Energy exchange management in a prosumer microgrid cluster: a piece of cake
Tóm tắt
The objective of this paper is to propose a proportional-fair energy exchange framework in a prosumer microgrid system taking into consideration the trading preferences of buyer microgrids. In fact, in a multi-microgrid system, there are potential seller microgrids with energy surplus and buyer microgrids with energy demand. The buyer microgrids may have different and,sometimes, competing trading preferences. The proposed framework ensures that each buyer gets a part of its energy requirement fulfilled from its preferred suppliers. Simultaneously, it incentivizes local energy trading over the central high-cost transactions with the main central grid. To this end, a Knapsack problem inspired approach is first suggested to unravel and fix the optimal market preferences of buyer microgrids. Afterwards, a self-organizing, transparent and anti-greed by design Last Diminisher protocol, which is a procedure of fair Cake-Cutting problem, is used to fairly meet the conflicting energy demand of buyers. The proposed approach is evaluated and validated through simulations. It has been shown that the suggested protocol can achieve an efficient and fair energy allocation among interconnected microgrids while reducing the total energy buying cost compared to other methods used in the literature.
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Tài liệu tham khảo
Facchini, A.: Distributed energy resources: planning for the future. Nat. Energy 2, 17129 (2017). https://doi.org/10.1038/nenergy.2017.129
Li, Q., Xu, Z., Yang, L.: Recent advancements on the development of microgrids. J. Mod. Power Syst. Clean Energy 2, 206–211 (2014). https://doi.org/10.1007/s40565-014-0069-8
Yao, L., Yang, B., Cui, H., et al.: Challenges and progresses of energy storage technology and its application in power systems. J. Mod. Power Syst. Clean Energy 4, 519–528 (2016). https://doi.org/10.1007/s40565-016-0248-x
Refaat, S.S., Ellabban, O., Bayhan, S., Abu-Rub, H., Blaabjerg, F., Begovic, M.M., Massaoudi, M., Refaat, S.S., Abu-Rub, H.: On the pivotal role of artificial intelligence toward the evolution of smart grids. In smart grid and enabling technologies (eds S.S. Refaat, O. Ellabban, S. Bayhan, H. Abu-Rub, F. Blaabjerg and M.M. Begovic) (2021). https://doi.org/10.1002/9781119422464.ch15
Mollah, M.B., Zhao, J., Niyato, D.T., Lam, K., Zhang, X., Ghias, A.M., Koh, L.H., Yang, L.: Blockchain for future smart grid: a comprehensive survey. IEEE Internet Things J. 8, 18–43 (2019). https://doi.org/10.1109/JIOT.2020.2993601
Hua, Weiqi, Chen, Ying, Qadrdan, Meysam, Jiang, Jing, Sun, Hongjian, Wu, Jianzhong: Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: a review. Renew. Sustain. Energy Rev. (2022). https://doi.org/10.1016/j.rser.2022.112308
Kakarott, J., Skwarek, V.: An enhanced DLT-based CO2 emission trading system, 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4), 2020, pp. 435-442. https://doi.org/10.1109/WorldS450073.2020.9210260
Eicke, L., Weko, S., Apergi, M., Marian, A.: Pulling up the carbon ladder? Decarbonization, dependence, and third-country risks from the European carbon border adjustment mechanism. Energy Res. Soc. Sci. 80, 102240 (2021). https://doi.org/10.1016/j.erss.2021.102240
Kühnbach, M., Bekk, A., Weidlich, A.: Towards improved prosumer participation: electricity trading in local markets. Energy 239, 122445 (2022). https://doi.org/10.1016/j.energy.2021.122445
Ali, L., Muyeen, S.M., Bizhani, H., Ghosh, A.: A peer-to-peer energy trading for a clustered microgrid - game theoretical approach. Int. J. Electr. Power Energy Syst. 133, 107307 (2021). https://doi.org/10.1016/j.ijepes.2021.107307
Paravantis, J.A., Kontoulis, N., Ballis, A., Tsirigotis, D., Dourmas, V.: A geopolitical review of definitions, dimensions and indicators of energy security. 2018 9th international conference on information, intelligence, systems and applications (IISA), pp. 1-8 (2018). https://doi.org/10.1109/IISA.2018.8633676
Yue, Z., Jianzhong, W., Chao, L., Wenlong, M.: State-ofthe- art analysis and perspectives for peer-to-peer energy trading. Engineering (2020). https://doi.org/10.1016/j.eng.2020.06.002
Dudkina, E., Crisostomi, E., Poli, D.: A review of P2P energy markets and a possible application for remote areas. 2020 IEEE PES innovative smart grid technologies Europe (ISGT-Europe), pp. 869-873 (2020). https://doi.org/10.1109/ISGT-Europe47291.2020.9248870
Zia, M.F., Benbouzid, M., Elbouchikhi, E., Muyeen, S.M., Techato, K., Guerrero, J.M.: Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis. IEEE Access 8, 19410–19432 (2020). https://doi.org/10.1109/ACCESS.2020.2968402
Mohsen, K., Amrit, P., Reza, R., Pierluigi, S.: A new method for peer matching and negotiation of prosumers in peer-to-peer energy markets. IEEE Trans. Smart Grid (2021). https://doi.org/10.1109/TSG.2020.3048397
Cheng, L., Yu, T.: Game-theoretic approaches applied to transactions in the open and ever-growing electricity markets from the perspective of power demand response: an overview. IEEE Access 7, 25727–25762 (2019). https://doi.org/10.1109/ACCESS.2019.2900356
Paudel, A., Chaudhari, K., Long, C., Gooi, H.B.: Peer-to-peer energy trading in a prosumer-based community microgrid: a game-theoretic model. IEEE Trans. Ind. Electron. 66(8), 6087–6097 (2019). https://doi.org/10.1109/TIE.2018.2874578
ALsalloum, H., Rahim, R., Merghem-Boulahia, L.: Prioritizing prosumers in the energy trading mechanism: a game theoretic approach. 2019 international conference on wireless and mobile computing, Networking and communications (WiMob), pp. 1-5 (2019). https://doi.org/10.1109/WiMOB.2019.8923202
Wu, Q., Xie, Z., Ren, H., Li, Q., Yang, Y.: Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: a comparison study. Energy 254, 124448 (2022). https://doi.org/10.1016/j.energy.2022.124448
Liu, N., Yu, X., Wang, C., Wang, J.: Energy sharing management for microgrids with PV prosumers: a Stackelberg game approach. IEEE Trans. Ind. Inform. 13(3), 1088–1098 (2017). https://doi.org/10.1109/TII.2017.2654302
Zaidi, SS.M.B.H., Ahmed, A., Sohail, M.B., Shah, S.H.H., Ahmed, A., Hussain, I.: Energy trading for shared facility control of a smart community using auction process. 2019 7th international electrical engineering congress (iEECON), pp. 1-4 (2019). https://doi.org/10.1109/iEECON45304.2019.8938966
Zaidi, B.H., Hong, S.H.: Combinatorial double auctions for multiple microgrid trading. Electr. Eng. 100, 1069–1083 (2018). https://doi.org/10.1007/s00202-017-0570-y
He, L., Zhang, J.: A community sharing market with PV and energy storage: an adaptive Bidding-based double-side auction mechanism. IEEE Trans. Smart Grid 12(3), 2450–2461 (2021). https://doi.org/10.1109/TSG.2020.3042190
Zhang, H., Zhang, H., Song, L., Li, Y., Han, Z.: Peer-to-Peer Energy Trading in DC Packetized Power Microgrids Using Iterative Auction. 2019 IEEE global communications conference (GLOBECOM), pp. 1-6 (2019). https://doi.org/10.1109/GLOBECOM38437.2019.9013179
Naz, K., Zainab, F., Mehmood, K.K., Bukhari, S.B.A., Khalid, H.A., Kim, C.-H.: An optimized framework for energy management of multi-microgrid systems. Energies 14, 6012 (2021). https://doi.org/10.3390/en14196012
Gregoratti, D., Matamoros, J.: Distributed energy trading: the multiple- microgrid case. IEEE Trans. Ind. Electron. 62(4), 2551–2559 (2015). https://doi.org/10.1109/TIE.2014.2352592
Paudel, A., Gooi, H.B.: Pricing in Peer-to-Peer Energy Trading Using Distributed Optimization Approach. 2019 IEEE power & energy society general meeting (PESGM), pp. 1-5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973868
Zhu, H., Ouahada, K., Abu-Mahfouz, A.M.: Peer-to-peer energy trading in smart energy communities: a Lyapunov-based energy control and trading system. IEEE Access 10, 42916–42932 (2022). https://doi.org/10.1109/ACCESS.2022.3167828
Mehdinejad, M., Shayanfar, H., Mohammadi-Ivatloo, B.: Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers. Energy 244, 122713–122731 (2022). https://doi.org/10.1016/j.energy.2021.122713
Karthik, P.K., Anand, R.: Energy Trading in Microgrids using BlockChain Technology. 2020 4th international conference on intelligent computing and control systems (ICICCS), pp. 884-888 (2020). https://doi.org/10.1109/ICICCS48265.2020.9121050
AlSkaif, T., Crespo-Vazquez, J.L., Sekuloski, M., van Leeuwen, G., Catalão, J.P.S.: Blockchain-based fully peer-to-peer energy trading strategies for residential energy systems. IEEE Trans. Ind. Inform. 18(1), 231–241 (2022). https://doi.org/10.1109/TII.2021.3077008
Joseph, A., Balachandra, P.: Smart grid to energy internet: a systematic review of transitioning electricity systems. IEEE Access 8, 215787–215805 (2020). https://doi.org/10.1109/ACCESS.2020.3041031
Ma, Z., Zhang, C., Qian, C.: The Development of Machine Learning In Energy Trading. 2019 1st international conference on industrial artificial intelligence (IAI), pp. 1-5 (2019). https://doi.org/10.1109/ICIAI.2019.8850824
Zhou, S., Hu, Z., Gu, W., Jiang, M., Zhang, X.: Artificial intelligence based smart energy community management: a reinforcement learning approach. CSEE J. Power nergy Syst. 5(1), pp. 1–10, (2019). https://doi.org/10.17775/CSEEJPES.2018.00840
Chen, T., Su, W.: Local energy trading behavior modeling with deep reinforcement learning. IEEE Access 6, 62806–62814 (2018). https://doi.org/10.1109/ACCESS.2018.2876652
Sun, H., Kitamura, S., Nikovski, D., Mori, K., Hashimoto, H.: Illegitimate Trade Detection for Electricity Energy Markets. 2020 international conference on smart grids and energy systems (SGES), pp. 338-343 (2020). https://doi.org/10.1109/SGES51519.2020.00066
Hwang, H.-K., Yoon, A.-Y., Kang, H.-K., Moon, S.-I.: Retail electricity pricing strategy via an artificial neural network-based demand response model of an energy storage system. IEEE Access 9, 13440–13450 (2021). https://doi.org/10.1109/ACCESS.2020.3048048
Jogunola, O., Wang, W., Adebisi, B.: Prosumers matching and least-cost energy path optimisation for peer-to-peer energy trading. IEEE Access 8, 95266–95277 (2020). https://doi.org/10.1109/ACCESS.2020.2996309
Essayeh, C., El-Fenni, M.R., Dahmouni, H.: Optimal Energy Exchange in Micro-Grid Networks: Cooperative Game Approach. 2018 renewable energies, power systems & green inclusive economy (REPS-GIE), pp. 1-6 (2018). https://doi.org/10.1109/REPSGIE.2018.8488865
Neely, MM.J., Saber Tehrani, A., Dimakis, A.G.: Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers. 2010 First IEEE international conference on smart grid communications, pp. 549-554 (2010). https://doi.org/10.1109/SMARTGRID.2010.5621993
Bai, L., Crisostomiy, E.: Distribution Loss Allocation in Peer-to-Peer Energy Trading in a Network of Microgrids. 2020 IEEE power & energy society general meeting (PESGM), pp. 1-5 (2020). https://doi.org/10.1109/PESGM41954.2020.9281382
Narayanan, A., Nardelli, P.: Profit Allocation in Renewables Based Community Microgrids with Aggregation and Self-Sufficiency.: IEEE 31st annual international symposium on personal. Indoor and mobile radio communications 2020, 1–6 (2020). https://doi.org/10.1109/PIMRC48278.2020.9217165
Gjorgievski, Vladimir Z., Cundeva, Snezana, Markovska, Natasa, Georghiou, George E.: Virtual net-billing: a fair energy sharing method for collective self-consumption. Part B Energy (2022). https://doi.org/10.1016/j.energy.2022.124246
Zhou, Y., Tan, M., Li, S., Wang, R.: A cooperative energy trading model for multi-energy microgrid cluster in an active distribution network. 2019IEEE 3rd conference on energy internet and energy system integration (EI2) pp. 625-630 (2019). https://doi.org/10.1109/EI247390.2019.9062011
Bai, L., Thomopulos, D., & Crisostomi, E.: Preference-based energy exchange in a network of microgrids (2019). https://doi.org/10.48550/arXiv.1906.11070
Amin, W., et al.: Consumers’ preference based optimal price determination model for p2p energy trading. Electr. Power Syst. Res. 187, 106488 (2020). https://doi.org/10.1016/j.epsr.2020.106488
Dukovska, I., Paterakis, N.G., Slootweg, H.J.G.: Local Energy Exchange Considering Heterogeneous Prosumer Preferences. 2018 International conference on smart energy systems and technologies (SEST), pp. 1-6 (2018). https://doi.org/10.1109/SEST.2018.8495865
Morstyn, T., McCulloch, M.D.: Multiclass energy management for peer to- peer energy trading driven by prosumer preferences. IEEE Trans. Power Syst. 34(5), 4005–4014 (2019). https://doi.org/10.1109/TPWRS.2018.2834472
Hahnel, U.J., Herberz, M., Pena-Bello, A., Parra, D., Brosch, T.: Becoming prosumer: revealing trading preferences and decision-making strategies in peer-to-peer energy communities. Energy Policy 137, 111098 (2020). https://doi.org/10.1016/j.enpol.2019.111098
Zerka, A., Ouassaid, M., Maaroufi, M., Rabeh, R.: Energy Exchange Management in Smart Grids Using a Knapsack Problem Inspired Approach 2022 IEEE 21st mediterranean electrotechnical conference (MELECON), pp. 1034-1039 (2022). https://doi.org/10.1109/MELECON53508.2022.9842885
Sánchez, M., Cruz-Duarte, J.M., Ortíz-Bayliss, J.C., Ceballos, H., Terashima-Marin, H., Amaya, I.: A systematic review of hyper-heuristics on combinatorial optimization problems. IEEE Access 8, 128068–128095 (2020). https://doi.org/10.1109/ACCESS.2020.3009318
Ezugwu, A.E., Pillay, V., Hirasen, D., Sivanarain, K., Govender, M.: A comparative study of meta-heuristic optimization algorithms for 0–1 Knapsack problem: some initial results. IEEE Access 7, 43979–44001 (2019). https://doi.org/10.1109/ACCESS.2019.2908489
Erel, Segal-Halevi.: Fairly Dividing a Cake after Some Parts Were Burnt in the Oven. MAS (2018). arXiv:1704.00726. https://doi.org/10.48550/arXiv.1704.00726
Legut, J.: Simple fair division of a square. J. Math. Econ. 86, 35–40 (2020). https://doi.org/10.1016/j.jmateco.2019.11.001
Bertsimas, D., Farias, V., Trichakis, N.: The Price of Fairness. Oper. Res. 59, 17–31 (2011). https://doi.org/10.1287/opre.1100.0865
Forouzan, A.R., Shahtalebi, K.: Price of fairness in digital subscriber line systems using dynamic spectrum management. IEEE Trans. Commun. 69(5), 2851–2862 (2021). https://doi.org/10.1109/TCOMM.2021.3053614
Ortega, J., Segal-Halevi, E.: Obvious manipulations in cake-cutting. Soc. Choice Welf. (2022). https://doi.org/10.1007/s00355-022-01416-4
Rothe, Jörg.: Economics and Computation: An introduction to algorithmic game theory. Computational social choice, and fair division. (2016). https://doi.org/10.1007/978-3-662-47904-9
Robertson, Jack, Webb, William: Cake-Cutting algorithms: be fair if you can. A. K. Peters, Natick, Massachusetts 78-1-56881-076-8. LCCN 97041258. OL 2730675W (1998)
Jain, K., Dhabu, M., Kakde, O., Funde, N.: Completely fair energy scheduling mechanism in a smart distributed multi-microgrid system. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.08.002
Funde NA, Dhabu MM, Deshpande PS, Patne NR,: SF-OEAP starvation-free optimal energy allocation policy in a smart distributed multimicrogrid system. IEEE Trans. Ind. Inf. 14(11), 4873–4883 (2018). https://doi.org/10.1109/TII.2018.2810816