Hierarchical Optimization Control of Charging Load for New Energy Electric Vehicles in Random Environments
With the popularity of new energy electric vehicles,challenges arise in dealing with uncertain charging demands,fluctuating charging resources,and variability in renewable energy supply.To address these challenges,this study proposes an improved multi-objective particle swarm optimization algorithm combined with fuzzy control-based charging load scheduling method.The proposed method considers multiple objectives including equivalent load peak-valley difference,distribution network total losses,and user charging costs,and seeks a balance between the interests of the grid and users through optimization strategies.Additionally,the method employs fuzzy control to handle complex charging demands and power variations by transforming them into fuzzy sets and adjusting charging power based on fuzzy rules.Experimental results demonstrate the effectiveness of the proposed approach in improving charging efficiency and reducing system load fluctuations.Therefore,this research holds significant theoretical and practical implications for promoting the widespread adoption of new energy electric vehicles and enhancing the reliability of charging systems.Future research can further optimize algorithms and models while integrating intelligent grid technologies to drive the sustainable development of new energy electric vehicles in stochastic environments.
new energy electric vehiclescharging loadstochastic environmentoptimization control
NETL
NSTL
万方数据
