DATA DRIVEN MODELING AND ANALYTICS FOR ENHANCED SYSTEM LAYER IMPLEMENTATION
This project is a part of ENERGISE program and is sponsored by the Office of Energy Efficiency and Renewable Energy SunShot Initiative of the Department of Energy (DoE). The Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) funding program develops distribution planning and operation solutions to enable dynamic, automated, and cost-effective management of distributed and variable generation sources, like solar, onto the grid. These software and hardware solutions are highly scalable, data-driven, and capable of real-time system operation and planning. Solutions developed under ENERGISE enable grid operators to gather up-to-the-minute measurements and forecast data from distributed energy sources and optimize system performance using sensors, communications, and data analytics technologies.
The challenges in achieving the goals which need to be addressed in this project are:
- Partial observability of the distribution system.
- Stochasticity of solar generation.
- Real-time operational planning on large-sized grids to ensure smooth grid operations.
The objective of DEEP SOLAR is to enable deep solar penetration through:
- Data-driven modeling for live grid status.
- Fast, robust predictive analytics for accurate load and generation prediction.
- A real-time scalable optimization framework for smooth grid operation.
- Dynamic “What-If” scenario analysis for operational planning.
The expected outcomes of DEEP SOLAR are:
- Scalable dynamic scenario analysis tools for operational planning under solar penetration.
- Demonstration of dynamic scenario analysis toolkit using 100 nodes HiL Sacramento Municipal Utility District (SMUD) testbed and large-scale simulations (1M+ nodes).