تامین توان الکتریکی ایستگاه¬های مخابرات سلولی در ریزشبکه-های مجزا با اولویت بکارگیری منابع تجدیدپذیر و خودروهای الکتریکی با ارائه رویکرد برنامه¬ریزی غیرخطی مختلط با اعداد صحیح
محورهای موضوعی : فناوری اطلاعات و ارتباطات
1 - پژوهشگاه ارتباطات و فناوری اطلاعات
2 - پژوهشگاه ارتباطات و فناوری اطلاعات
کلید واژه: دکل مخابراتی, تامین توان الکتریکی, خودرو های الکتریکی, ریزشبکه, گازهای گلخانه ای,
چکیده مقاله :
این مقاله، موضوع تامین توان اقتصادی و زیست محیطی برای ایستگاه های مخابرات سلولی در یک ریزشبکه مجزای مستقر در مناطق صعب العبور را مورد بررسی قرار داده است. ریزشبکه، شامل ژنراتوردیزلی، سیستم فتوولتاییک و منبع ذخیره هیدروژنی است. امکان تبادل انرژی ریزشبکه با باتری خودرو الکتریکی متصل شده به ریزشبکه، مورد بررسی قرار گرفت. با استفاده از شارژ/دشارژ هوشمند خودروهای الکتریکی تامین توان المان های ریزشبکه، صورت گرفته شده و حداکثر استفاده از انرژی های پاک به عمل آمده است. مدل ریاضی مسئله فوق به عنوان یک برنامه ریزی غیرخطی مختلط با اعداد صحیح ارئه شد که ماهیت غیرخطی المان ها را مدلسازی می کند. در قالب بهینه سازی چندهدفه، بجز هزینه، گازهای گلخانه ای نیز از اهداف مسئله بوده و تعادلی بین این دو هدف متناقض در جبهه پارتو ایجاد شده است. در این مسئله، پارامتر های غیرقطعی، مانند تولید انرژی خورشیدی، الگو های رفتاری راننده ها (زمان رسیدن به ایستگاه شارژ، زمان خروج از ایستگاه شارژ، و مسافت روزانه طی شده، تولید) توسط سناریو های تصادفی مدلسازی شده است. نتایج به دست آمده نشان می دهد که شارژ/دشارژ هوشمند خودروهای الکتریکی، سهم چشمگیری در کاهش ریسک عملیاتی ریزشبکه، هزینه ی کل و گازهای گلخانه ای دارد. به طوریکه، استفاده از روش برنامه ریزی اقتصادی/زیست-محیطی پیشنهاد شده، منجر به %60/18 کاهش در مقدار گازهای گلخانه ای می شود.
The cellular base stations are communication devices that ensure the connection in the world. Nevertheless, they are usually installed in remote places. This paper, studied the energy procurement of a cellular base stations in an independent microgrid with a hydrogen-based energy storage system, photovoltaic (PV) system, electric vehicles and a diesel generator. A new mixed-integer nonlinear programming model was used to deal with nonlinearities of the system components. The paper studied different uncertainties, such as the connection rate in cellular base stations, the driver of the electric vehicle, and PV generation, using stochastic programming method. The potency of the proposed method was studied in different case studies. The results prove that smart electric vehicle chargers reduce the risks and also cost/emission objective functions. The usage of this model can reduce the emissions as much as 18.60%.
[1] P. Nema, R. K. Nema, and S. Rangnekar, “Minimization of green house gases emission by using hybrid energy system for telephony base station site application,” Renewable and Sustainable Energy Reviews, vol. 14, no. 6. Pergamon, pp. 1635–1639, Aug. 01, 2010, doi: 10.1016/j.rser.2010.02.012.
[2] N. Nasiri et al., “A bi-level market-clearing for coordinated regional-local multi-carrier systems in presence of energy storage technologies,” Sustain. Cities Soc., vol. 63, p. 102439, Dec. 2020, doi: 10.1016/j.scs.2020.102439.
[3] J. Lian, Y. Zhang, C. Ma, Y. Yang, and E. Chaima, “A review on recent sizing methodologies of hybrid renewable energy systems,” Energy Conversion and Management, vol. 199. Elsevier Ltd, p. 112027, Nov. 01, 2019, doi: 10.1016/j.enconman.2019.112027.
[4] A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Abapour, and K. Zare, “Robust scheduling of hydrogen based smart micro energy hub with integrated demand response,” J. Clean. Prod., vol. 267, p. 122041, 2020, doi: 10.1016/j.jclepro.2020.122041.
[5] R. P. Shea, M. O. Worsham, A. D. Chiasson, J. Kelly Kissock, and B. J. McCall, “A lifecycle cost analysis of transitioning to a fully-electrified, renewably powered, and carbon-neutral campus at the University of Dayton,” Sustain. Energy Technol. Assessments, vol. 37, p. 100576, Feb. 2020, doi: 10.1016/J.SETA.2019.100576.
[6] Z. J. Lee et al., “Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging,” IEEE Trans. Smart Grid, vol. 12, no. 5, pp. 4339–4350, Sep. 2021, doi: 10.1109/TSG.2021.3074437.
[7] M. Dubarry, A. Devie, and K. McKenzie, “Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis,” J. Power Sources, vol. 358, pp. 39–49, Aug. 2017, doi: 10.1016/J.JPOWSOUR.2017.05.015.
[8] B. Qiao and J. Liu, “Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm,” Renew. Energy, vol. 154, pp. 316–336, Jul. 2020, doi: 10.1016/j.renene.2020.03.012.
[9] M. S. Hossain, A. Jahid, and D. M. Fayzur Rahman, “Quantifying Potential of Hybrid PV/WT Power Supplies for Off-Grid LTE Base Station,” Sep. 2018, doi: 10.1109/IC4ME2.2018.8465653.
[10] C. Zeljkovic et al., “A Monte Carlo Simulation Platform for Studying the Behavior of Wind-PV-Diesel-Battery Powered Mobile Telephony Base Stations,” Aug. 2020, doi: 10.1109/PMAPS47429.2020.9183576.
[11] E. Jannelli, M. Minutillo, A. Lubrano Lavadera, and G. Falcucci, “A small-scale CAES (compressed air energy storage) system for stand-alone renewable energy power plant for a radio base station: A sizing-design methodology,” Energy, vol. 78, pp. 313–322, Dec. 2014, doi: 10.1016/j.energy.2014.10.016.
[12] A. Jahid, M. K. H. Monju, M. E. Hossain, and M. F. Hossain, “Renewable Energy Assisted Cost Aware Sustainable Off-Grid Base Stations with Energy Cooperation,” IEEE Access, vol. 6, pp. 60900–60920, 2018, doi: 10.1109/ACCESS.2018.2874131.
[13] mdsanwar Hossain, M. Fayzur Rahman, M. Sanwar Hossain, S. Member, and C. Author, “Hybrid Solar PV/Biomass Powered Energy Efficient Remote Cellular Base Stations Characterization of the performance attributes of Graphene Nano Film based Surface Plasmon Resonance (SPR) Biosensor for Biomedical Applications View project lifetime measurement of monitoring system View project Hybrid Solar PV/Biomass Powered Energy Efficient Remote Cellular Base Stations,” 2020. Accessed: Feb. 05, 2021. [Online]. Available: https://www.researchgate.net/publication/340455867.
[14] E. Oh and B. Krishnamachari, “Energy savings through dynamic base station switching in cellular wireless access networks,” 2010, doi: 10.1109/GLOCOM.2010.5683654.
[15] J. Gong, S. Zhou, Z. Niu, and P. Yang, “Traffic-aware base station sleeping in dense cellular networks,” 2010, doi: 10.1109/IWQoS.2010.5542725.
[16] A. Spagnuolo, A. Petraglia, C. Vetromile, R. Formosi, and C. Lubritto, “Monitoring and optimization of energy consumption of base transceiver stations,” Energy, vol. 81, pp. 286–293, Mar. 2015, doi: 10.1016/j.energy.2014.12.040.
[17] Y. Choi and H. Kim, “Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost,” mdpi.com, doi: 10.3390/en9060462.
[18] S. Cordiner et al., “Fuel cell based Hybrid Renewable Energy Systems for off-grid telecom stations: Data analysis from on field demonstration tests,” Appl. Energy, vol. 192, pp. 508–518, Apr. 2017, doi: 10.1016/j.apenergy.2016.08.162.
[19] T. O. BA Aderemi, SP Chowdhury, “NoTechno-economic feasibility of hybrid solar photovoltaic and battery energy storage power system for a mobile cellular base station in Soshanguve, South Africa Title,” Energies, 2018.
[20] H. Turker and S. Bacha, “Optimal Minimization of Plug-In Electric Vehicle Charging Cost with Vehicle-to-Home and Vehicle-to-Grid Concepts,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10281–10292, 2018, doi: 10.1109/TVT.2018.2867428.
[21] P. Mesarić and S. Krajcar, “Home demand side management integrated with electric vehicles and renewable energy sources,” Energy Build., vol. 108, pp. 1–9, Dec. 2015, doi: 10.1016/J.ENBUILD.2015.09.001.
[22] M. Alirezaei, M. Noori, and O. Tatari, “Getting to net zero energy building: Investigating the role of vehicle to home technology,” Energy Build., vol. 130, pp. 465–476, Oct. 2016, doi: 10.1016/J.ENBUILD.2016.08.044.
[23] C. B. Robledo, V. Oldenbroek, F. Abbruzzese, and A. J. M. van Wijk, “Integrating a hydrogen fuel cell electric vehicle with vehicle-to-grid technology, photovoltaic power and a residential building,” Appl. Energy, vol. 215, pp. 615–629, Apr. 2018, doi: 10.1016/J.APENERGY.2018.02.038.
[24] F. Brahman, M. Honarmand, and S. Jadid, “Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system,” Energy Build., vol. 90, pp. 65–75, Mar. 2015, doi: 10.1016/J.ENBUILD.2014.12.039.
[25] T. Ma, J. Wu, and L. Hao, “Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub,” Energy Convers. Manag., vol. 133, pp. 292–306, Feb. 2017, doi: 10.1016/J.ENCONMAN.2016.12.011.
[26] M. Rastegar, M. Fotuhi-Firuzabad, H. Zareipour, and M. Moeini-Aghtaieh, “A Probabilistic Energy Management Scheme for Renewable-Based Residential Energy Hubs,” IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2217–2227, Sep. 2017, doi: 10.1109/TSG.2016.2518920.
[27] S. A. Alavi, A. Ahmadian, and M. Aliakbar-Golkar, “Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method,” Energy Convers. Manag., vol. 95, pp. 314–325, May 2015, doi: 10.1016/J.ENCONMAN.2015.02.042.
[28] M. Shafie-Khah and P. Siano, “A stochastic home energy management system considering satisfaction cost and response fatigue,” IEEE Trans. Ind. Informatics, vol. 14, no. 2, pp. 629–638, Feb. 2018, doi: 10.1109/TII.2017.2728803.
[29] X. Lu, Z. Liu, L. Ma, L. Wang, K. Zhou, and S. Yang, “A robust optimization approach for coordinated operation of multiple energy hubs,” Energy, vol. 197, p. 117171, Apr. 2020, doi: 10.1016/J.ENERGY.2020.117171.
[30] P. Zhao, W. Xu, S. Zhang, J. Wang, and Y. Dai, “Technical feasibility assessment of a standalone photovoltaic/wind/adiabatic compressed air energy storage based hybrid energy supply system for rural mobile base station,” Energy Convers. Manag., vol. 206, p. 112486, Feb. 2020, doi: 10.1016/j.enconman.2020.112486.
[31] M. A. Mosa and A. A. Ali, “Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique,” Electr. Power Syst. Res., vol. 192, p. 106971, Mar. 2021, doi: 10.1016/J.EPSR.2020.106971.
[32] B. Li, X. Wang, M. Shahidehpour, C. Jiang, and Z. Li, “Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators with Correlated Wind Power Generation,” IEEE Trans. Sustain. Energy, vol. 10, no. 4, pp. 1904–1915, Oct. 2019, doi: 10.1109/TSTE.2018.2875483.
[33] M. Zare Oskouei, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Shafiee, M. Marzband, and A. Anvari-Moghaddam, “A hybrid robust-stochastic approach to evaluate the profit of a multi-energy retailer in tri-layer energy markets,” Energy, vol. 214, p. 118948, Jan. 2021, doi: 10.1016/J.ENERGY.2020.118948.
[34] N. Nasiri, S. Zeynali, S. N. Ravadanegh, and M. Marzband, “A hybrid robust-stochastic approach for strategic scheduling of a multi-energy system as a price-maker player in day-ahead wholesale market,” Energy, vol. 235, p. 121398, Nov. 2021, doi: 10.1016/J.ENERGY.2021.121398.
[35] M. K. Kim and L. Baldini, “Energy analysis of a decentralized ventilation system compared with centralized ventilation systems in European climates: Based on review of analyses,” Energy Build., vol. 111, pp. 424–433, Jan. 2016, doi: 10.1016/J.ENBUILD.2015.11.044.
[36] S. Zeynali, N. Nasiri, M. Marzband, and S. N. Ravadanegh, “A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets,” Appl. Energy, vol. 300, p. 117432, Oct. 2021, doi: 10.1016/J.APENERGY.2021.117432.
[37] S. Zeynali, N. Rostami, A. Ahmadian, and A. Elkamel, “Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology,” Sustain. Energy Technol. Assessments, vol. 39, p. 100722, Jun. 2020, doi: 10.1016/j.seta.2020.100722.
[38] N. Nasiri, M. Reza Banaei, and S. Zeynali, “A hybrid robust-stochastic approach for unit commitment scheduling in integrated thermal electrical systems considering high penetration of solar power,” Sustain. Energy Technol. Assessments, vol. 49, p. 101756, Feb. 2022, doi: 10.1016/J.SETA.2021.101756.
[39] I. AlHajri, A. Ahmadian, and A. Elkamel, “Stochastic day-ahead unit commitment scheduling of integrated electricity and gas networks with hydrogen energy storage (HES), plug-in electric vehicles (PEVs) and renewable energies,” Sustain. Cities Soc., vol. 67, p. 102736, Apr. 2021, doi: 10.1016/J.SCS.2021.102736.
[40] S. Zeynali, N. Rostami, A. Ahmadian, and A. Elkamel, “Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model,” Sustain. Cities Soc., vol. 74, p. 103154, Nov. 2021, doi: 10.1016/J.SCS.2021.103154.
[41] S. Nojavan and K. Zare, “Optimal energy pricing for consumers by electricity retailer,” Int. J. Electr. Power Energy Syst., vol. 102, pp. 401–412, Nov. 2018, doi: 10.1016/j.ijepes.2018.05.013.
[42] “National household travel survey.” https://nhts.ornl.gov/.
[43] P. Fazlalipour, M. Ehsan, and B. Mohammadi-Ivatloo, “Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets,” Energy, vol. 171, pp. 689–700, Mar. 2019, doi: 10.1016/J.ENERGY.2018.12.173.
[44] P. Sheikhahmadi, S. Bahramara, A. Mazza, G. Chicco, and J. P. S. Catalão, “Bi-level optimization model for the coordination between transmission and distribution systems interacting with local energy markets,” Int. J. Electr. Power Energy Syst., vol. 124, p. 106392, Jan. 2021, doi: 10.1016/j.ijepes.2020.106392.
[45] M. Baradar and M. R. Hesamzadeh, “Calculating negative LMPs from SOCP-OPF,” ENERGYCON 2014 - IEEE Int. Energy Conf., pp. 1461–1466, 2014, doi: 10.1109/ENERGYCON.2014.6850615.
