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Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS)

21 Jan 21:44
@atribera atribera
v1.0.0
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v1.0.0

The Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS) software tool is a Python-based open-source tool for assessing the resource adequacy of the evolving electric power grid integrated with energy storage systems (ESS).

Key Features of ProGRESS 1.0.0


Key features of ProGRESS include:

  • Emphasis on Energy Storage Systems: ProGRESS is developed for analyzing the resource adequacy of power systems with a special focus on ESS. This tool offers unique features such as integrating failure and repair models of ESS in resource adequacy evaluation while preserving its charge/discharge dynamics and SOC update characteristics. Future updates to the tool will include sizing of ESS for grid reliability applications, economic analysis of using ESS for these purposes, more detailed ESS reliability models, and other ESS-centric features.

  • Stochastic Monte Carlo Simulation Engine: At the core of ProGRESS is a Markov Chain Monte Carlo-based engine that allows users to simulate practically unlimited scenarios involving diverse component failures and weather conditions. Each scenario is considered to be a sample of the Monte Carlo simulation and spans 8760 hours (one year). The users can choose as many samples as they want, the choice typically depending on factors such as system size, convergence criteria, and computational resources of the user.

  • Historical VER Data: ProGRESS allows users to conveniently download weather data using APIs. Data related to solar weather is downloaded by ProGRESS from NSRDB while wind-related weather data is downloaded from Wind Integration National Dataset Toolkits. ProGRESS then seamlessly converts the weather data to solar and wind power generation data using in-built functions. Users may utilize their own timeseries VRE generation datasets as well.

  • VER Uncertainty Handling: Proper handling of the uncertainty associated with VERs is crucial to accurate resource adequacy assessment and ESS sizing for maintaining grid reliability. ProGRESS uses innovative techniques to quantify uncertainty associated with VERs and ensures that these resources are represented appropriately within the simulation. A k-means clustering technique is used to cluster solar power generation while a transition rate matrix method is used for wind power generation.

  • Model Flexibility: Users can currently represent their power systems using a transportation or a copper-sheet model. The copper-sheet model runs significantly faster, especially for larger systems, while the transportation model generates more accurate results.

  • Modular Structure: The tool is constructed using an Object-Oriented Programming (OOP) structure and a modular design. This approach enables users to easily modify the backend programs to meet their specific requirements.

  • User-friendly Graphical User Interface: The interactive Graphical User Interface (GUI) offered by ProGRESS simplifies the process of input data upload, model building, and results interpretation.

  • Parallel Programming Capabilities: The backend includes code for parallel programming (using Python's mpi4py library), allowing users with access to high-performance computing resources to run longer simulations with larger systems for more accurate results. Currently, this functionality is not available through the GUI.

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