مشارکت هابهای انرژی تجدیدپذیر دارای ذخیرهسازهای هیدروژنی، حرارتی و هوای فشرده در بازار انرژی مبنی بر سیستم مدیریت انرژی
رضا سپه وند
1
(
دانشکده مهندسی و پرواز- دانشگاه افسری امام علی (ع)، تهران، ایران
)
کلید واژه: ذخیرهسازی, ذخیرهسازی حرارتی, هوای فشرده, هاب انرژی,
چکیده مقاله :
این مقاله به مشارکت هاب های انرژی تجدیدپذیر مجهز به مزارع بادی و واحدهای بیوگاز، و ذخیره سازهای هیدروژنی، حرارتی و هوای فشرده در بازار انرژی مبنی بر مدل تسویه قیمت بازار می پردازد. هاب ها همزمان در دو شبکه الکتریکی و حرارتی حضور دارند. واحد بیوگاز مجهز به فناوری ترکیبی برق و حرارت است، به طوری که آن همزمان در تولید انرژی الکتریکی و حرارتی نقش دارد. طرح پیشنهادی در قالب بهینه سازی دوسطحی است. سطح بالای آن بیشینه سازی سود مورد انتظار هاب با در نظر گرفتن قیود بهره برداری منابع و ذخیره سازهای مذکور را فرمول بندی می کند. در سطح پایین فرمول بندی استراتژی تسویه قیمت بازار لحاظ شده که آن کمینه سازی هزینه مورد انتظار واحدهای تولید الکتریکی و حرارتی مقید به معادلات پخش توان بهینه شبکه های الکتریکی و حرارتی را در نظر می گیرد. در ادامه روش کراش کان تاکر یک فرمول بندی تکسطحی برای طرح پیشنهادی به دست می آورد. بهینه سازی تصادفی برای مدل سازی عدمقطعیتهای بار و منابع تجدیدپذیر استفاده می شود. در نهایت نتایج عددی بهدست آمده بیانگر قابلیت طرح پیشنهادی در ارتقای وضعیت اقتصادی و بهره برداری شبکه های انرژی نسبت به مطالعات پخش بار بهینه (شبکه بدون هاب) در کنار استخراج زمان بندی بهینه انرژی هاب ها متناسب با ارتقای وضعیت اقتصادی آنها است. به طوری که ذخیره سازهای هیدروژنی، هوای فشرده و حرارتی منجر به ارتقای 2/11 درصد وضعیت اقتصادی هاب تجدیدپذیر می شوند. مدیریت بهینه انرژی هاب های تجدیدپذیر مبنی بر ذخیره ساز باعث ارتقای 27 درصد وضعیت اقتصادی یا بهره برداری شبکه های انرژی نسبت به مطالعات پخش بار بهینه شده است.
چکیده انگلیسی :
This paper concerns the participation of renewable energy hubs equipped with wind farms, bio-waste units, and hydrogen, thermal, and compressed air storage systems in the energy market based on the market clearing price model. Hubs are simultaneously present in both electrical and thermal networks. The bio-waste unit is equipped with combined heat and power technology, so it produces electrical and thermal energy. The proposed design is in the form of bi-level optimization. Its upper level formulates the maximization of the hub's expected profit considering the operational constraints of the mentioned resources and storage devices. The market clearing price strategy is included at the lower formulation level, considering minimizing the expected operation cost of electricity and thermal power generation units subject to the optimal power flow equations of electrical and thermal networks. The Karush-Kuhn-Tucker method obtains a single-level formulation for the design. The stochastic optimization is used to model uncertainties of load and renewable resources. Finally, the obtained numerical results indicate the proposed design's ability to improve the operation and economic status of energy networks compared with optimal power flow studies (the hub-less network), along with optimal power scheduling of hubs in accordance with improving their economic status. So, hydrogen, compressed air, and heat storage devices lead to an 11.2% enhancement in the economic status of the renewable hub. Optimal energy management of renewable hubs based on the storage system has led to a 27% enhancement in energy network operation status compared to optimal power flow studies.
[1] F. Khalafian, "Robust planning of the islanded hybrid system including renewable and non-renewable sources and stationary and mobile storages", Journal of Intelligent Procedures in Electrical Technology, vol. 14, no. 53, pp. 15-32, Sept. 2022 (in Persian) (dor: 20.1001.1.23223871.1402.14.53.2.6).
[2] K. Bradbury, L. Pratson, D. Patiño-Echeverri, "Economic viability of energy storage systems based on price arbitrage potential in real-time US electricity markets", Applied Energy, vol. 114, pp. 512-519, Feb. 2014 (doi: 10.1016/j.rser.2019.04.069).
[3] C. Iris, J.S. Lam, "A review of energy efficiency in ports: Operational strategies, technologies and energy management systems", Renewable and Sustainable Energy Reviews, vol. 112, pp. 170-182, Sept. 2019 (doi: 10.1016/j.rser.2019.04.069).
[4] M. Kazemi, T. Niknam, B.B. Firouzi, M. Nafar, "Energy hub flexibility, energy network, energy and reserve market, stochastic programming", Journal of Novel Researches on Electrical Power, vol. 4, no. 4, pp. 49-59, Dec. 2019 (in persian) (dor: 20.1001.1.23222468.1399.9.4.5.9).
[5] H.R. Zafarani, S.A. Taher, M. Shahidehpour, "Robust operation of a multicarrier energy system considering EVs and CHP units", Energy, vol. 192, pp.1-12, Feb. 2020 (doi: 10.1016/j.energy.2019.116703).
[6] A. Dini, S. Pirouzi, M.A. Norouzi, M. Lehtonen, "Grid-connected energy hubs in the coordinated multi-energy management based on day-ahead market framework", Energy, vol. 188, pp. 1-12, Dec. 2019 (doi: 10.1016/j.energy.2019.116055).
[7] K. Afrashi, B. Bahmani-Firouzi, M. Nafar, "Multicarrier energy system management as mixed integer linear Programming", Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, pp. 619-631, June 2021 (doi: 10.1007/s40998-020-00373-x).
[8] A. Heidari, S.S. Mortazavi, R.C. Bansal, "Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies", Applied Energy, vol. 261, Article Number: 114393, Mar. 2020 (doi: 10.1016/j.apenergy.2019.114393).
[9] M. Jalili, M. Sedighizadeh, A. Sheikhi-Fini, "Stochastic optimal operation of a microgrid based on energy hub including a solar-powered compressed air energy storage system and an ice storage conditioner", Journal of Energy Storage, vol. 33, Article Number: 102089, Jan. 2021 (doi: 10.1016/j.est.2020.102089).
[10] S. Geng, M. Vrakopoulou, I.A. Hiskens, "Optimal capacity design and operation of energy hub systems", Proceedings of the IEEE, vol. 108, no. 9, pp. 1475-1495, Sept. 2020 (doi: 10.1109/JPROC.2020.3009323).
[11] E. Akbari, S.F. Mousavi-Shabestari, S. Pirouzi, M. Jadidoleslam, "Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management”, Renewable Energy, vol. 206, pp. 295-308, Feb. 2023 (doi: 10.1016/j.renene.2023.02.050).
[12] A. Heidari, R.C. Bansal, J. Hossain, J. Zhu, "Strategic risk aversion of smart energy hubs in the joined energy markets applying a stochastic game approach", Journal of Cleaner Production, vol. 349, Article Number: 131386, May 2022 (doi: 10.1016/j.jclepro.2022.131386).
[13] A.R. Daneshvar-Garmroodi, F. Nasiri, F. Haghighat, "Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach", Journal of Energy Storage, vol. 57, Article Number: 106147, Jan. 2023 (doi: 10.1016/j.est.2022.106147).
[14] G. Zhang, Y. Ge, Z. Ye, M. Al-Bahrani, "Multi-objective planning of energy hub on economic aspects and resources with heat and power sources, energizable, electric vehicle and hydrogen storage system due to uncertainties and demand response", Journal of Energy Storage, vol. 57, pp. 106160, Jan. 2023 (doi: 10.1016/j.est.2022.106160).
[15] S.A.A. Ghappani, A. Karimi, "Optimal operation framework of an energy hub with combined heat, hydrogen, and power (CHHP) system based on ammonia", Energy, vol.266, pp. 126407, Mar. 2023 (doi: 10.1016/j.energy.2022.126407).
[16] M.R. Jokar, S. Shahmoradi, A.H. Mohammed, L.K. Foong, B.N. Le, S. Pirouzi, "Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization", Journal of Energy Storage, vol. 58, Article Number: 106389, Feb. 2023 (doi: 10.1016/j.est.2022.106389).
[17] M. Karami, M. Zadehbagheri, M.J. Kiani, S. Nejatian, "Retailer energy management of electric energy by combining demand response and hydrogen storage systems, renewable sources and electric vehicles", International Journal of Hydrogen Energy, vol. 48, no. 49, pp. 18775-18794, June 2023 (doi: 10.1016/j.ijhydene.2023.01.285).
[18] A.R. Azarhooshang, D. Sedighizadeh, M. Sedighizadeh, "Two-stage stochastic operation considering day-ahead and real-time scheduling of microgrids with high renewable energy sources and electric vehicles based on multi-layer energy management system", Electric Power Systems Research, vol. 201, Article Number: 107527, Dec. 2021 (doi: 10.1016/j.epsr.2021.107527).
[19] W. Shi, X. Han, X.Y. Wang, J. Li, "Optimization scheduling strategy with multi-agent training data rolling enhancement for regional power grid considering operation risk and reserve availability", Proceeding of the IEEE/ACPEE, pp. 177401781, Tianjin, China, April 2023 (doi: 10.1109/ACPEE56931.2023.10135875).
[20] D. Bertsimas, E. Litvinov, X.A. Sun, J. Zhao, T. Zheng, "Adaptive robust optimization for the security constrained unit commitment problem", IEEE Trans. on Power Systems, vol. 28, no. 1, pp. 52-63, Feb. 2013 (doi: 10.1109/TPWRS.2012.2205021).
[21] H.R. Hamidpour, J. Aghaei, S. Dehghan, S. Pirouzi, T. Niknam, "Integrated resource expansion planning of wind integrated power systems considering demand response programmes", IET Renewable Power Generation, vol. 13, no. 4, pp. 519-529, Mar. 2019 (doi: 10.1049/iet-rpg.2018.5835).
[22] J. Aghaei, M. Barani, M. Shafie-khah, A.A.S. Nieta, J.P.S. Catalão, "Risk-Constrained offering strategy for aggregated hybrid power plant including wind power producer and demand response provider", IEEE Trans. on Sustainable Energy, vol. 7, no. 2, pp. 513-525, April 2016 (doi: 10.1109/TSTE.2015.2500539).
[23] D. Chattopadhyay, "Application of general algebraic modeling system to power system optimization", IEEE Trans. on Power Systems, vol. 14, no. 1, pp. 15-22, Feb. 1999 (doi: 10.1109/59.744462).
[24] I. Abdulrahman, "MATLAB-based programs for power system dynamic analysis", IEEE Open Access Journal of Power and Energy, vol. 7, pp. 59-69, Nov. 2019 (doi: 10.1109/OAJPE.2019.2954205).
[25] A. Shabanpour-Haghighi, A.R. Seifi, "Multi-objective operation management of a multi-carrier energy system", Energy, vol. 88, pp. 430-442, Aug. 2015 (doi: 10.1016/j.energy.2015.05.063).
_||_