Abstract
The upward trends in renewable energy penetration, cross-border flow volatility and electricity actors’ proliferation pose new challenges in the power system management. Electricity and market operators need to increase collaboration, also in terms of more frequent and detailed system analyses, so as to ensure adequate levels of quality and security of supply. This work proposes a novel distributed load flow solver enabling for better cross border flow analysis and fulfilling possible data ownership and confidentiality arrangements in place among the actors. The model exploits an Inexact Newton Method, the Newton–Krylov–Schwarz method, available in the portable, extensible toolkit for scientific computation (PETSc) libraries. A case-study illustrates a real application of the model for the TSO–TSO (transmission system operator) cross-border operation, analyzing the specific policy context and proposing a test case for a coordinated power flow simulation. The results show the feasibility of performing the distributed calculation remotely, keeping the overall simulation times only a few times slower than locally.
Highlights
Power grids are among the most complex man-made systems
It’s worth keeping in mind that the minimization of linear iterations is of fundamental importance to minimize the amount of communication messages among workstations and keep the overall simulation time near standard-local power flow simulations
The paper showed the possibility to carry out remote power flow computations by taking advantage of the Newton–Krylov algorithm, preconditioned by a Domain decomposition methods (DDMs) (Schwarz)
Summary
Power grids are among the most complex man-made systems. The increasing penetration of fluctuating and only partially-predictable Renewable Energies Sources as well as new connections of distributed energy resources (DER) have even more increased the complexity of grid management for grid operators, while consisting in an opportunity to schedule resources more flexibly. Solve exactly a linear system and ensure fast computational time for the majority of cases that can be faced in practice. Such methods are not well suited to parallelization. It’s worth keeping in mind that the minimization of linear iterations is of fundamental importance to minimize the amount of communication messages among workstations and keep the overall simulation time near standard-local power flow simulations. This is the second main role of Schwarz preconditioner.
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