Multi-Scenario Optimization of Electric Vehicle Charging Station Allocation with Renewable Energy Integration in Radial Distribution Networks Using Hybrid Cuckoo Search-Grey Wolf Algorithm
Keywords:
Electric vehicle charging stations, renewable energy integration, wind turbines, photovoltaic systems, istribution network optimization, Cuckoo Search algorithm, Grey Wolf Optimizer, hybrid metaheuristic algorithms, power loss minimization, voltage stability enhancement, IEEE standard test systemsAbstract
The exponential growth in electric vehicle adoption necessitates strategic planning of charging infrastructure integrated with renewable energy sources to ensure sustainable power distribution networks. This research presents a comprehensive multi-scenario optimization framework using a novel hybrid Cuckoo Search-Grey Wolf Optimizer algorithm for optimal allocation of electric vehicle charging stations with photovoltaic systems and wind turbines in radial distribution networks. The proposed methodology synergistically integrates Cuckoo Search's Lévy flight-based global exploration mechanisms with Grey Wolf Optimizer's hierarchical local exploitation strategies. Six distinct operational scenarios are systematically evaluated on IEEE 33bus and 69-bus test systems: base case configuration, EVCSonly deployment, wind turbine integration, photovoltaic system integration, combined EVCS with PV, and comprehensive EVCS with PV and WT integration. Extensive comparative analysis against Particle Swarm Optimization, standalone Cuckoo Search, and Grey Wolf Optimizer demonstrates superior performance of the proposed hybrid algorithm. The IEEE 33-bus system achieves power loss reductions of 90.34 percent with voltage profile improvements from 0.9037 to 0.9812 per unit and voltage stability index enhancement from 0.6610 to 0.9289 per unit. The IEEE 69-bus system demonstrates 91.26 percent power loss reduction with voltage improvements from 0.9092 to 0.9845 per unit and voltage stability enhancement from 0.6817 to 0.9567 per unit.
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