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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

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  2/2023 - 3

Risk-based Optimal Bidding and Operational Scheduling of a Virtual Power Plant Considering Battery Degradation Cost and Emission

AKKAS, O. P. See more information about AKKAS, O. P. on SCOPUS See more information about AKKAS, O. P. on IEEExplore See more information about AKKAS, O. P. on Web of Science, CAM, E. See more information about CAM, E. on SCOPUS See more information about CAM, E. on SCOPUS See more information about CAM, E. on Web of Science
 
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Download PDF pdficon (1,631 KB) | Citation | Downloads: 487 | Views: 700

Author keywords
distributed power generation, energy management, power system planning, renewable energy sources, risk analysis

References keywords
power(41), energy(29), virtual(26), plant(23), scheduling(12), optimal(12), jijepes(9), stochastic(7), risk(7), markets(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-05-31
Volume 23, Issue 2, Year 2023, On page(s): 19 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.02003
Web of Science Accession Number: 001009953400003
SCOPUS ID: 85164345741

Abstract
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A virtual power plant (VPP) is a system combining various types of distributed energy resources (DERs) to provide a reliable power system operation. It provides the advantage of making changes in generation according to variety, price, and demand conditions with bringing renewable energy sources (RES) in a single portfolio and using their flexibility. In this study, it is tried to find optimal bidding and operational scheduling of a VPP containing Wind Power Plant (WPP), Photovoltaic Power Plant (PVPP), Heat-Only Unit (HOU), Battery Energy Storage System (BESS), Combined Heat and Power Plant (CHPP), and electrical/thermal demands and participating in the day-ahead electricity market in 24-h time interval. It is aimed to maximize profit and minimize emissions with considering the battery cost. A stochastic model is formed by considering the uncertainty arising from RES. In addition, CVaR (Conditional Value at Risk) as a risk measurement technique is applied against the risk arising from low profit scenarios. The proposed optimization problem formulated as a stochastic Mixed Integer Nonlinear Programming (MINLP) model and is solved by solver LINDO in GAMS (General Algebraic Modelling System). The case studies are implemented to show the applicability and effectiveness of the presented model.


References | Cited By  «-- Click to see who has cited this paper

[1] M. Rahimi, F. J.Ardakani, O. Olatujoye, and A. J. Ardakani, "Two-stage interval scheduling of virtual power plant in day-ahead and real-time markets considering compressed air energy storage wind turbine," Journal of Energy Storage, vol 45, pp. 103599, January 2022.
[CrossRef] [Web of Science Times Cited 19]


[2] M. Rahimi, F. J. Ardakani, and A. J. Ardakani, "Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant," Int J Elec Power, vol. 127, pp. 106658 May 2021.
[CrossRef] [Web of Science Times Cited 51]


[3] M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, A. Anvari-Moghaddam, and P. Siano, "Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties," Int J Elec Power, vol. 121, pp. 106126 October 2020.
[CrossRef] [Web of Science Times Cited 47]


[4] M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, M. Shafie-Khah, and J. P. S. Catalão, "Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs," IEEE T Smart Grid, vol. 12, no. 2, pp. 1405-1415, March 2021.
[CrossRef] [Web of Science Times Cited 61]


[5] Y. Pezhmani, M. A. Mirzaei, K. Zare, and B. Mohammadi-Ivatloo, "Robust self-scheduling of a virtual multi-energy plant in thermal and electricity markets in the presence of multi-energy flexible technologies," Int J Energy Res, vol. 46, no. 5, pp. 6225-6245, 2022.
[CrossRef] [Web of Science Times Cited 6]


[6] M. Jafari and A. A. Foroud, "A medium/long-term auction-based coalition-forming model for a virtual power plant based on stochastic programming," Electrical Power and Energy Systems, vol. 118, pp. 105784, June 2020.
[CrossRef] [Web of Science Times Cited 19]


[7] S, Yin, Q. Ai, Z. Li, Y. Zhang, and T. Lu, "Energy management for aggregate prosumers in a virtual power plant: a robust Stackelberg game approach," Int J Electr Power Energy Syst, vol. 117, pp. 105605, May 2020.
[CrossRef] [Web of Science Times Cited 65]


[8] M. A. Tajeddini, A. Rahimi-Kian, and A. Soroudi, "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energ, vol. 73, pp. 958-967, August 2014.
[CrossRef] [Web of Science Times Cited 113]


[9] A. J. Jordehi, "A stochastic model for participation of virtual power plants in futures markets, pool markets and contracts with withdrawal penalty," Journal of Energy Storage, vol. 50, pp. 104304, June 2022.
[CrossRef] [Web of Science Times Cited 30]


[10] R. Heydari, J. Nikoukar, and M. Gandamkor, "Optimal operation of virtual power plant with considering the demand response and electric vehicles," J Electr Eng Technol, vol. 16, pp. 2407-2419, 2021.
[CrossRef] [Web of Science Times Cited 6]


[11] D. Yang, S. He, M. Wang, and H. Pandzic, "Bidding strategy for virtual power plant considering the large-scale integrations of electric vehicles," IEEE Trans Ind Appl, vol. 56, no. 5, pp. 5890-5900, 2020.
[CrossRef] [Web of Science Times Cited 28]


[12] S. Hadayeghparast, A. S. Farsangi, and H. Shayanfar, "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, vol. 172, pp. 630-646, April 2019.
[CrossRef] [Web of Science Times Cited 105]


[13] L. Zhang, D. Liu, G. Cai, L. Lyu, L. H. Koh, and T. Wang, "An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading," Int J Elec Power, vol. 144, pp. 108558, January 2023.
[CrossRef] [Web of Science Times Cited 56]


[14] O. P. Akkas and E. Cam, "Optimal operational scheduling of a virtual power plant participating in day-ahead market with consideration of emission and battery degradation cost," Int Trans Electr Energ Syst, e12418, 2020.
[CrossRef] [Web of Science Times Cited 9]


[15] M. Lamari, Y. Amrane, M. Boudour, and B. Boussahoua, "Multi‐objective economic/emission optimal energy management system for scheduling micro‐grid integrated virtual power plant," Energy. Sci. Eng, vol. 10, pp. 3057-3074, 2022.
[CrossRef] [Web of Science Times Cited 5]


[16] G. Wu, H. Hua, and N. Dongxiao, "Low-carbon economic dispatch optimization of a virtual power plant based on deep reinforcement learning in China's carbon market environment," J Renew Sustain Ener, vol. 14, 056301, 2022.
[CrossRef] [Web of Science Times Cited 3]


[17] C. Yang, X. Du, D. Xu, J. Tang, X. Lin, K. Xie, and W. Li, "Optimal bidding strategy of renewable-based virtual power plant in the day-ahead market," Int J Elec Power, vol. 144, 108557, 2023.
[CrossRef] [Web of Science Times Cited 13]


[18] J. Wang, C. Guo, C. Yu, and Y. Liang, "Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning," Electr Pow Syst Res, vol. 205, 107714, 2023.
[CrossRef] [Web of Science Times Cited 28]


[19] A. H. Elgamal, M. Vahdati, and M. Shahrestani, "Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: A case study in Egypt," Sustainable Cities and Society, vol. 83, 103968, 2022.
[CrossRef] [Web of Science Times Cited 11]


[20] S. Sadeghi, H. Jahangir, B. Vatandoust, M. A. Golkar, A. Ahmadian, and A. Elkamel, "Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach," Int J Elec Power, vol. 127, 106646, 2021.
[CrossRef] [Web of Science Times Cited 59]


[21] M. Shafiekhani, A. Badri, M. Shafie-khah, and J. P. S. Catalão, "Strategic bidding of virtual power plant in energy markets: A bi-level multiobjective approach," Electrical Power and Energy Systems, vol. 113, pp. 208-219, 2019.
[CrossRef] [Web of Science Times Cited 79]


[22] M. Shafiekhani and A. Badri, "A risk based gaming framework for VPP bidding strategy in a joint energy and regulation market," IJST-T Electr Eng, vol. 43, pp. 545-558, 2019.
[CrossRef] [Web of Science Times Cited 6]


[23] A. Alahyari and D. Pozo, "Performance-based virtual power plant offering strategy incorporating hybrid uncertainty modeling and risk viewpoint," Electr Pow Syst Res, vol. 203, 107632, 2022.
[CrossRef] [Web of Science Times Cited 7]


[24] A. K. Pandey, V. K. Jadoun, and N. S. Jayalakshmi, "Real-time and day-ahead risk averse multi-objective operational scheduling of virtual power plant using modified Harris Hawk's optimization," Electr Pow Syst Res, vol. 220, 109285, 2023.
[CrossRef] [Web of Science Times Cited 6]


[25] B. Zhou, X. Liu, Y. Cao, C. Li, C. Y. Chung, and K. W. Chan, "Optimal scheduling of virtual power plant with battery degradation cost," IET Gener Transm Dis, vol. 10, no. 3, pp. 712-725, 2016.
[CrossRef] [Web of Science Times Cited 73]


[26] T. Depci, M. Inci, M. M. Savrun, and M. Büyük, "A review on wind power forecasting regarding impacts on the system operation, Technical Challenges, and Applications," Energy Technology, vol. 10, no. 8, 2101061, 2022.
[CrossRef] [Web of Science Times Cited 3]


[27] S. Nojavan, K. Zare, and B. Mohammadi-Ivaltloo, "Application of fuel cell and electrolyzer as hydrogen energy storage system in energy management of electricity energy retailer in the presence of the renewable energy sources and plug-in electric vehicles," Energy Conversion and Management, vol. 136, pp. 404-417, March 2017.
[CrossRef] [Web of Science Times Cited 99]


[28] V. Thakur and S. S. Chandel, "Maximizing the Solar Gain of a Grid-Interactive Solar Photovoltaic Power Plant," Energy Technology, vol. 1, no. 11, pp. 661-667, 2013.
[CrossRef] [Web of Science Times Cited 13]


[29] A. G. Zamani, A. Zakariazadeh, and S. Jadid, "Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant," International Journal of Electrical Power & Energy, vol. 82, pp. 608-620, November 2016.
[CrossRef] [Web of Science Times Cited 91]


[30] M. Nazari-Heris, S. Abapour, and B. Mohammadi-Ivatloo, "Optimal economic dispatch of FC-CHP based heat and power micro-grids," Applied Thermal Engineering, vol. 114, pp. 756-769, March 2017.
[CrossRef] [Web of Science Times Cited 188]


[31] M. Nazari-Heris, M. Mehdinejad, B. Mohammadi-Ivatloo, and G. Babamalek-Gharehpetian, "Combined heat and power economic dispatch problem solution by implementation of whale optimization method," Neural Computing and Applications, vol. 31, no. 2, pp. 421-436, 2019.
[CrossRef] [Web of Science Times Cited 84]


[32] A. Castillo, J. Flicker, C. W. Hansen, J-P. Watson, and J. Johnson, "Stochastic optimisation with risk aversion for virtual power plant operations: a rolling horizon control," IET Gener Transm Dis, vol. 13, no. 11, pp. 2063-2076, 2019.
[CrossRef] [Web of Science Times Cited 17]


[33] U. B. Filik and M. Kurban, "Optimal power flow analysis of the 22-bus 380-kV interconnected power system in Turkey," IEEE International Power and Energy Conference, pp. 276-279, 2006.
[CrossRef]


[34] B. Ozerdem, S. Ozer, and M. Tosun, "Feasibility study of wind farms: A case study for Izmir, Turkey," J Wind Eng Ind Aerod, vol. 94, no. 10, pp. 725-743, October 2006.
[CrossRef] [Web of Science Times Cited 69]




References Weight

Web of Science® Citations for all references: 1,469 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 42 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-26 20:11 in 193 seconds.




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