Controlling Learned Inverter Dynamics of Distributed Energy Resources and Long-term Planning for Long-duration Energy Storage

Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. In this work, we find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5/kWh, and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices by over 70% during times of high demand. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions (Staadecker, M. et al., Nature Communications, 2024).In the second project, we propose for the first time, a non-cooperative game framework that incorporates learned inverter dynamics of Distributed Energy Resources (DERs) from a nonlinear high-fidelity model to represent their participation in a Virtual Power Plant to meet regulation services in support of the upper-level grid (Serna-Torre, P. and Hidalgo-Gonzalez, P. PSCC, 2024). This work is first of its kind and it is a stepping stone to answering fundamental questions related to inverter dominated grids.

Speaker: Patricia Hidalgo-Gonzalez, UC San Diego