Building a Commercial Intelligence System for Renewable Energy

Client Background

Our client is one of Europe’s largest energy providers, with operations spanning multiple countries. We worked with their renewable energy division, which is responsible for identifying and developing new wind and solar power sites. The division faced growing pressure from increased competition, especially in Germany, to speed up and enhance site assessment processes.

The Challenge

Evaluating potential locations for wind and solar development was slow, manual, and inconsistent. The standard process involved using GIS tools to exclude unsuitable areas, manually testing possible layouts, and evaluating financial viability via Excel models. The approach used generalised inputs and did not support detailed simulations or integrated views. Several disconnected tools were in use, including early-stage Python scripts and standalone financial models, but they were not integrated.

With the market becoming more competitive, especially in Germany, it became critical to reduce the number of sites requiring full manual investigation and to prioritise the most promising opportunities more effectively.

Project Goals

The project was initiated as a Proof of Concept (PoC) with two primary objectives:

• Demonstrate a unified platform concept to business stakeholders that could justify a larger investment.
• Investigate suitable technology approaches and system architecture options for a potential production system.

There was internal debate about whether to build a standalone web application or embed the solution into their existing ArcGIS system. While the client had an ArcGIS Online subscription, data sovereignty concerns made them hesitant to upload proprietary datasets to Azure-based infrastructure.

Our Approach

We were tasked with designing and developing a working PoC to test both the user experience and technology integration possibilities. The project ran for approximately three months with a small, focused team of two developers.

Technology Exploration

We built a simple web application and evaluated several mapping frameworks, including ArcGIS components and Mapbox. The PoC integrated:

• OpenMap data for base layers and boundaries
• WindAtlas historical data to simulate wind conditions
• A proprietary financial model, translated from Excel into JavaScript
• An internal Python module that generated wind farm layouts based on GIS inputs

The application allowed users to identify viable land, select optimal turbine models, simulate potential layouts, and assess connection feasibility to the electrical grid. All this data was then fed into a unified financial model that produced a score from 0 to 100 to represent site viability.

We used a Kanban delivery approach, which suited the experimental nature of the work. We maintained a fast feedback loop with stakeholders, refining the product incrementally.

Results and Impact

The PoC clearly demonstrated that an integrated solution was not only viable but highly beneficial. Key outcomes included:

• Technology direction: The team recommended deploying an internal ArcGIS Enterprise environment, avoiding cloud-hosted risks while enabling deep system integration.
• Automation potential: The system's logic could be extended to evaluate hundreds of sites automatically in the background, drastically reducing manual workload.
• Prioritisation: A scoring system enabled the team to generate a ranked list of the most promising sites.
• Future enhancements identified: Integrating land ownership data and analysing road access to evaluate delivery feasibility were recognised as valuable future extensions.

Lessons Learned and Next Steps

The PoC became a pivotal step in reshaping how the client approached early-stage project evaluation. It gave stakeholders a clear vision of a future tool, surfaced key technical risks, and laid the foundation for scaling up development.

The client has since begun deploying ArcGIS Enterprise within their own data centre and exploring options for expanding the application into a full production-grade commercial intelligence platform.