Abstract
In this paper we introduce an approach to accelerate many-scenario (i.e., hundreds to thousands) power system simulations which is based on a highly scalable and flexible open-source software environment. In this approach, the parallel execution of simulations follows the single program, multiple data (SPMD) paradigm, where the dynamic simulation program is executed in parallel and takes different inputs to generate different scenarios. The power system is modeled using an existing Modelica library and compiled to a simulation executable using the OpenModelica Compiler. Furthermore, the parallel simulation is performed with the aid of a message-passing interface (MPI) and the approach includes dynamic workload balancing. Finally, benchmarks with the simulation environment are performed on high-performance computing (HPC) clusters with four test cases. The results show high scalability and a considerable parallel speedup of the proposed approach in the simulation of all scenarios.
Highlights
This paper introduces our approach to speed up power system studies that need to simulate a large set of scenarios
The approach relies on describing power system models with the object-oriented, equation-based Modelica programming language which is translated to C code and eventually compiled into an executable as well as an message-passing interface (MPI)- based implementation to parallelize multiple scenario simulations and balance workload among parallel processes
With the aid of MPI to parallelize simulations, the approach can be applied on both inexpensive commodity and highly performance custom clusters. This is realized through our lightweight htcsim module for Python, further utilizing open-source software such as OpenModelica, OMPython, and mpi4py
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The classical power system architecture centered around large generation units with very controllable power production is progressively evolving towards a model with more distributed generation based on renewable sources that are inherently less controllable. Renewable sources such as wind and solar are interfaced to the grid via power converters. The decommissioning of thermal power plants with synchronous machines and their replacement with converter interfaced generation leads to a gradual reduction of system inertia Both the lower level of rotating inertia in the power systems and the higher variability of the power generation are expected to render future power systems more difficult to operate because the margins to counteract contingency events will be tighter. These trends challenge the role of transmission system operators that would need to introduce innovative technologies in their control rooms to ensure sufficient levels of power system security
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