The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs) and grid providers. Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.

Power Grid Model provides a calculation engine that is increasingly essential for operators in this new environment. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.

Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.

Power Grid Model offers an independent open technology base that speeds and simplifies development of critical Power System technologies for DSO’s and related organizations.

Power Grid Model Benefits

Open Source Business Benefits

• Reduced project time and cost leveraging with field-proven Open Source components
• Rapid, relevant and ongoing innovation through open, community-driven development
• Shared community learning and best practice

Technical Benefits

• Standards-based speeding development, implementation and integration with broader systems
• Unrivalled performance through efficient C++ implementation, native shared memory multi-threading and support for parallel computing
• Ensured accuracy with integrated unit testing and validation test
• Optimized algorithms to capture the true characteristics of the distribution grid
• Full support of three-phase asymmetric calculation

Image: Power Grid Model – High-Level Concept

Features

• Power system calculation functionalities: power flow, state estimation, short circuit
• Linear methods available
• Symmetric and asymmetric calculation
• High-performance implementation in C++ with native parallelization
• API (Application Programming Interface) in Python and C
• Cross-platform

Open Source Community

Hosted by the Linux Foundation Energy, The Power Grid Model is now a vibrant Open Source project with a diverse, active and growing community, consisting of DSO’s, universities, research institutes and commercial parties.

The project is constantly evolving through a vibrant community-driven development process, with future scope to extend the existing libraries and develop more complete open-source applications. As part of this secure ongoing development, continuous validation is conducted through a CI pipeline in GitHub Actions.

Through the community, field validation of the library has been performed against the Power Grid Model reference models in over 80 test cases.

Collaboration

Integration

Architecture

The project consists of two main libraries: power-grid-model and power-grid-model-io.

power-grid-model

The core power-grid-model library is the main calculation engine, optimized for speed, to support real-time modelling, machine learning and powerful predictive analytics. Written in highly efficient C++, the library also offers native shared-memory multi-threading to enable parallelization in batch calculations.

A choice of C-API (with dynamic shared object) and a user-friendly Python API offers flexibility for developers. While the library runs across platforms including Windows (x64), Linux (x64/arm64), and macOS (x64/arm64), and publishes binary Python packages in official PyPI.

The calculation core is a C++ header only library. This is wrapped by a C-API providing direct access with dynamic shared object support for C-API developers. The C-API is then wrapped into a Python API to provide a more user-friendly option for Python developers.

The calculation core is thoroughly tested by its own unit tests and validation tests. While the model can also be validated on the Python side with the same test data.

More in-depth information on Power Grid Model can be found here: https://power-grid-model.readthedocs/en/stable.

power-grid-model-io

The power-grid-model-io library is a data conversion Python library to speed and simplify integration of Power Grid Model into broader system environments. This handles the conversion between the Power Grid Model format and other common grid data formats, with current support for conversion from Vision and pandapower.

More in-depth information on the data conversion library can be found here: https://power-grid-model-io.readthedocs.io/en/stable.

Integration and LF Energy Power Grid Suite

Power Grid Model offers a powerful stand-alone calculation engine. Using power-grid-model-io, it can be easily integrated into any broader systems architecture, with out-of-the-box integration modules for Vision and pandapower.

For organizations looking at building full active congestion management systems, Power Grid Model now forms part of a more comprehensive open source tool suite hosted by LF Energy.

Used together, the suite enables DSOs to create end-to-end smart energy software platforms stretching from capacity forecasting, through advanced modelling and calculation, through to intelligent grid-edge mitigation and the implementation of reactive market pricing for steering supply and demand.

The LF Energy Power Grid Suite also incorporates:

	OpenSTEF uses machine learning for accurate short term forecasting grid load and generation: based on measurements, weather forecasts, pricing on the energy market and other determining metrics.
	Shapeshifter offers a framework and libraries for building Smart Energy trading platforms based on the Universal Flex Trading Protocol (UFTP).

Power Grid Model Use Cases

• Power Grid Model is used by existing DSOs to generate simulations of different potential grid expansion plans in profile calculations over future decades.
• Monte-Carlo: Local operators are using Power Grid Model to simulate different scenarios for low voltage (LV) grid requirements in the coming decades. Simulations are based on different forecasts for the market penetration of electric vehicles and photovoltaics, and are used to identify potential bottlenecks within LV grids.
• Other current use cases include real-time what-if analysis including impact analysis of component failure and other anomalies on the current grid state.