Optimization of Distributed Photovoltaic and Wind Turbine Capacity Optimization Allocation Based on Improved Spider Monkey Optimization Algorithm

This research advances a refined computational intelligence framework for coordinating sustainable energy assimilation in modern power grids. Leveraging the IEEE 33-bus radial network benchmark, the methodology demonstrates enhanced convergence characteristics when resolving multivariate renewable coordination challenges, particularly in photovoltaic-wind hybrid configurations. Experimental validation confirms that optimized deployment of three wind conversion units (2.1-2.4 MW) or dual photovoltaic clusters (1.2-1.5 MW) achieves concurrent technical enhancements: 17.9-19.2% voltage stabilization, 22.4% mean energy loss reduction, and 14.3% network operational efficiency improvement. The algorithm exhibits superior parametric stability (variation coefficient < 0.12) against conventional swarm intelligence methods, effectively harmonizing intermittent generation profiles with nodal demand characteristics in decentralized architectures.