留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进Pareto算法的风/光/氢蓄储公路微电网调度决策优化

郝雪丽 赵美瑄 裴莉莉 李伟 刘状壮

郝雪丽, 赵美瑄, 裴莉莉, 李伟, 刘状壮. 基于改进Pareto算法的风/光/氢蓄储公路微电网调度决策优化[J]. 交通运输工程学报, 2024, 24(4): 71-82. doi: 10.19818/j.cnki.1671-1637.2024.04.006
引用本文: 郝雪丽, 赵美瑄, 裴莉莉, 李伟, 刘状壮. 基于改进Pareto算法的风/光/氢蓄储公路微电网调度决策优化[J]. 交通运输工程学报, 2024, 24(4): 71-82. doi: 10.19818/j.cnki.1671-1637.2024.04.006
HAO Xue-li, ZHAO Mei-xuan, PEI Li-li, LI Wei, LIU Zhuang-zhuang. Optimization on scheduling decision-making for wind/solar/hydrogen storage highway microgrid based on improved Pareto algorithm[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 71-82. doi: 10.19818/j.cnki.1671-1637.2024.04.006
Citation: HAO Xue-li, ZHAO Mei-xuan, PEI Li-li, LI Wei, LIU Zhuang-zhuang. Optimization on scheduling decision-making for wind/solar/hydrogen storage highway microgrid based on improved Pareto algorithm[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 71-82. doi: 10.19818/j.cnki.1671-1637.2024.04.006

基于改进Pareto算法的风/光/氢蓄储公路微电网调度决策优化

doi: 10.19818/j.cnki.1671-1637.2024.04.006
基金项目: 

国家重点研发计划 2021YFB1600201

详细信息
    作者简介:

    郝雪丽(1987-),女,山东莱芜人,长安大学高级工程师,工学博士,从事交通信息工程及控制、交通与能源融合研究

    通讯作者:

    裴莉莉(1995-),女,河北邯郸人,长安大学讲师,工学博士

  • 中图分类号: U495

Optimization on scheduling decision-making for wind/solar/hydrogen storage highway microgrid based on improved Pareto algorithm

Funds: 

National Key Research and Development Program of China 2021YFB1600201

More Information
  • 摘要: 针对公路能源微电网在高峰时段负荷大、突发性负荷波动频繁等问题,提出了一种基于改进Pareto算法的风/光/氢蓄储公路微电网日内调度决策优化模型,以实现微电网在日内运行周期中的电力削峰填谷,完成电力系统最大化消纳风/光机组出力,并达到不同类型能源间的互补和最优化利用;以微电网系统日内运行总成本最低、碳排放量最小、风/光消纳率最大为主要准则构建了目标函数,考虑电功率平衡、风/光能源出力、氢蓄储能以及与外电网交互等多种约束条件,按照优先消纳可再生能源政策制定了出力策略,并输出了区域内微电网最佳调度决策结果;为验证提出模型的有效性、精确度和实用性,以新疆S21公路吉力湖路段的气象数据和电力负荷数据为实例进行了分析。研究结果表明:基于提出的优化模型构建的风/光/氢蓄储公路微电网系统能有效提升风光能源的消纳,与传统Pareto算法和多目标粒子群优化等算法相比,改进后的Pareto算法可使系统在相同微电网结构下的日内运行总成本分别降低8.5%和3.7%,可再生能源消纳率分别提高3.6%和10.1%,碳排放量分别减少14.4%和23.9%。由此可见,基于改进Pareto算法的风/光/氢蓄储日内调度决策优化模型能够在确保公路微电网系统平稳运行的同时,提升风/光/氢蓄储系统的可靠性。

     

  • 图  1  风/光/氢蓄储公路微电网系统结构

    Figure  1.  Structure of wind/solar/hydrogen storage highway microgrid system

    图  2  基于改进Pareto算法的多目标优化模型求解流程

    Figure  2.  Solution process of multi-objective optimization model based on improved Pareto algorithm

    图  3  典型日内风/光机组出力功率预测曲线

    Figure  3.  Prediction curves of typical intraday wind/solar turbine units output powers

    图  4  典型电负荷需求功率日内变化曲线

    Figure  4.  Intraday variation curves of typical electric load demand power

    图  5  各典型日调度优化方案

    Figure  5.  Optimization scheduling schemes for each typical day

    表  1  电负荷系统试验参数

    Table  1.   Test parameters for electric load system

    能耗项目 设备名称 设备数量 平均额定功率/W
    监控系统 LED可变信息标志(悬臂式) 12 900
    LED可变信息标志(门架式) 14 1 900
    高清网络摄像机 51 18
    高速球型摄像机 35 40
    微波雷达车辆检测 30 10
    通信系统 光端机 70 5
    交换机 6 500
    英特尔处理器 30 270
    路由器 10 300
    收费系统 车道控制器 4 100
    车道通行信号灯 4 50
    车道检测线圈 4 10
    自动栏杆机 4 60
    内部有线紧急对讲及报警系统 4 70
    费用额度显示器 4 40
    车道摄像机 4 55
    收费计算机、显示器 4 200
    收费亭内配电机柜 1 3 000
    收费亭内摄像机 4 55
    收费天棚照明 12 100
    广场高杆照明灯 8 1 500
    服务生活系统 通风机 15 700
    空调 20 580
    电锅炉 2 2 200
    台式电脑 20 200
    电视机 5 135
    中低杆照明灯具 80 300
    高压钠灯 100 400
    无极荧光灯 100 120
    白光LED灯具 80 55
    监控摄像 20 10
    高清红外摄像 50 18
    暖风机 10 1 800
    电解槽 2 2 200
    下载: 导出CSV

    表  2  风/光/氢蓄储能公路微电网系统设备运行参数

    Table  2.   Operation parameters of wind/solar/hydrogen storage highway microgrid system equipment

    设备名称 参数 数值 参数 数值
    电转气 产气效率 0.55 容量上限/kW 200
    损失系数 0.31 容量下限/kW 0
    最大爬坡功率/kW 200 运维成本/(元·h-1) 4.2
    储氢罐 储氢效率 0.92 容量上限/m3 500
    放氢效率 0.85 容量下限/m3 30
    最大放氢容量/m3 100 运维成本/(元·h-1) 1.5
    氢燃料电池 氢气流率/(L·min-1) 100 裸堆功率密度/(kW·L-1) 4.7
    电池效率 0.45 氢气利用率 0.64
    最大放电功率/kW 20 运维成本/(元·h-1) 3.1
    蓄电池 充电效率 0.85 容量上限/kW 500
    放电效率 0.85 容量下限/kW 10
    最大充、放功率/kW 100 运维成本/(元·h-1) 2.1
    电动汽车充电站 充电效率 0.87 容量上限/kW 300
    放电效率 0.87 容量下限/kW 30
    最大充、放功率/kW 50 运维成本/(元·h-1) 2.8
    风电机组 额定输出功率/kW 250 损失系数 0.55
    轮毂高度/m 70 装机容量/kW 1 500
    风电转换效率 0.45 运维成本/(元·h-1) 15.7
    光伏机组 额定输出功率/kW 230 损失系数 0.78
    光场面积/m2 4 600 装机容量/kW 1 000
    光电转换效率 0.22 运维成本/(元·h-1) 11.8
    下载: 导出CSV

    表  3  各典型日最优配置方案

    Table  3.   Optimal configuration schemes for each typical day

    参数名称 极热日 极冷日 过渡日
    风电机组数量 3 1 3
    光伏电站数量 2 1 2
    蓄电池组件数量 25 60 30
    氢燃料电池组件数量 16 0 10
    40L储氢罐数量 10 0 5
    电动汽车充电数量 46 28 70
    日内运行总成本/(元·d-1) 1 482.73 1 985.14 1 697.08
    可再生能源消纳率/% 97.4 32.1 95.3
    碳排放量/t 0.007 2 0.018 2 0.008 9
    下载: 导出CSV

    表  4  分时电价

    Table  4.   Time-sharing tariffs

    时段 购电电价/[元·(kW·h)-1] 售电电价/[元·(kW·h)-1]
    0:00~6:00 0.48 0.25
    6:00~10:00、12:00~16:00 0.65 0.44
    10:00~12:00、16:00~18:00 0.83 0.58
    18:00~00:00 1.15 0.95
    下载: 导出CSV

    表  5  不同方案下的系统运行参数对比结果

    Table  5.   Comparison results of system operation parameters under different schemes

    方案 优化算法 风/光发电 主网出力 氢储模块 蓄储模块 系统日内运行总成本/(元·d-1) 可再生能源消纳率/% 碳排放量/t
    1 改进Pareto算法 1 896.57 0.0 0.031 2
    2 改进Pareto算法 1 579.29 68.2 0.017 5
    3 改进Pareto算法 1 501.34 76.3 0.014 2
    4 改进Pareto算法 1 697.08 95.3 0.008 9
    5 Pareto算法 1 854.25 91.7 0.010 4
    6 MOPSO算法 1 761.92 85.2 0.011 7
    7 SA-PSO算法 1 743.41 89.4 0.009 2
    下载: 导出CSV
  • [1] MA Yi-xiang, YU Le-an, ZHANG Guo-xing, et al. Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling[J]. Renewable Energy, 2023, 219: 119483. doi: 10.1016/j.renene.2023.119483
    [2] RAHMAN M A, MUKTA M Y, ASYHARI A T, et al. Renewable energy re-distribution via multiscale IoT for 6G-oriented green highway management[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 23771-23780. doi: 10.1109/TITS.2022.3203208
    [3] 武程浩. 可持续视角下高速公路服务区光伏发电站投资决策研究[D]. 北京: 华北电力大学, 2021.

    WU Cheng-hao. Investment decision of photovoltaic power station in expressway service area from the perspective of sustainability[D]. Beijing: North China Electric Power University, 2021. (in Chinese)
    [4] GU Xiang, CHEN Zhe. Multi-time-scale scheduling optimization of regional multi-energy systems considering source-load uncertainty[C]//IEEE. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). New York: IEEE, 2021: 198-201.
    [5] 郭靖琪, 李常生, 付荣之, 等. 基于源荷不确定性的虚拟电厂负荷优化调度方法[J]. 自动化技术与应用, 2023, 42(10): 73-76.

    GUO Jing-qi, LI Chang-sheng, FU Rong-zhi, et al. Load optimal dispatching method of virtual power plant based on source load uncertainty[J]. Techniques of Automation and Applications, 2023, 42(10): 73-76. (in Chinese)
    [6] KATARAY T, NITESH B, YARRAM B, et al. Integration of smart grid with renewable energy sources: opportunities and challenges—a comprehensive review[J]. Sustainable Energy Technologies and Assessments, 2023, 58: 103363. doi: 10.1016/j.seta.2023.103363
    [7] ZHANG De-long, DU Ming-xing, ZHANG Zi-yang, et al. Effect of multi-energy storage systems on improving the synergy of integrated energy system[C]//IEEE. 2023 3rd New Energy and Energy Storage System Control Summit Forum (NEESSC). New York: IEEE, 2023: 90-93.
    [8] ZHU Ying, TONG Quan-ling, YAN Xia-xia, et al. Optimal design of multi-energy complementary power generation system considering fossil energy scarcity coefficient under uncertainty[J]. Journal of Cleaner Production, 2020, 274: 122732. doi: 10.1016/j.jclepro.2020.122732
    [9] WANG Jun-song, YIN Xiu-xing, LIU Yu-kang, et al. Optimal design of combined operations of wind power-pumped storage-hydrogen energy storage based on deep learning[J]. Electric Power Systems Research, 2023, 218: 109216. doi: 10.1016/j.epsr.2023.109216
    [10] 何飞帆, 高文根, 於跃. 基于模糊控制的光伏微电网复合储能控制策略优化研究[J]. 重庆工商大学学报(自然科学版), 2023, 40(3): 56-63.

    HE Fei-fan, GAO Wen-gen, YU yue, et al. Research on the composite energy storage control strategy optimization of fuzzy control-based photovoltaic micro-grid[J]. Journal of Chongqing Technology and Business University (Natural Science Edition), 2023, 40(3): 56-63. (in Chinese)
    [11] 孔令国. 风光氢综合能源系统优化配置与协调控制策略研究[D]. 北京: 华北电力大学, 2017.

    KONG Ling-guo. Research on optimal sizing and coordinated control strategy of integrated energy system of wind photovoltaic and hydrogen[D]. Beijing: North China Electric Power University, 2017. (in Chinese)
    [12] DOZEIN M G, JALALI A, MANCARELLA P. Fast frequency response from utility-scale hydrogen electrolyzers[J]. IEEE Transactions on Sustainable Energy, 2021, 12(3): 1707-1717. doi: 10.1109/TSTE.2021.3063245
    [13] 陶鹏, 张冰玉, 韩桂楠, 等. 计及源荷双侧风险管理的光储微网两阶段低碳运行优化研究[J]. 智慧电力, 2023, 51(11): 1-6.

    TAO Peng, ZHANG Bing-yu, HAN Gui-nan, et al. Two-stage low carbon operation optimization of photovoltaic storage microgrid considering risk management of both source and load sides[J]. Smart Power, 2023, 51(11): 1-6. (in Chinese)
    [14] GANESHAN A, HOLMES D G, MEEGAHAPOLA L, et al. Enhanced control of a hydrogen energy storage system in a microgrid[C]//IEEE. 2017 Australasian Universities Power Engineering Conference (AUPEC). New York: IEEE, 2017: 1-6.
    [15] 陈继明, 徐乾, 李勇, 等. 计及源荷不确定性和碳捕集虚拟电厂的电-气互联系统优化调度[J]. 太阳能学报, 2023, 44(10): 9-18.

    CHEN Ji-ming, XU Qian, LI Yong, et al. Optimal dispatch of electricity-natural gas interconnection system considering source-load uncertainty and virtual power plant with carbon capture[J]. Acta Energiae Solaris Sinica, 2023, 44(10): 9-18. (in Chinese)
    [16] HUANG Li-ming, SHI Ru-xin, WANG Di, et al. Low-carbon economic dispatch of virtual power plant considering u ncertainty of wind power[C]//IEEE. 2023 10th International Forum on Electrical Engineering and Automation (IFEEA). New York: IEEE, 2023: 761-766.
    [17] 王侃宏, 赵政通, 刘欢, 等. 基于HOMER和SA-PSO算法的风/光氢储系统的优化匹配[J]. 水电能源科学, 2020, 38(5): 207-210.

    WANG Kan-hong, ZHAO Zheng-tong, LIU Huan, et al. Optimization matching analysis of wind-photovoltaic-hydrogen-storage system based on HOMER and SA-PSO algorithm[J]. Water Resources and Power, 2020, 38(5): 207-210. (in Chinese)
    [18] LAMARI M, AMRANE Y, BOUDOUR M, et al. Multi-objective economic/emission optimal energy management system for scheduling micro-grid integrated virtual power plant[J]. Energy Science and Engineering, 2022, 10(8): 3057-3074. doi: 10.1002/ese3.1188
    [19] ZHAO Lin, HOU Yi-xin, JIANG Hai-wei, et al. Multi-objective optimization scheduling of integrated energy system interval under multiple uncertainty environment[J]. Soft Computing, 2023, https://doi.org/10.1007/s00500-023-08222-9.
    [20] ZHANG Xi-zheng, WANG Ze-yu, LU Zhang-yu. Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm[J]. Applied Energy, 2022, 306: 118018. doi: 10.1016/j.apenergy.2021.118018
    [21] WANG Zhen, DUAN Li-qiang, ZHANG Zu-xian. Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions[J]. Energy, 2022, 261: 125279. doi: 10.1016/j.energy.2022.125279
    [22] QIAO Yi-yang, HU Fan, XIONG Wen, et al. Multi-objective optimization of integrated energy system considering installation configuration[J]. Energy, 2023, 263: 125785. doi: 10.1016/j.energy.2022.125785
    [23] 戚野白, 刘开欣, 刘杰, 等. 高速公路服务区光伏与虚拟电厂协同控制策略研究[J]. 交通节能与环保, 2022, 18(2): 15-21.

    QI Ye-bai, LIU Kai-xin, LIU Jie, et al. Study on cooperative control strategy of photovoltaic and virtual power plant in Expressway service area[J]. Transport Energy Conservation and Environmental Protection, 2022, 18(2): 15-21. (in Chinese)
    [24] 王宁玲, 窦潇潇, 李承周, 等. 含P2G和复合储能的高速公路服务区综合能源系统日前优化调度[J]. 华北电力大学学报(自然科学版), 2024, 51(2): 53-61, 69.

    WANG Ning-ling, DOU Xiao-xiao, LI Cheng-zhou, et al. Day-ahead optimization scheduling of integrated energy system in highway service area with P2G and composite energy storage[J]. Journal of North China Electric Power University (Natural Science Edition), 2024, 51(2): 53-61, 69. (in Chinese)
    [25] DHAKAL R, SEDAI A, POL S, et al. A novel hybrid method for short-term wind speed prediction based on wind probability distribution function and machine learning models[J]. Applied Sciences, 2022, 12(18): 9038. doi: 10.3390/app12189038
    [26] 柳柏松. 高速公路自洽能源系统综合评价及优化调度研究[D]. 西安: 长安大学, 2023.

    LIU Bai-song. Research on comprehensive evaluation and optimal dispatch of highway self-consistent energy system[D]. Xi'an: Chang'an University, 2023. (in Chinese)
    [27] WANG Yan-liang, WANG Bo, FARJAM H. Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids[J]. Engineering Science and Technology, 2024, 51: 101649.
    [28] 田文, 杨帆, 尹嘉男, 等. 航路时空资源分配的多目标优化方法[J]. 交通运输工程学报, 2020, 20(6): 218-226. doi: 10.19818/j.cnki.1671-1637.2020.06.019

    TIAN Wen, YANG Fan, YIN Jia-nan, et al. Multi-objective optimization method of air route space-time resources allocation[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 218-226. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.06.019
    [29] 赵志宏, 李晴, 李乐豪, 等. LSTM Encoder-Decoder方法预测设备剩余使用寿命[J]. 交通运输工程学报, 2021, 21(6): 269-277.

    ZHAO Zhi-hong, LI Qing, LI Le-hao, et al. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 269-277. (in Chinese)
    [30] 杨晓霞, 张蕊, 李永行, 等. 火灾爆发时地铁站乘客疏散多目标路径优化方法[J]. 交通运输工程学报, 2023, 23(5): 192-209.

    YANG Xiao-xia, ZHANG Rui, LI Yong-xing, et al. A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 192-209. (in Chinese)
  • 加载中
图(5) / 表(5)
计量
  • 文章访问数:  203
  • HTML全文浏览量:  67
  • PDF下载量:  29
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-02-10
  • 网络出版日期:  2024-09-26
  • 刊出日期:  2024-08-28

目录

    /

    返回文章
    返回