Smart Grid 2.0: Modeling Peer-to-Peer Trading Community and Incentives for Prosumers in the Transactive Energy Grid


  • Manal Mahmoud Khayyat Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
  • Sami Ben Slama The Applied College, King Abdulaziz University, Jeddah, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13470-13480 | April 2024 |


Smart Grid 2.0 (SG 2.0) implementation constitutes an additional challenge in the industry and research fields. Energy consumption decreases when producers exchange excess energy consumers, including intelligent consumers, Distributed Generation (DG), such as wind and solar, and Electric Vehicles (EVs). By utilizing Demand Response (DR) based on Real-Time Pricing (RTP), the operation of every device in a smart home can be scheduled. Allowing users to trade energy directly with other energy producers (prosumers) rather than exclusively relying on the grid, peer-to-peer (P2P) energy trading in smart homes lowers energy prices for users. This article focuses on how the DR P2P energy trading affects consumers. The study conducted utilizes a two-stage scheduling technique to reduce consumers' electricity expenses. The initial stage involves arranging each device in the smart home based on RTP employing a deep learning method. The P2P energy trading between consumers in the second phase is made more accessible by the DR and the simulation results exhibit that energy trading decreases electricity bills in smart homes. Utility companies can reduce load during peak hours using DR-based P2P energy trading.


artificial intelligence, deep reinforcement learning, peer-to-peer energy trading, smart community, photovoltaic-array, energy market


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S. Ben Slama, "Prosumer in smart grids based on intelligent edge computing: A review on Artificial Intelligence Scheduling Techniques," Ain Shams Engineering Journal, vol. 13, no. 1, Jan. 2022, Art. no. 101504.

R. Rodriguez et al., "Sizing of a fuel cell–battery backup system for a university building based on the probability of the power outages length," Energy Reports, vol. 8, pp. 708–722, Nov. 2022.

G. K. Jabash Samuel and J. Jasper, "MANFIS based SMART home energy management system to support SMART grid," Peer-to-Peer Networking and Applications, vol. 13, no. 6, pp. 2177–2188, Nov. 2020.

B. M. Manjunatha, S. N. Rao, A. S. Kumar, V. L. Devi, P. R. Mohan, and K. Brahmanandam, "An Enhanced Z-Source Switched MLI Capacitor for Integrated Micro-Grid with Advanced Switching Pattern Scheme," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8936–8941, Aug. 2022.

T. Khan, M. Yu, and M. Waseem, "Review on recent optimization strategies for hybrid renewable energy system with hydrogen technologies: State of the art, trends and future directions," International Journal of Hydrogen Energy, vol. 47, no. 60, pp. 25155–25201, Jul. 2022.

Y. Liu, L. Wu, and J. Li, "Peer-to-peer (P2P) electricity trading in distribution systems of the future," The Electricity Journal, vol. 32, no. 4, pp. 2–6, May 2019.

L. Novoa, R. Flores, and J. Brouwer, "Optimal renewable generation and battery storage sizing and siting considering local transformer limits," Applied Energy, vol. 256, Dec. 2019, Art. no. 113926.

J. L. Rojas-Renteria, T. D. Espinoza-Huerta, F. S. Tovar-Pacheco, J. L. Gonzalez-Perez, and R. Lozano-Dorantes, "An Electrical Energy Consumption Monitoring and Forecasting System," Engineering, Technology & Applied Science Research, vol. 6, no. 5, pp. 1130–1132, Oct. 2016.

A. F. Moreno Jaramillo, D. M. Laverty, D. J. Morrow, J. Martinez del Rincon, and A. M. Foley, "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, vol. 179, pp. 445–466, Dec. 2021.

S. Nebili, I. Benabdallah, and A. Cherif, "Decoupling Control Applied to the Smart Grid Power Dispatching Problem," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8960–8966, Aug. 2022.

Y. Dai, Y. Gao, H. Gao, and H. Zhu, "Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers," Neurocomputing, vol. 260, pp. 149–156, Oct. 2017.

J. Shu, R. Guan, L. Wu, and B. Han, "A Bi-Level Approach for Determining Optimal Dynamic Retail Electricity Pricing of Large Industrial Customers," IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2267–2277, Mar. 2019.

H. Thomas, H. Sun, and B. Kazemtabrizi, "Closest Energy Matching: Improving peer-to-peer energy trading auctions for EV owners," IET Smart Grid, vol. 4, no. 4, pp. 445–460, 2021.

S. Mohammadi, F. Eliassen, and Y. Zhang, "Effects of false data injection attacks on a local P2P energy trading market with prosumers," in IEEE PES Innovative Smart Grid Technologies Europe, The Hague, Netherlands, Oct. 2020, pp. 31–35.

A. Al-Sorour, M. Fazeli, M. Monfared, A. Fahmy, J. R. Searle, and R. P. Lewis, "Enhancing PV Self-Consumption Within an Energy Community Using MILP-Based P2P Trading," IEEE Access, vol. 10, pp. 93760–93772, 2022.

M. I. Azim, W. Tushar, T. K. Saha, C. Yuen, and D. Smith, "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, vol. 169, Nov. 2022, Art. no. 112908.

H. Zang and J. Kim, "Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market," Energies, vol. 14, no. 14, Jan. 2021, Art. no. 4131.

Z. He, K. P. Tran, S. Thomassey, X. Zeng, J. Xu, and C. Yi, "Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning," Journal of Manufacturing Systems, vol. 62, pp. 939–949, Jan. 2022.

A. Sheffrin, "Empirical Evidence of Strategic Bidding in the California ISO Real-time Market," in Electricity Pricing in Transition, A. Faruqui and B. K. Eakin, Eds. Boston, MA, USA: Springer, 2002, pp. 267–281.

Y. Wu, Z. Liu, B. Li, J. Liu, and L. Zhang, "Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff," Renewable Energy, vol. 200, pp. 558–570, Nov. 2022.

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, Sep. 2021.

K. Chaurasia and H. R. Kamath, "New Approach using Artificial Intelligence-Machine Learning in Demand Side Management of Renewable Energy integrated Smart Grid for Smart City," in 4 th International Conference on Innovative Computing and Communication, Delhi, India, Feb. 2021, pp. 1–5.

Y. Wang, X. Wang, C. Shao, and N. Gong, "Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach," Renewable Energy, vol. 155, pp. 513–530, Aug. 2020.

F. Zeng, Y. Chen, L. Yao, and J. Wu, "A novel reputation incentive mechanism and game theory analysis for service caching in software-defined vehicle edge computing," Peer-to-Peer Networking and Applications, vol. 14, no. 2, pp. 467–481, Mar. 2021.

M. B. Roberts, A. Bruce, and I. MacGill, "Impact of shared battery energy storage systems on photovoltaic self-consumption and electricity bills in apartment buildings," Applied Energy, vol. 245, pp. 78–95, Jul. 2019.

B. D. Raj, A. Sarkar, and D. Goswami, "An efficient framework for brownout based appliance scheduling in microgrids," Sustainable Cities and Society, vol. 83, Aug. 2022, Art. no. 103936.

J. Wang, H. Zhong, Q. Xia, G. Li, and M. Zhou, "Sharing Economy for Renewable Energy Aggregation," in Sharing Economy in Energy Markets: Modeling, Analysis and Mechanism Design, J. Wang, H. Zhong, Q. Xia, G. Li, and M. Zhou, Eds. New York, NY, USA: Springer, 2022, pp. 107–142.

S. Benjaafar, G. Kong, X. Li, and C. Courcoubetis, "Peer-to-Peer Product Sharing," in Sharing Economy: Making Supply Meet Demand, M. Hu, Ed. New York, NY, USA: Springer, 2019, pp. 11–36.

P. R. Padghan, S. Arul Daniel, and R. Pitchaimuthu, "Grid-tied energy cooperative trading framework between Prosumer to Prosumer based on Ethereum smart contracts," Sustainable Energy, Grids and Networks, vol. 32, Dec. 2022, Art. no. 100860.

P. Pediaditis, D. Papadaskalopoulos, A. Papavasiliou, and N. Hatziargyriou, "Bilevel Optimization Model for the Design of Distribution Use-of-System Tariffs," IEEE Access, vol. 9, pp. 132928–132939, 2021.

M. Maldet et al., "Trends in local electricity market design: Regulatory barriers and the role of grid tariffs," Journal of Cleaner Production, vol. 358, Jul. 2022, Art. no. 131805.

Y. Takeda and K. Tanaka, "Bidding Agent Model for P2P Energy Trading," IEEJ Transactions on Industry Applications, vol. 140, no. 10, pp. 738–745, Oct. 2020.

M. Aloud, "Adaptive GP agent-based trading system under intraday seasonality model," Intelligent Decision Technologies, vol. 11, no. 2, pp. 235–251, Jan. 2017.

S. Ben Slama, "Design and implementation of home energy management system using vehicle to home (H2V) approach," Journal of Cleaner Production, vol. 312, Aug. 2021, Art. no. 127792.

B. S. Sami, N. Sihem, and Z. Bassam, "Design and implementation of an intelligent home energy management system: A realistic autonomous hybrid system using energy storage," International Journal of Hydrogen Energy, vol. 43, no. 42, pp. 19352–19365, Oct. 2018.

S. Zamanloo, H. Askarian Abyaneh, H. Nafisi, and M. Azizi, "Optimal two-level active and reactive energy management of residential appliances in smart homes," Sustainable Cities and Society, vol. 71, Aug. 2021, Art. no. 102972.

J. Liu, H. Yang, and Y. Zhou, "Peer-to-peer energy trading of net-zero energy communities with renewable energy systems integrating hydrogen vehicle storage," Applied Energy, vol. 298, Sep. 2021, Art. no. 117206.

I. Hammou Ou Ali, M. Ouassaid, and M. Maaroufi, "Optimal appliance management system with renewable energy integration for smart homes," in Renewable Energy Systems, A. T. Azar and N. A. Kamal, Eds. Cambridge, MA, USA: Academic Press, 2021, pp. 533–552.

D. Hemkumar, S. Ravichandra, and D. V. L. N. Somayajulu, "Impact of data correlation on privacy budget allocation in continuous publication of location statistics," Peer-to-Peer Networking and Applications, vol. 14, no. 3, pp. 1650–1665, May 2021.

L. Ma, L. Wang, and Z. Liu, "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, vol. 285, Mar. 2021, Art. no. 116399.

N. E. H. Bourebia and C. Li, "A greedy energy efficient clustering scheme based reinforcement learning for WSNs," Peer-to-Peer Networking and Applications, vol. 15, no. 6, pp. 2572–2588, Nov. 2022.


How to Cite

M. M. Khayyat and S. Ben Slama, “Smart Grid 2.0: Modeling Peer-to-Peer Trading Community and Incentives for Prosumers in the Transactive Energy Grid”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13470–13480, Apr. 2024.


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