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This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers’ privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.01% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

The extent of the damage to Puerto Rico from Hurricane Maria in September 2017 led to outages in electricity service that persisted for months. Power system operators attempting to restore critical facilities faced challenges on almost every front, from supply chain interruptions to the inaccessibility of key assets. After a disaster of this magnitude, it is critical, but challenging, to prioritize how limited resources are directed toward rebuilding and fortifying the electric power system. To inform these decisions, the U.S. Department of Energy funded efforts investigating methodologies to identify critical vulnerabilities to the Puerto Rican power system, and to provide data-driven recommendations on how to harden and operate the system for greater resilience. This work presents the Risk-based Contingency Analysis Tool (RCAT), a framework developed as a part of that resilience initiative. The framework can qualitatively and quantitatively describe the most critical system vulnerabilities with an understanding of both likelihood of occurrence and impact. It evaluates the effectiveness of candidate remediation strategies in reducing overall risk to the system from future hurricane events. This paper will describe RCAT, with an emphasis on how different modeling capabilities have been integrated along with probabilistic methods and analytical metrics to better describe risk.

Research on demand response and transactive energy systems often require granular, appliance-level demand data. However, there is no existing test system with such appliance-level data with proper temporospatial diversity in a realistic distribution system. This paper develops a 240-node real distribution test system with appliance-level demand data for responsive loads. The residential appliance-level demand data are derived from smart meters connected to 1,120 homes in a real distribution system from Iowa State, hereafter called Midwest 240-Node test distribution system. A queueing load model was used to derive the appliance-level data from the smart meter data. The Midwest 240-Node test distribution system provides granular appliance-level information for all homes in the distribution system (i.e., individual appliances that constitute the home load), and the aggregate of all customer load emulates the actual smart meter data. The performance of the Midwest 240-Node test distribution system is evaluated by comparing the aggregated appliance-level demand with the actual measured smart meter data from the utility. The one-year appliance data has a mean absolute percentage error of 2.58% compared to the measured smart meter data. The test system is modeled in OpenDSS and GridLAB-D and is openly available to researchers to enable demand response and transactive energy studies with active end-users.