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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
