Modeling, Simulation, and Analysis
From Grid Optics
The electric infrastructure has served the nation remarkably well, but is likely to see more changes over the next decade than it has seen over the past century. In particular, the widespread deployment of renewable generation, smart-grid controls, energy storage, and new conducting materials will require fundamental changes in algorithms and tools for modeling, simulation, and analysis.
Building on the foundation of our network and data management research, we developed new classes of stochastic forecasting and control processes for the grid made necessary by national policy objectives of increased clean power generation through renewable resources. This research area focused on advancing modeling, simulation, and analysis to drive changes in the following areas:
- Next-Generation Power Grid Models: Expand power grid models to include smart consumer devices (smart appliances, hybrid vehicles) and intermittent energy sources, and develop new modeling approaches for integrated, multi-scale transmission and distribution analysis.
- Real-Time Power Grid Simulation: Develop new algorithms and computational platforms for real-time power grid analysis. This also includes the design and development of integrated hardware/software architectures to meet real-time computational needs.
- Long-Term Power Grid Performance Assessment: Design computational tools for the long-term assessment of the power grid, supporting both utility asset utilization and policy decisions.
With the penetration of smart loads and distributed generation (especially small wind turbines and roof-top photovoltaic panels) the need for detailed modeling of the lower voltage networks in the grid becomes apparent. Additionally, it is not feasible to model air conditioning units in 200 million+ homes and businesses in the U.S. based on the electro-thermal conversion equations. In this Initiative, we explored statistical approaches and behavior modeling to characterize the stochastic nature of smart grid devices and increased level of human intervention.
In contrast with the traditional leadership class advanced computing (HPC) problems, the power grid operation has strong real-time requirements. To achieve these, we envisioned a solution that advances the science on two fronts: real-time HPC and parallel algorithms and solvers. We believe that multiple HPC systems will have to be deployed because the geographically distributed data, tight real-time requirements, and the large data volume prohibit a single centralized computer system.
The eventual long-term assessment of power grid performance involves larger-scale models and higher uncertainties and thus requires a multi-scale model and an extreme scale simulator capable of simulating the entire national grid. Using the modeling efforts presented earlier, this multi-scale model will integrate both the transmission grid models with millions of variables and the distribution grid models with millions of customers.
We characterized the impact on the power grid of market incentives and novel grid features, such as increased reliance on bidirectional power flow, smart meters, renewable energy, smart loads, and the resulting adaptive consumer behavior.
List of Projects:
- Market Design Analysis Tool
- Linear Algebra Solvers and Assocaited Matrix-Vector Kernels for Power Grid Simulations
- Future Power Grid Control Paradigm
- GridPACK: Grid Parallel Advanced Computational Kernels
- Real-time High-Performance Computing Infrastructure for Next-Generation Power Grid Analysis
- Modeling of Distributed Energy Resources in the Smart Grid
- A Statistical State Prediction Methodology