This technology is a method of predicting disruptive events in power grid systems to communicate contingency and real-time management decisions to prevent electricity outages, referred to as a Distribution Contingency Management System (DCMS).
Energy distribution relies on a complex power grid that can be prone to errors. Real-time measurement of all recordings in the grid allows utilities to understand which components of the system are defective and need to be repaired in the event of an electricity outage. Current distribution management systems record these disruptive events at their occurrence, but do not provide information in order to prevent outages from happening. Although some researchers are attempting to develop a system to prevent outages from occurring, there is no methodical system that has been implemented to predict future disrupting events and inform utilities to perform future improvements on their power distribution systems.
This technology describes the DCMS, a computational method that uses real-time measurements from power distribution systems to predict disruptive events in order to prevent electricity outages. Using a reinforcement learning algorithm, data can be processed to predict points of disruption, allowing rapid management decisions to be made to remediate the problemed area. Additionally, DCMS can estimate future needs to provide accurate, controller-based pricing, curtailment, and load pocket management mechanisms.
IR M09-054
Licensing Contact: Richard Nguyen