Tootfinder

Sometimes I daydream of an Anarchist navy, silently sailing across the Salish Sea, smuggling to avoid the tariffs and annoy the terfs. I dream of them sneaking by CPB, who is then deployed with the rest of the federal forces to occupy cities, in stolen ships repaired with materials stolen from prison construction sites... and with the sale of tariff skipped good, they fund a Cascadian version of the AIF.
And this dream brings a smile to my face.
Good luck to anyone who is inspired to join the Anarchist Yacht Club.

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration
Cong Bai, Salish Maharjan, Han Wang, Zhaoyu Wang
https://arxiv.org/abs/2508.13306

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration
Extended outages in distributed systems (DSs) dominated by distributed energy resources (DERs) require innovative strategies to efficiently and securely deploy black start (BS) resources. To address the need, this paper proposes a two-stage stochastic resource allocation method within synchronizing dynamic microgrids (MGs) for black start (SDMG-BS), enabling risk-averse and adaptive restoration across various scenarios while ensuring frequency security. Virtual synchronous generator (VSG)-contr…

Data-driven quantification and visualization of resilience metrics of power distribution system
Dingwei Wang, Salish Maharjan, Junyuan Zheng, Liming Liu, Zhaoyu Wang
https://arxiv.org/abs/2508.12408

Data-driven quantification and visualization of resilience metrics of power distribution system
This paper presents a data-driven approach for quantifying the resilience of distribution power grids to extreme weather events using two key metrics: (a) the number of outages and (b) restoration time. The method leverages historical outage records maintained by power utilities and weather measurements collected by the National Oceanic and Atmospheric Administration (NOAA) to evaluate resilience across a utility's service territory. The proposed framework consists of three stages. First, outag…

Grid Edge Intelligence-Assisted Model Predictive Framework for Black Start of Distribution Systems with Inverter-Based Resources
Junyuan Zheng, Salish Maharjan, Zhaoyu Wang
https://arxiv.org/abs/2508.12937

Grid Edge Intelligence-Assisted Model Predictive Framework for Black Start of Distribution Systems with Inverter-Based Resources
The growing proliferation of distributed energy resources (DERs) is significantly enhancing the resilience and reliability of distribution systems. However, a substantial portion of behind-the-meter (BTM) DERs is often overlooked during black start (BS) and restoration processes. Existing BS strategies that utilize grid-forming (GFM) battery energy storage systems (BESS) frequently ignore critical frequency security and synchronization constraints. To address these limitations, this paper propo…

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Cong Bai, Salish Maharjan, Zhaoyu Wang
https://arxiv.org/abs/2507.12569

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Black start (BS) of the distribution system (DS) with high penetration of distributed energy resources (DERs) requires advanced control frameworks to ensure secure and efficient restoration. This paper proposes a model predictive black start (MPBS) framework incorporating an inrush current feasibility module to dynamically generate real-time feasible and optimal restoration sequences. Short-term forecasts of DER output and transmission grid (TG) availability are utilized to construct adaptive c…