Manage Energy Network Performance With TigerGraph
Growth in primary energy consumption for 2017
People affected by power outage in 2017
Losses due to power outages in 8 key US markets
The Demands on Energy Networks Are Increasing
The world’s energy needs are growing at a steady pace. The global energy consumption increased 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.
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Legacy Systems Are Insufficient at Balancing Supply and Demand
The vast majority of grid monitoring systems are many decades old and the tools used to monitor them provide only limited visibility. Many utility companies, therefore, are hindered 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 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 grids, 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.
Database for Energy Management System?
Monitor Internet of Things Data Using Graph Analytics
From meter readings to information from network sensors, utility companies are being flooded with data – a typical network can contain as many as 10 billion devices, each one providing minute-by-minute updates.
Balancing a 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 data, network 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.
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Balance Supply and Demand Using Graph Analytics
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 energy management system must be capable of completing execution within a Supervisory Control And Data Acquisition sample cycle which is, typically, five5 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 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. That portion of the time is eliminated using TigerGraph’s graph analytics.
FAQ
An energy management system is a platform that helps utilities monitor, balance, and optimize power generation, transmission, distribution, demand, and grid performance. It is critical because energy networks are becoming more complex, demand is increasing, and outages can create significant operational, financial, and customer impact.
Graph databases improve energy management systems by modeling substations, meters, sensors, feeders, transformers, transmission lines, customers, and grid assets as connected data. Unlike relational databases that require complex joins across separate tables, graph databases can traverse grid topology in real time to analyze dependencies, constraints, demand changes, and outage impact.
TigerGraph’s energy management system solution supports real-time, deep link analytics across massive connected power networks. It can analyze multi-hop relationships across grid assets, IoT sensors, meters, demand signals, supply sources, and customers to improve grid visibility, balance supply and demand, reduce operational risk, and improve reliability.
Yes, TigerGraph can help utilities balance supply and demand by analyzing changing grid conditions, demand spikes, supply drops, asset capacity, and network topology in real time. This helps operators identify mismatches faster, prioritize critical areas, reroute power when needed, and reduce the operational impact of outages or instability.
Real-time graph analytics helps utilities understand how changes in one part of the grid affect connected assets, customers, and service areas. Instead of relying on delayed reports or static models, operators can monitor live sensor, meter, and network data to detect risks, assess downstream impact, and respond before issues escalate.
The main challenges include aging grid infrastructure, fragmented monitoring systems, growing IoT data volumes, renewable energy variability, demand volatility, and limited visibility across complex grid topology. Traditional systems often struggle to analyze connected grid data quickly. A graph database addresses these challenges by analyzing energy network relationships directly and at scale.
TigerGraph supports AI and machine learning for energy management by generating graph-based features from connected grid data, such as asset dependencies, load patterns, outage paths, demand clusters, and network criticality. These features help models improve outage prediction, demand forecasting, grid optimization, and operational recommendations across complex energy networks.
