With a decade long economic expansion United States and sustained economic growth
in India and China, worldâ€™s energy needs are growing at a steady pace. The global
primary energy consumption went up by 2.2% in 2017, the fastest growth since 2013.
The economic impact of a power outage is massive, with eight key U.S. market segments
studied by energy consultant E Source losing about $27 billion per year due to power outages. Now
more than ever, all power grid operators need faster energy management systems to balance and
even out the spikes in demand or drops in supply to minimize the impact of outages.
Traditional Solutions Are Missing the Mark
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.
Why TigerGraph a Native Parallel Graph Database for Energy Management System?
Deep Link Analysis of Power Grid IoT Data
From meter readings to the constant flow of information from sensors and network
components, utility companies are being flooded with the IoT sensor data. Working
closely with the leading energy and utility companies, TigerGraph has pioneered Native
Parallel Graph approaches that help companies monitor and analyze power flows,
detect bottlenecks, and alert personnel about grid performance issues.
power grid requires consolidating signals from multiple levels of the power infrastructure
and matching demand and supply with complex linear equations, which is deep link
analytics taken to the extreme. Using TigerGraph to process all of their power grid IoT
(Internet of Things) sensor data, operators can respond immediately to sudden spikes in
demand or drops in supply, thus reducing operational risk and operating costs while
improving reliability, efficiency, and customer experience.
Faster than Real-time Analysis of Power Grid IoT Data
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