Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks
It addresses resilience issues for energy utilities and operators facing climate-related disruptions, but appears incremental in applying decentralized AI to an existing domain.
This paper tackles the problem of enhancing energy network resilience against climate-induced events like wildfires, by proposing a decentralized strategy using distributed storage and demand response, aimed at providing utilities and policymakers with a clearer decision-making framework.
This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires. A recent example is PG&E's forced power outage to contain wildfire risk which led to widespread power disruption. This paper focuses on answering two questions: who is responsible for resilience? and how to quantify the monetary value of resilience? To this end, we first provide preliminary definitions of resilience for power systems. We then investigate the role of natural hazards, especially wildfire, on power system resilience. Finally, we will propose a decentralized strategy for a resilient management system using distributed storage and demand response resources. Our proposed high fidelity model provides utilities, operators, and policymakers with a clearer picture for strategic decision making and preventive decisions.