Research on Performance Optimization of Cloud Rendering Technology in Simulation of Pumped Storage Power Station Construction

This study presents a cloud-native simulation framework integrating physics-informed particle swarm optimization (PSO) with GPU-accelerated cloud rendering for pumped storage power station (PSPS) operations. The framework addresses three critical challenges in dynamic hydraulic optimization under renewable energy volatility: 1) A hybrid master-worker cloud architecture achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.8 \times$</tex> computational acceleration through adaptive GPU resource allocation, reducing 24-hour simulation time by 58% compared to conventional high-performance computing clusters; 2) A flow-priority PSO algorithm enhances power output by 23% through asymmetric reservoir optimization and hydraulic inertia modeling; 3) Predictive swarm damping mechanisms maintain grid response stability within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 3 \%$</tex> error margins under <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 12 \%$</tex> stochastic load fluctuations. Experimental results demonstrate logarithmic energy storage growth (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8 \times 10^{\wedge} 6 ~\text{Wh}$</tex> at 24 -hour operation) with sub-hourly climate adaptation capabilities, establishing PSPS as dynamic grid stabilizers that reconcile thermodynamic constraints with renewable intermittency.