Fueling the Future: Cadent Gas Redefines Energy Distribution with Quantiphi’s Viability Scoring Model
Energy & UtilitiesBusiness Impacts
Increased data capacity by 60%, handling up to 12 GB, far exceeding the limitations of MS Excel.
Enhanced efficiency and robustness in processing large datasets, with significantly reduced latency.
Customer Key Facts
- Country : UK
- Industry : Energy & Utilities
- About : Cadent Gas is a British gas distribution company that owns and maintains the UK’s largest gas distribution network, serving 11 million homes and businesses. They manage over 82,000 miles of mostly underground pipes, delivering gas across the North West, West Midlands, East Midlands, South Yorkshire, East of England, and North London.
Assessing Geographical Viability for Hydrogen Supply
Cadent Gas, the largest natural gas distribution network operator in the United Kingdom, sought to transition from natural gas to hydrogen gas in select regions. To achieve this, they needed a software solution equipped with advanced viability logic to identify the most suitable geographical areas for conversion.
Challenges
- Minimizing the use of natural gas in the UK was a significant challenge for Cadent Gas.
- The existing Excel-based system lacked the ability to handle the complex logic needed for accurate demand forecasting and location identification.
- Excel’s limitations in handling large volumes of data prevented Cadent Gas from working at a granular level.
- There was no capability to save and share scenarios with other users, hindering collaboration.
Technologies Used
Big Query
Cloud Storage
Cloud Composer
Cloud SQL
Cloud Build
Cloud Run
Firebase
Solutions
- Quantiphi developed a software solution in just 10 weeks that integrated geographical information and existing logic to accurately identify locations for conversion from natural gas to hydrogen gas.
- Data preprocessing was performed to prepare base tables, including converting coordinates to geolocation and filling in missing elements. The logic was then transferred from Excel to BigQuery after thorough evaluation and understanding.
- A web application was built to display the output, with added functionality for creating scenarios to predict future energy demand.
Results
- Precise identification of optimal locations on a geographical representation of the client’s supply network.
- Development of 5 charts, 3 metric tables, and a geospatial map within the web application for comprehensive network visualization.
- Simplified access to region-specific data, with options to view and download through interactive charts and metric tables.
