پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 446 |
| تعداد مقالات | 5,719 |
| تعداد مشاهده مقاله | 8,018,497 |
| تعداد دریافت فایل اصل مقاله | 6,527,293 |
Decentralized Optimization in the Scheduling of Three Virtual Power Plants with Non-Convex Constraints | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 1، دوره 56، شماره 1، 2024، صفحه 3-18 اصل مقاله (2.03 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2024.23304.5364 | ||
| نویسندگان | ||
| Amir Hossein Gholami* 1؛ Amir Aboulfazl Suratgar1؛ Mohammad Bagher Menhaj1؛ Mohammad Reza Hesamzadeh2 | ||
| 1Distributed Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University, Tehran, Iran | ||
| 2Department of Electrical Engineering and Computer Science, KTH, Stockholm, Sweden | ||
| چکیده | ||
| Virtual power plant planning (VPP) has received much attention in recent years. VPP refers to the integration of multiple power units, considered as a single power plant. In this paper, three VPPs are considered, each consisting of different power plant units and expected to supply the desired load. In addition to providing the desired load, they must maximize their profits. A decentralized optimization method was used to optimize these three VPPs. The reason for using a decentralized approach is to increase network security and eliminate the need for a central computer. However, using decentralized optimization increases the speed of problem-solving. Finally, the obtained results are compared with the centralized method. Simulations show that almost the same results are achieved using different optimization methods. These results increase the trend of using decentralized methods in VPP. Another feature of decentralized methods compared to the centralized method is the reduction in the speed of problem-solving, which in this article has greatly reduced the solution time. If the considered network becomes wider and the number of problem variables and their limitations increases, the use of decentralized methods will become more efficient, and in those problems, the difference in problem-solving time by centralized and decentralized methods will increase. | ||
| کلیدواژهها | ||
| Decentralized Optimization؛ Virtual Power Plant (VPP)؛ ADMM Algorithm؛ Fast ADMM Algorithm؛ Fast ADMM with Restart Algorithm | ||
| مراجع | ||
|
[1]. X. Dong, Y. Hua, Y. Zhou, Z. Ren, Y. Zhong, “Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems”, IEEE Transactions on Automation Science and Engineering, 2018, 16(1), 229–240.
[2]. W. Gao, J. Gao, K. Ozbay, Z. Jiang, “Reinforcement-learning-based cooperative adaptive cruise control of buses in the Lincoln-tunnel corridor with time–varying topology”. IEEE Transactions on Intelligent Transportation Systems, 2019.
[3]. Z. Zhang, J. Du, K. Zhu, J. Guo, M. Li, T. Xu, “Optimization scheduling of virtual power plant with carbon capture and waste incineration considering P2G coordination”, Energy Reports, 2022.
[4]. Y. Wang, M. Zhang, J. Ao, Z. Wang, H. Dong, M. Zeng, “Profit Allocation Strategy of Virtual Power Plant Based on Multi-Objective Optimization in Electricity Market”, sustainability, 2022.
[5]. W. Chen, Y. Xiang, J. Liu, “Optimal operation of virtual power plants with shared energy storage”, IET Smart Grid, 2022.
[6]. A. Shahkoomahalli, A. Koochaki, and H. Shayanfar, “Two-stage Operational Planning of a Virtual Power Plant in the Presence of a Demand Response Program”, Journal of Applied Dynamic Systems and Control, Vol.5, No.1, 2022.
[7]. Y. Chen, Q. Du, M. Wu, L. Yang, H. Wang, and Z. Lin, “Two-stage optimal scheduling of virtual power plant with wind-photovoltaic-hydro-storage considering flexible load reserve”, 2022 The 4th International Conference on Clean Energy and Electrical Systems (CEES 2022), 2–4 April, 2022.
[8]. J. Cao, Y. Zheng, X. Han, D. Yang, J. Yu, N. Tomin, P. Dehghanian, “Two-stage optimization of a virtual power plant incorporating with demand response and energy complementation”, Energy Reports 8, 2022.
[9]. H. Wang, Y. Jia, C. S. Lai, K. Li, “Optimal Virtual Power Plant Operational Regime under Reserve Uncertainty”, IEEE, 2022.
[10]. H. Sharma, S. Mishra, “Optimization of Solar Grid‑Based Virtual Power Plant Using Distributed Energy Resources Customer Adoption Model: A Case Study of Indian Power Sector”, Arabian Journal for Science and Engineering, 2021.
[11]. Z. Ullah, A. Arshad, H. Hassanin, “Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources”, energies, 2022.
[12]. X. Wei, Y. Xu, H. Sun, H. Zhao, “Bi-level Hybrid Stochastic/Robust Optimization for Low-Carbon Virtual Power Plant Dispatch”, CSEE Journal of Power and Energy Systems, 2021.
[13]. Z. Ullah, A. Arshad, H. Hassanin, J. Cugley, M. A. Alawi, “Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty”, energies, 2022.
[14]. S. Maiz, L. Baringo, R. G. Bertrand, “Expansion planning of a price-maker virtual power plant in energy and reserve markets”, Sustainable Energy, Grids and Networks, ELSEVIER, 2022.
[15]. A. H. Gholami, A. A. Suratgar, M. B. Menhaj, M. R. Hesamzadeh, “Establishment of a Virtual Power Plant in Grid for Maximizing Producers' Profits and Minimizing Pollutant Emissions and Investment Costs”, 30th International Conference on Electrical Engineering (ICEE), 2022.
[16]. L. Yavuz, A. Önen, S. M. Muyeen, and I. Kamwa, “Transformation of microgrid to virtual power plant—A comprehensive review”, IET Gener., Transmiss. Distrib., vol. 13, no. 11, pp. 1994–2005, Jun. 2019.
[17]. B. Hu, H. Wang, T. Niu, C. Shao, C. Li, “Multi-time-scale coordinated optimal dispatch of virtual power plant under unreliable communication”, CSEE Journal of Power and Energy Systems”, pp. 1-10, 2021.
[18]. W. Liu, H. Xu, X. Wang, S. Zhang, T. Hu, “Optimal dispatch strategy of virtual power plants using potential game theory”, The 5th International Conference on Electrical Engineering and Green Energy, CEEGE, vol. 8, pp. 1069-1079, 2022
[19]. W. Chen, Y. Xiang, J. Liu, “Optimal operation of virtual power plants with shared energy storage”, IET Smart Grid, No. 24, 2022.
[20] H. Eisazadeh, M. M. Moghaddam, B. Alizadeh, “Optimal frequency response of VPP-based power systems considering participation coefficient”, International Journal of Electrical Power and Energy Systems, ELSEVIER, vol. 129, 2021.
[21] A. Suratgar, M. B. Menhaj, Distributed Optimization and Its Application in Electricity Grids, Including Electrical Vehicle, Book chapter of “Electric Transportation Systems in Smart Power Grids: Integration, Aggregation, Ancillary Services, and Best Practices”, Chapter 19, Taylor & Francis, 2023.
[22] SM Malakouti, MB Menhaj, AA Suratgar, The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction , Cleaner Engineering and Technology 15, 100664, 2023
[23] A. Saadati Moghadam, A. A. Suratgar, M. R. Hesamzadeh, S. K. Y. Nikravesh, A Distributed Approach for Solving AC-DC Multi-Objective OPF Problem, International Journal of Electrical Power & Energy Systems, Vol.153, 2023.
[24] Amir Hossein Gholami, Amirabolfazl Suratgar, Mohammad Bagher Menhaj, Mohammad Reza Hesamzadeh, Distributed and Decentralized Optimization with Unknown Agents: A Virtual Power Plant Scheduling Application, SSRN, 2023.
[25] Z. Goudarzi, J. Bagherinejad, M. Rafiee. A. A. Suratgar, Optimizing Investment of Pumped storage Systems for Renewable Energy Future, Journal of Optimization and Industrial Engineering, Vol.16. pp 167-184, 2023. DOI10.22094/JOIE.2023.1974939.2022
[26] B. Farzanegan, M.B. Menhaj, A. A. Suratgar, M. Zanmani, Distributed optimal control for continuous-time nonaffine nonlinear interconnected systems, International Journal of Control, 95 (12), pp: 3462-3476, 2022.
[27] A. Saadati Moghadam, A. A. Suratgar, M. R. Hesamzadeh, S. K. Y. Nikravesh, Multi-objective ACOPF using distributed gradient dynamics, International Journal of Electrical Power & Energy Systems, Vol.141, 2022.
[28] S. Asgari, A. A. Suratgar, M. G. Kazemi, Feedforward fractional order PID load frequency control of microgrid using harmony search algorithm, Vol. 45, No.4, pp1369-1381,2021.
[29] S Asgari, MB Menhaj, AA Suratgar, MG Kazemi, A Disturbance Observer Based Fuzzy Feedforward Proportional Integral Load Frequency Control of Microgrids, International Journal of Engineering 34 (7), 1694-1702, 2021.
[30] E Ranjbar, MB Menhaj, AA Suratgar, J Andreu-Perez, M Prasad, Design of a fuzzy PID controller for a MEMS tunable capacitor for noise reduction in a voltage reference source, SN Applied Sciences, Springer International Publishing, Vol.3, pp:1-17, 2021.
[31] B. Farzanegan, M. Zamani, A.A. Suratgar, M.B. Menhaj, A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations, Control Theory and Technology, vol19, no.2, 283-294, 2021 .
[32] Jafar Tavoosi, Amir Abolfazl Suratgar, Mohammad Bagher Menhaj, Amir Mosavi, Ardashir Mohammadzadeh, and Ehsan Ranjbar, Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction, Sustainability,Vol.13, no.6 , pp:1-21, 2021.
[33] S. Hashempour, A. A. Suratgar, A.Afshar, Distributed Non-Convex Optimization for Energy Efficiency in Mobile Ad-Hoc Networks, IEEE System Journals, Vol.15, no.4, pp:5683-5693, 2021.
[34]. O. P. Akkas, E. Cam, " Optimal operational scheduling of a virtual power plant participating in day-ahead market with consideration of emission and battery degradation cost", International Transaction Electric Engineering System, 2020.
[35]. Z. Jing, J. Zhu, R. Hu, "Sizing optimization for island microgrid with pumped storage system considering demand response", MPCE (J. Mod. Power Syst. Clean Energy), 2017.
[36]. R. Nishihara, L. Lessard, B. Recht, A. Packard, M. I. Jordan, “A General Analysis of the Convergence of ADMM”, 32 nd International Conference on Machine Learning, 2015.
[37]. T. Goldstein, B. O. Donoghue, S. Setzer, R. Baraniuk, “Fast Alternating Direction Optimization Methods”, Industrial and Applied Mathematics, SIAM, 2014.
[38]. M. Shivaie, M. Mokhayeri, M. K. Moghaddam, A. Ashouri-Zadeh, “A reliability-constrained cost-effective model for optimal sizing of an autonomous hybrid solar/wind/diesel/battery energy system by a modified discrete bat search algorithm”, Solar Energy, pp. 344-356, 2019.
[39]. B. M. Ivatloo, M. M. Dalvand, A. Rabiee, “Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients”, Electric Power Systems Research, pp. 9-18, 2013. | ||
|
آمار تعداد مشاهده مقاله: 706 تعداد دریافت فایل اصل مقاله: 378 |
||