Massively Parallel Data Analytics for Smart Grid Applications
Complexity involved in operating modern power and energy systems is constantly increasing given the volatility induced by the rapid integration of intermittent renewable energy sources. In order to operate the power grid in secure and reliable way, a plethora of uncertain parameters need to be considered and hundreds of thousands of different power grid scenarios need to be rapidly evaluated. This works analyzes the computational aspects in massively parallel simulations from the perspective of efficient hardware utilization. A method for efficiently managing and processing the computational tasks is presented, carefully considering the level of parallelism in order to avoid computational bottlenecks and efficiently utilizing modern multicore architectures with deep memory hierarchies. An extensive set of numerical experiments is presented, considering multiple aspects of the computational pipeline. The numerical experiments are performed using mathematical models typically used in the power grid problems, including linear and quadratic programs as well as the models containing the discrete variables. The optimized high-throughput computation strategy has been shown to significantly reduce response times by preventing the memory bottlenecks for various computational models.
Sustainable Energy, Grids and Networks
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high-throughput scheduling, massively parallel computing, numerical optimization, power grid, optimal power flow, unit commitment