基于改进的PSO风光互补公路隧道配置优化
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1.中交第二公路勘察设计研究院有限公司;2.重庆交通大学

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U458.1

基金项目:

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项目(2022B0101070001)。


Improved PSO-based configuration optimization for wind-solar complementary highway tunnels
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1.China Communications Second Highway Survey,Design and Research Institute Co,Ltd;2.Chongqing Jiaotong University

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    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498m长的公路隧道负荷为算例,利用基于改进的粒子群算法(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:1.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;2.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%。3.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年的时间内收回,在设备20年的使用寿命内,风光储互补发电系统将节省697.12万元的用电费用。

    Abstract:

    The rapid increase of highway tunnel mileage also marks the gradual rise of operation cost, and the problem of high electricity operation cost of highway tunnel needs to be solved urgently. In order to reduce the electricity operation cost of highway tunnel and achieve energy saving and emission reduction. Under the “dual carbon” background, from the perspective of optimizing energy structure, explore the application prospect of renewable energy supply system in highway tunnel, and establish a wind, light, storage complementary power generation system. Taking a 498m long highway tunnel load as an example, using the improved particle swarm optimization (PSO) algorithm, with the lowest equipment construction cost and maintenance cost of the whole life cycle as the goal, with the power shortage load rate and energy storage capacity as the constraints, the wind-light-storage complementary system is optimized. The results show that: 1. The improved discrete adaptive particle swarm algorithm obtained the optimal solution after the 20th iteration, and the standard particle swarm algorithm obtained the optimal solution after nearly the 300th iteration. The discrete adaptive particle swarm algorithm has stronger optimization ability; 2. Compared with the standard particle swarm algorithm, the improved discrete adaptive particle swarm algorithm reduced the equipment investment and use cost of wind, light and storage by 578,300 yuan, about 17.37%. 3. Compared with the annual electricity cost of 515,000 yuan for the example tunnel, _________________

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李金,林志,于冲冲,等. 基于改进的PSO风光互补公路隧道配置优化[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-04-29
  • 最后修改日期:2024-06-05
  • 录用日期:2024-06-08
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