An Integrated Situational Awareness Tool for Resilience-Driven Restoration With Sustainable Energy Resources

Abstract

Integrating sustainable energy resources transforms the distribution grid into an active system with higher variations observed in load and generation. Estimating distributed generation, gross load, and cold load pick-up (CLPU) become more challenging with behind-the-meter (BTM) distributed energy resources (DERs), especially in case of outages caused by extreme events. This work proposes a resilience-driven restoration scheme using the most updated information from an integrated and enhanced situational awareness tool (ESAT) using kernelized Bayesian state-space inference (KBSI) with Markov Chain Monte Carlo (MCMC) and multiple optimization algorithms. ESAT consists of the BTM load/ DER estimation and disaggregation, CLPU estimation, and network topology estimation with de-energized islands. The proposed work provides solutions to establish informed restoration schemes considering resilience criteria for a quick recovery of high-priority loads. A resilience metric is utilized after outages to measure the effectiveness of ESAT-driven restoration for the supposed threats. The performance of developed ESAT is demonstrated using actual field datasets and validated using the emulated real scenarios on a benchmark model.