یک مدل چندلایه‌ای برای مدیریت انرژی سیستم‌های چندریزشبکه‌ای ادغام شده با خانه‌های هوشمند و خودروهای الکتریکی

مهدی حق پرست ¹ (دانشکده فنی و مهندسی- واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران)
امیر احمری نژاد ² (دانشکده فنی و مهندسی- واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران)
احمد رضائی جوردهی ³ (گروه مهندسی برق- واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران)
شهرام جوادی ⁴ (دانشکده فنی و مهندسی- واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران)
محمود حسینی علی آبادی ⁵ (دانشکده فنی و مهندسی- واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران)

تاریخ ارسال : 1402/02/22 تاریخ تایید : 1402/06/05

کلید واژه: انرژی‌های تجدیدپذیر, ریزشبکه, خانه‌های هوشمند, وسایل نقلیه الکتریکی, بهینه‌سازی چندلایه‌ای, سرویس‌های انعطاف‌پذیری,

چکیده مقاله :

در این مقاله یک استراتژی بهینه‌سازی سه‌لایه‌ای برای مدیریت انرژی سیستم توزیع فعال تحت نفوذ بالای منابع انرژی تجدیدپذیر بادی و خورشیدی معرفی‌ شده است، که در آن ریزشبکه‌ها مسئول تأمین سرویس‌های انعطاف‌پذیری برای شبکه توزیع اصلی هستند. در استراتژی پیشنهادی، بهره‌برداران ریزشبکه‌ها سرویس‌های انعطاف‌پذیری را از طریق منابع تولید پراکنده، سیستم‌های ذخیره‌ساز الکتریکی، خانه‌های هوشمند و خودروهای الکتریکی تأمین می‌کنند. در لایه اول استراتژی پیشنهادی، خانه‌های هوشمند با در نظر گرفتن بازارهای انرژی و انعطاف‌پذیری برنامه‌ریزی شده و سپس برنامه نهایی خود را به بهره‌بردار ریزشبکه‌ اعلام می‌کنند. در لایه دوم، ریزشبکه‌ها با توجه به برنامه‌های دریافتی از خانه‌های هوشمند، برنامه‌ریزی ناحیه خود را انجام داده و برنامه مشارکت خود در بازارهای انرژی و انعطاف‌پذیری را برای بهره‌بردار شبکه توزیع اصلی می‌فرستند. در نهایت، در لایه سوم، بهره‌بردار شبکه اصلی برنامه‌ریزی بازارهای انرژی و انعطاف‌پذیری را با توجه به برنامه‌های دریافتی از ریزشبکه‌ها انجام می‌دهد. استراتژی سه ‌مرحله‌ای پیشنهادی به صورت یک مسئله برنامه‌ریزی خطی مختلط عدد صحیح مدل شده و توسط حل‌کننده CPLEX در نرم‌افزار گمز حل می‌شود. استراتژی بهینه‌سازی پیشنهادی بر روی چندین مورد مطالعاتی پیاده‌سازی شده و نتایج شبیه‌سازی نشان می‌دهند که این استراتژی به طور مؤثری توانسته ظرفیت‌های انعطاف‌پذیری مورد نیاز برای بهره‌برداری پایدار را از طریق منابع ارزان درون ریزشبکه‌ها تأمین کند و بدینوسیله هزینه‌های روزانه ریزشبکه‌ها و شبکه توزیع را به طور قابل ‌توجهی کاهش دهد.

چکیده انگلیسی :

In this paper, a three-layer optimization strategy for flexible energy management of the active distribution system under the high penetration of wind and solar renewable energy sources is introduced, in which microgrids are responsible for providing the flexibility services to the main distribution network. In the proposed strategy, microgrid operators provide flexibility services through distributed generation resources, electrical storage systems, smart homes, and electric vehicles. In the first layer of the proposed strategy, smart homes are planned considering the energy and flexibility markets and then announce their final plan to the microgrid operator. In the second layer, microgrids plan their service area according to the plans received from smart homes and then send their participation plan in the energy and flexibility markets to the main distribution network operator. Eventually, in the third layer, the main distribution network operator plans the energy and flexibility markets according to the plans received from the microgrids. The proposed three-stage strategy is modeled as a mixed integer linear programming problem and solved by CPLEX solver in GAMS. The proposed optimization strategy has been implemented on several case studies and the simulation results demonstrate that this strategy can effectively provide the flexibility capacities required for sustainable operation through cheap resources within microgrids, thereby significantly reducing the daily costs of microgrids and distribution network.

منابع و مأخذ:

[1] M. Tostado-Véliz, A. Rezaee-Jordehi, S.A. Mansouri, F. Jurado, "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model", Applied Energy, vol. 328, Article Number: 120257, Dec. 2022 (doi:10.1016/j.apenergy.2022.120257).

[2] M. Tostado-Véliz, S.A. Mansouri, A. Rezaee-Jordehi, D. Icaza-Alvarez, F. Jurado, "Information gap decision theory-based day-ahead scheduling of energy communities with collective hydrogen chain", International Journal of Hydrogen Energy, vol. 48, pp. 7154-7169, March 2023 (doi: 10.1016/j.ijhydene.2022.11.183).

[3] A. Sahoo, P.K. Hota, "Impact of energy storage system and distributed energy resources on bidding strategy of micro-grid in deregulated environment", Journal of Energy Storage, vol. 43, Article Number: 103230, Nov. 2021 (doi: 10.1016/j.est.2021.103230).

[4] S.A. Mansouri, M.S. Javadi, "A robust optimisation framework in composite generation and transmission expansion planning considering inherent uncertainties", Journal of Experimental and Theoretical Artificial Intelligence, vol. 29, pp. 717-730, Nov. 2017 (doi: 10.1080/0952813X.2016.1259262).

[5] N.E. Koltsaklis, J. Knápek, "Assessing flexibility options in electricity market clearing", Renewable and Sustainable Energy Reviews, vol. 173, Article Number: 113084, Mar 2023 (doi: 10.1016/j.rser.2022.113084).

[6] M. Debouza, A. Al-Durra, T.H.M. EL-Fouly, H.H. Zeineldin, "Survey on microgrids with flexible boundaries: Strategies, applications, and future trends", Electric Power Systems Research, vol. 205, Article Number: 107765, April 2022 (doi: 10.1016/j.epsr.2021.107765).

[7] Z. Yu, F. Lu, Y. Zou, X. Yang, "Quantifying energy flexibility of commuter plug-in electric vehicles within a residence–office coupling virtual microgrid. Part II: Case study setup for scenario and sensitivity analysis", Energy and Buildings, vol. 254, Article Number: 111552, Jan. 2022 (doi: 10.1016/j.enbuild.2021.111552).

[8] T. Hai, J. Zhou, A.K. Alazzawi, T. Muranaka, "Management of renewable-based multi-energy microgrids with energy storage and integrated electric vehicles considering uncertainties", Journal of Energy Storage, vol. 60, Article Number: 106582, April 2023 (doi: 10.1016/j.est.2022.106582).

[9]  S.A. Shirmardi, M. Joorabian, H. Barati, "Flexible-reliable operation of green microgrids including sources and energy storage-based active loads considering ANFIS-based data forecasting method", Electric Power Systems Research, vol. 210, Article Number: 108107, Sept. 2022  (doi: 10.1016/j.epsr.2022.108107).

[10]  S.M. Hashemi, H. Arasteh, M. Shafiekhani, M. Kia, J.M. Guerrero, "Multi-objective operation of microgrids based on electrical and thermal flexibility metrics using the NNC and IGDT methods", International Journal of Electrical Power and Energy Systems, vol. 144, Article Number: 108617, Jan. 2023 (doi: 10.1016/j.ije­pes.20­22.108617).

[11] A. Majzoobi, A. Khodaei, "Application of microgrids in supporting distribution grid flexibility", IEEE Trans. on Power Systems, vol. 32, pp. 3660-3669, Sept. 2017 (doi: 1109/TPWRS.2016.2635024).

[12] F. Garcia-Torres, C. Bordons, J. Tobajas, R. Real-Calvo, I. Santiago, S. Grieu, "Stochastic optimization of microgrids with hybrid energy storage systems for grid flexibility services considering energy forecast uncertainties", IEEE Trans. on Power Systems, vol. 36, pp. 5537-5547, Nov. 2021 (doi: 10.1109/TPW­RS.­20­21.3071867).

[13] C. Huang, M. Zhang, C. Wang, N. Xie, Z. Yuan, "An interactive two-stage retail electricity market for microgrids with peer-to-peer flexibility trading", Applied Energy, vol. 320, Article Number: 119085, Aug. 2022 (doi: 10.1016/j.apenergy.2022.119085).

[14] W. Hua, H. Xiao, W. Pei, W.Y. Chiu, J. Jiang, H. Sun, P. Matthews, "Transactive energy and flexibility provision in multi-microgrids using Stackelberg game", CSEE Journal of Power and Energy Systems, vol. 9, pp. 505-515, March 2023 (doi: 10.17775/CSEEJPES.2021.04370).

[15] S. Bahramara, P. Sheikhahmadi, A. Mazza, G. Chicco, "Day-ahead self-scheduling from risk-averse micr­ogrid operators to provide reserves and flexible ramping ancillary services", International Journal of Electrical Power and Energy Systems, vol. 142, Article Number: 108381, Nov. 2022 (doi: 10.1016/j.ijep­es.20­22.­10­8381).

[16] A. Kathirgamanathan, M. Rosa, E. Mangina, D.P. Finn, "Data-driven predictive control for unlocking building energy flexibility: A review", Renewable and Sustainable Energy Reviews, vol. 135, Article Number: 110120, Jan. 2021 (doi: 10.1016/j.rser.2020.110120).

[17] H. Khajeh, H. Firoozi, H. Laaksonen, "Flexibility potential of a smart Home to provide TSO-DSO-level services", Electric Power Systems Research, vol. 205, Article Number: 107767, April 2022 (doi: 10.1016/j.ep­sr.20­21.107767).

[18] M. Khatibi, S. Rahnama, P. Vogler-Finck, J. Dimon Bendtsen, A. Afshari, "Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme", Applied Energy, vol. 333, Article Number: 120562, March 2023 (doi: 10.1016/j.apenergy.2022.120562).

[19] S.A. Mansouri, A. Ahmarinejad, E. Nematbakhsh, M.S. Javadi, A.R. Jordehi, J.P.S. Catalão, "Energy manag­eme­nt in microgrids including smart homes: A multi-objective approach", Sustainable Cities and Society, vol. 69, Article Number: 102852, June 2021 (doi: 10.1016/j.scs.2021.102852).

[20] S.A. Mansouri, A. Ahmarinejad, M.S. Javadi, A.E. Nezhad, M. Shafie-Khah, J.P.S. Catalão, "Demand resp­on­se role for enhancing the flexibility of local energy systems", Distributed Energy Resources in Local Integrated Energy Systems, pp. 279-313, March 2021 (doi: 10.1016/B978-0-12-823899-8.00011-X).

[21] P. Balakumar, T. Vinopraba , K. Chandrasekaran, "Machine learning based demand response scheme for IoT enabled PV integrated smart building", Sustainable Cities and Society, vol. 89, Article Number: 104260, Feb. 2023 (doi: 10.1016/j.scs.2022.104260).

[22] G.R. Aghajani, I. Heydari, "Energy management in microgrids containing electric vehicles and renewable ‎energy sources considering demand response", Journal of Operation and Automation in Power Engineering, vol. 9, pp. 34-48, April 2021 (doi: 10.22098/joape.2020.5854.1438).

[23] S. Gorji, S. Zamanian, M. Moazzami, "Techno-economic and environmental base approach for optimal energy management of microgrids using crow search algorithm", Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 49-68, Sept. 2020 (dor: 20.1001.1.23223871.1399.11.43.4.7).

[24] M. Zare, S.A. Saeed, H. Akbari, "Demand response programs modeling in multiple energy and structure management in microgrids equipped by combined heat and power generation", Journal of Intelligent Procedures in Electrical Technology, vol. 14, no. 53, pp. 99-120, Feb. 2022 (dor: 20.1001.1.23223871.14­02.1­4.53.6.0).

[25] J.A. Aguado, Á. Paredes, "Coordinated and decentralized trading of flexibility products in Inter-DSO local electricity markets via ADMM", Applied Energy, vol. 337, Article Number: 120893, May 2023 (doi: 10.1016/j.apenergy.2023.120893).

[26] Y. Ye, D. Papadaskalopoulos, Q. Yuan, Y. Tang, G. Strbac, "Multi-agent deep reinforcement learning for coordinated energy trading and flexibility services provision in local electricity markets", IEEE Trans. on Smart Grid, vol. 14, pp. 1541-1554, Feb. 2022 (doi: 10.1109/tsg.2022.3149266).

[27] H. Nagpal, I-I. Avramidis, F. Capitanescu, A.G. Madureira, "Local energy communities in service of sustainability and grid flexibility provision: hierarchical management of shared energy storage", IEEE Trans. on Sustainable Energy, vol. 13, pp. 1523-1535, July 2022 (doi: 10.1109/tste.2022.3157193).

[28] Y. Chen, W. Wei, H. Wang, Q. Zhou, J.P.S. Catalao, "An energy sharing mechanism achieving the same flexibility as centralized dispatch", IEEE Trans. on Smart Grid, vol. 12, pp. 3379-3389, July 2021 (doi: 10.1109/tsg.2021.3060380).

[29] I. Avramidis, F. Capitanescu, G. Deconinck, "Grid-friendly smart sustainable buildings: flexibility-to-cost mapping", IEEE Trans. on Sustainable Energy, vol. 13, pp. 1857-1860, July 2022 (doi: 10.1109/tste.20­22.31­608­86).

[30] M. Babagheibi, S. Jadid, A. Kazemi, "An incentive-based robust flexibility market for congestion manage­ment of an active distribution system to use the free capacity of Microgrids", Applied Energy, vol. 336, Article Number: 120832, April 2023 (doi: 10.1016/j.apenergy.2023.120832).

[31] N. Pourghaderi, M. Fotuhi‐Firuzabad, M. Moeini‐Aghtaie, M. Kabirifar, P. Dehghanian, "A local flexibility market framework for exploiting DERs’ flexibility capabilities by a technical virtual power plant", IET Renewable Power Generation, vol. 17, pp. 681-695, Feb. 2023 (doi: 10.1049/rpg2.12624).

[32] Z. Foroozandeh, S. Ramos, J. Soares, Z. Vale, "Goal programming approach for energy management of smart building". IEEE Access, vol. 10, pp. 25341-25348, Feb. 2022 (doi: 10.1109/access.2022.3153341).

[33] M. Ebrahimi, A.S. Gazafroudi, M. Ebrahimi, H. Laaksonen, M. Shafie-Khah, J.P.S. Catalao, "Iterative game approach for modeling the behavior of agents in a competitive flexibility trading", IEEE Access, vol. 9, pp. 165227-165238, Dec. 2021 (doi: 10.1109/access.2021.3134937).

[34] M. Yu, J. Jiang, X. Ye, X. Zhang, C. Lee, S.H. Hong, "Demand response flexibility potential trading in smart grids: A multileader multifollower stackelberg game approach", IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 53, pp. 2664-2675, May 2023 (doi: 10.1109/tsmc.2022.3218039).

[35] X. Lu, X. Ge, K. Li, F. Wang, H. Shen, P. Tao, Hu J, Lai J, Zhen Z, Shafie-khah M, Catalão J.P.S, "Optimal bidding strategy of demand response aggregator based on customers’ responsiveness behaviors modeling under different incentives", IEEE Trans. on Industry Applications, vol. 57, pp. 3329-3340, Augst 2021 (doi: 10.1109/tia.2021.3076139).

[36] S. Cordova, C.A.C. nizares, A. Lorca, D.E. Olivares, "Aggregate modeling of thermostatically controlled loads for microgrid energy management systems", IEEE Trans. on Smart Grid, vol. 336, pp. 1-13, March 2023 (doi: 10.1109/tsg.2023.3254655).

[37] X. Zhang, D. Shin, Y. Son, H. Woo, S.Y. Kim, S. Choi, "Three-stage flexibility provision framework for radial distribution systems considering uncertainties", IEEE Trans. on Sustainable Energy, vol. 14, pp. 948-961, April 2023 (doi: 10.1109/tste.2022.3230774).

[38] K. Antoniadou-Plytaria, D. Steen, L.A. Tuan, O. Carlson, B. Mohandes, M.A.F. Ghazvini, "Scenario-based stochastic optimization for energy and flexibility dispatch of a microgrid", IEEE Trans. on Smart Grid, vol. 13, pp. 3328-3341, Sept. 2022 (doi: 10.1109/tsg.2022.3175418).

[39] G. Tsaousoglou, N. Efthymiopoulos, P. Makris, E. Varvarigos, "Multistage energy management of coordi­nated smart buildings: a multiagent markov decision process approach", IEEE Trans. on Smart Grid, vol. 13, pp. 2788-2797, July 2022 (doi: 10.1109/tsg.2022.3162915).

[40] Á. Paredes, J.A. Aguado, P. Rodrí-guez, "Uncertainty-aware trading of congestion and imbalance mitigation services for multi-DSO local flexibility markets", IEEE Trans. on Sustainable Energy, pp. 1-13, March 2023 (doi: 10.1109/tste.2023.3257405).

[41] S.A. Mansouri, A. Rezaee Jordehi, M. Marzband, M. Tostado-Véliz, F. Jurado, J.A. Aguado, "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster", Applied Energy, vol. 333, Article Number: 120560, March 2023 (doi: 10.1016/j.apenergy.2022.120560).

[42] S.A. Mansouri, A. Ahmarinejad, E. Nematbakhsh, M.S. Javadi, A. Esmaeel Nezhad, J.P.S. Catalão, "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources", Energy, vol. 245, Article Number: 123228, April 2022 (doi: 10.1016/j.energy.2022.123228).

[43] S.A. Mansouri, A. Ahmarinejad, M.S. Javadi, J.P.S. Catalão, "Two-stage stochastic framework for energy hubs planning considering demand response programs", Energy, vol. 206, pp. 118124, Sept. 2020 (doi: 10.1016/j.energy.2020.118124).