Price

€2000

Research Area

Information and Communication Technologies

Contact

Karl Quinn

Inventors

Federico Milano

Resource Files

Terms and Conditions

Topics

Power Systems Simulation Synthesis of Controllers and Stability Analysis Power Systems Modelling Sparse Matrix Factorization

DOME

Dome is a Python-based suite for power system modelling and simulation

Overview
Dome is a C and Python-based set of libraries for electric power system analysis and control. It is a Unix command line application that links to the state-of-the-art of mathematical libraries for sparse matrix factorization, synthesis of controllers and stability analysis. Parallel computing and GPU support is also provided. A reduced version of the tool can also run on Windows.
How It Works
Dome solves steady-state and dynamic analyses of high-voltage power systems. These include: power flow, optimal power flow (both linear and nonlinear programming models), continuation power flow (for voltage stability analysis), small-signal stability analysis (for angle stability analysis), time domain integration (for transient stability analysis), 2D and 3D visualization, short circuit analysis, and equivalencing techniques. It is specifically oriented to research on modern power systems, smart grids, integration of power systems and telecommunications as well as other energy systems. It allows modelling discontinuous models, stochastic processes and functional equations (e.g., time delays).
Benefits
Dome is not just another power system analysis tool. It provides a modular and flexible platform where virtually any physical device and control system can be implemented and tested over a wide range of analysis techniques. The ability to link to state-of-the-art mathematical libraries makes Dome an ideal tool to develop high-performance computing while maintaining the fast prototyping and flexibility of the Python scripting language. Dome is specifically designed to efficiently design new device models, control schemes as well as novel algorithms for large scale problems. Parallelization, multi-processing and heterogeneous CPU-GPU-based solutions are also available.
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