[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Social Network Membership
Linkedin
Researchgate
..
Indexing Databases
..
DOI
کلیک کنید
..
ِDOR
..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 9, Issue 4 (11-2020) ::
ieijqp 2020, 9(4): 1-12 Back to browse issues page
Analytical and Statistical Analysis of the Effect of Electric Vehicle Aggregator on the Stochastic Behavior of LMP Using LMP Decomposition
Mohammad Ebrahim Hajiabadi * 1, Hadise Ghanbari1 , Mahdi Samadi1
1- hakim sabzevari university
Abstract:   (2931 Views)
The main goal of this paper is to analytically analyze of the effect of the electric vehicle aggregators on the statistical behaviors of Locational Marginal Prices (LMPs) of busses, considering network congestion. In order to achieve this aim, at the first step, by extending the LMP decomposition into 6 sections in Lemma1, the sensitivities of LMPs to the power generation of aggregators in each bus are analyzed analytically. With the help of the coefficients of LMP decomposition, it can be possible to rank the busses to the presence of electric vehicle aggregators, based on the impact on the random behavior of electricity prices. It must be noted that this ranking does not same with the cap of electric vehicle aggregators. Finally, in order to confirm the analytical results, at second step,  the random behaviors of power generations of aggregators in each bus are simulated in the IEEE-24-bus system, and the STandard Deviations (STD) of LMPs are calculated for all basses. The correlation of STD with LMP decomposition coefficients indicates the accuracy of LMP decomposition in the prediction of the effect of the presence of electric vehicle aggregators on the random behavior of LMP.
Keywords: Electric Vehicle Aggregator, LMP decomposition, Price sensitivity coefficient
Full-Text [PDF 1562 kb]   (612 Downloads)    
Type of Study: Research |
Received: 2019/05/28 | Accepted: 2020/10/24 | Published: 2020/12/2
References
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]



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hajiabadi M E, Ghanbari H, Samadi M. Analytical and Statistical Analysis of the Effect of Electric Vehicle Aggregator on the Stochastic Behavior of LMP Using LMP Decomposition. ieijqp 2020; 9 (4) :1-12
URL: http://ieijqp.ir/article-1-632-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 9, Issue 4 (11-2020) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
Persian site map - English site map - Created in 0.05 seconds with 40 queries by YEKTAWEB 4645