آمار

تعداد نشریات26
تعداد شماره‌ها391
تعداد مقالات3,429
تعداد مشاهده مقاله5,270,136
تعداد دریافت فایل اصل مقاله3,604,429
Cheshme-Khavar, S., Abdolahi, A., Gazijahani, F.S., Kalantari, N.T., Guerrero, J.M.. (1403). Short-Term Scheduling of Cryogenic Energy Storage Systems in Microgrids Considering CHP-Thermal-Heat-Only Units and Plug-in Electric Vehicles. سامانه مدیریت نشریات علمی, 12(3), 233-244. doi: 10.22098/joape.2023.10963.1812
S. Cheshme-Khavar; A. Abdolahi; F.S. Gazijahani; N.T. Kalantari; J.M. Guerrero. "Short-Term Scheduling of Cryogenic Energy Storage Systems in Microgrids Considering CHP-Thermal-Heat-Only Units and Plug-in Electric Vehicles". سامانه مدیریت نشریات علمی, 12, 3, 1403, 233-244. doi: 10.22098/joape.2023.10963.1812
Cheshme-Khavar, S., Abdolahi, A., Gazijahani, F.S., Kalantari, N.T., Guerrero, J.M.. (1403). 'Short-Term Scheduling of Cryogenic Energy Storage Systems in Microgrids Considering CHP-Thermal-Heat-Only Units and Plug-in Electric Vehicles', سامانه مدیریت نشریات علمی, 12(3), pp. 233-244. doi: 10.22098/joape.2023.10963.1812
Cheshme-Khavar, S., Abdolahi, A., Gazijahani, F.S., Kalantari, N.T., Guerrero, J.M.. Short-Term Scheduling of Cryogenic Energy Storage Systems in Microgrids Considering CHP-Thermal-Heat-Only Units and Plug-in Electric Vehicles. سامانه مدیریت نشریات علمی, 1403; 12(3): 233-244. doi: 10.22098/joape.2023.10963.1812

Short-Term Scheduling of Cryogenic Energy Storage Systems in Microgrids Considering CHP-Thermal-Heat-Only Units and Plug-in Electric Vehicles

Journal of Operation and Automation in Power Engineering
مقاله 6، دوره 12، شماره 3، 2024، صفحه 233-244    اصل مقاله (653.16 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10963.1812
نویسندگان
S. Cheshme-Khavar1؛ A. Abdolahi* 2؛ F.S. Gazijahani2؛ N.T. Kalantari2؛ J.M. Guerrero3
1Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
2Departmant of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
3Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark
چکیده
With the exponential penetration of renewable energy sources (RES), the need for compatible scheduling of these has increased from economic and environmental points of view. Due to the high-efficiency and fast-response features of combined heat and power (CHP) generation units, these units can immunize the system against RES fluctuations. To address the operational challenges associated with RES, this paper aims to schedule the arbitrage of cryogenic energy storage (CES) not only to maximize its owner but also to minimize RES variability. On the other hand, plug-in electric vehicles (PEV) are applied in the proposed model as responsible loads to smooth the system's load profile by changing the consumers' consumption patterns. The proposed problem is modeled as second-order cone programming and solved by the dominated group search optimization algorithm. To verify the applicability and effectiveness of the proposed approach, four different case studies have been executed. 
کلیدواژه‌ها
Cryogenic energy storage؛ Energy arbitrage؛ Microgrid؛ Plug-in electric vehicles؛ Renewable energy sources
مراجع
  1. Hatziargyriou, “Architectures and control,” Microgrids: John Wiley & Sons, 2014.
  2. Liang, W. Zhuang, “Stochastic modeling and optimization in a microgrid: A survey,” Energies, vol. 7, no. 4, pp. 2027–2050, 2014.
  3. A. Nguyen, M. L. Crow, “Stochastic optimization of renewable-based microgrid operation incorporating battery operating cost,” IEEE Trans. Power Syst., vol. 31, no. 3, pp. 2289–2296, 2016.
  4. Piagi, R. H. Lasseter, “Autonomous control of microgrids,” in proc. of the Power Eng. Society General Meeting, Montreal, QC, Canada, 2006, pp. 8–pp.
  5. Zheng, B.M. Jenkins, K. Kornbluth, C. Træholt, “Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage,” Renewable Energy, vol. 123, pp. 204–217, 2018
  6. S. Tabar, M.A. Jirdehi, R. Hemmati, “Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option,” Energy, vol. 118, pp. 827–39, 2017.
  7. Komeili, P. Nazarian, A. Safari, M. Moradlou, “Robust optimal scheduling of CHP-based microgrids in presence of wind and photovoltaic generation units: An IGDT approach,” Sustainable Cities Soc., vol. 78, p. 103566, 2022.
  8. Afroozeh, H. R. Abdolmohammadi, M. E. Nazari, “Economic-environmental dispatch of integrated thermalCHP-heat only system with temperature drop of the heat pipeline using mutant gray wolf optimization algorithm,” Electr. Power Syst. Res., vol. 212, p. 108227, 2022.
  9. Alipour, B. Mohammadi-Ivatloo, K. Zare, “Stochastic scheduling of renewable and CHP-based microgrids,” IEEE Trans. Ind. Inf., vol. 11, no. 5, pp. 1049–1058, 2019.
  10. H. Imani, M.J. Ghadi, S. Ghavidel, L. Li, “Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs,” Renewable Sustainable Energy Rev., vol. 94, pp. 486–499, 2018.
  11. H. Shams, M. Shahabi, M. E. Khodayar, “Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response,” Energy, vol. 155, pp. 326–338, 2018.
  12. Liu, L. He, X. Yu, L. Ma, “Multiparty energy management for grid-connected microgrids with heat-and electricitycoupled demand response,” EEE Trans. Ind. Inf., vol. 14, no. 5, pp. 1887–1897, 2018.
  13. Kalavani, B. Mohammadi-Ivatloo, K. Zare, “Optimal stochastic scheduling of cryogenic energy storage with wind power in the presence of a demand response program,” Renewable Energy, vol. 130, pp. 268–280, 2019.
  14. Sedighizadeh, M. Esmaili, N. Mohammadkhani, “Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles,” J. Cleaner Prod., vol. 195, pp. 301–317, 2018.
  15. Mohammadkhani, M. Sedighizadeh, M. Esmaili, “Energy and emission management of CCHPs with electric and thermal energy storage and electric vehicle,” Therm. Sci. Eng. Prog., vol. 8, pp. 494–508, 2018.
  16. S. Langeroudi, M. Sedaghat, S. Pirpoor, R. Fotouhi, M. A. Ghasemi, “Risk-based optimal operation of power, heat and hydrogen-based microgrid considering a plug-in electric vehicle,” Int. J. Hydrogen Energy, vol. 46, no. 58, pp. 30031–30047, 2021.
  17. Nouri, H. Khodaei, A. Darvishan, S. Sharifian, N. Ghadimi, “Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: an epsilon constraint method and fuzzy satisfying approach,” Energy, vol. 159, pp. 121–133, 2018.
  18. M. Sadati, A. Rastgou, M. Shafie-khah, S. Bahramara, S. Hosseini-hemati, “Energy management modeling for a community-based electric vehicle parking lots in a power distribution grid,” J. Energy Storage, vol. 38, p. 102531, 2021.
  19. S. Gazijahani, J. Salehi, “Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids,” IET Renewable Power Gener., vol. 11, no. 14, pp. 1749–1759, 2017.
  20. Kalavani, B. Mohammadi-Ivatloo, K. Zare, “Optimal stochastic scheduling of cryogenic energy storage with wind power in the presence of a demand response program,” Renewable Energy, vol. 130, pp. 268–280, 2019.
  21. Li, H. Chen, Y. Ding, “Fundamentals, and applications of cryogen as a thermal energy carrier: A critical assessment,” Int. J. Therm. Sci.,vol. 49, no. 6, pp. 941–949, 2010.
  22. Khani, M.R. Zadeh, “Real-time optimal dispatch and economic viability of cryogenic energy storage exploiting arbitrage opportunities in an electricity market,” IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 391–401, 2015.
  23. Zhang, I.E. Grossmann, C. F. Heuberger, A. Sundaramoorthy, J. M. Pinto, “Air separation with cryogenic energy storage: optimal scheduling considering electric energy and reserve markets,” AlChE J. vol. 61, no. 5, pp. 1547–1558, 2015.
  24. Salehi, F.S. Gazijahani, A. Safari, “Stochastic simultaneous planning of interruptible loads, renewable generations and capacitors in distribution network,” J. Oper. Autom. Power Eng., vol. 10, no. 2, pp. 113–121, 2022.
  25. Taghizadegan, S. Cheshmeh Khavar, A. Abdolahi, F. Arasteh, R. Ghoreyshi, “Dominated GSO algorithm for optimal scheduling of renewable microgrids with penetration of electric vehicles and energy storages considering DRP,” Int. J. Ambient Energy, vol. 43, no. 1, pp. 6380–6391, 2022.
  26. Olamaei, M.E. Nazari, S. Bahravar, “Economic environmental unit commitment for integrated CCHP-thermalheat only system with considerations for valve-point effect based on a heuristic optimization algorithm,” Energy, vol. 159, pp. 737–750, 2018.
  27. Makhdoomi, J. Moshtagh, “Optimal scheduling of electrical storage system and flexible loads to participate in energy and flexible ramping product markets,” J. Oper. Autom., vol. 11, no. 3, pp. 203–212, 2023
  28. Daryani, M.T. Hagh, and S. Teimourzadeh, “Adaptive group search optimization algorithm for multi-objective optimal power flow problem,” Appl. Soft Comput., vol. 38, pp. 1012–1024, 2016.
  29. He, Q. H. Wu, and J. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior,” EEE Trans. Evol. Comput., vol. 13, no. 5, pp. 973–990, 2009.
  30. S. Gazijahani, S. N. Ravadanegh, J. Salehi, “Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies,” ISA Trans., vol. 73, pp. 100–111. 2018.
  31. Jani, H. Karimi, S. Jadid, “Hybrid energy management for islanded networked microgrids considering battery energy storage and wasted energy,” J. Energy Storage, vol. 40, p. 102700, 2021.
  32. Abdolahi, F.S. Gazijahani, A.A. Alizadeh, N. T. Kalantari, “Chance-constrained CAES and DRP scheduling to maximize wind power harvesting in congested transmission systems considering operational flexibility,” Sustainable Cities Soc., vol. 51, p. 101792, 2019.
  33. Olamaei, M.E. Nazari, S. Bahravar, “Economic environmental unit commitment for integrated CCHP-thermalheat only system with considerations for valve-point effect based on a heuristic optimization algorithm,” Energy, vol. 159, pp. 737–750, 2018.
  34. Alipour, B. Mohammadi-Ivatloo, K. Zare, “Stochastic scheduling of renewable and CHP-based microgrids,” IEEE Trans. Ind. Inf., vol. 11, no. 5, pp. 1049–1058, 2015.
  35. Abdolahi, J. Salehi, F.S. Gazijahani, A. Safari, “Assessing the potential of merchant energy storage to maximize social welfare of renewable-based distribution networks considering risk analysis,” Electr. Power Syst. Res., vol. 188, p. 106522, 2022
  36. Shamshirband, J. Salehi, F.S. Gazijahani, “Decentralized trading of plug-in electric vehicle aggregation agents for optimal energy management of smart renewable penetrated microgrids with the aim of CO2 emission reduction,” J. Cleaner Prod., vol. 200, pp. 622–640, 2018.
  37. Li, Z. Yang, G. Li, D. Zhao, W. Tian, “Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties,” EEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1565–1575, 2018.
  38. Bornapour, R.A. Hooshmand, M. Parastegari, “An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids,” Renewable energy, vol. 130, pp. 1049–1066, 2019.
  39. Zeng, M. S. Nazir, M. Khaksar, K. Nishihara, H. Tao, “A Day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm,” J. Energy Storage, vol. 33, p. 102021, 20121.
  40. Li, K. Li, Z. Yang, Y. Yu, R. Xu, M. Yang, “Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach,” J. Cleaner Prod., vol. 30, p. 129840, 2022.
  41. W. Liu, L.L. Li, M.L. Tseng, M.K. Lim, M. Helmi Ali, “Optimal scheduling of combined cooling, heating, and power microgrid based on a hybrid gray wolf optimizer,” J. Ind. Prod. Eng., vol. 39, no. 4, pp. 277–292, 2022.
  42. Khorram?Nia, B. Bahmani?Firouzi, M. Simab, “Optimal scheduling of reconfigurable microgrids incorporating the PEVs and uncertainty effects,” IET Renewable Power Gener., vol. 16, no. 16, pp. 3531–3543, 2022.
  43. Luo, S.S. Abdulkareem, A. Rezvani, M. R. Miveh, S. Samad, N. Aljojo, M. Pazhoohesh, “Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty,” J. Energy Storage, vol. 28, p. 101306, 2020.
آمار
تعداد مشاهده مقاله: 521
تعداد دریافت فایل اصل مقاله: 653


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب