Your browser does not support JavaScript!

access icon openaccess An electric power trading framework for smart residential community in smart cities

This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.


    1. 1)
      • 13. Liu, N., Cheng, M., Yu, X., et al: ‘Energy-sharing provider for pv prosumer clusters: a hybrid approach using stochastic programming and stackelberg game’, IEEE Trans. Ind. Electron., 2018, 65, (8), pp. 67406750.
    2. 2)
      • 9. Shamsi, P., Xie, H., Longe, A., et al: ‘Economic dispatch for an agent-based community microgrid’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 23172324.
    3. 3)
      • 3. Schweppe, F.C.: ‘Power systems2000’: hierarchical control strategies’, IEEE Spectr., 1978, 15, (7), pp. 4247.
    4. 4)
      • 10. Long, C., Wu, J., Zhang, C., et al: ‘Peer-to-peer energy trading in a community microgrid’. IEEE Power & Energy Society General Meeting, 2017, Chicago, IL, USA, 2017, pp. 15.
    5. 5)
      • 22. Arun, S.L., Selvan, M.P.: ‘Dynamic demand response in smart buildings using an intelligent residential load management system’, IET Gener. Transm. Distrib., 2017, 11, (17), pp. 43484357.
    6. 6)
      • 1. Wang, C., Ekanayake, J., Wu, J.: ‘Smart electricity distribution networks’ (Taylor & Francis; CRC Press, Boca Raton, Florida, USA, 2017, 1st edn.).
    7. 7)
      • 17. Morstyn, T., Darby, S.J., Farrell, N., et al: ‘Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants’, Nature Energy, 2018, 3, pp. 94101.
    8. 8)
      • 2. Paudel, A., Chaudhari, K., Long, C., et al: ‘Peer-to-peer energy trading in a prosumer-based community microgrid: A game-theoretic model’, IEEE Trans. Ind. Electron., 2019, 66, (8), pp. 60876097.
    9. 9)
      • 7. Celik, B., Roche, R., Bouquain, D., et al: ‘Coordinated neighborhood energy sharing using game theory and multi-agent systems’. 2017 IEEE Manchester PowerTech. Manchester, United Kingdom, 2017, pp. 16.
    10. 10)
      • 8. Cintuglu, M.H., Martin, H., Mohammed, O.A.: ‘Real-time implementation of multiagent-based game theory reverse auction model for microgrid market operation’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 10641072.
    11. 11)
      • 4. GWAC. ‘Gridwise transactive energy framework version 1.0 department of energy Washington’, January 2015. Available at, Accessed 01 Jan 2019.
    12. 12)
      • 12. Zhou, S., Hu, Z., Gu, W., et al: ‘Artificial intelligence based smart energy community management: A reinforcement learning approach’, CSEE J. Power Energy Syst., 2019, 5, (1), pp. 110.
    13. 13)
      • 5. McArthur, S.D.J., Davidson, E.M., Catterson, V.M., et al: ‘Multi-agent systems for power engineering applications part i: concepts, approaches, and technical challenges’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 17431752.
    14. 14)
      • 15. Zhang, C., Wu, J., Cheng, M., et al: ‘A bidding system for peer-to-peer energy trading in a grid-connected microgrid’, Energy Procedia, 2016, 103, pp. 147152.
    15. 15)
      • 19. Arun, S.L., Selvan, M.P.: ‘Intelligent residential energy management system for dynamic demand response in smart buildings’, IEEE Syst. J., 2018, 12, (2), pp. 13291340.
    16. 16)
      • 16. Zhang, C., Wu, J., Long, C., et al: ‘Review of existing peer-to-peer energy trading projects’, Energy Procedia, 2017, 105, pp. 25632568.
    17. 17)
      • 6. Alvaro-Hermana, R., Fraile-Ardanuy, J., Zufiria, P.J., et al: ‘Peer to peer energy trading with electric vehicles’, IEEE Intell. Transp. Syst. Mag., 2016, 8, (3), pp. 3344.
    18. 18)
      • 21. Arun, S.L., Selvan, M.P.: ‘Prosumer based demand response for profitable power exchange between end-user and utility’. 2018 20th National Power Systems Conf. (NPSC), Tiruchirappalli, India, 2018, pp. 16.
    19. 19)
      • 11. Liu, N., Yu, X., Wang, C., et al: ‘Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 35693583.
    20. 20)
      • 20. Katsigiannis, Y.A., Georgilakis, P.S., Karapidakis, E.S.: ‘Hybrid simulated annealing–tabu search method for optimal sizing of autonomous power systems with renewables’, IEEE Trans. Sustain. Energy, 2012, 3, (3), pp. 330338.
    21. 21)
      • 18. Singh, S., Roy, A., Selvan, M.P.: ‘Smart load node for nonsmart load under smart grid paradigm: a new home energy management system’, IEEE Consum. Electron. Mag., 2019, 8, (2), pp. 2227.
    22. 22)
      • 14. Long, C., Wu, J., Zhang, C., et al: ‘Feasibility of peer-to-peer energy trading in low voltage electrical distribution networks’, Energy Procedia, 2017, 105, pp. 22272232.

Related content

This is a required field
Please enter a valid email address