یک چارچوب بهینه احتمالی جهت بهره‌برداری از سپهر‌های انرژی ادغام‌شده با بارهای پاسخگوی سرمایشی، حرارتی و الکتریکی و سیستم ذخیره‌ساز یخ توسط الگوریتم بهینه‌سازی خود-تطبیق کپک مخاطی بهبودیافته

فناوری های نوین مهندسی برق در سیستم انرژی سبز

دوره 2، شماره 1 - شماره پیاپی 5، خرداد 1402، صفحه 77-95   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.30486/teeges.2022.1969195.1043

نویسندگان

محمد عمادی¹؛ حمید رضا مسرور^{* 2}؛ اسمعیل رک رک³؛ امین سامان فر¹

¹گروه مهندسی برق، دانشکده مهندسی برق، واحد خرم آباد، دانشگاه آزاد اسلامی، خرم آباد، لرستان، ایران

²گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران

³گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه لرستان، خرم آباد، لرستان، ایران

چکیده

به دنبال گسترش استفاده از سپهرهای چند حاملی انرژی در صنایع، این مقاله یک چارچوب تصادفی جامع جهت مدیریت بهینه و برنامه‌ریزی روزانه یک سپهر انرژی ادغام‌شده با منابع انرژی تجدید پذیر و بارهای پاسخگوی سرمایشی، حرارتی و الکتریکی و سیستم ذخیره‌ساز یخ ارائه می‌دهد. برای حل این چالش، از روش تخمین نقطه‌ای2m+1 جهت ارزیابی دقیق عدم قطعیت‌های سیستم با پیچیدگی محاسباتی کم استفاده می‌شود. روش تخمین نقطه‌ای2m+1 یک روش تحلیل عدم قطعیت سریع بر اساس سری تیلور است. در این روش عدم قطعیت منابع انرژی تجدید پذیر و بارهای الکتریکی و حرارتی سپهر انرژی و همچنین قیمت مبادله با شبکه‌های مختلف توزیع انرژی بالادستی در نظر گرفته‌شده است. این مقاله همچنین یک روش بهینه‌سازی خود-تطبیق جدید به نام الگوریتم بهینه‌سازی خود-تطبیق بهبودیافته کپک مخاطی (SMSMA) را جهت حل مسئله پیچیده غیرخطی برنامه‌ریزی بهینه روزانه یک سپهر انرژی ارائه می‌دهد. روش خود-تطبیق بهبودیافته شده بر مبنای تئوری موجک است که قابلیت و توانایی الگوریتم اصلی کپک مخاطی را جهت حل مسئله برنامه‌ریزی بهینه روزانه یک سپهر انرژی یکپارچه بهبود می‌بخشد. نتایج عددی نشان می‌دهد که چارچوب برنامه‌ریزی تصادفی روزانه پیشنهادی، همراه با الگوریتم بهینه‌سازی SMSMA پیشنهادی، هزینه‌های بهره‌برداری سپهر انرژی را کاهش می‌دهد.

کلیدواژه‌ها

الگوریتم کپک مخاطی؛ تولید چند انرژی؛ روش تخمین نقطه‌ای؛ سپهر انرژی یکپارچه؛ مدیریت تصادفی سپهر انرژی

مراجع

[1] K. Kampouropoulos, F. Andrade, E. Sala, A. G. Espinosa, and L. Romeral, "Multiobjective optimization of multi-carrier energy system using a combination of ANFIS and genetic algorithms," IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 2276-2283, 2016.

[2] X. Zhang, M. Shahidehpour, A. Alabdulwahab, and A. Abusorrah, "Optimal expansion planning of energy hub with multiple energy infrastructures," IEEE Transactions on Smart grid, vol. 6, no. 5, pp. 2302-2311, 2015.

[3] N. Liu, L. Tan, H. Sun, Z. Zhou, and B. Guo, "Bilevel Heat–Electricity Energy Sharing for Integrated Energy Systems With Energy Hubs and Prosumers," IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3754-3765, 2021.

[4] M. Z. Oskouei, B. Mohammadi-Ivatloo, M. Abapour, M. Shafiee, and A. Anvari-Moghaddam, "Strategic operation of a virtual energy hub with the provision of advanced ancillary services in industrial parks," IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2062-2073, 2021.

[5] Y.-G. Son, B.-C. Oh, M. A. Acquah, R. Fan, D.-M. Kim, and S.-Y. Kim, "Multi energy system with an associated energy hub: a review," IEEE Access, 2021.

[6] https://www.eia.gov/todayinenergy/detail.php?id=36692

[7] B. Zhao, Z. Zhao, M. Huang, X. Zhang, Y. Li, and R. Wang, "Model Predictive Control of Solar PV-Powered Ice-Storage Air-Conditioning System Considering Forecast Uncertainties," IEEE Transactions on Sustainable Energy, vol. 12, no. 3, pp. 1672-1683, 2021.

[8] Y. Xie, S. Lin, W. Liang, Y. Yang, and M. Liu, "An Interval Probabilistic Energy Flow Calculation Method for CCHP Campus Microgrids," IEEE Systems Journal, 2022.

[9] M. Geidl and G. Andersson, "Optimal power flow of multiple energy carriers," IEEE Transactions on power systems, vol. 22, no. 1, pp. 145-155, 2007.

[10] H. R. Massrur, T. Niknam, J. Aghaei, M. Shafie-Khah, and J. P. Catalao, "Fast decomposed energy flow in large-scale integrated electricity–gas–heat energy systems," IEEE Transactions on Sustainable Energy, vol. 9, no. 4, pp. 1565-1577, 2018.

[11] H. R. Massrur, T. Niknam, M. Fotuhi‐Firuzabad, and A. Nikoobakht, "Hourly electricity and heat Demand Response in the OEF of the integrated electricity‐heat‐natural gas system," IET Renewable Power Generation, vol. 13, no. 15, pp. 2853-2863, 2019.

[12] S. M. Ezzati, F. Faghihi, H. M. Shourkaei, S. B. Mozafari, and S. Soleymani, "Optimum operation of multi-energy carriers in the context of an energy hub considering a wind generator based on linear programming," Journal of Renewable and Sustainable Energy, vol. 10, no. 1, p. 014702, 2018.

[13] H. R. Massrur, T. Niknam, and M. Fotuhi-Firuzabad, "Investigation of carrier demand response uncertainty on energy flow of renewable-based integrated electricity–gas–heat systems," IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 5133-5142, 2018.

[14] A. M. El-Zonkoly, A. Y. Abdelaziz, and A. M. Eladl, "Double-layer firefly algorithm for simultaneous optimal sizing and operation of energy hubs," Electric Power Components and Systems, vol. 45, no. 16, pp. 1846-1857, 2017.

[15] A. Najafi, A. Tavakoli, M. Pourakbari-Kasmaei, and M. Lehtonen, "A risk-based optimal self-scheduling of smart energy hub in the day-ahead and regulation markets," Journal of Cleaner Production, vol. 279, p. 123631, 2021.

[16] M. C. Bozchalui, C. A. Canizares, and K. Bhattacharya, "Optimal energy management of greenhouses in smart grids," IEEE transactions on smart grid, vol. 6, no. 2, pp. 827-835, 2014.

[17] N. Neyestani, M. Yazdani-Damavandi, M. Shafie-Khah, G. Chicco, and J. P. Catalão, "Stochastic modeling of multienergy carriers dependencies in smart local networks with distributed energy resources," IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1748-1762, 2015.

[18] S. Moazeni, A. H. Miragha, and B. Defourny, "A risk-averse stochastic dynamic programming approach to energy hub optimal dispatch," IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 2169-2178, 2018.

[19] M. Alipour, K. Zare, and M. Abapour, "MINLP probabilistic scheduling model for demand response programs integrated energy hubs," IEEE Transactions on Industrial Informatics, vol. 14, no. 1, pp. 79-88, 2017.

[20] Y. Wang, N. Zhang, C. Kang, D. S. Kirschen, J. Yang, and Q. Xia, "Standardized matrix modeling of multiple energy systems," IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 257-270, 2017.

[21] Z. Zhao et al., "Distributed robust model predictive control-based energy management strategy for islanded multi-microgrids considering uncertainty," IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 2107-2120, 2022.

[22] M. Rastegar, M. Fotuhi-Firuzabad, H. Zareipour, and M. Moeini-Aghtaieh, "A probabilistic energy management scheme for renewable-based residential energy hubs," IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2217-2227, 2016.

[23] M. Jadidbonab, B. Mohammadi-Ivatloo, M. Marzband, and P. Siano, "Short-term self-scheduling of virtual energy hub plant within thermal energy market," IEEE Transactions on industrial electronics, vol. 68, no. 4, pp. 3124-3136, 2020.

[24] T. Ha, Y. Xue, K. Lin, Y. Zhang, V. Van Thang, and T. Nguyen, "Optimal Operation of Energy Hub Based Microenergy Network with Integration of Renewables and Energy Storages," Journal of Modern Power Systems and Clean Energy, 2020.

[25] X. Gao, K. W. Chan, S. Xia, X. Zhang, and G. Wang, "Bidding strategy for coordinated operation of wind power plants and NGG-P2G units in electricity market," CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 212-224, 2021.

[26] L. Liu, W.-d. Li, J. Shen, C. Jin, and K. Wen, "Probabilistic Assessment of beta for Thermal Unit Using Point Estimate Method Adopted to a Low-Order Primary Frequency Response Model," IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 1931-1941, 2018.

[27] S. Bahrami and A. Sheikhi, "From demand response in smart grid toward integrated demand response in smart energy hub," IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 650-658, 2015.

[28] T. Savola and I. Keppo, "Off-design simulation and mathematical modeling of small-scale CHP plants at part loads," Applied thermal engineering, vol. 25, no. 8-9, pp. 1219-1232, 2005.

[29] A. Shabanpour-Haghighi and A. R. Seifi, "An integrated steady-state operation assessment of electrical, natural gas, and district heating networks," IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3636-3647, 2015.

[30] N. Holjevac, T. Capuder, N. Zhang, I. Kuzle, and C. Kang, "Corrective receding horizon scheduling of flexible distributed multi-energy microgrids," Applied energy, vol. 207, pp. 176-194, 2017.

[31] Y. Tao, J. Qiu, S. Lai, and J. Zhao, "Integrated electricity and hydrogen energy sharing in coupled energy systems," IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1149-1162, 2020.

[32] J. Wei, Y. Zhang, J. Wang, X. Cao, and M. A. Khan, "Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method," Applied Energy, vol. 260, p. 114188, 2020.

[33] L. Ju et al., "A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion," Energy, vol. 196, p. 117078, 2020.

[34] S. A. Gallego, J. F. Franco, and L. G. Cordero, "A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation," International Journal of Electrical Power & Energy Systems, vol. 131, p. 107049, 2021.

[35] M. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili, "Slime mould algorithm: A new method for stochastic optimization," Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.

[36] M. Gouri and R. S. Balan, "Enhancement of multimedia security using random permutation with wavelet function," Computers & Electrical Engineering, vol. 63, pp. 41-52, 2017.

[37] S. Clegg and P. Mancarella, "Storing renewables in the gas network: modelling of power‐to‐gas seasonal storage flexibility in low‐carbon power systems," IET Generation, Transmission & Distribution, vol. 10, no. 3, pp. 566-575, 2016.

[38] B. Mihaylov, T. R. Betts, A. Pozza, H. Müllejans, and R. Gottschalg, "Uncertainty estimation of temperature coefficient measurements of PV modules," IEEE Journal of Photovoltaics, vol. 6, no. 6, pp. 1554-1563, 2016.