Data-driven State of Risk Prediction and Mitigation in Support of the Net-zero Carbon Electric Grid
Abstract
An approach for reaching the net-zero carbon electricity grid is to intensify the deployment of distributed renewable generation resources such as photovoltaic (PV) solar and wind generation, complemented with stationary and mobile (electric vehicle) battery energy storage systems (BESS). This paper assumes a scenario where the PV renewable generation and BESS are integrated into a nano-Grid (n-Grid), a prosumer-owned virtual power plant installed at the residential or commercial sites. To be profitable, this distributed energy resource (DER) needs to be managed effectively to support its own load and the wholesale and retail market services. Our paper introduces a risk-based, data-driven approach focused on predicting the State of Risk (SoR) of the utility grid outages. The SoR prediction enables the development of optimal mitigation strategies aiming at reducing the impact of the grid outages by harvesting the n-Grid flexibility. Several data analytics tools to assist the n-Grid operators and aggregators for n-Grid participation in the wholesale market ancillary service products are introduced, and some preliminary implementation results are demonstrated.
Keywords
Cite
@article{arxiv.2207.03472,
title = {Data-driven State of Risk Prediction and Mitigation in Support of the Net-zero Carbon Electric Grid},
author = {Mladen Kezunovic and Rashid Baembitov and Mohammad Khoshjahan},
journal= {arXiv preprint arXiv:2207.03472},
year = {2022}
}
Comments
In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada