برنامه‌ريزي بهينه الکتریکی -گرمایی نيروگاه مجازي با رويکرد احتمالاتی و مدل‌سازی پاسخ بار جامع و ريسک
الموضوعات :
فاطمه فتاحی اردکانی
1
,
سیدبابک مظفری
2
,
سودابه سلیمانی مورچه خورتی
3
1 - گروه مهندسی برق (قدرت و کنترل)- واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران
2 - گروه مهندسی برق (قدرت و کنترل)- واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران
3 - گروه مهندسی برق (قدرت و کنترل)- واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران
الکلمات المفتاحية: احتمالاتي, پاسخ بار جامع, ريسک, مدل مقاوم, نيروگاه مجازي ,
ملخص المقالة :
چكيده: نیروگاه مجازی یک رویکرد نوین برای مدیریت یکپارچه واحدهای حرارتی و واحدهای تجدیدپذیر جهت تامین تقاضا و سود مشارکت در بازار است. در این مقاله یک مدل بهینه برای برنامه‌ریزی روزپیش یک نیروگاه مجازی ارائه شده است. در این مطالعه، منابع انرژی تولید پراکنده تجدیدپذیر و واحدهای تولید فسیلی و خودروهای الکتریکی به گونه‌ای برنامه‌ریزی می‌شوند که علاوه بر تامین بارهای الکتریکی و گرمایی از حداکثر سود مشارکت در بازار برق بهره‌مند شوند. عدم قطعیت‌های مربوط به تولید انرژی تجدیدپذیر باد و خورشید، عدم قطعیت بار و قیمت بازار طی یک رویکرد سناریو محور در نظر گرفته شده است. پس از تولید سناریوها با استفاده از تابع چگالی احتمال پارامترهای تصادفی، برای کاهش سناریو از روش بهینه‌سازی برنامه‌ریزی خطی آمیخته با اعداد صحیح استفاده شده و سناریوهای با احتمال بیشتر جهت برنامه‌ریزی بهینه انتخاب شده‌اند. مدل مساله ذاتاً به صورت غیرخطی است اما با استفاده از مدل‌های خطی مناسب برای منابع سنتی و منحنی شارژ خودروهای الکتریکی و استفاده از مدل سناریو محور، به صورت یک مساله خطی مدل شده است. منابع تولید همزمان برق و گرما و واحدهای تولیدی خورشیدی-گرمایی در تامین بارهای گرمایی و الکتریکی به صورت همزمان و بهینه برنامه‌ریزی شده است. همچنین پاسخ بار جامع الکتریکی-گرمایی جهت بهبود عملکرد سیستم ارائه شده است. ریسک سیستم با در نظر گرفتن یک عدم قطعیت اضافه به تابع هدف به صورت مقاوم مدل‌سازی شده است. مدل ارائه شده برای سیستم توزیع اصلاح شده 33 شین IEEE برای سناریوهای منتخب پیاده‌سازی و نتایج مقایسه شده است. نتایج نشان‌دهنده بهبود ضریب بار سیستم، افزایش سود و کاهش قطع بار با در نظر گرفتن مدل پیشنهادی است. همچنین میزان سود سیستم با در نظر گرفتن ضریب مقاوم بیشتر، با ریسک کمتری حاصل شده است.
[1] F.H. Aghdam, M.S. Javadi, J.P. Catalão, "Optimal stochastic operation of technical virtual power plants in reconfigurable distribution networks considering contingencies", International Journal of Electrical Power and Energy Systems, vol. 147, Article Number: 108799, May 2023 (doi: 10.1016/j.ijepes.2022.108799).
[2] X. Yang, Y. Zhang, "A comprehensive review on electric vehicles integrated in virtual power plants", Sustainable Energy Technologies and Assessments, vol. 48, Article Number: 101678, Dec. 2021 (doi: 10.101¬6/j.s¬eta.2021.101678).
[3] J.F. Venegas-Zarama, J.I. Muñoz-Hernandez, L. Baringo, P. Diaz-Cachinero, I. De Domingo-Mondejar, "A review of the evolution and main roles of virtual power plants as key stakeholders in power systems", IEEE Access, pp. 47937-47964, May 2022 (doi: 10.1109/ACCESS.2022.3171823).
[4] S. Yin, Q. Ai, Z. Li, Y. Zhang, T. Lu, "Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach", International Journal of Electrical Power and Energy Systems, vol. 117, Article Number: 105605, May 2020 (doi: 10.1016/j.ijepes.2019.105605).
[5] C. Tan, J. Wang, S. Geng, L. Pu, Z. Tan, "Three-level market optimization model of virtual power plant with carbon capture equipment considering copula–CVaR theory", Energy, vol. 237, Article Number: 121620, Dec. 2021(doi: 10.1016/j.energy.2021.121620).
[6] L. Ju, Q. Tan, Y. Lu, Z. Tan, Y. Zhang, Q. Tan, "A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances", International Journal of Electrical Power and Energy Systems, vol. 107, pp. 628-643, May 2019 (doi: 10.1016/j.ijepes.2018.12.012).
[7] H. Liu, J. Qiu, J. Zhao, "A data-driven scheduling model of virtual power plant using Wasserstein distributi¬onally robust optimization", International Journal of Electrical Power and Energy Systems, vol. 137, Article Number: 107801, May 2022 (doi: 10.1016/j.ijepes.2021.107801).
[8] G. Cavazzini, A. Benato, G. Pavesi, G. Ardizzon, "Techno-economic benefits deriving from optimal schedul¬ing of a virtual power plant: pumped hydro combined with wind farms", Journal of Energy Storage, vol. 37, Article Number: 102461, May 2021 (doi: 0.1016/j.est.2021.102461).
[9] M. Rahimi, F.J. Ardakani, O. Olatujoye, A.J. Ardakani, "Two-stage interval scheduling of virtual power plant in day-ahead and real-time markets considering compressed air energy storage wind turbine", Journal of Energy Storage, vol. 45, Article Number: 103599, Jan. 2022 (doi: 10.1016/j.est.2021.103599).
[10] Y. Zhang, F. Liu, Z. Wang, Y. Su, W. Wang, S. Feng, "Robust scheduling of virtual power plant under exogenous and endogenous uncertainties", IEEE Trans. on Power Systems, vol. 37, no. 2, pp. 1311-1325, Mar. 2022 (doi: 10.1109/TPWRS.2021.3105418).
[11] F.G. Olanlari, T. Amraee, M. Moradi‐Sepahvand, A. Ahmadian, "Coordinated multi‐objective scheduling of a multi‐energy virtual power plant considering storages and demand response", IET Generation, Transmission and Distribution, vol. 16, issue. 17, pp. 3539-3562, Sept. 2022 (doi: 10.1049/gtd2.12543).
[12] J. Wang, C. Guo, C. Yu, Y. Liang, "Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning", Electric Power Systems Research, vol. 205, Article Number: 107714, Apr. 2022 (doi: 10.1016/j.epsr.2021.107714).
[13] M. Hannan, M.G. Abdolrasol, M. Faisal, P.J. Ker, R. Begum, A. Hussain, "Binary particle swarm optimization for scheduling MG integrated virtual power plant toward energy saving", IEEE Access, vol. 7, pp. 107937-107951, Aug. 2019 (doi: 10.1109/ACCESS.2019.2933010).
[14] A. Alahyari, M. Ehsan, M. Mousavizadeh, "A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties", Journal of Energy Storage, vol. 25, Article Number: 100812, Oct. 2019 (doi: 10.1016/j¬.est.2019.100812).
[15] A. Alahyari, M. Ehsan, D. Pozo, M. Farrokhifar, "Hybrid uncertainty‐based offering strategy for virtual power plants", IET Renewable Power Generation, vol. 14, no. 13, pp. 2359-2366, Oct. 2020 (doi: 10.1049/iet-rpg.2020.¬0249).
[16] F. Sheidaei, A. Ahmarinejad, "Multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs", International Journal of Electrical Power and Energy Systems, vol. 120, Article Number: 106047, Sept. 2020 (doi: 10.1016/j.ijepes.2020.106047).
[17] D. Falabretti, F. Gulotta, D. Siface, "Scheduling and operation of RES-based virtual power plants with e-mobility: A novel integrated stochastic model", International Journal of Electrical Power and Energy Systems, vol. 144, Article Number: 108604, Jan. 2023 (doi: 10.1016/j.ijepes.2022.108604).
[18] A. Alahyari, D. Pozo, "Performance-based virtual power plant offering strategy incorporating hybrid uncertainty modeling and risk viewpoint", Electric Power Systems Research, vol. 203, Article Number: 107632, Feb. 2022 (doi: 10.1016/j.epsr.2021.107632).
[19] R.M. Lima, A.J. Conejo, L. Giraldi, O.L. Maitre, I. Hoteit, O.M. Knio, "Sample average approximation for risk-averse problems: A virtual power plant scheduling application", EURO Journal on Computational Optimization, vol. 9, Article Number: 100005, Mar. 2021 (doi: 10.1016/j.ejco.2021.100005)..
[20] T.M. Alabi, L. Lu, Z. Yang, "Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy", Applied Energy, vol. 314, Article Number: 118997, May 2022 (doi: 10.1016/j.apenergy.20¬22¬.118997) .
[21] M. Rahimi, F.J. Ardakani, A.J. Ardakani, "Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant", International Journal of Electrical Power and Energy Systems, vol. 127, Article Number: 106658, May 2021 (doi: 10.1016/j.ijepes.2020.106658).
[22] M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, M. Shafie-Khah, J.P. Catalão, "Risk-averse optimal energy and reserve scheduling for virtual power plants incorporating demand response programs", IEEE Trans. on Smart Grid, vol. 12, issue. 2, pp. 1405-1415, Mar. 2021 (doi: 10.1109/TSG.2020.3026971).
[23] J. Dixon, W. Bukhsh, C. Edmunds, K. Bell, "Scheduling electric vehicle charging to minimise carbon emiss¬ions and wind curtailment", Renewable Energy, vol. 161, pp. 1072-1091, Dec. 2020 (doi: 10.1016/j.r¬ene¬ne.2¬020.07.017).
[24] A. Geletu, M. Klöppel, H. Zhang, P. Li, "Advances and applications of chance-constrained approaches to systems optimisation under uncertainty", International Journal of Systems Science, vol. 44, issue. 7, pp. 1209-1232, July 2013 (doi: 10.1080/00207721.2012.670310).
[25] D. Zou, D. Gong, "Differential evolution based on migrating variables for the combined heat and power dynamic economic dispatch", Energy, vol. 238, Article Number: 121664, Jan. 2022 (doi: 10.1016/j.en¬ergy.¬20¬21.121664).
[26] J. Wang, N.E. Redondo, F.D. Galiana, "Demand-side reserve offers in joint energy/reserve electricity markets", IEEE Trans. on power systems, vol. 18, issue. 4, pp. 1300-1306, Nov. 2003 (doi: 10.1109/TPWRS.20¬03.81¬85¬93).
[27] E. Heydarian-Forushani, M. E. Golshan, M. Shafie-khah, "Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration", Applied Energy, vol. 179, pp. 338-349, Oct. 2016 (doi: 10.1016/j.apenergy.2016.06.145).
[28] A. Gholami, T. Shekari, F. Aminifar, M. Shahidehpour, "Microgrid scheduling with uncertainty: The quest for resilience", IEEE Trans. on Smart Grid, vol. 7, issue. 6, pp. 2849-2858, Nov. 2016 (doi: 10.11¬09/TSG¬.201¬6.25¬98¬80¬2).
[29] A.T. Dahiru, D. Daud, C.W. Tan, Z.T. Jagun, S. Samsudin, A.M. Dobi, "A comprehensive review of demand side management in distributed grids based on real estate perspectives", Environmental Science and Pollution Research, vol. 30, pp. 81984–82013, Jan. 2023 (doi: 0.1016/j.ijepes.2019.02.029).
[30] R. Khalili, A. Khaledi, M. Marzband, A.F. Nematollahi, B. Vahidi, P. Siano, "Robust multi-objective optim¬izat¬ion for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs", Applied Energy, vol. 334, Article Number: 120737, Mar. 2023 (doi: 10.1016/j.apenergy.2023.120737).
[31] H. Karimi, G. Gharehpetian, R. Ahmadiahangar, A. Rosin, "Optimal energy management of grid-connected multi-microgrid systems considering demand-side flexibility: A two-stage multi-objective approach", Electric Power Systems Research, vol. 214, Article Number: 108902, Jan. 2023 (doi: 10.1016/j.epsr.2022.108902).
[32] F. Zhao, Z. Li, D. Wang, T. Ma, "Peer-to-peer energy sharing with demand-side management for fair revenue distribution and stable grid interaction in the photovoltaic community", Journal of Cleaner Production, vol. 383, Article Number: 135271, Jan. 2023 (doi: 10.1016/j.jclepro.2022.135271).
[33] A. Bayatian, A. Ahmarinejad, "A three-level framework for determining the optimal strategy of microgrids to participate in the day-ahead competitive market by considering electric vehicles and demand response programs", Journal of Intelligent Procedures in Electrical Technology, vol. 14, no. 56, pp. 97-118, Mar. 2024 (in persian).
[34] G.G. Dranka, P. Ferreira, "Review and assessment of the different categories of demand response potentials", Energy, vol. 179, pp. 280-294, July 2019 (doi: 10.1016/j.energy.2019.05.009).
[35] B. Park, J. Dong, B. Liu, T. Kuruganti, "Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks", Applied Energy, vol. 330, Article Number: 120303, Jan. 2023 (doi: 10.1016/j.apenergy.2022.120303).
[36] F.F. Ardakani, S.B. Mozafari, S. Soleymani, "Scheduling energy and spinning reserve based on linear chance constrained optimization for a wind integrated power system", Ain Shams Engineering Journal, vol. 13, Article Number: 101582, May 2022 (doi: 10.1016/j.asej.2021.09.009).
[37] 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, June 2023 (in Persian).
[38] A.R. Jordehi, "Optimisation of demand response in electric power systems, a review", Renewable and sustainable energy reviews, vol. 103, pp. 308-319, Apr. 2019 (doi: 10.1016/j.rser.2018.12.054).
[39] L. Wang, J. Lin, H. Dong, Y. Wang, M. Zeng, "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system", Energy, Article Number: 126893, May. 2023 (doi: 10.1016/j.energy.2023.126893).
[40] H. Karimi, S. Jadid, "Optimal energy management for multi-microgrid considering demand response prog¬rams: A stochastic multi-objective framework", Energy, vol. 195, Article Number: 116992, Mar. 2020 (doi: 10.1016/j.energy.2020.116992).
[41] A.G. Zamani, A. Zakariazadeh, S. Jadid, "Day-ahead resource scheduling of a renewable energy based virtual power plant", Applied Energy, vol. 169, pp. 324-340, May 2016 (doi: 10.1016/j.apenergy.2016.02.011).
[42] S. Yu, F. Fang, Y. Liu, J. Liu, "Uncertainties of virtual power plant: Problems and countermeasures", Applied energy, vol. 239, pp. 454-470, Apr. 2019 (doi:10.1016/j.apenergy.2019.01.224).
[43] A.G. Zamani, A. Zakariazadeh, S. Jadid, A. Kazemi, "Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant", International Journal of Electrical Power and Energy Systems, vol. 82, pp. 608-620, Nov. 2016 (doi: 10.1016/j.ijepes.2016.04.024).
[44] M. Shafiekhani, A. Ahmadi, O. Homaee, M. Shafie-khah, J.P. Catalao, "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads", Energy, vol. 239, Article Number: 122379, Jan. 2022. (doi: 10.1016/j.energy.2021.122379).
[45] S. Hadayeghparast, A.S. Farsangi, H. Shayanfar, "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant", Energy, vol. 172, pp. 630-646, Apr. 2019 (doi: 10.1016/j.energy.2019.01.143).
[46] M. Nikzad, A. Samimi, "Integration of optimal time-of-use pricing in stochastic programming for energy and reserve management in smart micro-grids", Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 44, pp. 1449-1466, May 2020 (doi: 10.1007/s40998-020-00342-4).
[47] M. Nikzad, A. Samimi, "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems", Applied Energy, vol. 282, Article Number: 116163, Jan. 2021 (doi: 10.1016/j.apenergy.2020.116163).
[48] J.M. Mulvey, R.J. Vanderbei, S.A. Zenios, "Robust optimization of large-scale systems", Operations Research, vol. 43, no. 2, pp. 264-281, Apr. 1995 (doi: 10.1287/opre.43.2.264).
[49] K. Apornak, S. Soleymani, F. Faghihi, B. Mozafari, "Propose a model to maximize the retailer profit in electricity market contracts based on demand response and price elasticity while calculating the optimal value of the risk limitation", Journal of Novel Researches on Smart Power Systems, vol. 9, no. 2, pp. 9-18, Sept. 2020 (in Persian).
[50] M. Seyfi, M. Mehdinejad, B. Mohammadi-Ivatloo, H. Shayanfar, "Scenario-based robust energy management of CCHP-based virtual energy hub for participating in multiple energy and reserve markets", Sustainable Cities and Society, vol. 80, Article Number: 103711, May. 2022 (doi: 10.1016/j.scs.2022.103711).
[51] N. Naval, J.M. Yusta, "Virtual power plant models and electricity markets-A review", Renewable and Sustainable Energy Reviews, vol. 149, Article Number: 111393, Oct. 2021 (doi: 10.1016/j.rser.2021.111393).
[52] C. Crozier, K. Baker, "The effect of renewable electricity generation on the value of cross-border interconnection", Applied Energy, vol. 324, Article Number: 119717, Oct. 2022 (doi: 10.1016/j.apenergy.202¬2.11¬9717).
