افزایش دادن سود مالکان واحدهای تولید پراکنده همراه با کاهش تلفات سیستم توزیع با استفاده از الگوریتم گرگ خاکستری بهبودیافته

محورهای موضوعی : مهندسی برق قدرت

سید امیر محمد لاحقی ¹ , بهروز ذاکر ²

1 - دانشکده مهندسي برق و کامپیوتر، دانشگاه شیراز، شیراز، فارس، ايران
2 - دانشکده مهندسي برق و کامپیوتر، دانشگاه شیراز، شیراز، فارس، ايران

تاریخ دریافت : 1402/12/22 تاریخ پذیرش : 1403/03/22 تاریخ انتشار : 1403/12/29

کلید واژه: تولید پراکنده, قیمت‌گذاری بهینه, بهینه‌سازی گرگ خاکستری, درخت تصمیم,

چکیده مقاله :

این مقاله یک راهکار جامع برای بهینه‌سازی عملکرد واحدهای تولید پراکنده در سیستم‌های توزیع ارائه می‌دهد. با تمرکز بر کاهش تلفات شبکه توزیع، راهکار پیشنهادی از قیمت‌گذاری نقطه به نقطه استفاده می‌کند تا قیمت‌ها را در سراسر سیستم توزیع تعیین کند. هدف بهینه‌سازی بر کمینه کردن تلفات شبکه تمرکز دارد و از قیمت‌های مشارکتی اعلام‌شده توسط مالکان واحدهای تولید پراکنده استفاده می‌کند. هم‌چنین بهینه‌سازی قیمت‌ها با استفاده از الگوریتم بهینه‌سازی گرگ خاکستری بهبودیافته انجام می‌شود که برای بهبود آن از یک مدل درخت تصمیم استفاده شده است که اجازه تشخیص راهکارهای بهینه در هر تکرار را فراهم می‌کند که این اقدام باعث افزایش سرعت و دقت در هر مرحله از آموزش الگوریتم می‌شود. کارایی روش پیشنهادی بر روی دو سیستم توزیع آزمایشی 33شینه و 69شینه IEEE در نرم‌افزار MATLAB ارزیابی می‌شود که نتایج آن حاکی از بهبودی چشمگیر در سرعت و دقت راهکار ارائه‌شده نسبت به روش‌های قبلی است. به طور کلی، این مطالعه می‌تواند به پیشرفت استراتژی‌های کارآمد برای مدیریت واحدهای تولید پراکنده در سیستم‌های توزیع، با تاکید بر سودآوری و حل چالش‌های بهینه‌سازی شبکه، کمک شایانی کند.

چکیده انگلیسی:

This paper presents a comprehensive solution for optimizing the performance of distributed generation units in distribution systems. Focusing on reducing distribution network losses, the proposed solution utilizes point-to-point pricing method to determine prices across the distribution system. The optimization objective is to minimize network losses, utilizing participatory prices declared by the owners of distributed generation units. Furthermore, price optimization is carried out using an improved grey wolf optimization algorithm, which employs a decision tree model to identify optimal solutions in each iteration, enhancing speed and accuracy at each stage of the algorithm training. The efficacy of the proposed method is evaluated on two IEEE 33-bus and 69-bus test distribution systems in MATLAB software, showing significant improvement in the speed and accuracy of the proposed solution compared to previous methods. Overall, this study can contribute to the advancement of efficient strategies for managing distributed generation units in distribution systems, emphasizing profitability and addressing network optimization challenges.

منابع و مأخذ:

[1] M. Dehghani, M. Ghiasi, T. Niknam, A. Kavousi-Fard, M. Shasadeghi, N. Ghadimi, F. Taghizadeh-Hesary, “Blockchain-Based Securing of Data Exchange in a Power Transmission System Considering Congestion Management and Social Welfare,” Sustainability, vol. 13, no. 90, pp. 1-21, December 2020, doi: 10.3390/su13010090.
[2] H. M. Alzoubi, G. Ahmed, A. Al-Gasaymeh, B. Al Kurdi, “Empricial study on sustainable supply chain strategies and its impact on competitive priorities: The mediating role of supply chain collaboration,” Management Science Letters, vol. 10, no. 3, pp. 703-708, September 2019, doi: 10.526/j.msl.2019.9.008.
[3] H. Abdeltawab and Y. A. -R. I. Mohamed, “Energy Storage Planning for Profitability Maximization by Power Trading and Ancillary Services Participation,” IEEE Systems Journal, vol. 16, no. 2, pp. 1909-1920, June 2022, doi: 10.1109/JSYST.2021.3069671.
[4] B. Zaker, “A New Dynamic Equivalent Model for Microgrids Including Distributed Generation Units and Static Compensators,” Technovations of Electrical Engineering in Green Energy System, vol. 3, no. 1, pp. 1-16, October 2023, doi: 10.30486/teeges.2023.1997093.1095.
[5] M. Farhangnia, M. Haghighatdar-Fesharaki, “Peek Shaving of Industrial Customers through Combined Installation of Photovoltaic Power Plant and Energy Storage System,” Technovations of Electrical Engineering in Green Energy System, vol. 3, no. 1, pp. 17-32, February 2024, doi: 10.30486/teeges.2023.1997093.1095.
[6] M. Rostamnia, M. S. Rostamnia, E. Heydarian-Forushani, S. F. Zarei, S. H. Hosseianian, “Decentralized Agent-Based Protection Coordination for Distribution Networks with Renewable Distributed Generations using Intelligent Electronic Devices,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 3, pp. 54-75, November 2023, doi: 10.30486/teeges.2023.1986361.1072.
[7] P. M. Sotkiewicz and J. M. Vignolo, “The value of intermittent wind DG under Nodal Prices and Amp-mile Tariffs,” 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), Montevideo, Uruguay, 2012, pp. 1-7, doi: 10.1109/TDC-LA.2012.6319114.
[8] P. M. Sotkiewicz and J. M. Vignolo, “Nodal pricing for distribution networks: efficient pricing for efficiency enhancing DG,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 1013-1014, May 2006, doi: 10.1109/TPWRS.2006.873006.
[9] S. A. M. Lahaghi, E. Azad-Farsani, “A risk-averse approach for distribution locational marginal price calculation in electrical distribution networks,” Energy, vol. 291, March 2024, doi: 10.1016/j.energy.2024.130383.
[10] R. Tang, S. Wang, H. Li “Game theory based interactive demand side management responding to dynamic pricing in price-based damnd response of smart grids,” Applied Energy, vol. 250, September 2019, doi: 10.1016/j.apenergy.2019.04.177.
[11] E. Azad-Farsani, I. Goroohi Sardou, S. Abedini, “Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA,” Energy, vol. 291, January 2021, doi: 10.1016/j.energy.2020.119146.
[12] D. Rabah, C. Abdelghani, H. Abdelchafik, “Efficiency of some optimization approaches with the charge simulation method for calculating the electric field under extra high voltage power lines.” IET Generation, Transmission & Distributiion, vol. 11, no. 17, November 2017, pp. 4167-4174, doi: 10.1049/iet-gtd.2016.1297.
[13] M. Zhou, T. Hu, K. Bian, W. Lai, F. Hu, O. Hamrani, Z. Zhu, “Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization.” Energies, vol. 14, no. 16, August 2021, doi: 10.3390/en14164890.
[14] B. Dey, B. Bhattacharyya, R. Devarapalli, “A novel hybrid algorithm for solving emerging electricity market problem of microgrid,” International Journal of Intelligent Systems, vol. 36, no. 2, pp. 919-961, November 2020, doi: 10.1002/int.22326.
[15] K. Bhatia, R. Mittal, J. Varanasi, M. M. Tripathi, “An ensemble approach for electricity price forecasting in markets with renewable energy sources,” Utilities Policy, vol. 70, June 2021, doi: 10.1016/j.jup.2021.101185.
[16] W. Yang, J. Wang, T. Niu, P. Du, “A novel system for multi-step electricity price forecasting for electricity market management,” Applied Soft Computing, vol. 88, March 2020, doi: 10.1016,/j.asacp.2019.106029.
[17] F. B. Ozsoydan, “Effects of dominant wolves in grey wolf optimization algorithm,” Applied Soft Computing, vol. 83, October 2019, doi: 10.1016,/j.asacp.2019.105658.
[18] Y. Y. Song, Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch Psychiatry, vol. 27, no. 2, pp. 130-135, April 2015, doi: 10.11919/j.issn.1002-0829.215044.
[19] S. H. Dolatabadi, M. Ghorbanian, P. Siano and N. D. Hatziargyriou, “An Enhanced IEEE 33 Bus Benchmark Test System for Distribution System Studies,” IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2565-2572, May 2021, doi: 10.1109/TPWRS.2020.3038030.
[20] S. Ghosh, Y. J. Isbeih and M. S. E. Moursi, “Assessment of Bus Inertia to Enhance Dynamic Flexibility of Hybrid Power Systems With Renewable Energy Integration,” IEEE Transactions on Power Delivery, vol. 38, no. 4, pp. 2372-2386, August 2023, doi: 10.1109/TPWRD.2023.3241721.
[21] W. Qi, N. Zhang, G. Zong, S. -F. Su, H. Yan and R. -H. Yeh, “Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm,” IEEE Transactions on Cybernetics, vol. 53, no. 10, pp. 6503-6515, October 2023, doi: 10.1109/TCYB.2023.3253701.
[22] C. Srinivas, V. Bhargavi, N. S. Babu, P. Harika and P. Kranthi, “Minimization of Power Losses in the Distribution System by Controlling Tap Changing Transformer using the PSO Algorithm,” 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 740-745, doi: 10.1109/IDCIoT56793.2023.10053479.
[23] F. Olsina, F. Graces, H. J. Haubrich, “Modeling long-term dynamics of electricity markets,” Energy Policy, vol. 34, no. 12, pp. 1411-1433, August 2006, doi: 10.1016/j.enpol.2004.11.003.
[24] M. H. Nazari, M. Bagheri Sanjareh, A. Khodadadi, M. Torkashvand, S. H. Hosseinian, “An economy-oriented DG-based scheme for reliability improvement and loss reduction of active distribution network based on game-theoretic sharing strategy,” Sustainable Energy, Grids and Network, vol. 27, September 2021, doi: 10.1016/j.segan.2021.100514.
[25] K. Zhang, S. Hanif, C. M. Hackl, T. Hamacher, “A Framework for Multi-Regional Real-Time Pricing in Distribution Grids,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6826-6838, November 2019, doi: 10.1109/TSG.2019.2911996.