CUSTOMER STORY
How Volue Optimized Power Grid Management with Memgraph
Customer
Volue
Use case
Power grid management
Industry
Energy management
Profile
Building with Memgraph
Challenge
Managing the power grid with traditional databases was slow and rigid, requiring costly infrastructure upgrades and delaying real-time decision-making.
Solution
By using an in-memory graph database, Volue achieved real-time power flow analysis, faster load forecasting, and dynamic energy balancing, eliminating infrastructure bottlenecks.
Reading time: 6min
About Volue
Volue is a Norwegian technology company specializing in power grid data management. Their solutions help energy providers optimize grid operations, improve forecasting, and balance supply and demand efficiently. Volue’s Spark project is dedicated to improving real-time grid analytics, ensuring power grids remain reliable, cost-effective, and scalable in the face of increasing energy demands.
Impact Highlights
Real-time power grid management
With Memgraph, Volue can now instantly analyze network connectivity, impact areas, and potential grid overloads.
Better load forecasting
Volue predicts power usage patterns and provides incentives in real time for customers to shift usage off-peak.
Flexible, dynamic data modeling
Unlike traditional databases, Memgraph allows grid topology to be updated in real time, without expensive data reprocessing.
"When this [power grid] was initially set up, it was built for home appliances. But things have changed. We now use more electricity for powering cars and water heaters, which adds a lot of strain to the system. Instead of upgrading infrastructure, which is expensive, we needed a way to optimize power distribution dynamically."
Dinesh Mathur, Team Lead at Volue
Key Memgraph Features for Volue
- Graph-based networking modeling
- Power grids are highly interconnected, and Memgraph’s graph database structure perfectly maps relationships between substations, transformers, and consumers.
- Pattern-based querying
- Instead of writing low-level traversal logic, Memgraph’s Cypher query language makes it easy to find power routes, detect network stress, and analyze failure points.
- In-memory processing
- Unlike disk-based Neo4j, Memgraph operates in memory and makes graph traversals significantly faster.
- Kubernetes deployment
- Memgraph’s Helm charts allowed Volue to deploy and scale graph processing across Azure Kubernetes clusters.
Backstory
Before adopting Memgraph, Volue relied on traditional Network Information Systems (NIS) and relational databases (PostGIS, PostgreSQL) to manage power grid data. While these systems were sufficient for static record-keeping, they proved inefficient and slow when it came to real-time analysis, forecasting, and decision making.
Initially, they experimented with JanusGraph + Gremlin, but the experience was not good. Query development was too slow as a simple topology trace took two months to optimize. Deployment was complex with multiple components which had to work together. For example, JanusGraph required multiple external components such as Apache Cassandra, HBase, and Elasticsearch just to function properly. This became a major operational burden.
Initially, they experimented with JanusGraph + Gremlin, but the experience was not good. Query development was too slow as a simple topology trace took two months to optimize. Deployment was complex with multiple components which had to work together. For example, JanusGraph required multiple external components such as Apache Cassandra, HBase, and Elasticsearch just to function properly. This became a major operational burden.
"We started with traditional network information systems that stored power grid topology in relational databases. The problem was every time we made a change, we had to rebuild the entire network model, and that was time-consuming."
Dinesh Mathur, Team Lead at Volue
Furthermore, performance was lacking even after optimization. Gremlin queries were inefficient for large-scale grid data. While JanusGraph worked well for small-scale tests, query performance deteriorated rapidly as the dataset grew.
Realizing that a graph-first approach was necessary, they looked for a database that could handle real-time grid topology changes and run high-speed queries. This is when they found Memgraph.
Realizing that a graph-first approach was necessary, they looked for a database that could handle real-time grid topology changes and run high-speed queries. This is when they found Memgraph.
Challenge
Power grid management is not just about infrastructure–it’s about data. Volue wanted to balance energy loads dynamically without relying on expensive infrastructure upgrades.
Traditional NIS solutions were too slow. Every update to the grid topology required manual rebuilding and extensive reprocessing, making real-time analysis impossible.
Scalability was also an issue. As grid complexity increased, relational databases struggled with querying large-scale power network connections.
For Volue, real-time insights are critical. Power demand fluctuates based on time of day, weather, and local energy production. This is why they needed a faster, more flexible solution.
Scalability was also an issue. As grid complexity increased, relational databases struggled with querying large-scale power network connections.
For Volue, real-time insights are critical. Power demand fluctuates based on time of day, weather, and local energy production. This is why they needed a faster, more flexible solution.
Why Memgraph?
Memgraph offered everything Volue needed: ease of deployment, fast performance, a simple Cypher-based query language, and a built-in visualization tool (Memgraph Lab) for intuitive power grid exploration.
- Milliseconds-scale querying for real-time grid analysis
- As Volue needed a database which could process changes instantly, Memgraph in-memory architecture provided near-instant downstream and connected traces. This included faster data ingestion while analytics enabled real-time monitoring and forecasting.
- Easy to deploy and without complex architecture
- Memgraph is easy to deploy and maintain with a single-container setup that just worked. Without needing any additional indexing layers (like ElasticSearch) this meant less overhead and faster performance.
- Visualization with Memgraph Lab
- This solved Volue’s challenge of making power grid topology data easier to analyze and debug. Their engineers visually explored grid failures, traced power disruptions, and identified overloaded nodes in seconds—without needing third-party visualization tools.
- Direct engineering support
- Memgraph provided direct access to engineers via Slack and Discord. Response times were often within minutes, not days. Also, feature requests (like spatial data support) were prioritized into the roadmap and actively developed.
"Something that also made Memgraph easy to use is Lab. Other graph databases have similar tools, but I wouldn't say they are on par with Memgraph Lab. It's easily the best."
Inge Hegge, Backend Developer at Volue
Results
Here’s how improvement looked like for Volue after implementing Memgraph. Power grid queries that once took 2 seconds in NIS now execute in milliseconds, enabling instant analysis of energy distribution and network failures. Then by intelligently balancing energy loads, Volue has avoided costly hardware upgrades, ensuring a more efficient and scalable power grid.
Last but not least, Memgraph replaced a complex, multi-component setup (JanusGraph, Gremlin, and ElasticSearch) with a single, high-performance graph database, reducing operational overhead.
"If you compare the times, a connected trace took several seconds in our previous setup, but in Memgraph, it's milliseconds. And that's a huge difference when working with real-time energy data."
Inge Hegge, Backend Developer at Volue
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