Distribution Grid Model

[Aide Broeckel]

Theglobal 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 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.

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.

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.

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 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.

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.

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).

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The U.S. Department of Energy works closely with the electricity industry to identify challenges and proactively address grid transformation issues. Policies, changing customer preferences, and innovative technologies are all transforming power system planning and operations, particularly at the distribution grid.

DOE works closely with various organizations representing state officials to examine issues and advance best practices relating to distribution system transformation and grid-edge evolution. Additional information, including reports and educational materials, are available from the Center for Partnerships & Innovation of the National Association of Regulatory Utility Commissioners.

power-grid-model is a library for steady-state distribution power system analysis.It is distributed for Python and C.The core of the library is written in C++.Currently, it supports both symmetric and asymmetric calculations for the following calculation types:

This electric grid test case repository contains open-source test cases, models, and datasets to help power system researchers and engineers evaluate their ideas for future energy systems with high penetrations of renewables .

The Multi-Area Frequency Response Integration Tool (MAFRIT) is the only software tool of its kind that integrates primary frequency response (turbine governor control) with secondary frequency response (automatic generation control). It simulates the power system dynamic response in full time spectrum with variable time steps from millisecond to minutes to hours and days. Capable of simulating both normal and event conditions, this tool can represent real power system operations and thus evaluate the primary and secondary reserves adequacy. This unique interaction of a turbine governor model and a novel automatic generation control model places special emphasis on electric power systems with high penetrations of renewable generation.

The Integrated Grid Modeling System (IGMS) is a novel electric power system modeling platform for integrated transmission-distribution analysis that co-simulates off-the-shelf tools on high-performance computing platforms to offer unprecedented resolution from independent system operator markets down to appliances and other end uses. The system simultaneously models hundreds or thousands of distribution systems in co-simulation with detailed independent system operator markets and automatic generation control-level reserve deployment.

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We are pleased to invite you to the Power Grid Model Workshop on 18 January 2024 . The workshop will be virtual. The meet-up will be hosted by Alliander, a Dutch distribution system operator (DSO) which operates around one-third of Dutch distribution grid.

In this three-hour workshop, you will learn about Power Grid Model and its advantages over other software, in addition to getting hands-on experience by performing power flow calculations for a single timestep, N-1 and time-series.

This project is part of Linux Foundation Energy. Please subscribe our mailing list to be kept updated on new project developments. You can easily subscribe by sending an empty email to: powergridmod...@lists.lfenergy.org

Some formats may be similar to the format used by power-grid-model, but use a different underlying data structure.While we do not formally support those formats, you may find examples in this documentation for some of them.

As Black & Veatch points out, the term DSO'' is often used to describe a set of actors, roles and responsibilities, rather than a single organization. While some regions of the U.S. will see DSO activities managed by the local distribution utility, other regions may see independent entities or community choice aggregators take on the role of the DSO.

Simply put, the DSO is responsible for managing local grid conditions while enabling complex interactions to occur among grid-connected energy resources. These include interactions between distribution-connected devices and the bulk (transmission-level) power system.

Every potential DSO model is inherently multiparty. The ability to call upon local resources to support the grid requires the collaboration of DER owners, aggregators, distribution system operators, and transmission system operators.

Sharing data across these parties is a crucial first step. That means bringing together customer device data with information about the state of the distribution grid and connectivity to the transmission system operator, wholesale energy market, and third-party DER aggregators. Ideally, that information is available from a single, shared data exchange for privileged access by relevant parties.

For utilities accustomed to multi-year technology implementations, such a high degree of integration may seem daunting. But utilities like Vermont Electric Cooperative have made rapid progress in pulling these data sources together and using the improved situational awareness to deliver cost savings and reliability benefits.

With the data foundation in place, aspiring DSOs can then use the data to peer into the future via forecasting and to provide grid-aware orchestration of local resources. Eventually that leads to the creation of programs and markets to fairly compensate resources for supporting the grid with distribution and transmission-level services.

Production cost modeling is used to conduct detailed simulations of grid operations and costs. Production cost modeling and capacity expansion modeling are similar in that they both use optimization to find the least-cost dispatch of grid resources. However, whereas capacity expansion modeling selects new resources to add to the grid over a range of future years, production cost modeling uses one static set of resources on the grid and usually examines a snapshot in time (e.g., a single year). This narrower focus allows grid modelers to do much more detailed analysis of grid operations before the optimization becomes too complex.

Many of the simplifications often made in capacity expansion modeling are not used in production cost modeling. This type of modeling almost always examines all 8,760 hours in a year (if not sub-hourly grid operations), it usually includes much more detailed transmission topology, and it more accurately simulates the intricacies of power plant dispatch. (If you want to get even wonkier, ask me about the joys of accurately modeling power plant heat rate curves.)

One real-world example is that the California Independent System Operator (CAISO) uses this type of modeling to conduct power flow analysis and develop local resource adequacy requirements, a critical element of maintaining grid reliability in California. The idea behind this type of analysis is that you can examine contingencies, such as a power plant or transmission line tripping offline, and determine if the grid will fail as a result.

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