ارائه یک چارچوب بهینه سازی مبتنی بر الگوریتم ADMM به منظور توسعه مبادلات همتابه همتای انرژی در یک شبکه توزیع فعال با استفاده از پوش های انعطاف پذیری پویا
محمد صداقت
1
(
دانشکده مهندسي، گروه برق قدرت، داشگاه لرستان، خرم آباد، ايران
)
اسمعیل رک رک
2
(
دانشکده مهندسي، گروه برق قدرت، داشگاه لرستان، خرم آباد، ايران
)
میثم دوستی زاده
3
(
دانشکده مهندسي، گروه برق قدرت، داشگاه لرستان، خرم آباد، ايران
)
کلید واژه: الگوریتم ADMM, پوش های انعطاف پذیری پویا, مجتمع انرژی, مبادلات همتا به همتای انرژی, شبکه توزیع فعال ,
چکیده مقاله :
با توسعه منابع تولید پراکنده، مبادلات همتا به همتای انرژی در بازارهای برق محلی متصل به شبکه های توزیع نیز افزایش یافته اند. یکی از الزامات پیاده سازی موفق مبادلات همتا به همتای انرژی، حفظ قیود فنی شبکه و بهره گیری از حداکثر ظرفیت منابع تولید پراکنده در طول مبادلات می باشد. روش های گوناگونی به منظور مدلسازی و حفظ قیود فنی شبکه در ناحیه مجاز در طول مبادلات همتا به همتای انرژی ارائه شده است. در پژوهش های اخیر، از پوش های بهره برداری به صورت استاتیک و دینامیک که حدود مجاز بهره برداری از شبکه را در بازه های زمانی مختلف مشخص می کنند، استفاده شدهاست. این پوش ها اغلب توسط اپراتور شبکه توزیع محاسبه شده و به کاربران شبکه ابلاغ می شوند و درنتیجه ترجیحات کاربران درنظر گرفته نمی شود. در این مقاله چارچوبی موسوم به پوش های انعطاف پذیری پویا ارائه شده که در آن از توافق میان مجتمع های انرژی و اپراتور شبکه توزیع، آستانه ایمن مبادلات همتابه همتا با توجه به عدم قطعیت مجتمع های انرژی مشخص می شود. روش پیشنهادی در قالب یک مسئله بهینه سازی غیرمتمرکز با استفاده الگوریتم ADMM بر روی شبکه استاندارد 69 شینه IEEE و با استفاده از نرم افزار GAMS پیاده سازی میشود. نتایج عددی، افزایش حجم مبادلات توان حقیقی و راکتیو را در مقایسه با روش های دیگر نشان می دهد. همچنین در این مقاله، کارایی روش پیشنهادی در پیاده سازی عدم قطعیت تولیدات پراکنده با بررسی فاصله اطمینان های مختلف اثبات می شود.
چکیده انگلیسی :
As distributed generation resources continue to grow, there has been a rise in p2p energy trading in local electricity markets connected to distribution networks. A key requirement for effectively implementing peer-to-peer energy transactions is to uphold the technical constraints of the network and optimize the capacity of distributed generation resources during these exchanges. Several approaches have been suggested for modeling and maintaining the network's technical constraints within the allowable limits during peer-to-peer energy transactions. Recent research has utilized both static and dynamic operation thresholds that define the acceptable limits for network operations across various time periods. Typically, these thresholds are determined by the distribution system operator (DSO) and conveyed to network participants, often overlooking user preferences in the process. In this paper, a framework called dynamic flexibility envelopes (DFE) is presented, in which the safe threshold of peer-to-peer exchanges is determined based on the agreement between energy communities (ECs) and DSO, considering the uncertainty of ECs. The proposed method is implemented in the form of a decentralized optimization problem using the ADMM algorithm on the IEEE standard 69-bus network and using GAMS software. Numerical results show an increase in the volume of real and reactive power transactions compared to other methods. Also, in this paper, the efficiency of the proposed method in implementing the uncertainty of distributed generations is proven by examining different confidence levels.
T. Capper et al., "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, vol. 162, p. 112403, 2022, doi:10.1016/j.rser.2022.112403.
J. Guerrero, A. C. Chapman, and G. Verbič, "Decentralized P2P energy trading under network constraints in a low-voltage network," IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5163-5173, 2018, doi: 10.1109/TSG.2018.2878445.
M. Khorasany, A. S. Gazafroudi, R. Razzaghi, T. Morstyn, and M. Shafie-khah, "A framework for participation of prosumers in peer-to-peer energy trading and flexibility markets," Applied energy, vol. 314, p. 118907, 2022, doi:10.1016/j.apenergy.2022.118907.
J. Cochran et al., "Flexibility in 21st century power systems," National Renewable Energy Laboratory (NREL), Golden, CO (United States)2014.
Y. Xia, Y. Huang, and J. Fang, "A generalized Nash-in-Nash bargaining solution to allocating energy loss and network usage cost of buildings in peer-to-peer trading market," Sustainable Energy, Grids and Networks, p. 101628, 2025, doi:10.1016/j.segan.2025.101628.
H. Hou, Z. Wang, B. Zhao, L. Zhang, Y. Shi, and C. Xie, "Peer-to-peer energy trading among multiple microgrids considering risks over uncertainty and distribution network reconfiguration: A fully distributed optimization method," International Journal of Electrical Power & Energy Systems, vol. 153, p. 109316, 2023, doi:10.1016/j.ijepes.2023.109316.
H. J. Kim, Y. S. Chung, S. J. Kim, H. T. Kim, Y. G. Jin, and Y. T. Yoon, "Pricing mechanisms for peer-to-peer energy trading: Towards an integrated understanding of energy and network service pricing mechanisms," Renewable and Sustainable Energy Reviews, vol. 183, p. 113435, 2023, doi:10.1016/j.rser.2023.113435.
M. H. Ullah and J.-D. Park, "DLMP integrated P2P2G energy trading in distribution-level grid-interactive transactive energy systems," Applied Energy, vol. 312, p. 118592, 2022, doi:10.1016/j.apenergy.2022.118592.
Z. Lu, L. Bai, J. Wang, J. Wei, Y. Xiao, and Y. Chen, "Peer-to-peer joint electricity and carbon trading based on carbon-aware distribution locational marginal pricing," IEEE transactions on power systems, vol. 38, no. 1, pp. 835-852, 2022, doi: 10.1109/TPWRS.2022.3167780.
M. I. Azim, W. Tushar, and T. K. Saha, "Coalition graph game-based P2P energy trading with local voltage management," IEEE transactions on smart grid, vol. 12, no. 5, pp. 4389-4402, 2021, doi: 10.1109/TSG.2021.3070160.
A. Koirala, F. Geth, and T. Van Acker, "Day-ahead dynamic operating envelopes using stochastic unbalanced optimal power flow," Sustainable Energy, Grids and Networks, vol. 40, p. 101528, 2024, doi:10.1016/j.segan.2024.101528.
G. Lankeshwara and R. Sharma, "Dynamic operating envelopes-enabled demand response in low-voltage residential networks," in 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022, pp. 1-7: IEEE, doi: 10.1109/APPEEC53445.2022.10072108.
M. I. Azim et al., "Dynamic operating envelope-enabled P2P trading to maximize financial returns of prosumers," IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1978-1990, 2023, doi: 10.1109/TSG.2023.3297366.
M. M. Hoque, M. Khorasany, M. I. Azim, R. Razzaghi, and M. Jalili, "Dynamic operating envelope-based local energy market for prosumers with electric vehicles," IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1712-1724, 2023, doi: 10.1109/TSG.2023.3302270.
P. Mochi and K. S. Pandya, "Dynamic Operating Envelope for Cost Optimization in Local Peer-to-Peer Energy Market," in 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE), 2023, pp. 1-6: IEEE, doi: 10.1109/ICEMCE57940.2023.10433960.
B. Liu and J. H. Braslavsky, "Robust dynamic operating envelopes for DER integration in unbalanced distribution networks," IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 3921-3936, 2023, doi: 10.1109/TPWRS.2023.3308104.
K. Petrou et al., "Ensuring distribution network integrity using dynamic operating limits for prosumers," IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 3877-3888, 2021, doi: 10.1109/TSG.2021.3081371.
J. Kainz et al., "Grid-friendly renewable energy communities using operating envelopes provided by DSOS," in IET Conference Proceedings CP823, 2023, vol. 2023, no. 6, pp. 2822-2827: IET, doi:10.1049/icp.2023.1040.
P. Scott and S. Thiebaux, "Network-aware co-optimisation of residential DER in energy and FCAS markets," 2020, doi: 10.1016/j.epsr.2020.106730.
K. Xing, L. Luo, S. Lu, W. Gu, X. Wang, and Y. Bai, "Improve operational flexibility of distribution systems using transportable resources," Renewable and Sustainable Energy Reviews, vol. 204, p. 114788, 2024, doi:10.1016/j.rser.2024.114788.
A. Attarha, S. M. N. RA, M. Mahmoodi, J. Iria, and P. Scott, "Shaped operating envelopes: Distribution network capacity allocation for market services," Electric Power Systems Research, vol. 234, p. 110639, 2024, doi: 10.1016/j.epsr.2024.110639.
ttarha, R. A. S. M. Noori, P. Scott, and S. Thiébaux, “Networksecure envelopes enabling reliable DER bidding in energy and reserve markets,” IEEE Trans. Smart Grid, vol. 13, no. 3, pp. 2050–2062, May 2022, doi: 10.1109/TSG.2021.3138099.
M. Zhu and E. Frazzoli, "Distributed robust adaptive equilibrium computation for generalized convex games," Automatica, vol. 63, pp. 82-91, 2016, doi: 10.1016/j.automatica.2015.10.012.
A. Aminlou, B. Mohammadi-Ivatloo, K. Zare, R. Razzaghi, and A. Anvari-Moghaddam, "Activating demand side flexibility market in a fully decentralized P2P transactive energy trading framework using ADMM algorithm," Sustainable Cities and Society, vol. 100, p. 105021, 2024, doi:10.1016/j.scs.2023.105021.
S. Feng, W. Wei, and Y. Chen, "Day-ahead scheduling and online dispatch of energy hubs: A flexibility envelope approach," IEEE Transactions on Smart Grid, vol. 15, no. 3, pp. 2723-2737, 2023, doi: 10.1109/TSG.2023.3337629.
S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004, ISBN:9780521833783, 0521833787.
K. Subbaramaiah and P. Sujatha, "Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis," Electric Power Systems Research, vol. 214, p. 108869, 2023, doi.org/10.1016/j.epsr.2022.108869.
M. Z. Golambahri, M. Shakarami, and M. Doostizadeh, "Security-aware joint energy and flexibility trading in electricity-heat networks: A novel clearing and validation analysis," International Journal of Electrical Power & Energy Systems, vol. 157, p. 109901, 2024, doi: 10.1016/j.ijepes.2024.109901.
X. Zhou, S. A. Mansouri, A. R. Jordehi, M. Tostado-Véliz, and F. Jurado, "A three-stage mechanism for flexibility-oriented energy management of renewable-based community microgrids with high penetration of smart homes and electric vehicles," Sustainable Cities and Society, vol. 99, p. 104946, 2023, doi:10.1016/j.scs.2023.104946.