Energy-saving optimal scheduling under multi-mode “source-network-load-storage” combined system in metro station based on modified Gray Wolf Algorithm

• Autorzy: Tian Jingjing , Qian Yu , Zhao Feng , Mo Shenglin , Xiao Huaxuan , Zhu Xiaotong , Liu Guangdi

Identyfikatory

DOI

10.24425/aee.2024.148861

• Języki publikacji: EN

Abstrakty

EN

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “source network-load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

Słowa kluczowe

EN

bilevel optimization; grey wolf optimization algorithm; multi-mode; peak shaving; valley-filling; source-network-load-storage

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2024
• Tom: Vol. 73, nr 1
• Strony: 121--143
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr., wz.

Twórcy

autor

Tian Jingjing

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Qian Yu

autor

Zhao Feng

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China
• Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education Lanzhou, China

autor

Mo Shenglin

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Xiao Huaxuan

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Zhu Xiaotong

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Liu Guangdi

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

Bibliografia

• [1] Song Jie, Zheng Yi et al., Research on Performance Optimization of Ventilation and Air Conditioning Systems for Equipment Management Rooms in Subway Station Based on Actual Measurements, Shanghai Energy Saving (in Chinese), no. 6, pp. 806–813 (2023).
• [2] Xie Shaofeng, Fang Manqi, Xia Guohua, Influence of PV generation system accessing to traction power supply system on power quality, Electric Power Automation Equipment (in Chinese), vol. 38, no. 10, pp. 53–59 (2018).
• [3] Chen Weirong, Wang Xuan, Li Qi et al., Review on the Development Status of PV Power Station Accessing to Traction Power Supply System for Rail Transit, Power System Technology (in Chinese), vol. 43, no. 10, pp. 3663–3670 (2019).
• [4] Tang Jie, Li Yiran, Huang Ruanyuan et al., Capacity Allocation off BESS in Secondary Frequency Regulation with the Goal of Maximum Net Benefit, Transactions of China Electrotechnical Society (in Chinese), vol. 43, no. 8, pp. 3048–3059 (2023).
• [5] Hu Haitao, Ge Yinbo, Huang Yi et al., “Source-network-train-storage” Integrated Power Supply System for Electric Railways, Proceedings of the CSEF (in Chinese), vol. 42, no. 12, pp. 4374–4391 (2022).
• [6] Andoni S., Aitor M., Haizea G. et al., Co-Optimization of Storage System Sizing and Control Strategy for Intelligent Photovoltaic Power Plants Market Integration, IEEE transactions on sustainable energy, vol. 7, no. 4, pp. 1749–1761 (2016), DOI: 10.1109/TSTE.2016.2555704.
• [7] Deng Wenli, Dai Zhaohua, Chen Weirong et al., Review on the Development Status of PV Power Station Accessing to Traction Power Supply System for Rail Transit, Power System Technology (in Chinese), vol. 39, no. 19, pp. 5692–5702+5897 (2019).
• [8] Loakimidis C., Thomas D., Rycerski P. et al., Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot, Energy, vol. 148, pp. 148–158 (2018), DOI: 10.1016/j.energy.2018.01.128.
• [9] Garcia-Plaza M., Eloy-Garcia Carrascoet J., Alonso-Martinez J. et al., Peak shaving algorithm with dynamic minimum voltage tracking for battery storage systems in microgrid applications, Journal of Energy Storage, vol. 20, pp. 41–48 (2018), DOI: org/10.1016/j.est.2018.08.021.
• [10] Li Junhui, Zhang Jiahui, Mu Gang et al., Day-ahead optimal scheduling strategy of peak regulation for energy storage considering peak and valley characteristics of load, Electric Power Automation Equipment (in Chinese), vol. 40, no. 7, pp. 128–133+140+134–136 (2020).
• [11] Hu Haitao, Zheng Zheng, He Youzheng et al., The Framework and Key Technologies of Traffic Energy Internet, Proceedings of the CSEE (in Chinese), vol. 38, no. 1, pp. 12–24+339 (2018).
• [12] Liao Haizhu, Hu Haitao, Huang Yi et al., Day-ahead energy optimization and scheduling strategy of “source-network-train-storage” coordinated power supply system for electrified railways, Electric Drive for Locomotives (in Chinese), no. 3, pp. 1–9 (2022), DOI: 10.13890/j.issn.1000-128X.2022.03.001.
• [13] Qi Yan, Shang Junxue, Nie Jingyu et al., Optimization of CCHP micro-grid operation based on improved multi-objective grey wolf algorithm, Electrical Measurement & Instrumentation (in Chinese), vol. 59, no. 6, pp. 12–19+52 (2022).
• [14] Zhao Rui, Tan Zhongfu, Degejirifu U. et al., Two-level coordinated operation optimization model of the source-storage-load in microgrid considering demand response, Renewable Energy Resources (in Chinese), vol. 39, no. 16, pp. 4808–4818+4982 (2019).
• [15] Song Wenqin, Lv Jinli, Zhao Linxia et al., Study on the economic dispatch strategy of power system with combined operation of concentrated solar power and wind farm, Power System Protection and Control (in Chinese), vol. 48, no. 5, pp. 95–102 (2020).
• [16] Yang Zhipin, Li Kerun, Wang Ninglin et al., Economic Analysis of Peaking Regulation of Coal-fired Generating Units Under Big Data, Proceedings of the CSEE (in Chinese), vol. 44, no. 8, pp. 2754–2760 (2020).
• [17] Lv Zhipeng, Lu Limin, Lu Huaigu et al., Research on multi-mode energy management strategy of DC microgrid, Distribution & Utilization (in Chinese), vol. 36, no. 7, pp. 44–51 (2019).
• [18] Brahmendra Kumar V.G., Palanisamy K., Energy management of renewable energy-based microgrid system with HESS for various operation modes, Frontiers in Energy Research, vol. 10 (2022).
• [19] Zhao Huirong, Mao Haifei, Peng Daogang, Multi-object Nonlinear Economic Predictive Controller for Dynamic Energy Efficiency Optimization of MGT-LiBr CCHP Under Variable Working Conditions, Proceedings of the CSEF (in Chinese), vol. 43, no. 8, pp. 3048–3059 (2023).
• [20] Yu Xiaobo, Geng Yuqing, Complementary Configuration Research of New Combined Cooling, Heating, and Power System Driven by Renewable Energy under Energy Management Modes, Energy Technology, vol. 7, no. 10 (2019).
• [21] Li Jiayuan, Li Yaonan, Hui Jilu, Overview of Gray Wolf Algorithm Applications, Digital Technology & Application Voltage Engineering (in Chinese), vol. 34, no. 5, pp. 963–972 (2019).
• [22] Wang Zhi, Tao Hongjun, Cai Wenkui et al., Optimal scheduling research of multi-time-scale-rolling optimization in CCHP system, Acta Energiae Solar Sinica (in Chinese), vol. 44, no. 2, pp. 298–308 (2023).
• [23] Kang Ligai, Yang Junhong, An Qingsong et al., Complementary configuration and performance comparison of CCHP-ORC system with a ground source heat pump under three energy management modes, Energy Conversion and Management, no. 135 (2017).
• [24] Muhyaddin R., Abdullah A., Hussain B. et al., Economical-technical-environmental operation of power networks with wind-solar-hydropower generation using analytic hierarchy process and improved grey wolf algorithm, Ain Shams Engineering Journal, vol. 12, no. 3 (2021), DOI: 10.1016/j.asej.2021.02.004.
• [25] Zhao Chao, Wang Bin, Sun Zhixin et al., Optimal configuration optimization of islanded microgrid using improved grey wolf optimizer algorithm, Acta Energiae Solar Sinica (in Chinese), vol. 43, no. 1, pp. 256–262 (2022).
• [26] Mao Jingfeng, Wu Jian, Zhang Zhenmeng et al., Multi-objective optimization of island-type DC microgrid based on AFPSO, Acta Energiae Solar Sinica (in Chinese), vol. 42, no. 6, pp. 63–71 (2021).
• [27] Farayi M., Alireza K., Mojtaba S.A. et al., Multi-objective optimization of a biomass gasification to generate electricity and desalinated water using Grey Wolf Optimizer and artificial neural network, Chemosphere, vol. 287, part 2 (2021), DOI: 10.1016/j.chemosphere.2021.131980.
• [28] Gujarathi K.P., Shah A.V., Lokhande M.M., Grey wolf algorithm for multidimensional engine optimiza tion of converted plug-in hybrid electric vehicle, Transportation Research Part D, vol. 63 (2018), DOI: org/10.1016/j.trd.2018.06.003.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).