Grid Modeling & Planning

Electric utilities and grid operators increasingly face the limits of traditional planning tools. Networks must handle more renewables, adapt to climate uncertainty, and prepare for a surge in distributed energy and electric vehicles. Yet many existing models are static, siloed, and hard to maintain.

At DKL, we approached this challenge as software and data engineers. Our goal was to build a standardized, scalable modeling framework that transforms heterogeneous data — from historical operations to future demand projections — into consistent, reproducible power flow analyses. The result: a technical foundation for grid planning that evolves with the data itself.

We began by uniting strong data engineering foundations with a software architecture designed for flexibility and growth. Instead of treating modeling as a black box, we established transparent workflows for data ingestion, transformation, and scenario generation. This allowed planners to move beyond static spreadsheets and toward systematic, repeatable analysis.

The core of our implementation was a standardized modeling layer built on PyPSA, enhanced with compatibility for commercial solvers. This integration enabled large-scale stochastic scenario generation in cloud environments, allowing efficient simulation of multiple grid futures that account for weather variability, renewable penetration, and demand growth.

Our team developed an end-to-end data pipeline to clean, align, and enrich operational and forecast datasets, ensuring analytical consistency. We structured the modeling stack so that datasets — including load profiles, renewable generation, and infrastructure topology — flow seamlessly into the simulation layer.

On the software side, we built modular components that orchestrate data processing, scenario simulation, and post-analysis reporting. This architecture supported reproducible, scalable power flow studies across multiple horizons — from short-term operational analysis to long-term expansion planning.

The result is a comprehensive Grid Modeling & Planning platform that enables planners to analyze network behavior under different future conditions. Rather than focusing on real-time operations, the platform supports structured, data-driven simulations to assess grid resilience to extreme weather events, renewable intermittency, and the evolving impact of batteries and electric vehicles.

By combining open-source flexibility (PyPSA) with commercial solver performance, the system balances transparency with computational scale — allowing users to expand model coverage, run large scenario batches in the cloud, and retain full control over data and modeling assumptions.

• Standardization and scalability: A unified modeling framework that integrates data pipelines, power flow simulation, and scenario analysis under one architecture.
• Reproducible analytics: Every simulation is fully traceable and versioned, enabling consistent comparison across scenarios and time periods.
• Operational transparency: Open-source PyPSA core ensures visibility and adaptability, while commercial solver compatibility delivers performance for large-scale runs.
• Resilience insights: The platform quantifies grid performance under stress — from renewable volatility to extreme weather and infrastructure outages.
• Future readiness: Enables planners to evaluate the combined effects of renewables, storage systems, and electric mobility on network stability and investment strategy.
• Cloud-native simulation: Stochastic scenario generation at scale, leveraging cloud infrastructure for computational efficiency and flexibility.