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Client
The client is a 130+ year-old American multinational utility corporation that generates, transmits and distributes electricity, serving almost two-thirds of Oregon’s commercial and industrial activity. The distribution system was changing rapidly with more renewables and other types of distributed energy resources (DERs), such as battery energy storage and electric vehicles. So, the client needed to build capabilities for efficient and frequent iterative studies instead of their legacy methods and annual assessments.
Need for change
The electricity and utility industry is at a turning point. As more homes and businesses switch to electric vehicles and appliances, demand for electricity is rising fast—putting pressure on an aging grid that wasn’t built for today’s needs. Moreover, the push to meet climate goals means we need to bring clean, distributed energy sources online much faster than before. But the current system is struggling to keep up. With extreme weather becoming more common and many parts of the grid showing their age, reliability is becoming a serious concern. On top of that, people’s expectations are changing—they want more control over their energy use, lower bills, and cleaner options. The traditional way of delivering power simply isn’t flexible enough to keep up. To meet these challenges head-on, the industry needs to rethink how it operates and make meaningful changes—starting now, setting the stage for harnessing innovative technology such as virtual power plants (VPPs).
VPPs the Power of Many
VPPs aggregate distributed energy resources to function as a unified system. They mimic traditional power plants while offering greater flexibility.
Core components of a VPP
- Distributed generators: Solar arrays, wind turbines, and combined heat and power systems that generate electricity locally.
- Energy storage: Battery systems and thermal storage that capture excess energy for later use.
- Flexible consumers: Smart appliances and industrial equipment that can adjust energy usage based on grid needs.
- Control infrastructure: Advanced software and communication networks that coordinate all VPP components.
Future impact of VPPs
VPPs have powerful capabilities that make energy workflows more efficient, eco-friendly and sustainable. They facilitate:
- Renewable integration: VPPs will enable higher penetration of clean energy sources. Grid stability improves despite variability in generation.
- Environmental benefits: Lower carbon emissions and reduced environmental impact. Grid resilience increases against extreme weather events.
- Consumer empowerment: Energy independence and cost savings for participants. Communities gain more control over their energy systems.
- Grid transformation: Critical role in transitioning to smart, decentralized infrastructure. The power system will become more democratic and adaptive.
VPPs will enable customer choice, control and flexibility in shifting energy use to off-peak times. By 2030, the VPP will scale to achieve 25% peak usage offset while serving 100% of customer energy needs. Thus, the energy industry is on the brink of a complete transformation by leveraging the power of VPPs.
Figure 1: Sources for VPP
Challenges
Data inaccuracies
Since the DER source files (like active generator list and Scada keys) involved manual intervention, there was a chance of data inaccuracies. the data may be inaccurate. This could lead to issues when integrating it with the client’s data in Snowflake.
Data validation process
Needed to implement an automated, multi-step validation system for DER and customer data integration before exporting to DERMS. Traditionally, this capability didn’t exist, leading to manual checks, delays, and risk of exporting inaccurate data. The new system must flag records as “passed” or “failed,” ensure only validated data is exported, and generate error reports for quick issue resolution—accelerating and improving data quality management.
Orchestration issues
There was a need for a system to orchestrate data quality checks during every data ingestion. Manual execution of these checks is inefficient, prone to errors, and lacks timely monitoring and alerts. Without orchestration, validation steps may be missed or delayed, risking data quality and downstream reliability. Implementing an automated orchestration system will ensure consistent, real-time data validation and prompt notifications of any failures, improving data integrity and operational efficiency.
Lack of data visualization
Business users needed visibility of DER records to process the ingestion. We needed to set up a dashboard or an interface where users could run the ingestion process based on real-time data.
LTIMindtree’s solution
With the above challenges in the client’s legacy system and workflow, we designed the VPP system (refer Figure 2 in below) to perform the following:
Orchestrated the data loading process using AWS Step Functions instead of Matillion
Leveraging Step Functions enabled us to build complex workflows in AWS by chaining together Lambda functions, AWS services, and other tasks. This also provided the capability to email respective teams in case of any fails that occurred during the ingestion process.
Data validation using Snowflake stored procedures instead of Matillion Python code
- We implemented data quality checks using Snowflake stored procedures to validate key rules such as uniqueness, value ranges, and special character restrictions. Any validation failures were automatically captured in an error log table within Snowflake, ensuring a clear audit trail.
- Additionally, real-time alerts were sent to notify the relevant teams immediately when issues occurred, enabling quick resolution.
- This approach leveraged Snowflake’s scalable computing power and built-in security, resulting in faster execution, simplified architecture, and reduced reliance on external tools compared to Matillion Python code.
Snowflake Streamlit for data visualization of DER data, connecting AWS to the run ingestion process
We empowered business users to interact with real-time DER data and track the ingestion and validation processes. Streamlit refreshed at intervals to display the latest data and validation status.
Figure 2: VPP architecture
Tech stack
| Back-end technologies | AWS S3, Lambda, Step Function |
| Front-end technologies | Python, Streamlit |
| Database | Snowflake |
| DevOps | CI/CD Pipeline |
Business Benefits
Our solution provided a wide range of benefits to the client including:
Cost savings
Improved automation and scalability
Faster and reliable data validation
Seamless user experience
Tighter cloud integration
Reduced operational overhead
Conclusion
Our data warehouse solution was an innovation in the distributed energy resources (DER) space. We built a modern data warehouse that brings together customer and DER data seamlessly, making it easy to send accurate, timely information to the distributed energy resource management system (DERMS). This empowered our client to see, organize, and control energy resources in real-time—helping them meet important grid needs like capacity and reserves across their network.
By delivering fast, reliable data to DERMS and speeding up analysis, our VPP enables customers to have more choice and control—shifting their energy use to off-peak times when it’s cheaper and greener.
Looking ahead, by 2030, this VPP is projected to reduce peak energy demand by 25% while fully meeting all customer energy needs. This case study demonstrates how the energy industry is on the brink of transformation—powered by smarter data, real-time integration, and flexible energy management through VPPs.
Ready to revolutionize energy operations with increased savings with your own virtual power plant?
Contact eugene.comms@ltimindtree.com to know more!
