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Risk Based Energy Management of Renewable Based Smart EV Parking Lot | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 2، شماره 4 - شماره پیاپی 10، اردیبهشت 2023، صفحه 23-30 اصل مقاله (1.3 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2024.32772.1138 | ||
| نویسندگان | ||
| Mahsa Salari* ؛ Mehdi Jafari Shahbazzadeh؛ Mahdiyeh Eslami | ||
| Department of Electrical Engineering Kerman Branch, Islamic Azad University, Kerman, Iran. | ||
| تاریخ دریافت: 03 دی 1402، تاریخ پذیرش: 09 بهمن 1402 | ||
| چکیده | ||
| Electric Vehicles (EVs) have penetrated the modern distribution system in the last decade. On the other hand, Renewable Energies (RE) play a serious task in such Micro-Grids (MG). A typical MG consists of several Distributed Energy Resources (DER) including Distributed Generations (DGs) and Demand Response (DR) as well as EV charge/discharge stations. In this paper, optimal charging and discharging strategies based on DR programs are applied to Electric Vehicle charging stations equipped with renewable energies. To avoid profit loss due to renewable uncertainties, Peer-to-peer (P2P) energy bartering between EV charging stations as prosumers are suggested in this paper. Hence the management system for the charge and discharge of EVs and station batteries, as well as the Energy Management System (EMS) are developed in this paper. To this end firstly developed EMS applied to the individual station. Secondly, the P2P power transaction was added to the model in order to smoothen volatile uncertain load and renewables. The proposed model is a Mixed-Integer Linear Programming (MILP) and has been solved by GAMS/CPLEX. Numerical studies have shown that aggregator deployment is more beneficiary for Virtual Power Plant (VPP). | ||
| کلیدواژهها | ||
| Demand Energy Resources؛ Renewable Energy؛ Uncertainty؛ Demand Response؛ Energy Management System؛ Peer to Peer | ||
| مراجع | ||
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[1] S. Hunkin and K. Krell. Renewable energy communities, policy brief from the policy learning platform on low-carbon economy, Aug. 2018. [Online]. Available: https://interregeurope.eu/
[2] S. Minniti, N. Haque, P. Nguyen, and G. Pemen, “Local markets for flexibility trading: Key stages and enablers,” Energies, vol. 11, no. 11, Nov. 2018.
[3] I. Baäekovi and P. A. Østergaard, “Local smart energy systems and cross-system integration,” Energy, vol. 151, pp. 812-825, May 2018.
[4] G. Raveduto, V. Croce, M. Antal, C. Pop, I. Anghel, and T. Cioara, “Dynamic coalitions of prosumers in virtual power plants for energy trading and profit optimization,” IEEE 20th Mediterranean Electrotechnical Conference (MELECON), pp. 541-546, Jun. 2020.
[5] M. Yazdanie, M. Densing, and A.Wokaun, “The nationwide characterization and modeling of local energy systems: Quantifying the role of decentralized generation and energy resources in future communities,” Energy Policy, vol. 118, pp. 516-533, Jul. 2018.
[6] J.-H. Kim and A. Shcherbakova, “Common failures of demand response,” Energy, vol. 36, no. 2, pp. 873-880, Feb. 2011.
[7] M. Beaudin and H. Zareipour, “Home energy management systems: A review of modelling and complexity,” Renewable and Sustainable Energy Reviews, vol. 45, pp. 318-335, May 2015.
[8] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen, “A distributed algorithm for managing residential demand response in smart grids,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2385-2393, Nov. 2014.
[9] A. H. Mohsenian-Rad and A. Leon-Garcia, “Optimal residential load control with price prediction in real-time electricity pricing environments,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 120-133, Sep. 2010.
[10] F. D. Angelis, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, and Q. Wei, “Optimal home energy management under dynamic electrical and thermal constraints,” IEEE Transactions on Industrial Informatics, vol. 9, no. 3, pp. 1518-1527, Aug. 2013.
[11] A. Soares, A. Gomes, C. H. Antunes, and C. Oliveira, “A customized evolutionary algorithm for multi-objective management of residential energy resources,” IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 492-501, Apr. 2017.
[12] M. Pipattanasomporn, M. Kuzlu, and S. Rahman, “An algorithm for intelligent home energy management and demand response analysis,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2166-2173, Dec. 2012.
[13] N. G. Paterakis, A. Tackaraolu, O. Erdin, A. G. Bakirtzis, and J. P. S. Catalo, “Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households,” IEEE Transactions on Industrial Informatics, vol. 12, no. 4, pp. 1529-1539, Aug. 2016.
[14] I. Y. Joo and D. H. Choi, “Optimal household appliance scheduling considering consumer’s electricity bill target,” IEEE Transactions on Consumer Electronics, vol. 63, no. 1, pp. 19-27, Feb. 2017.
[15] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen, “Optimal residential load management in smart grids: A decentralized framework,” IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 1836-1845, Jul. 2016.
[16] B. Celik, R. Roche, D. Bouquain, and A. Miraoui, “Decentralized neighborhood energy management with coordinated smart home energy sharing,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6387-6397, Nov. 2018.
[17] B. Celik, R. Roche, D. Bouquain, A. Miraoui, T. Hansen, and S. Suryanarayanan, “Increasing local renewable energy use in smart neighborhoods through coordinated trading,” Cyber-Physical-Social Systems and Constructs in Electric Power Engineering. Edison, NJ, USA: Institution of Engineering and Technology, Oct. 2016, ch. 9.
[18] M. Jadidbonab, B. Mohammadi-Ivatloo, M. Marzband, and P. Siano, “Short-term self-scheduling of virtual energy hub plant within thermal energy market,” IEEE Transactions on Industrial Electronics, vol. 68, no. 4, pp. 3124-3136, Apr. 2021.
[19] H. R. Gholinejad, A. Loni, J. Adabi, and M. Marzband, “A hierarchical energy management system for multiple home energy hubs in neighborhood grids,” Journal of Building Engineering, vol. 28, Mar. 2020, Art. no. 101028.
[20] M. Nazari-Heris, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Marzband, and S. Asadi, “Economic-environmental effect of power to gas technology in coupled electricity and gas systems with price-responsive shiftable loads,” Journal of Cleaner Production, vol. 244, Jan. 2020, Art. no. 118769.
[21] M. A. Mirzaei, A. Sadeghi-Yazdankhah, B. Mohammadi-Ivatloo, M. Marzband, M. Shafie-khah, and J. P. S. Catalão, “Integration of emerging resources in IGDT-based robust scheduling of combined power and natural gas systems considering _exible ramping products,” Energy, vol. 189, Dec. 2019, Art. no. 116195.
[22] M. A. Mirzaei, M. Hemmati, K. Zare, M. Abapour, B. Mohammadi-Ivatloo, M. Marzband, and A. Anvari-Moghaddam, “A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets,” International Journal of Electrical Power & Energy Systems, vol. 123, Dec. 2020, Art. no. 106293.
[23] M. A. Mirzaei, M. Nazari-Heris, K. Zare, B. Mohammadi-Ivatloo, M. Marzband, S. Asadi, and A. Anvari-Moghaddam, “Evaluating the impact of multi-carrier energy storage systems in optimal operation of integrated electricity, gas and district heating networks,” Applied Thermal Engineering, vol. 176, Jul. 2020, Art. no. 115413.
[24] F. Safdarian, O. Ciftci, and A. Kargarian, “A time decomposition and coordination strategy for power system multi-interval operation,” IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5, Aug. 2018. [25] A. Engelmann, Y. Jiang, T. Muhlpfordt, B. Houska, and T. Faulwasser, “Toward distributed OPF using ALADIN,” IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 584-594, Jan. 2019. [26] S. Fan, Z. Li, J. Wang, L. Piao, and Q. Ai, “Cooperative economic scheduling for multiple energy hubs: A bargaining game theoretic perspective,” IEEE Access, vol. 6, pp. 27777-27789, May. 2018.
[27] Z. Tan, P. Yang, and A. Nehorai, “An optimal and distributed demand response strategy with electric vehicles in the smart grid,” IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 861-869, Jan. 2014.
[28] M. shafie-khah, and P. Siano, “A stochastic home energy management system considering satisfaction cost and response fatigue,” IEEE Transactions on Industrial Informatics, vol.14, no.2, pp.629-638, Feb.2018.
[29] L. Gkatzikis, I. Koutsopoulos, and T. Salonidis, “The role of aggregators in smart grid demand response markets,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp. 1247-1257, Jun. 2013.
[30] The italian electricity market - gestore dei mercati energetici (GME), 2016. [Online]. Available: http://www.mercatoelettrico.org.
[31] Salman Habib , Sina Aghakhani , Mobin GhasempourNejati , Mahdi Azimian , Youwei Jia , EmadM. Ahmed , ‘Energy management of an intelligent parking lot equipped with hydrogen storage systems and renewable energy sources using the stochastic p-robust optimization approach’, 1 September 2023, 1278 | ||
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