Abstract
The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.
Highlights
The growth in energy dependence has led to increased complexity in the planning and operation of power systems [1]
Algorithms 2021, 14, 275 works of PHC techniques in power systems since 80% of the articles published during the last five years correspond to GPU implementations
We presented a review of PHC techniques in power systems applications, such as power flow analysis, transient stability, contingency analysis, and smart grids, among others
Summary
The growth in energy dependence has led to increased complexity in the planning and operation of power systems [1]. CPU clusters connected through Message Passing Interface (MPI) were the first alternative Technologies, such as fog and cloud computing were used to reduce simulation time and as a solution to handle all the measured data of smart grids. The review classifies the works into two groups: works where GPU is used and works that use CPU clusters, fog or cloud computing, or FPGA in power system studies and applications. This classification helps to organize the review since GPU applications represent more than half of all the articles.
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