برنامه‌ریزی پیشگیرانه و امکانی- احتمالاتی ریزشبکه‌های الکتریکی در برابر حوادث طبیعی و در حضور خودروهای برقی
محورهای موضوعی : مهندسی برق و کامپیوترامیرحسین نثری 1 , امیر عبداللهی 2 , مسعود رشیدی‌نژاد 3
1 - دانشگاه شهید باهنر کرمان
2 - دانشگاه شهید باهنر کرمان
3 - دانشگاه شهید باهنر کرمان
کلید واژه: برنامه‌ریزی امکانی- احتمالاتی ریزشبکهپارکینگ خودروهای برقیمنابع تجدیدپذیرZ-numberعدم قطعیت,
چکیده مقاله :
این مقاله ساختاری امکانی- احتمالاتی برای برنامه‌ریزی یک روز پیش ریزشبکه‌ها در حضور پارکینگ خودروهای برقی و منابع تولید پراکنده ارائه می‌دهد. برنامه‌ریزی ریزشبکه بر اساس عملکرد آن در حالت عادی و حالت جزیره به دلیل رخداد خطا در شبکه اصلی انجام می‌گردد. در این مطالعه ابتدا عدم قطعیت تعداد خودروهای موجود در پارکینگ در هر ساعت با روش Z-number تعیین می‌گردد. در گام بعد میزان توان تولیدی توربین بادی و پنل‌های فتوولتاییک، قیمت بازار و میزان بار به صورت احتمالاتی با استفاده از روش مونت‌کارلو مدل میگردند. همچنین رخداد حوادث در شبکه بالادست که جزیره‌شدن ریزشبکه را در پی دارد، به صورت سناریومحور و بر اساس زمان شروع رخداد و مدت‌زمان تأثیرگذاری آن در نظر گرفته می‌شود. علاوه بر این، در برنامه‌ریزی بهینه ریزشبکه، ساختاری مبتنی بر عدم قطعیت و شارژ و دشارژ خودروهای برقی برای بهره‌برداری از پارکینگ‌ها پیشنهاد شده است. در این مدل، هزینه بهره‌برداری ریزشبکه در شرایط عملکرد معمول و هزینه عدم تأمین بار و بهره‌برداری به صورت توأمان در حالت رخداد خطا به عنوان توابع هدف پیشنهادی در نظر گرفته شده‌اند. نتایج حاصل از اجرای ساختار پیشنهادی بر روی ریزشبکه اصلاح‌شده 33 باس IEEE، اهمیت این مدل را در بهبود وضعیت امنیت و بهره‌برداری ریزشبکه نشان می‌دهد.
This paper proposes a preventive and probabilistic–possibilistic framework for day-ahead scheduling of Electric Vehicles (EVs) parking lot and distributed generation in a microgrid. The suggested scheduling is performed in normal and emergency conditions when a natural phenomenon appears and the microgrid is disconnected from the upstream network. Furthermore, the uncertainty of EVs number in a parking lot is considered by Z-number as a probabilistic-possibilistic model. Moreover, the uncertainties of photovoltaic units generation, wind turbine output power, market price, and load demand are modeled by Monte Carlo as a probabilistic method. Furthermore, natural phenomena occurrences are modeled by considering multifarious scenarios according to when the phenomenon unfolds and how much it takes. In the suggested framework, the operation of parking lots is based on the uncertainty and EVs charging/discharging schedule. The operational cost in normal condition and load shedding cost in addition to operational cost are considered as the objective functions of the proposed structure. To evaluate the performance of the suggested structure, the modified 33-bus IEEE distribution network is employed.
[1] H. Farhangi, "The path of the smart grid," IEEE Power and Energy Mag., vol. 8, no. 1. Jan./Feb. 2010.
[2] U. S. Department of Energy, DOE Microgrid Workshop Report, Off. Electr. Deliv. Energy Reliab. Smart Grid R&D Program, San Diego, California, USA, pp. 1-32, 2011.
[3] Y. Xu and C. Singh, "Power system reliability impact of energy storage integration with intelligent operation strategy," IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 1129-1137, Mar. 2014.
[4] X. Lu, K. Zhou, S. Yang, and H. Liu, "Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles," J. Clean. Prod., vol. 195, pp. 187-199, Sept. 2018.
[5] M. D. Galus, R. A. Waraich, and G. Andersson, "Predcitive, distributed, hierarchical charging control of PHEVs in the distribution system of a large urban area incorporating a multi-agent transportation simulation," in Proc. 17th Power Syst. Comput. Conf., 7 pp. 2011.
[6] H. Wang and J. Huang, "Joint investment and operation of microgrid," IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 833-845, Mar. 2017.
[7] Z. Wang, B. Chen, J. Wang, and J. Kim, "Decentralized energy management system for networked microgrids in grid-connected and islanded modes," IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 1097-1105, Mar. 2016.
[8] W. Su, J. Wang, and J. Roh, "Stochastic energy scheduling in microgrids with intermittent renewable energy resources," IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1876-1883, Jul. 2014.
[9] Y. Wu, G. J. Lim, and J. Shi, "Stability-constrained microgrid operation scheduling incorporating frequency control reserve," IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1007-1017, Mar. 2020.
[10] G. Liu, T. Jiang, T. B. Ollis, X. Zhang, and K. Tomsovic, "Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics," Appl. Energy, vol. 239, pp. 83-95, Apr. 2019.
[11] W. Chen, Y. Zeng, and C. Xu, "Energy storage subsidy estimation for microgrid: a real option game-theoretic approach," Appl. Energy, vol. 239, pp. 373-382, Apr. 2019.
[12] E. Mortaz, A. Vinel, and Y. Dvorkin, "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Appl. Energy, vol. 242, pp. 1649-1660, May 2019.
[13] M. R. Ebrahimi and N. Amjady, "Adaptive robust optimization framework for day-ahead microgrid scheduling," Int. J. Electr. Power Energy Syst., vol. 107, pp. 213-223, May 2019.
[14] D. E. Olivares, C. A. Canizares, and M. Kazerani, "A centralized energy management system for isolated microgrids," IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1864-1875, Jul. 2014.
[15] S. Singh, S. Jagota, and M. Singh, "Energy management and voltage stabilization in an islanded microgrid through an electric vehicle charging station," Sustain. Cities Soc., vol. 41, pp. 679-694, Aug. 2018.
[16] S. Cai, Y. Xie, Q. Wu, and Z. Xiang, "Robust MPC-based microgrid scheduling for resilience enhancement of distribution system," Int. J. Electr. Power Energy Syst., vol. 121, Article ID 106086, Oct. 2020.
[17] A. Sefidgar-Dezfouli, M. Joorabian, and E. Mashhour, "A multiple chance-constrained model for optimal scheduling of microgrids considering normal and emergency operation," Int. J. Electr. Power Energy Syst., vol. 112, pp. 370-380, Nov. 2019.
[18] A. Khodaei, "Microgrid optimal scheduling with multi-period islanding constraints," IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1383-1392, Nov. 2014.
[19] C. Gouveia, J. Moreira, C. L. Moreira, and J. A. Pecas Lopes, "Coordinating storage and demand response for microgrid emergency operation," IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 1898-1908, Apr. 2013.
[20] S. Bahramirad, W. Reder, and A. Khodaei, "Reliability-constrained optimal sizing of energy storage system in a microgrid," IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2056-2062, Dec. 2012.
[21] J. Mitra and M. R. Vallem, "Determination of storage required to meet reliability guarantees on island-capable microgrids with intermittent sources," IEEE Trans. Power Syst., vol. 27, no. 4, pp. 2360-2367, Aug. 2012.
[22] A. Kavousi-Fard, A. Zare, and A. Khodaei, "Effective dynamic scheduling of reconfigurable microgrids," IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5519-5530, Mar. 2018.
[23] S. E. Ahmadi and N. Rezaei, "A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response," Int. J. Electr. Power Energy Syst., vol. 118, Article ID 105760, Jun. 2020.
[24] A. Khodaei, "Provisional microgrids," IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1107-1115, Mar. 2015.
[25] L. A. Zadeh, "A note on Z-numbers," Inf. Sci., vol. 181, no. 14, pp. 2923-2932, Jul. 2011.
[26] A. Soroudi and T. Amraee, "Decision making under uncertainty in energy systems: State of the art," Renew. Sustain. Energy Rev., vol. 28, pp. 376-384, Dec. 2013.
[27] B. Kang, D. Wei, Y. Li, and Y. Deng, "A method of converting Z-number to classical fuzzy number," Inf. Comput. Sci., vol. 9, no. 3, pp. 703-709, Mar. 2012.
[28] "NHTS Home Page." [Online]. Available: http://nhts.ornl.gov/. [Accessed: 07-Jul-2017].
[29] S. Han, S. Han, and K. Sezaki, "Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: case studies for market participation," IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 632-641, Dec. 2011.
[30] R. Coppi, P. D'Urso, P. Giordani, and A. Santoro, "Least squares estimation of a linear regression model with LR fuzzy response," Comput. Stat. Data Anal., vol. 51, no. 1, pp. 267-286, Nov. 2006.
[31] H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control, Birkhäuser, 2006.
[32] L. Dong, W. Cheng, H. Bao, and Y. Yang, "Probabilistic load flow analysis for power system containing wind farms," in Proc Asia-Pacific, Power Energy Eng. Conf., 4 pp., Chengdu, China, 28-31 Mar. 2010.
[33] H. Haddadian and R. Noroozian, "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Appl. Energy, vol. 185, pp. 650-663, Jan. 2017.
[34] M. Chis, M. M. A. Salama, and S. Jayaram, "Capacitor placement in distribution systems using heuristic search strategies," IEE Proc.-Generation, Transm. Distrib., vol. 144, no. 3, pp. 225-230, May 1997.
[35] H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, "Enhancing power system resilience through hierarchical outage management in multi-microgrids," IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2869-2879, Nov. 2016.
[36] H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, "Developing a stochastic approach for optimal scheduling of isolated microgrids," in Proc. 23rd Iran. Conf. Electr. Eng., vol. 10, pp. 1671-1676, 10-14 May 2015.
[37] F. H. Pereyra Zamora, et al., "Evaluation of the impact of electric vehicles on distribution systems combining deterministic and probabilistic approaches," IET Conf. Publ., Aritcle ID 0861, Stockholm, Sweden, 10-13 Jun. 2013.
[38] R. Wegmann, V. Doge, J. Becker, and D. U. Sauer, "Optimized operation of hybrid battery systems for electric vehicles using deterministic and stochastic dynamic programming," J. Energy Storage, vol. 14, pp. 22-38, Dec. 2017.
[39] Y. Jingwei, L. Diansheng, Y. Shuang, and H. Shiyu, "Charging load forecasting for electric vehicles based on fuzzy inference," Commun. Comput. Inf. Sci., vol. 484, pp. 585-594, Nov. 2014.
[40] A. Khodaei, "Resiliency-oriented microgrid optimal scheduling," IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1584-1591, Jul. 2014.
