LF Energy is a new umbrella organization designed to establish open, interoperable frameworks for accelerating the energy transition independent of hardware, silicon, cloud, or operating system.

The transition from centralized to distributed energy resources is heavily fragmented, with multiple proprietary stacks. LF Energy’s goal is to foster a unified, approach to the non-differentiating code that will enable the world’s power systems to transform rapidly. LF Energy provides open frameworks and reference architectures that bring complementary projects under one central umbrella to create collaborative solutions that are compatible and support the entire power systems ecosystem from generation and aggregation, to transmission, distribution, and demand reduction and flexibility.

LF Energy is comprised of six initial projects. You can discover more about them below. However, it is important visitors understand – because of the breadth of the energy sector and the scope of use cases – these projects are the very beginning. LF Energy intends to include projects from across the entire electricity and power systems lifecycle to enable and facilitate the acceleration of the energy transition. We welcome code, developer involvement, and partnership collaborations to further define and focus projects into relevant and strategically critical opportunities. Please reach out to LF Energy directly.

We invite all developers who are interested in contributing to LF Energy to sign up to stay informed on our mailing lists.

LF Energy Projects


 

OperatorFabric Logo

A Smart Assistant For System Operators

OperatorFabric is a modular, extensible, industrial-strength and field-tested platform for use in electricity, water, and other utility operations.

  • System visualization and console integration
  • Precise alerting
  • Workflow scheduling
  • Remedial action manager
  • Historian
  • Scripting (ex: Python, JavaScript)
  • Reference implementation: Let’s Coordinate

Operator Fabric Webinar
Operator Fabric Demo Video
Operator Fabric on GitHub
OperatorFabric Mailing List


 

Logo POWSYBL

A High-Performance Computing Framework For Grid Simulation and Planning

PowSyBl provides the code building blocks for the simulations and analyses of power systems, for horizons from real-time operation to investment planning.

  • Grid data model, described with Java classes and possibility to define extensions with plugins
  • Data management system
  • Importers and exporters for several formats (CIM, CGMES, UCTE…)
  • APIs to various computation modules (load-flow, security analysis, short-circuit computation, sensitivity computation, optimizers, AMPL)
  • Distribution framework for HPC (tested on a 10,000 cores platform)
  • JavaFX user interface framework
  • Scripting
  • Advanced functions: grid model merging, remedial actions manager
  • Modular architecture based on plugins

PowSyBl Webinar
PowSyBl on GitHub
PowsyBl Mailing List


 

RIAPS Logo

An Effective Distributed Software Platform for Smart Grid Apps

RIAPS: The Resilient Information Architecture Platform for Smart Grid (RIAPS) provides core infrastructure and services for building effective, secure and powerful distributed Smart Grid applications. Examples include monitoring and control, data collection and analytics, energy management, microgrid control, and protection applications. The RIAPS technology stack features:

  • Component-oriented programming model for distributed real-time software running on embedded nodes dispersed throughout the power grid
  • Support for low-latency, hard real-time applications via a low-overhead messaging layer
  • Services for application management, fault tolerance, security, high-precision time synchronization, distributed coordination
  • Uniform device access with support for various industrial protocols
  • Development toolkit for developing and deploying apps
  • Implementation languages: Python, C++, Simulink/Stateflow, and others

RIAPS Webinar
RIAPS Demo Video
RIAPS on GitHub
RIAPS Docs
RIAPS Mailing List
More Info from Vanderbilt


 

Consistent treatment of energy meter data for demand flexibility

Using the OpenEEmeter, private companies, utilities, and regulators can consistently calculate changes in energy consumption for building efficiency projects and portfolios with confidence in the methods and replicability of results.

The OpenEEmeter generates consistent and replicable results by always using the same methods to determine changes in energy consumption-there are no discretionary independent variables that change from calculation to calculation. Site level changes in consumption will reflect the same underlying methods across programs and implementations.

OpenEEmeter features:

    • Contains reference implementations of standard CalTRACK methods
    • Enforces standards compliance by incorporating data sufficiency checking and first-class warnings reporting
    • Facilitates integration with external systems and testing of methodological variations with modular design
    • Uses public weather sources by default, but allows flexibility
    • Is built on top of the popular python scientific stack (scipy/pandas)
    • Includes visualization and debugging tools

This project was contributed by Recurve, formerly Open Energy Efficiency.

OpenEEmeter webinar
OpenEEmeter on GitHub
More Info from OpenEE (or How it works)
Energy Market Methods Consortium
CalTRACK
Project History
OpenEEmeter Mailing List


 

Energy Market Methods Consortium (EM2)

Reducing the costs of scaling demand-side energy through collaboration

Energy Market Methods Consortium (EM2) is developing standardized methods, linked to open source code, to enable demand flexibility as a resource, supporting energy programs and distributed energy resource (DER) ​markets. It is made up of industry stakeholders committed to collaboration to reduce the costs of scaling demand-side energy programs and procurements.

The methods development process is split into three working groups, each representing a core challenge in the development of scalable energy markets on the demand side. The core tenets uniting the methods development processes are transparency, empirical testing, and consensus. 

The three working groups are focused on standard methods for:  

  • CalTRACK – calculating avoided energy use
  • GRID – adjustments to avoided energy use for grid integration
  • SEAT – enabling secure data sharing  

This project was contributed by a diverse group of stakeholders that includes utilities, regulators, evaluators, software companies, and load shape aggregators, through a multi-year process that was led by Recurve, formerly Open Energy Efficiency.

EM2 webinar
EM2 on Github
Energy Market Methods Consortium
CalTRACK
GRID
SEAT
EM2 Mailing List


Improving Data Access to Enable Advanced Analytics and Research Innovation

The Open Energy Data Initiative (OEDI) provides tools, methods, and data catalogs designed and curated to promote open data exchange within the energy sector. OEDI was developed by the National Renewable Energy Laboratory (NREL) as part of the Open Energy Information (OpenEI) project, a Department of Energy initiative to accelerate and transform the use of data related to the generation, transmission, and use of energy.

OEDI is in active development, creating a data catalog which curates multiple data sets in a cloud-based “data lake”, as well as tools and algorithms to access and analyze the data and to leverage data science tools available through cloud service providers. Datasets being developed include wind, solar, and meteorological data history as well as historical data from 17 US national labs.

Details:

  • OEDI provides curated datasets related to energy resource data, with other high-value resource datasets planned
  • OEDI is used by data scientists, utilities, energy companies, and other interested parties to analyze energy data, identify useful patterns, and share with others
  • Major features include a cloud-based data lake consisting of multiple curated datasets, tools and algorithms to access and analyze the data, and expertise for creating and curating new datasets
  • Primary components include the data lake and its datasets, cloud-based tools and code fragments with algorithms to access the data, and a robust community

OEDI Webinar
OEDI website at OpenEI
OEDI Mailing List