportunities to
advance the science for multisector adaptation and tradeoffs. We focus on power
system operational models, which typically represent key physical and economic
aspects of grid operations over days to a year and assume a fixed power grid
infrastructure. Due to computational burden, models are typically customized to
reflect regional resource opportunities, data availability, and applications of
interest. While there are model intercomparison papers, there is however no
model-agnostic characterization and systematic overview of the state-of-the-art
process representations in operational power system models. To address our
objective, we conceptualize power system operational models with four core
processes: physical grid assets (generation, transmission, loads, and storage),
model objectives and purpose, institutions and decision agents, and performance
metrics. We taxonomize the representations of these core processes based on a
review of 23 existing open-source and commercial models. As we acknowledge the
computational burden of certain representations, we leverage this taxonomy to
describe tradeoffs in process fidelity and tractability that have been adopted
by the research community to address interactions between the power grid and
hydrometeorological uncertainties, global change, and/or technological
innovation. The core process taxonomy along with the existing computational
tradeoffs are used to identify technical gaps and recommend future model
development needs and research directions to better represent power grid
operations as part of integrated multisector dynamics modeling and
interdisciplinary research.
