基于APSO算法的公路光伏声屏障储充系统优化

Capacity Optimization of PV Battery Charging System Using APSO Algorithm

为实现公路光伏声屏障发电系统的能源自洽,基于自适应粒子群算法提出了一套从光伏出力、蓄电池储能到充电桩用电的“光储充”容量配置方案。根据项目所在地的温度、辐射强度及光伏组件性能衰减规律建立光伏出力模型,结合公路光伏声屏障吸声降噪效果,计算光伏组件最佳倾角并获取该倾角下的光伏资源禀赋。基于光伏出力与负荷需求,获取蓄电池逐时荷电状态,利用雨流计数法和等效循环寿命法计算蓄电池的预估使用寿命,建立了涵盖初始投资、运行维护和设备更换3部分费用的成本模型,相较于传统固定寿命的成本模型更加具有实际意义。然后,以最小化总成本为优化目标,考虑系统的最小功率、占地面积、蓄电池荷电状态、系统负荷缺电率和系统弃能率5个约束条件,建立了公路“光储充”系统容量配置协调优化模型。采用自适应的粒子群算法,通过改进惯性权重提高了粒子的全局和局部搜索能力,得到了给定光伏板数量下蓄电池及充电桩的最优容量配置方案。结果表明:提出的公路“光储充”模型的能源自洽率达95%以上,对加快公路交通能源融合具有重要意义。针对蓄电池荷电状态,设定荷电状态全年处于0.2~0.8之间,防止了蓄电池的过充过放,显著提高了蓄电池的使用寿命;针对粒子寻优能力,采用自适应粒子群算法使得粒子寻优速度提升将近90%;最终优化结果同时满足5%的负荷缺电率及10%的系统弃能率,保证了系统可靠性。

To achieve energy self-consistency in the power generation systems of highway photovoltaic(PV)sound barriers,PV battery and charging(PBC)capacity configuration schemes based on the adaptive particle swarm optimization(APSO)algorithm,which include PV power output,battery energy storage,and charging station power consumption,are proposed.A PV power output model is established based on the temperature,radiation intensity at the project site,and performance attenuation law of the PV component.The optimal inclination angle of the PV component is then calculated considering the sound absorption and noise reduction effects of the highway PV sound barrier,and the PV resource endowment at this angle is obtained.Based on the PV output and load demand,the hourly charging state of the battery is obtained,and the estimated service life of the battery is calculated using the rain flow counting and equivalent cycle life methods.A cost model covering the initial investment,operation,maintenance,and equipment replacement is established,which is more practical than the traditional fixed-life cost model.An optimization model for the capacity configuration of a PBC system is developed using cost as the objective and considering five constraints:minimum power consumption,land area,battery state of charge(S),lack of power supply ratio(Q),and loss of energy ratio(L).By improving the inertia weight,the APSO algorithm improves the global and local search capabilities of the particles,resulting in the optimal capacity configuration scheme for the batteries and charging piles under several PV panels.The results indicate that the energy self-consistency rate of the proposed PBC system is greater than 95%,which is significant for accelerating the integration of energy into highway transportation.The S remains between 0.2 and 0.8 throughout the year,preventing overcharging and overdischarging of the battery and significantly improving the battery lifespan.Regarding particle optimization ability,the APSO algorithm increased the particle optimization speed by nearly 90%.The optimization results simultaneously achieve 5%Q and 10%L,effectively ensuring the reliability of the PBC system.