1. 1. Liu Z, Wu Q, Huang S, Wang L, Shahidehpour M, Xue Y. Optimal day-ahead charging scheduling of electric vehicles through an aggregative game model. IEEE Trans Smart Grid. 2018;9(5):5173-84. [ DOI:10.1109/TSG.2017.2682340] 2. 2. حاجیآبادی م. مطالعه آماری قیمت برق و مدلسازی آن به کمک قضیه حد مرکزی جهت بررسی سطح رقابتپذیری. Vol. رساله دکتری. [دانشگاه فردوسی مشهد]: فردوسی مشهد; 1392. 3. 3. شاهمیرزایی ع, قدیری ع, پارسامقدم م. تأثیر خودروهای الکتریکی بر ر وی تراکم خطوط شبکه و قیمت های گره ای در شبکه توزیع. چهارمین کنفرانس سالانه انرژی پاک. کرمان، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته; 1393. 4. 4. Ortega-Vazquez MA, Bouffard F, Silva V. Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement. IEEE Trans Power Syst. 2013;28(2):1806-15. [ DOI:10.1109/TPWRS.2012.2221750] 5. 5. Zhao Y, Feng C, Lin Z, Wen F, He C, Lin Z. Development of Optimal Bidding Strategy for an Electric Vehicle Aggregator in a Real-Time Electricity Market. In: 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE; 2018. p. 288-93. [ DOI:10.1109/ISGT-Asia.2018.8467845] 6. 6. Han S, Han S, Sezaki K. Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: Case studies for market participation. IEEE Trans Smart Grid. 2011;2(4):632-41. [ DOI:10.1109/TSG.2011.2160299] 7. 7. Han S, Han SH, Sezaki K. Probabilistic Analysis on the V2G Power Capacity Regarding Frequency Regulation. IFAC Proc Vol. 2011;44(1):11707-12. [ DOI:10.3182/20110828-6-IT-1002.02649] 8. 8. Li B, Wang X, Shahidehpour M, Jiang C, Li Z. Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators with Correlated Wind Power Generation. IEEE Trans Sustain Energy. 2018; [ DOI:10.1109/TSTE.2018.2875483] 10. 9. Pal S, Kumar R. Electric Vehicle Scheduling Strategy in Residential Demand Response Programs With Neighbor Connection. IEEE Trans Ind Informatics. 2018;14(3):980-8. [ DOI:10.1109/TII.2017.2787121] 11. 10. Clairand J-M, others. Participation of Electric Vehicle Aggregators in Ancillary Services Considering Users' Preferences. Sustainability. 2019;12(1):1-17. [ DOI:10.3390/su12010008] 12. 11. Karfopoulos EL, Panourgias KA, Hatziargyriou ND. Distributed coordination of electric vehicles providing V2G regulation services. IEEE Trans Power Syst. 2016;31(4):2834-46. [ DOI:10.1109/TPWRS.2015.2472957] 13. 12. Sun L, Wang X, Liu W, Lin Z, Wen F, Ang SP, et al. Optimisation model for power system restoration with support from electric vehicles employing battery swapping. IET Gener Transm Distrib. 2016;10(3):771-9. [ DOI:10.1049/iet-gtd.2015.0441] 15. 13. Tian M-W, Yan S-R, Tian X-X, Kazemi M, Nojavan S, Jermsittiparsert K. Risk-involved stochastic scheduling of plug-in electric vehicles aggregator in day-ahead and reserve markets using downside risk constraints method. Sustain Cities Soc. 2020;55:102051. [ DOI:10.1016/j.scs.2020.102051] 16. 14. Quinn C, Zimmerle D, Bradley TH. The effect of communication architecture on the availability, reliability, and economics of plug-in hybrid electric vehicle-to-grid ancillary services. J Power Sources. 2010;195(5):1500-9. [ DOI:10.1016/j.jpowsour.2009.08.075] 17. 15. Gitizadeh M, Kheradmand Khanekehdani H. Modeling operation of electric vehicles aggregator with energy storage system in reserve services market. J Renew Sustain Energy. 2016;8(1):15702. [ DOI:10.1063/1.4940406] 18. 16. Farahmand-Zahed A, Nojavan S, Zare K. Robust Scheduling of Plug-In Electric Vehicles Aggregator in Day-Ahead and Reserve Markets. In: Electricity Markets. Springer; 2020. p. 199-212. [ DOI:10.1007/978-3-030-36979-8_9] 19. 17. Rashidizadeh-Kermani H, Vahedipour-Dahraie M, Najafi HR, Anvari-Moghaddam A, Guerrero JM. A stochastic bi-level scheduling approach for the participation of EV aggregators in competitive electricity markets. Appl Sci. 2017;7(10):1100. [ DOI:10.3390/app7101100] 20. 18. Rassaei F, Soh W-S, Chua K-C. Demand response for residential electric vehicles with random usage patterns in smart grids. IEEE Trans Sustain Energy. 2015;6(4):1367-76. [ DOI:10.1109/TSTE.2015.2438037] 21. 19. Vahedipour-Dahraie M, Rashidizaheh-Kermani H, Najafi HR, Anvari-Moghaddam A, Guerrero JM. Coordination of EVs participation for load frequency control in isolated microgrids. Appl Sci. 2017;7(6):539. [ DOI:10.3390/app7060539] 22. 20. Perez-Diaz A, Gerding E, McGroarty F. Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators. arXiv Prepr arXiv181007063. 2018; 23. 21. Hajiabadi ME, Mashhadi HR. LMP decomposition: A novel approach for structural market power monitoring. Electr Power Syst Res. 2013;99:30-7. [ DOI:10.1016/j.epsr.2013.02.003] 24. 22. استیری م. تعیین سطح قدرت بازار واحدهای نیروگاهی در سطوح مختلف بار برمبنای تغییر سود واحدها با استفاده از تجزیه ساختاری بازار برق. Vol. پایان نامه کارشناسی ارشد. دانشگاه حکیم سبزواری; 1396. 25. 23. Ghaznavi A, Hajiabadi ME, Khaliliyan M. Cost‐worth analytical assessment of demand‐side management (DSM) programs, considering energy losses with the structural generation decomposition: A market‐based approach. Int Trans Electr Energy Syst. 2018;e2584. [ DOI:10.1002/etep.2584] 26. 24. غزنوی ع, حاجیآبادی م, خلیلیان م. تجزیه ساختاری تولید جهت ارزیابی تحلیلی تلفات شبکه با در نظر گرفتن تراکم شبکه انتقال. مجله مهندسی برق دانشگاه تبریز. 1397;1-12. 27. 25. Vayá MG, Andersson G. Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty. IEEE Trans Power Syst. 2015;30(5):2375-85. [ DOI:10.1109/TPWRS.2014.2363159] 28. 26. Dawar V, Lesieutre BC. Impact of electric vehicles on energy market. In: Power and Energy Conference at Illinois (PECI), 2011 IEEE. IEEE; 2011. p. 1-7. [ DOI:10.1109/PECI.2011.5740487] 29. 27. Shafie-Khah M, Moghaddam MP, Sheikh-El-Eslami MK, Catalão JPS. Optimised performance of a plug-in electric vehicle aggregator in energy and reserve markets. Energy Convers Manag. 2015;97:393-408. [ DOI:10.1016/j.enconman.2015.03.074] 30. 28. Tomić J, Kempton W. Using fleets of electric-drive vehicles for grid support. J Power Sources. 2007;168(2):459-68. [ DOI:10.1016/j.jpowsour.2007.03.010] 31. 29. Vagropoulos SI, Bakirtzis AG. Optimal bidding strategy for electric vehicle aggregators in electricity markets. IEEE Trans power Syst. 2013;28(4):4031-41. [ DOI:10.1109/TPWRS.2013.2274673] 32. 30. Sánchez Amaro R, Baringo Morales L. A Stochastic Robust Optimization Approach for the Bidding Strategy of an Electric Vehicle Aggregator. 2017; 33. 31. Vayá MG, Baringo L, Andersson G. Integration of PEVs into power markets: a bidding strategy for a fleet aggregator. In: Plug In Electric Vehicles in Smart Grids. Springer; 2015. p. 233-60. [ DOI:10.1007/978-981-287-302-6_9] 34. 32. Alipour M, Mohammadi-Ivatloo B, Moradi-Dalvand M, Zare K. Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets. Energy. 2017;118:1168-79. [ DOI:10.1016/j.energy.2016.10.141] 35. 33. Barhagh SS, Mohammadi-Ivatloo B, Anvari-Moghaddam A, Asadi S. Risk-involved participation of electric vehicle aggregator in energy markets with robust decision-making approach. J Clean Prod. 2019;239:118076. [ DOI:10.1016/j.jclepro.2019.118076] 36. 34. Cao Y, Huang L, Li Y, Jermsittiparsert K, Ahmadi-Nezamabad H, Nojavan S. Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique. Int J Electr Power Energy Syst. 2020;117:105628. [ DOI:10.1016/j.ijepes.2019.105628] 37. 35. Saini D, Saxena A, Bansal RC. Electricity price forecasting by linear regression and SVM. In: Recent Advances and Innovations in Engineering (ICRAIE), 2016 International Conference on. IEEE; 2016. p. 1-7. [ DOI:10.1109/ICRAIE.2016.7939509] 38. 36. Rashidizadeh-Kermani H, Najafi HR, Anvari-Moghaddam A, Guerrero JM. Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets. J Oper Autom Power Eng. 2018;1-19. [ DOI:10.3390/en11092413] 39. 37. Dalton J, Herre L, Söder L. Exploring the Business Case of a Risk-Averse Electric Vehicle Aggregator in the Nordic Market. In: 2nd E-Mobility Power System Integration Symposium. 2018. 40. 38. Rashidizadeh-Kermani H, Najafi H, Anvari-Moghaddam A, Guerrero J. Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets. Energies. 2018;11(9):2413. [ DOI:10.3390/en11092413] 41. 39. Xu Z, Hu Z, Song Y, Wang J. Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty. IEEE Trans Smart Grid. 2017;8(1):96-105. [ DOI:10.1109/TSG.2015.2477101] 42. 40. Aliasghari P, Mohammadi-Ivatloo B, Abapour M, Ahmadian A, Elkamel A. Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market. Energies. 2020;13(7):1771. [ DOI:10.3390/en13071771] 43. 41. Moghaddam SZ, Akbari T. Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach. Energy. 2018;151:478-89. [ DOI:10.1016/j.energy.2018.03.074] 44. 42. Gao X, Chan KW, Xia S, Zhou B, Lu X, Xu D. Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator. Energy. 2019;177:183-91. [ DOI:10.1016/j.energy.2019.04.048] 46. 43. Nanduri V, Das TK. A reinforcement learning model to assess market power under auction-based energy pricing. IEEE Trans Power Syst. 2007;22(1):85-95. [ DOI:10.1109/TPWRS.2006.888977] 47. 44. Kalinowski B, Anders G. A new look at component maintenance practices and their effect on customer, station and system reliability. Int J Electr Power Energy Syst. 2006;28(10):679-95. [ DOI:10.1016/j.ijepes.2006.03.023]
|