GPU-Based Parallel Acceleration Method for Electromagnetic Transient Simulation of Large-Scale Renewable Energy Power Grids
The integration of high-penetration renewable energy sources imposes severe computational bottlenecks on electromagnetic transient (EMT) simulation of large-scale power grids containing thousands of power electronic devices. Graphics processing units (GPUs) offer high-throughput parallelism, but direct application is hindered by three obstacles: thread warp divergence caused by heterogeneous control topologies, non-coalesced memory access from graph-based computation, and instruction redundancy from dynamic parsing. This paper proposes a heterogeneous parallel acceleration method for large-scale renewable energy grids. By introducing graph isomorphism, a two-stage graph clustering algorithm identifies and aggregates control systems at the topological level, eliminating instruction flow divergence from heterogeneous controllers. A GPU-oriented automatic code generation framework transforms aggregated models into branchless, memory-coalesced computational kernels. Experimental results on a scenario with thousands of renewable devices demonstrate a nearly 3x speedup over conventional simulation methods, with excellent scalability. Specifically, compressing effective execution sequence variants from 20 to 1 reduces DRAM-related access cycles from 18,595 to 1,502 and raises L2 cache hit rate from 16.09% to 84.54%. Static specialization eliminates dynamic graph parsing logic, reducing instruction count per control step by 91.8% and achieving a 2.7x kernel-level speedup. The method provides a high-fidelity, highly versatile, and high-performance EMT simulation solution for large-scale renewable energy systems.