Longroad Energy (LRE) is a leader in making clean wind and solar energy a reality, with 7,000+ wind and solar projects across North America. LRE manages the entire process of generating clean energy and delivering it to the grid. The company handles the whole project lifecycle, from site selection through construction and operation. Keeping solar and wind operations producing electricity for tens of thousands of customers also meant handling mission-critical maintenance. To manage this ambitious lifecycle, LRE assembled a portfolio of off-the-shelf SaaS products, spanning tools for land rights research, engineering, construction oversight, permitting, as well as both preventative and reactive maintenance.
The Challenge
After 15 years of experience and expansion, the portfolio of off-the-shelf SaaS business processes and operational systems had grown increasingly difficult to manage. Data integration became more and more difficult. Imagine if you had to coordinate data from many different operational and business systems; get it wrong, and your ROI evaporates. Worse yet, your utility customers suffer needless power interruptions.
LRE needed a continuous, consistent information flow of actionable insights across the organization to simplify decision-making. From tech support to the executive suite, business professionals needed clear, well-organized, readily quarriable data. Inputs from power plants, field maintenance systems, and business processes (e.g., Finance) combined with IoT capture, secure file sharing, ERP, and project work order management – all were essential for launching and operating green power sites.
The Solution
As SaaS development and cloud data engineering experts, CloudGeometry was a natural choice for end-to-end integration. LRE’s constant growth needed a flexible data pipeline approach, covering use cases from day-to-day Operational Analytics to Business Intelligence. CloudGeometry experts designed and built a set of cloud processing components to run an integrated data pipeline. Using the broad portfolio of AWS tools and services, CloudGeometry took API output from the existing commercial off-the-self SaaS systems and architected it to enrich the data value chain. These include:
- All-in-one loT signals collection from wind and solar plants, pre-aggregated by sources and consumers
- Computed thresholds to trigger automated repair work orders and to drive better MTBF/MTTR
- ERP finance and accounting system for logical integration of data sources and business rules
- Algorithms tying geographic data to weather history and forecasts
- Data distribution to various Regional Power Transmission Organizations and asset owners
CloudGeometry integrated these discrete data sources into a tiered data pipeline. The pipeline enabled multiple decision support use cases. In addition, CloudGeometry created a comprehensive reporting application that makes it possible for users to easily gain insights. These insights include how and where energy has been generated, and reports can be created in multiple formats.
Careful design for software reuse also enabled the rapid creation of new features. Changes can now be designed and implemented more quickly with days, not months. These new features include both operational improvements and the ability to leverage new AWS cloud-based analytics services.
The Benefits
To quote AWS CTO Werner Vogels, “Operations are forever.” For LRE, it’s nothing less than keeping the lights on. Their systems now collect and process data and monitor the data pipeline. They also match plant-level events and anomalies with work order data. End users at all levels get a well-aligned view of operational data, incidents, and maintenance events. By resolving multiple sources and data models into a single versatile stream, LRE, and its customers benefit from timely information generated from the SaaS systems they already rely on.
Key Features
<div class="case__txt--cols"><div><h4>Virtualized SaaS Data Pipeline</h4><p>S3 buckets store partitioned raw data, enabling support for ad-hoc queries from Amazon Athena.</p></div><div><h4>Serverless data orchestration</h4><p>Event-driven serverless functions and batch processing generate hourly, daily, and monthly aggregates, combining microservices and AWS Lambda.</p></div><div><h4>Multi-Tenancy Data Warehouse</h4><p>Stored in RDS Aurora, an easy-to-administer database with security features and scalable processing.</p></div></div>
