مدیریت انرژی و برنامه ریزی عملیاتی میکروگریدهای شبکه شده در یک محیط تصادفی

محورهای موضوعی : مهندسی برق قدرت

گیلدا حسینی ¹ , سیدبابک مظفری ² , سودابه سلیمانی ³

1 - دانشکده مهندسی برق، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده مهندسی برق- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
3 - دانشکده مهندسی برق- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

تاریخ دریافت : 1402/08/23 تاریخ پذیرش : 1402/10/26 تاریخ انتشار : 1403/03/01

کلید واژه: مدیریت انرژی, برنامه پاسخگویی بار, ریزشبکه,

چکیده مقاله :

در این مقاله جهت مدیریت بهینه انرژی و افزایش سود حاصله، یک مدل خطی دو مرحله ای برای هماهنگی میکروگرید های شبکه شده به صورت پیش اقدامانه و اصلاحی ارایه شده است. در مرحله اول، یک برنامه ریزی یک روز جلوتر، بدون در نظر گرفتن عدم قطعیت و در یک محیط قطعی برای میکروگرید ها صورت می گیرد. در مرحله دوم با بهره گیری از یک مدل تصادفی، عدم قطعیت هر میکروگرید در بهره برداری شبکه در زمان واقعی در نظر گرفته می شود. در مدلسازی تابع هدف مسئله، اختلاف ناشی از مرحله های پیش اقدامانه و اصلاحی محاسبه و لحاظ می گردد. سناریوهای عدم قطعیت در تولید انرژی باد، خورشید و تقاضا با استفاده از توابع توزیع احتمال از شبیه سازی مونت کارلو بدست می آیند که سناریوهای نماینده با یک روش کاهش سناریو انتخاب می شوند. در این مقاله از الگوریتم K-means برای کاهش سناریوها و از شاخص DB برای خوشه بندی اتوماتیک استفاده شده است. همچنین مدیریت و کنترل بار با استفاده از برنامه پاسخگویی بار می باشد. راهکار پاسخ به تقاضای پیشنهاد شده برای میکروگرید ها با سه سطح از بار مفروض می باشد که فقط سطح غیر بحرانی بار میکروگرید ها براساس سود اقتصادی شبکه قابلیت کنترل پذیری دارد. مدل ارایه شده بهینه سازی، برنامه ریزی عدد صحیح مختلط می باشد که در محیط نرم افزار گمز شبیه سازی و حل شده است. هدف اصلی مدل دو مرحله ای پیشنهاد شده مدیریت بهینه انرژی مبتنی بر اثر بخشی اقتصادی و با عملکرد مناسب شبکه می باشد که نتایج حاصله کارایی مدل را نشان می دهد.

چکیده انگلیسی:

This article introduces a two-stage linear model designed for the coordination of networked microgrids, aimed at optimizing energy management and enhancing profitability through proactive and corrective strategies. Initially, the first stage involves day-ahead hourly planning for microgrids, executed in a deterministic environment without accounting for uncertainties. Subsequently, the second stage addresses these uncertainties in real-time network operation through a stochastic programming approach. The model's objective function quantifies and incorporates the variations resulting from both the proactive and corrective phases. To handle uncertainties in wind and solar energy production as well as load demand, probability distribution functions derived from Monte Carlo simulations are utilized. From these, representative scenarios are chosen using a scenario reduction technique. Specifically, the K-means algorithm is employed for scenario clustering, with the Davies-Bouldin (DB) index facilitating automatic clustering. Additionally, load management is conducted via a demand response program. The proposed model stipulates that, within microgrids, only non-critical load levels can be modulated based on the network's economic benefit. This optimization model, formulated as mixed integer programming, is simulated and resolved in the GAMS software environment. The primary goal of this two-stage model is to achieve optimal energy management by balancing economic efficiency with robust network performance. The results obtained validate the model's effectiveness.

منابع و مأخذ:

1. M. M. dos Anjos, D. R. Tenenwurcel, L. A. Santos, W. R. Ferreira, A. L. Costa, and E. Pereira, "Low Carbon Transition through Renewables Sources–An Overview ofthe Renewable Energy Program in the State of Minas Gerais," Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 8, pp. 252-267, 2020, https://doi.org/10.13044/j.sdewes.d7.0295.


2. D. S. Kirschen and G. Strbac, Fundamentals of power system economics: John Wiley & Sons, 2018, https://doi.org/10.1002/0470020598.


3. K. Jalilpoor, R. Khezri, A. Mahmoudi, and A. Oshnoei, "Optimal sizing of energy storage system," in Variability, Scalability and Stability of Microgrids, ed: Institution of Engineering and Technology, 2019, pp. 263-289, https://doi.org/10.1049/PBPO139E_ch8.


4. S. Kakran and S. Chanana, "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, vol. 81, pp. 524-535, 2018, https://doi.org/10.1016/j.rser.2017.07.045.


5. A. Senthilvadivu, K. Gayathri, and K. Asokan, "Exchange Market algorithm based Profit Based Unit Commitment for GENCOs Considering Environmental Emissions," Int. J. Appl. Eng. Res, vol. 13, pp. 14997-15010, 2018.


6. M. Rahmani, S. H. Hosseinian, and M. Abedi, "Stochastic two-stage reliability-based Security Constrained Unit Commitment in smart grid environment," Sustainable Energy, Grids and Networks, vol. 22, p. 100348, 2020, https://doi.org/10.1016/j.segan.2020.100348.


7. K. Jalilpoor, A. Oshnoei, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam, "Network hardening and optimal placement of microgrids to improve transmission system resilience: A two-stage linear program," Reliability Engineering & System Safety, vol. 224, p. 10.8536, 2022, https://doi.org/10.1016/j.ress.2022.108536.


8. C. Zhang and L. Yang, "Distributed AC security-constrained unit commitment for multi-area interconnected power systems," Electric Power Systems Research, vol. 211, p. 108197, 2022, https://doi.org/10.1016/j.epsr.2022.108197.


9. A. Cretì and F. Fontini, Economics of electricity: Markets, competition and rules: Cambridge University Press, 2019, https://doi.org/10.1017/9781316884614.


10. D. Putz, D. Schwabeneder, H. Auer, and B. Fina, "A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment," International Journal of Electrical Power & Energy Systems, vol. 125, p. 106426, 2021, https://doi.org/10.1016/j.ijepes.2020.106426.


11. K. Jalilpoor, M. T. Ameli, S. Azad, and Z. Sayadi, "Resilient energy management incorporating energy storage system and network reconfiguration: A framework of cyber‐physical system," IET Generation, Transmission & Distribution, vol. 17, pp. 1734-1749, 2023, https://doi.org/10.1049/gtd2.12478.


12. M. T. Ameli, K. Jalilpoor, M. M. Amiri, and S. Azad, "Reliability analysis and role of energy storage in resiliency of energy systems," in Energy Storage in Energy Markets, ed: Elsevier, 2021, pp. 399-416, https://doi.org/10.1016/B978-0-12-820095-7.00011-X.


13. W. Abdulrazzaq Oraibi, B. Mohammadi-Ivatloo, S. H. Hosseini, and M. Abapour, "Multi Microgrid Framework for Resilience Enhancement Considering Mobile Energy Storage Systems and Parking Lots," Applied Sciences, vol. 13, p. 1285, 2023, https://doi.org/10.3390/app13031285.


14. S. S. K. R. Vaka and S. K. Matam, "Optimal sizing of hybrid renewable energy systems for reliability enhancement and cost minimization using multiobjective technique in microgrids," Energy Storage, vol. 5, p. e4.19,2023, https://doi.org/10.1002/est2.419.


15. N. Kumar, S. Dahiya, and K. Singh Parmar, "Multi-objective Economic Emission Dispatch Optimization Strategy Considering Battery Energy Storage System in Islanded Microgrid," Journal of Operation and Automation in Power Engineering, 2023, https://doi.org/10.22098/joape.2023.11399.1852.


16. A. Bhatt and W. Ongsakul, "Optimal techno-economic feasibility study of net-zero carbon emission microgrid integrating second-life battery energy storage system," Energy Conversion and Management, vol. 266, p. 115825, 2022, https://doi.org/10.1016/j.enconman.2022.115825.


17. M. Majidi and S. Nojavan, "Optimal sizing of energy storage system in a renewable-based microgrid under flexible demand side management considering reliability and uncertainties," Journal of Operation and Automation in Power Engineering, vol. 5, pp. 205-214, 2017, https://doi.org/10.22098/joape.2017.3356.1268.


18. N. Nasiri, M. R. Banaei, and S. Zeynali, "A hybrid robust-stochastic approach for unit commitment scheduling in integrated thermal electrical systems considering high penetration of solar power," Sustainable Energy Technologies and Assessments, vol. 49, p. 101756, 2022, https://doi.org/10.1016/j.seta.2021.101756.


19. B. Dey, S. Misra, and F. P. G. Marquez, "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, vol. 334, p. 120717, 2023, https://doi.org/10.1016/j.apenergy.2023.120717.


20. R. Mannini, J. Eynard, and S. Grieu, "A survey of recent advances in the smart management of microgrids and networked microgrids," Energies, vol. 15, p. 7009, 2022, https://doi.org/10.3390/en15197009.


21. K. Jalilpoor, S. Nikkhah, M. S. Sepasian, and M. G. Aliabadi, "Application of precautionary and corrective energy management strategies in improving networked microgrids resilience: A two-stage linear programming," Electric Power Systems Research, vol. 204, p. 107704, 2022, https://doi.org/10.1016/j.epsr.2021.107704.


22. H. Xie, X. Teng, Y. Xu, and Y. Wang, "Optimal energy storage sizing for networked microgrids considering reliability and resilience," IEEE Access, vol. 7, pp. 86336-86348, 2019, https://doi.org/10.1109/ACCESS.2019.2922994.


23. A. Soroudi, Power system optimization modeling in GAMS vol. 78: Springer, 2017, https://doi.org/10.1007/978-3-319-62350-4.


24. A. Nafisi, R. Arababadi, A. Moazami, and K. Mahapatra, "Economic and emission analysis of running emergency generators in the presence of demand response programs," Energy, vol. 255, p. 124529, 2022, https://doi.org/10.1016/j.energy.2022.124529.


25. M. H. Imani, P. Niknejad, and M. Barzegaran, "Implementing Time-of-Use Demand Response Program in microgrid considering energy storage unit participation and different capacities of installed wind power," Electric Power Systems Research, vol. 175, p. 105916, 2019, https://doi.org/10.1016/j.epsr.2019.105916.


26. A. Soroudi, M. Aien, and M. Ehsan, "A probabilistic modeling of photo voltaic modules and wind power generation impact on distribution networks," IEEE Systems Journal, vol. 6, pp. 254-259, 2011, https://doi.org/10.1109/JSYST.2011.2162994.


27. S. Nikkhah, K. Jalilpoor, E. Kianmehr, and G. B. Gharehpetian, "Optimal wind turbine allocation and network reconfiguration for enhancing resiliency of system after major faults caused by natural disaster considering uncertainty," IET Renewable Power Generation, vol. 12, pp. 1413-1423, 2018, https://doi.org/10.1049/iet-rpg.2018.5237.


28. S. S. Fazlhashemi, M. Sedighizadeh, and M. E. Khodayar, "Day-ahead energy management and feeder reconfiguration for microgrids with CCHP and energy storage systems," Journal of Energy Storage, vol. 29, p. 101301, 2020, https://doi.org/10.1016/j.est.2020.101301.


29. F. Zheng, X. Meng, L. Wang, and N. Zhang, "Operation Optimization Method of Distribution Network with Wind Turbine and Photovoltaic Considering Clustering and Energy Storage," Sustainability, vol. 15, p. 2184, 2023, https://doi.org/10.3390/su15032184.


30. G. Alkhayat and R. Mehmood, "A review and taxonomy of wind and solar energy forecasting methods based on deep learning," Energy and AI, vol. 4, p. 100060, 2021, https://doi.org/10.1016/j.egyai.2021.100060.


31. M. Sedighizadeh, S. S. Fazlhashemi, H. Javadi, and M. Taghvaei, "Multi-objective day-ahead energy management of a microgrid considering responsive loads and uncertainty of the electric vehicles," Journal of Cleaner Production, p. 121562, 2020, https://doi.org/10.1016/j.jclepro.2020.121562.


32. E. Mortaz and J. Valenzuela, "Microgrid energy scheduling using storage from electric vehicles," Electric Power Systems Research, vol. 143, pp. 554-562, 2017, https://doi.org/10.1016/j.epsr.2016.10.062.