The vast majority of grid monitoring systems are many decades old with extremely limited visibility. Thus most utility companies are very limited in their ability to plan, optimize, and respond to dynamic demand and supply changes. This results in increased operational risks, higher costs, and outages. Some companies have tried to employ traditional business intelligence technologies to address the problem. Most power systems are modeled using relational databases in a collection of interlinked tables. As different components of power systems are stored in separate tables, they need to be linked together using shared key values to model the connectivity and topology of the power system. Connecting or linking across separate tables (database joins) typically takes about 25% of the processing time for power flow calculation and 35% for power grid state estimation. With the data size and complexity of modern power grid, traditional energy management systems based on relational databases are slow, expensive, and generally incapable of analyzing the massive quantity and complexity of energy and utilities data.

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Creating a faster than real-time Energy Management System (EMS) has been the holy grail for the power industry. Such a system must be able to identify mismatches between the power demand and supply, lower the power consumption for non-critical parts of the grid, divert the power to higher priority areas for industrial output and national security, and be able to accomplish all of this in a few seconds. A faster than real-time EMS must be capable of completing execution within a Supervisory control and data acquisition (SCADA) sample cycle, typically 5 seconds. The standard approach for solving large-scale linear equations for power management requires bulky, time-intensive matrix operations. Modeling a power system as a graph (instead of a matrix) represents connections and topology more naturally. No data preparation is needed, cutting 25-35% of the generally required time for power flow calculation and state estimation. Bus ordering and admittance graph formation are performed with all graph nodes processing in parallel. Core calculations are all conducted on the graph, and solved values are stored as attributes of vertices and edges of the graph - rather than unknown variables in the vector or matrix. Using TigerGraph, solved values are stored as attributes of vertices and edges on a graph - forgoing the need for a mapping process. Output visualization takes about 70% of total time for power flow calculation, and 28% for state estimation when using the conventional approach. Using the TigerGraph native graph database and graph computing, that portion of the time is eliminated.

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