Volume 24 Issue 4
Aug.  2024
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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
shu

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

doi: 10.19818/j.cnki.1671-1637.2024.04.006
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2021YFB1600201

More Information
  • Author Bio:

    HAO Xue-li(1987-), female, senior engineer, PhD, xuelihao@chd.edu.cn

    PEI Li-li(1995-), female, assistant professor, PhD, peilili@chd.edu.cn

  • Received Date: 2024-02-10
    Available Online: 2024-09-26
  • Publish Date: 2024-08-28
    Abstract
  • To address the challenges of large loads and frequent sudden load fluctuations during peak hours in highway energy microgrid, an intraday scheduling decision-making optimization model for wind/solar/hydrogen storage highway microgrid was proposed based on the improved Pareto algorithm, to ensure the power peak shaving and valley filling in the microgrid during the intraday operation cycle, help the power system maximize the consumption of wind/solar unit output, and achieve the complementary and optimal utilization of different types of energies. The objective function was constructed by using the lowest intraday operating cost, the lowest carbon emission, and the highest wind/solar consumption rate of the microgrid system as the main criteria. In view of various constraints such as the electric power balance, wind/solar energy output, hydrogen storage energy, and interaction with the external grid, the output strategy was formulated according to the policy of preferential consumption of renewable energy, and the optimal scheduling decision-making results of the microgrid in the region were output. To verify the validity, accuracy, and practicality of the proposed model, the meteorological data and electric load data of Jili Lake section of Xinjiang S21 Highway were analyzed. Research results show that the wind/solar/hydrogen storage highway microgrid system constructed based on the proposed optimization model can effectively improve the consumptions of wind/solar energies. Compared with the traditional Pareto algorithm and multi-objective particle swarm optimization algorithm, the improved Pareto algorithm reduces the total intraday operating cost of the system by 8.5% and 3.7% under the same microgrid structure, increases the renewable energy consumption rate by 3.6% and 10.1%, and lowers the carbon emission by 14.4% and 23.9%, respectively. Thus, the wind/solar/hydrogen storage intraday scheduling decision-making optimization model based on the improved Pareto algorithm can improve the reliability of the wind/solar/hydrogen storage system while ensuring the smooth operation of the highway microgrid system. 5 tabs, 5 figs, 30 refs.

     

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    • References(30)
  • [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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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)
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