بهره‌برداری بهینه هاب انرژی با استفاده از روش کنترل پیش‌بین مبتنی بر مدل
محورهای موضوعی : مهندسی برق و کامپیوترزهرا هاشمی 1 , امین رمضانی 2 * , محسن پارسامقدم 3
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
3 - دانشگاه تربیت مدرس
کلید واژه: هاب انرژی کنترل پیش‌بین مبتنی بر مدل بهره‌برداری بارهای کنترل‌پذیر,
چکیده مقاله :
مفهوم سامانه هاب انرژی به عنوان مدلی برای مطالعه یک‌پارچه زیرساخت‌های مختلف انرژی مطرح می‌شود. هاب انرژی مجموعه‌ای متشکل از مبدل‌ها و ذخیره‌کننده‌هاست که از یک سو حامل‌های انرژی را از شبکه بالادست دریافت می‌کند و از سوی دیگر به تأمین تقاضای مصرف‌کنندگان انرژی می‌پردازند. در این مقاله مدل‌سازی فضای حالت برای این سامانه ارائه می‌شود، سپس برای بهره‌برداری این سامانه به علت داشتن ساختار دینامیکی و به لحاظ داشتن عدم قطعیت ناشی از پیش‌بینی پروفایل بار و قیمت، روش کنترل پیش‌بین مبتنی بر مدل ارائه می‌شود. روش ارائه‌شده بر روی هاب انرژی شامل ترانسفورماتور، بویلر، CHP و ذخیره‌سازهای حرارتی و الکتریکی با در نظر گرفتن برنامه پاسخ‌گویی بار اعمال می‌شود. نتایج به دست آمده، اثربخشی روش پیشنهادی را برای مسأله بهره‌برداری این سامانه نشان می‌دهد.
Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for optimal operation of the EH.
[1] M. Arnold, R. R. Negenborn, G. Andersson, and B. De Schutter, "Model-based predictive control applied to multi-carrier energy systems," in Proc. IEEE Power & Energy Society General Meeting. PES'09, 8 pp., Jul. 2009.
[2] M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson, and K. Frohlich, "Energy hubs for the future," IEEE Power and Energy Magazine, vol. 5, no. 1, pp. 24-30, Jan./Feb. 2007.
[3] R. Frik and P. Favre-Perrod, Proposal for a Multifunctional Energy Bus and Its Interlink with Generation and Consumption, Master Thesis, ETH Zurich: High voltage Laboratory, 2004.
[4] G. Koeppel and G. Andersson, "The influence of combined power, gas, and thermal networks on the reliability of supply," in Proc. the 6th World Energy System Conf., pp. 10-12, Torino, Italy, Jun. 2006.
[5] K. Alanne and A. Saari, "Distributed energy generation and sustainable development," Renewable and Sustainable Energy Reviews, vol. 10, no. 6, pp. 539-558, Dec. 2006.
[6] G. Koeppel and G. Andersson, "Reliability modeling of multi-carrier energy systems," Energy, vol. 34, no. 3, pp. 235-244, Mar. 2009.
[7] A. Hajimiragha, C. Canizares, M. Fowler, M. Geidl, and G. Andersson, "Optimal energy flow of integrated energy systems with hydrogen economy considerations," in Proc. Bulk Power System Dynamics and Control-VII, 11 pp., 2007.
[8] M. Schulze, L. Friedrich, and M. Gautschi, "Modeling and optimization of renewables: applying the energy hub approach," in Proc. ICSET IEEE Int. Conf. on Sustainable Energy Technologies, pp. 83-88, Dec. 2008.
[9] A. Sheikhi, A. Ranjbar, and H. Oraee, "Financial analysis and optimal size and operation for a multicarrier energy system," Energy and Buildings, vol. 48, pp. 71-78, May 2012.
[10] M. D. Galus, R. La Fauci, and G. Andersson, "Investigating PHEV wind balancing capabilities using heuristics and model predictive control," in Proc. IEEE Power and Energy Society General Meeting, 8 pp., Jul. 2010.
[11] P. Ahcin, "Simulating demand response and energy storage in energy distribution systems," in Proc. Int. Conf. on Proc. Power System Technology, POWERCON'10, 7 pp., Hangzhou, China, Oct. 2010.
[12] F. Kienzle, P. Ahcin, and G. Andersson, "Valuing investments in multi-energy conversion, storage, and demand-side management systems under uncertainty," IEEE Trans. on Sustainable Energy, vol. 2, no. 2, pp. 194-202, Apr. 2011.
[13] A. Parisio, C. Del Vecchio, and A. Vaccaro, "A robust optimization approach to energy hub management," International J. of Electrical Power & Energy Systems, vol. 42, no. 1, pp. 98-104, 2012.
[14] M. Arnold, R. Negenborn, G. Andersson, and B. De Schutter, "Distributed predictive control for energy hub coordination in coupled electricity and gas networks," in R. R. Negenborn, Zofia Lukszo, Hans Hellendoorn Eds., Intelligent Infrastructures, Ch. 10, Springer, pp. 235-273, 2010.
[15] S. Pazouki, M. R. Haghifam, and A. Moser, "Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response," International J. of Electrical Power & Energy Systems, vol. 61, pp. 335-345, Oct. 2014.
[16] E. F. Camacho and C. B. Alba, Model Predictive Control, Springer Science & Business Media, 2013.
[17] L. Wang, Model Predictive Control System Design and Implementation Using MATLAB®, Springer Science & Business Media, 2009.
[18] M. Geidl, Integrated Modeling and Optimization of Multi-carrier Energy Systems, TU Graz, 2007.