高级检索

基于多目标粒子群算法的光伏制氢系统容量优化方法

Capacity Optimization Method for Photovoltaic Hydrogen Production Systems Based on Multi-Objective Particle Swarm Algorithm

  • 摘要:
    目的 制氢系统利用太阳能将水转化为氢气,旨在减少碳排放并提升可再生能源的利用效率。然而,光伏发电出力的随机性和波动性严重影响光伏制氢系统的稳定供氢。
    方法 文章提出了一种基于多目标粒子群算法的光伏制氢系统容量优化方法。在光伏制氢系统中引入了化学能电池组和储氢罐,构建了光伏制氢-储能-供氢模型,并设计了氢储能优先的系统运行策略。
    结果 以系统的经济性成本、弃光率和购电率为优化目标,使用多目标粒子群算法求解了系统各组件的容量配置。在保证持续稳定供氢的情况下,优化结果显示系统的经济性成本、弃光率和购电率均得到了有效降低。
    结论 算例分析结果表明,所提出的容量优化方法能够有效降低光伏制氢系统的经济性成本,减少弃光和购电,显著提升系统的运行稳定性。

     

    Abstract:
    Objective The hydrogen production system utilizes solar energy to convert water into hydrogen, aiming to reduce carbon emissions and improve the efficiency of renewable energy utilization. However, the randomness and volatility of photovoltaic power output severely impact the stable hydrogen supply of the system.
    Method This paper proposed a capacity optimization method for photovoltaic hydrogen production systems based on a multi-objective particle swarm algorithm. In the photovoltaic hydrogen production system, chemical energy battery packs and hydrogen storage tanks were integrated to construct a photovoltaic hydrogen production-storage-supply model. A system operation strategy prioritizing hydrogen storage was designed.
    Result Taking the economic cost of the system, curtailment rate of solar power, and electricity purchase rate as optimization objectives, the multi-objective particle swarm algorithm was employed to solve the capacity configuration of the system components. The optimization results demonstrated that, while ensuring a continuous and stable hydrogen supply, the economic cost, curtailment rate, and electricity purchase rates of the system were effectively reduced.
    Conclusion The results of case analysis indicate that the proposed capacity optimization method can effectively reduce the economic costs of the system, decrease curtailment and electricity purchases, and significantly enhance the operational stability.

     

/

返回文章
返回