Fortune 150 Electric Utility's 4-Year Grid Modernization Partnership — From Siloed Data to Unified Analytics Platform on AWS
At a glance
A Fortune 150 electric utility serving 8+ million customers across six states faced a critical challenge: valuable operational data was locked in siloed systems across weather forecasting, SCADA operations, GIS, and grid topology—preventing the real-time analytics needed for grid modernization, cost deferral, and renewable energy integration.
Over a 4+ year strategic partnership, NMD built a unified, cloud-native analytics platform on AWS that integrates disparate data sources and enables advanced capabilities across solar forecasting, powerflow analysis, demand response coordination, EV infrastructure planning, and GIS consolidation. Using a proven “POC-to-production-to-scale” approach, NMD delivered working solutions that started in single jurisdictions and expanded across the utility’s entire service territory.
The platform enables the utility to defer billions in costly grid upgrades by maximizing infrastructure life, avoid emergency power purchases through better load forecasting and demand response, and make faster, data-driven decisions about capital investments—all while maintaining FERC and NERC compliance.
Industry
Use Case
- Predictive Forecasting & Analytics
- Energy Management & Optimization
- Asset & Infrastructure Management
- Workflow & Process Automation
- Analytics & Business Intelligence
- Environmental Monitoring / Climate Tech
Solution implemented
Unified Data Platform Foundation:
- Cloud-native data lake on Amazon S3 consolidating multi-source weather data, SCADA/PI historian streams, GIS data, grid topology models, and AMI customer data
- Real-time streaming infrastructure using AWS IoT Core and Kinesis ingesting operational data from distributed assets
- Graph database (Amazon Neptune) modeling distribution and transmission network connectivity with validation rules
- Automated data quality, governance, and validation frameworks ensuring regulatory compliance
Core Analytics Capabilities:
- Solar Forecasting: Multi-source weather integration, ensemble forecasting with SageMaker, human-in-the-loop interface for meteorologists, performance monitoring dashboards
- Powerflow Analytics: Hosting capacity analysis, optimal tie switch placement, upgrade planning engine, fuse curve analysis, interactive GIS dashboards
- Demand Response: Pre-event simulation, feeder-level coordination with localized temperature forecasting, load shaping analytics, OpenADR protocol integration
- EV Infrastructure: Load forecasting combining demographics and AMI data, satellite imagery analysis for parked vehicle patterns, grid upgrade prioritization
- GIS Consolidation: Multi-system integration, cloud migration to AWS, location-based analytics for real-time situational awareness
The value equation
- Cost deferral through infrastructure optimization; Maximizes useful life of expensive grid equipment, delaying billions in capital replacement costs
- Avoided emergency power purchases; Better load forecasting eliminates costly purchases from neighboring utilities
- Faster capital investment decisions; Powerflow constraints inform multi-billion dollar infrastructure planning
- Scalable platform foundation; Unified data architecture supports expansion from single-jurisdiction POCs to enterprise-wide deployment
- Cross-system integration; Weather forecasting, topology data, SCADA operations, and GIS unified for analytics
- Accelerated time-to-insight; POC-to-production-to-scale approach delivers working analytics in months instead of years
Company Snapshot
A Fortune 150 electric utility serving approximately 8+ million customers across six states in the southeastern and midwestern United States. The company operates 800+ solar sites and is actively navigating the energy transition through renewable expansion, grid modernization, and distributed energy resource (DER) integration while managing aging infrastructure and rising load demands.
Location
United States (Multi-state utility)
Customer Situation
A Fortune 150 electric utility serving 8+ million customers across six states confronted a fundamental challenge: aging infrastructure, rising load demands, and rapid DER growth were driving new grid planning complexities—but the data needed to address these challenges was locked in disconnected systems.
Weather forecasting data came from multiple vendors in inconsistent formats. Operational data streamed from SCADA systems across hundreds of substations. Grid topology lived in legacy engineering systems. Geospatial asset data was fragmented across multiple GIS platforms. Customer and load data resided in separate AMI systems. Planning tools operated in isolation from real-time operations.
The business impact was significant:
Without unified analytics, the utility couldn’t accurately forecast when and where grid capacity would be exceeded, forcing reactive approaches to infrastructure upgrades. Multi-billion dollar capital decisions—new transformers, substation upgrades, feeder extensions—were made with incomplete visibility into actual constraints, load patterns, and future demand.
During peak demand events, manual processes limited the utility’s ability to coordinate demand response. Without real-time integration of temperature forecasting, SCADA data, and DER control systems, operators couldn’t efficiently “shape” load to avoid purchasing expensive power from neighboring utilities.
The utility owned expensive grid equipment—transformers, substations, feeders the size of shipping containers—with 20-30 year lifecycles. Maximizing infrastructure life through data-driven load management could defer billions in capital costs. But without integrated analytics showing real-time utilization, forecasted demand growth, and equipment health, the utility couldn’t confidently extend asset lifecycles or optimize replacement timing.
For the utility’s 800+ solar sites, manual reconciliation of weather predictions with on-site generation data created bottlenecks. Meteorologists spent hours reconciling conflicting forecasts, delaying grid operator submissions and leading to suboptimal panel positioning. As EV adoption accelerated, the utility needed to forecast charging infrastructure requirements proactively but lacked integrated analytics to model where and when EV charging would stress existing circuits.
The utility’s GIS environment had evolved into a fragmented landscape with multiple platforms serving different departments, complicating synchronization and preventing real-time situational awareness during outages.
Beyond immediate operational challenges, the utility recognized a strategic imperative:
Without a modern, unified analytics foundation, they would struggle with effective capital deployment, regulatory compliance (FERC, NERC, clean energy mandates), and competitive positioning in an evolving energy landscape. They needed a partner who could deliver production-ready analytics incrementally—proving value in specific jurisdictions before scaling enterprise-wide, not traditional multi-year “big bang” platform projects.
NMD Solution
NMD approached the challenge with a strategic insight: the path to grid modernization wasn’t a single massive platform project, but rather focused, high-value capabilities built on a unified data foundation—each proving business value in a specific jurisdiction before scaling enterprise-wide.
Rather than proposing 18-24 months of requirements gathering followed by “big bang” implementation, NMD worked with the utility to identify highest-priority analytics needs and built working solutions rapidly using a proven pattern:
Phase 1: POC in Single Jurisdiction – Start with one geographic area or specific use case. Integrate necessary data sources. Build analytics capability. Demonstrate measurable business value in 8-16 weeks.
Phase 2: Production Deployment – Refine based on user feedback. Harden for operational use. Deploy to production systems. Train users. Measure outcomes.
Phase 3: Enterprise Scale – Expand successful capabilities across all jurisdictions. Ensure platform architecture supports growth without rebuild.
Critically, each project built on the same underlying AWS-native data platform foundation. Weather data ingested for solar forecasting also supports demand response. Topology models built for powerflow analysis inform GIS visualizations. SCADA data used for solar monitoring enables DER coordination and EV load analysis. The utility wasn’t building disconnected point solutions, but accumulating integrated capabilities.
NMD’s technical approach leveraged AWS managed services to minimize operational overhead for the utility’s teams while ensuring enterprise-grade security, scalability, and compliance:
- Amazon S3 data lakes storing raw and processed data with appropriate retention policies for regulatory requirements
- AWS Glue and custom ETL handling heterogeneous data integration across vendors, protocols, and formats
- Amazon Neptune graph database modeling complex grid topology with validation rules and connectivity queries
- AWS IoT Core and Kinesis providing real-time streaming infrastructure for SCADA and operational data
- Amazon SageMaker hosting ML models for forecasting, optimization, and predictive analytics
- AWS Lambda and Step Functions orchestrating automated workflows and scheduled executions
- Amazon QuickSight delivering interactive dashboards and performance monitoring
- API Gateway exposing secure, low-latency data access for internal applications
The solution architecture was explicitly designed for incremental expansion:
New data sources could be added without disrupting existing pipelines. New analytics capabilities could leverage existing integrated data. New jurisdictions could onboard using proven patterns and infrastructure.
NMD brought critical domain expertise to the partnership—understanding utility planning workflows, grid operations terminology, regulatory requirements (FERC, NERC), and industry-standard tools. This meant solutions matched the utility’s actual operational processes rather than forcing workflow changes to fit generic platforms.
Importantly, NMD maintained human-in-the-loop design principles throughout. For solar forecasting, expert meteorologists retained authority to adjust AI-generated predictions. For demand response, operators could simulate events before execution. For powerflow analysis, planners could explore “what-if” scenarios with interactive tools.
What We Delivered Over 4+ Years
Over four years, NMD delivered multiple integrated analytics capabilities, each following the POC-to-production-to-scale pattern:
Weather Data Foundation & Solar Forecasting
Challenge: 800+ solar sites with manual forecast reconciliation across multiple weather vendors causing delays and suboptimal panel positioning.
Solution: Multi-source weather integration platform (Meteomatics, Solcast, NOAA), ensemble forecasting with pvlib-based ML models, human-in-the-loop interface for meteorologists, performance monitoring.
Impact: Significantly improved solar forecast accuracy enabling better panel positioning and energy yield. Eliminated manual reconciliation bottlenecks, reducing publication time from hours to minutes. Weather data foundation reused for subsequent demand response and load forecasting. Scaled from POC to production across entire 800+ site portfolio.
Status: Deployed and operating in production
Powerflow Analytics & Grid Planning
Challenge: Multi-billion dollar infrastructure decisions made with incomplete visibility into grid constraints. Needed to maximize equipment life to defer costly upgrades.
Solution: Hosting capacity analysis, interactive GIS dashboards visualizing assets and connectivity hierarchy, optimal tie switch placement, upgrade planning engine, fuse curve and short circuit analysis.
Impact:
- Cost deferral: Data-driven load management maximizes life of existing transformers, substations, and feeders—delaying billions in capital replacement
- Faster investment decisions: Real-time hosting capacity and constraint analysis guides infrastructure planning with confidence
- Improved reliability: Optimal tie switch placement and protection coordination reduce outage risk
- Deployed in southeastern regions, expanding to midwestern service territory
Status: Production in southeastern regions; midwestern expansion in progress
Demand Response & Der Coordination
Challenge: Manual coordination across vendor systems. Couldn’t efficiently “shape” load to avoid costly power purchases from neighboring utilities.
Solution: Pre-event simulation testing demand response actions before execution, feeder-level coordination using localized temperature forecasting integrated with SCADA data, load shaping analytics, OpenADR protocol integration.
Impact:
- Avoided costly power purchases: Better forecasting and coordinated demand response eliminates emergency purchases during peak events
- Precise load management: Granular, feeder-level control informed by temperature forecasting
- Risk reduction: Pre-event simulation ensures targets met, reducing failure risk and penalties
- Platform value demonstrated: temperature forecasting data collected for one use case now powers another
Status: Deployed and operational
EV Infrastructure & Load Analytics
Challenge: Accelerating EV adoption creating uncertain load growth. Need to forecast charging infrastructure requirements and plan grid upgrades proactively.
Solution: EV load forecasting combining demographics and AMI data, satellite/drone imagery analysis quantifying EV adoption by region, household-level load profiles, grid upgrade prioritization.
Impact: Proactive grid upgrade planning enables circuit upgrades before capacity exceeded. Demographic analysis identifies optimal charging station locations. Data-driven planning ensures investments deployed where most needed.
Status: Analytics deployed; ongoing refinement
GIS Consolidation & Topology Management
Challenge: Fragmented GIS environment with multiple platforms. Limited collaboration and no real-time situational awareness.
Solution: Multi-system GIS integration unifying platforms, cloud migration to AWS, location-based analytics for real-time asset monitoring. Graph database (Neptune) modeling grid connectivity with automated validation and API access.
Impact:
- Unified environment: Eliminated department silos, improving collaboration
- Real-time awareness: Current view of asset status and location during outages
- Platform integration: GIS data powers powerflow analytics, hosting capacity visualizations, upgrade planning
- Single source of truth: Centralized topology model supports hosting capacity, powerflow simulations, outage response
Status: onsolidation and migration in progress
The Platform Thinking Advantage
What differentiates this partnership is the cumulative, compounding value of the platform approach:
- Weather data initially for solar forecasting now powers demand response and load forecasting
- Topology models for powerflow analytics inform GIS visualizations and outage management
- SCADA data for solar monitoring enables DER coordination and EV load analysis
- Real-time streaming infrastructure supports multiple use cases simultaneously
The utility isn’t accumulating disconnected point solutions—they’re building an integrated analytics ecosystem where each new capability leverages existing data and infrastructure, accelerating time-to-value and reducing marginal cost for expansion.
The POC-to-production-to-scale pattern proves ROI in single jurisdictions before enterprise-wide commitment, de-risking innovation while building confidence across business units and leadership.
Strategic Partnership Model
Over 4+ years, NMD has become embedded in the utility’s grid modernization journey as a strategic technology partner supporting ongoing transformation:
- Deep domain integration: Understanding operational workflows, regulatory environment, and planning processes—solutions match actual utility needs
- Platform stewardship: Ensuring architectural consistency, data quality, and technical sustainability as the platform evolves
- Continuous innovation: New capabilities build on proven foundation with accelerating delivery speed
- Knowledge transfer: Utility teams gain expertise in AWS-native analytics, ML operations, and modern data platform management
The relationship demonstrates how utilities can modernize incrementally—proving value at each step while building toward comprehensive grid intelligence.
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