基于改进的PSO风光互补公路隧道配置优化  

Improved PSO-Based Configuration Optimization for Wind-Solar Complementary Highway Tunnels

作　　者：李金[1,2] 林志 于冲冲[3,4] 尹恒 黄可心[3] 刘超铭 LI Jin;LIN Zhi;YU Chong-chong;YIN Heng;HUANG Ke-xin;LIU Chao-ming(China Communications Second Highway Survey,Design and Research Institute Co.,Ltd.,Wuhan 430050,China;Tunnel and Underground Space Engineering Technology Research and Development Center of CCCC,Wuhan 430056,China;School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China;China Construction Eighth Engineering Divsion Co.,Ltd.,Tianjin 300450,China)

机构地区：[1]中交第二公路勘察设计研究院有限公司,武汉430050 [2]中交集团隧道与地下空间工程技术研发中心,武汉430056 [3]重庆交通大学土木工程学院,重庆400074 [4]中国建筑第八工程局有限公司,天津300450

出　　处：《科学技术与工程》2025年第6期2578-2584,共7页Science Technology and Engineering

基　　金：国家自然科学基金(52078089,52274176);重庆英才创新创业领军人才项目(CQYC20220302517);重庆市自然科学基金创新发展联合基金(CSTB2022NSCQ-LZX0079);重庆市自然科学基金(cstc2021jcyj-msxmX1075);广东省重点领域研发计划(2022B0101070001)。

摘　　要：公路隧道里程的快速增加也标志着运营成本的逐渐上升,高昂的公路隧道电力运营成本问题急需解决。为了降低公路隧道电力运营成本,实现节能减排。在“双碳”背景下,从优化能源结构的角度思考,探究可再生能源供电系统在公路隧道上的应用前景,建立一个风、光、储互补发电系统。以一条498 m长的公路隧道负荷为算例,利用基于改进的粒子群算法(particle swarm optimization,PSO),以全生命周期的设备建设成本和维护成本最低为目标,以缺电负荷率、储能容量为约束,针对风光储互补系统进行寻优。结果表明:经改进的离散型自适应粒子群算法在第20次迭代后得到了最优解,标准粒子群算法在近第300次迭代得到最优解,离散型自适应粒子群算法寻优能力更强;改进后的离散型自适应粒子群算法对比标准的粒子群算法,寻优结果的风、光、储的设备投资使用成本降低了57.83万元,约17.37%。对比算例隧道一年的用电成本51.50万元,风、光、储互补系统的全生命周期成本为332.88万元,投资成本将在7 a的时间内收回,该风光互补系统的投资回报率是10.47%。在设备20 a的使用寿命内,风光储互补发电系统将节省697.12万元的用电费用。The rapid increase in highway tunnel mileage also signifies a gradual rise in operational costs,and the pressing issue of high electricity operation costs for highway tunnels urgently needs to be addressed.To reduce the electricity operation costs of highway tunnels and achieve energy conservation and emission reduction,it is essential to consider optimizing the energy structure under the“dual carbon”background.This involves exploring the application prospects of renewable energy supply systems in highway tunnels and establishing a wind-solar-storage complementary power generation system.Taking a 498 m long highway tunnel load as an example,an optimization based on an improved PSO(particle swarm optimization)algorithm was conducted.The goal was to minimize the full lifecycle costs of equipment construction and maintenance,with constraints on the power shortage load rate and storage capacity,specifically for the wind-solar-storage complementary system.The results show as follows.The improved discrete adaptive particle swarm algorithm obtained the optimal solution after the 20th iteration,while the standard particle swarm algorithm reached the optimal solution near the 300th iteration,indicating a stronger optimization capability of the discrete adaptive particle swarm algorithm.Compared to the standard particle swarm algorithm,the improved discrete adaptive particle swarm algorithm reduced the investment and usage costs of the wind,solar,and storage equipment by 578300 yuan,approximately 17.37%.Compared to the annual electricity cost of the example tunnel,which is 515000 yuan,the full lifecycle cost of the wind-solar-storage complementary system is 3328800 yuan.The investment cost will be recouped within 7 years,and the investment return rate of this wind-solar complementary system is 10.47%.Over the 20-year lifespan of the equipment,the wind-solar-storage complementary power generation system will save 6971200 yuan in electricity expenses.

关 键 词：公路隧道运营成本 粒子群算法 风光储互补系统 可再生能源 

分 类 号：U458.1[建筑科学—桥梁与隧道工程]