تجارت برق همتا به همتا در جامعه مبتنی بر نانوشبکه‌ها برپایۀ رویکرد تئوری بازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی برق، دانشکدۀ فنی، دانشگاه گیلان، رشت، ایران

2 دانشجوی دکتری، گروه مهندسی برق، دانشکدۀ فنی، دانشگاه گیلان، رشت، ایران

3 دانشیار، گروه مهندسی برق، دانشکدۀ فنی، دانشگاه گیلان، رشت، ایران

10.22059/ses.2024.382114.1094

چکیده

با توجه به افزایش نفوذ منابع انرژی توزیع‌شده (DER) در سیستم‌های توزیع فعال و اهمیت بالای تجارت انرژی همتا به همتا (P2P)، یافتن نوعی رویکرد مناسب برای بهره‌برداری از این منابع در نانوشبکه‌ها (NG) ضروری است. در این ساختار، NGها به‌ عنوان شرکت‌کنندگان فعال در معاملات انرژی P2P رفتار می‌کنند و با استفاده از الگوریتم تئوری بازی ارائه‌شده به تعادل نش می‌رسند. این تحقیق یک مدل جامع را برای بررسی پویایی سیستم انرژی با استفاده از رویکرد تئوری بازی به نام تابع نیکایدو ـ ایزودا و الگوریتم آزادسازی (NIRA) توسعه داده است. همچنین در این مطالعه، با تأکید بر ضرورت انعطاف‌پذیری و سازگاری با تغییرات در اکوسیستم انرژی، تأثیر عوامل مختلف بر درآمد و زیان‌ اقتصادی NGها ناشی از افزایش قیمت گاز طبیعی، افزایش تولیدات تجدیدپذیر و انفصال از شبکۀ بالادست مورد تجزیه‌و‌تحلیل قرار گرفته است. نتایج شبیه‌سازی نشان می‌دهد به‌ترتیب با دو برابر شدن تولیدات تجدیدپذیر و سه برابر شدن نرخ سوخت در مطالعه موردی، مجموع میزان مشارکت NGها در معاملات P2P با رشد 113‌ و افت 5 درصد نسبت به سناریوی نرمال همراه بوده است. بنابراین، روش ارائه‌شده به بهبود بهره‌وری انرژی، کاهش هزینه‌ها و افزایش پایداری سیستم قدرت منجر می‌شود. نتایج این مقاله می‌تواند به ‌عنوان نوعی راهکار عملی در گسترش استفاده از انرژی‌های تجدیدپذیر و تشکیل بازارهای انرژی محلی به ‌کار رود و به سیاست‌گذاران و مدیران انرژی برای طراحی تکنیک‌های مفید به منظور دستیابی به سیستم‌های انرژی پایدارتر و انعطاف‌پذیرتر کمک کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Peer-to-Peer Electricity Trading in Nanogrid-based Community based on Game Theory Approach

نویسندگان [English]

  • Keivan Malekzadeh Viayeh 1
  • Masoumeh Javadi 2
  • Alfred Baghramian 3
1 MSc student, Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
2 PhD student, Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
3 Associate Professor, Electrical Engineering Department, Faculty of Engineering, University of Guilan, Rasht, Iran
چکیده [English]

Owing to the augmenting penetration of distributed energy resources (DER) in active distribution systems and the high significance of peer-to-peer (P2P) energy trading, uncovering a proper approach to operate these resources in nanogrids (NG) is essential. In this structure, NGs behave as active participants in P2P energy dealings and reach Nash equilibrium utilizing the presented game theory algorithm. This research has developed a comprehensive model to examine the energy system dynamics employing a game theory approach named the Nikaido-Isoda function and Relaxation algorithm (NIRA). Besides, this study has analyzed the influences of various factors on the economic earnings and losses of NGs, consisting of natural gas price upsurges, renewable energy production boosts, and disconnection from the upstream grid, stressing the need for flexibility and adaptability to alterations in the energy ecosystem. Simulation outcomes reveal that with a doubling of renewable production and a tripling of fuel price in the case study, the whole NGs' participation in the P2P trades has encountered 113% growth and 5% reduction compared to the normal scenario. Therefore, the presented method leads to improved energy efficiency, decreased costs, and enhanced power system sustainability. The results of this paper could serve as a practical way to expand the usage of renewable energy and form local energy markets, helping policymakers and energy managers to design useful techniques for gaining more sustainable and flexible energy systems.

کلیدواژه‌ها [English]

  • Peer-to-Peer Electricity Trading
  • Game Theory
  • Nanogrid

مراجع

[1]        Sahebi H, Khodoomi M, Seif M, Pishvaee MS, Hanne T. The benefits of peer-to-peer renewable energy trading and battery storage backup for local grid. J Energy Storage. 2023;63(September 2022).
[2]        Tushar W, Yuen C, Mohsenian-Rad H, Saha T, Poor HV, Wood KL. Transforming energy networks via peer-to-peer energy trading: The potential of game-theoretic approaches. IEEE Signal Process Mag. 2018;35(4):90–111.
[3]        Zambroni de Souza AC, Castilla M. Microgrids design and implementation. Microgrids Design and Implementation. 2018. 1–538 p.
[4]        Chowdhury S, Chowdhury SP, Crossley P. Microgrids and active distribution networks [Internet]. Microgrids and Active Distribution Networks. Institution of Engineering and Technology; 2009. 1–298 p.
[5]        Maaref MK, Salehi J. Peer-to-Peer Electricity Trading in Microgrids with Renewable Sources and Uncertainty Modeling Using IGDT. J Oper Autom Power Eng. 2024;12(3):195–205.
[6]        Tushar W, Saha TK, Yuen C, Smith D, Poor HV. Peer-to-Peer Trading in Electricity Networks: An Overview. IEEE Trans Smart Grid. 2020;11(4):3185–200.
[7]        Sousa T, Soares T, Pinson P, Moret F, Baroche T, Sorin E. Peer-to-peer and community-based markets: A comprehensive review. Renew Sustain Energy Rev. 2019;104:367–78.
[8]        Moret F, Pinson P. Energy Collectives: A Community and Fairness Based Approach to Future Electricity Markets. IEEE Trans Power Syst. 2019;34(5):3994–4004.
[9]        Morstyn T, Teytelboym A, McCulloch MD. Designing decentralized markets for distribution system flexibility. IEEE Trans Power Syst. 2019;34(3):1–12.
[10]      Zhang Z, Tang H, Wang P, Huang Q, Lee WJ. Two-Stage Bidding Strategy for Peer-to-Peer Energy Trading of Nanogrid. IEEE Trans Ind Appl. 2020;56(2):1000–9.
[11]      Dong J, Dou X, Liu D, Bao A, Wang D, Zhang Y, et al. Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors. Front Energy Res. 2023;11(April):1–18.
[12]      Paudel A, Chaudhari K, Long C, Gooi HB. Peer-to-peer energy trading in a prosumer-based community microgrid: A game-theoretic model. IEEE Trans Ind Electron. 2019;66(8):6087–97.
[13]      Thomas A, Abraham MP, G AM. Analysis of Peer-to-Peer Energy Trading in a Dynamic Environment Using Stackelberg Game. In: 2021 Seventh Indian Control Conference (ICC). IEEE; 2021. p. 412–7.
[14]      Cui S, Wang YW, Shi Y, Xiao JW. A New and Fair Peer-to-Peer Energy Sharing Framework for Energy Buildings. IEEE Trans Smart Grid. 2020;11(5):3817–26.
[15]      Cui S, Xiao JW. Game-based peer-to-peer energy sharing management for a community of energy buildings. Int J Electr Power Energy Syst. 2020;123:106204.
[16]      Luo X, Shi W, Jiang Y, Liu Y, Xia J. Distributed peer-to-peer energy trading based on game theory in a community microgrid considering ownership complexity of distributed energy resources. J Clean Prod. 2022;351:131573.
[17]      Huang H, Nie S, Lin J, Wang Y, Dong J. Optimization of peer-to-peer power trading in a microgrid with distributed PV and battery energy storage systems. Sustain. 2020;12(3):923.
[18]      Lee S, Jin H, Vecchietti LF, Hong J, Park KB, Son PN, et al. Cooperative decentralized peer-to-peer electricity trading of nanogrid clusters based on predictions of load demand and PV power generation using a gated recurrent unit model. IET Renew Power Gener. 2021;15(15):3505–23.
[19]      Tushar W, Saha TK, Yuen C, Azim MI, Morstyn T, Poor HV, et al. A coalition formation game framework for peer-to-peer energy trading. Appl Energy. 2020;261:114436.
[20]      Malik S, Duffy M, Thakur S, Hayes B, Breslin J. A priority-based approach for peer-to-peer energy trading using cooperative game theory in local energy community. Int J Electr Power Energy Syst. 2022;137:107865.
[21]      Pereira H, Gomes L, Vale Z. Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning. Energy Informatics. 2022;5(S4):44.
[22]      Imahori M, Hase R, Shinomiya N. A Multi-Objective Optimization Method on Consumer ’ s Benefit in Peer-to-peer Energy Trading. Fifteenth Int Conf Syst. 2020;(c):56–61.
[23]      Marzband M, Javadi M, Domínguez-García JL, Moghaddam MM. Non-cooperative game theory based energy management systems for energy district in the retail market considering DER uncertainties. IET Gener Transm Distrib. 2016;10(12):2999–3009.
[24]      Javadi M, Marzband M, Mirhosseini Moghaddam M. Optimal energy management of microgrids in the retail market with bidding strategy based on game theory approach considering coalition formation among generation units. Tabriz J Electr Eng. 2016;46(4):95–107. [Persian]
[25]      Javadi M, Baghramian A. Electricity trading of multiple home microgrids through V2X based on game theory. Sustain Cities Soc. 2024;101:105046.
[26]      Zhang H, Zhang S, Hu X, Cheng H, Gu Q, Du M. Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion. Appl Energy. 2022;306:118040.
[27]      Su W, Huang AQ. A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers. Appl Energy. 2014;119:341–50.
[28]      Kynigos M, Yerasimou Y, Efthymiou V, Georghiou GE. Design of a Nanogrid for Increasing Energy Efficiency of Buildings. IET Conf Proc. 2020;2020(5):308–11.
[29]      energysage [Internet]. Solar Panel Buyer’s Guide. Available from: https://www.energysage.com/solar-panels/rec/2544/REC400AA_Pure
[30]      Gerhardsen T. energyprices.eu. Electricity spot price in Italy. Available from: https://www.energyprices.eu/electricity/italy-calabria
[31]      statista [Internet]. Natural gas prices pre-tax for household consumers in Italy from 2nd half 2012 to 1st half 2023. Available from: https://www.statista.com/statistics/785605/natural-gas-prices-for-household-consumers-in-italy/
فصلنامه سیستم های انرژی پایدار
دوره 3، شماره 4
مهر 1403
صفحه 419-442

فایل ها

هم رسانی

ارجاع به این مقاله

آمار