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access icon free Data-driven-based dynamic pricing method for sharing rooftop photovoltaic energy in a single apartment building

  • Author(s): Xu Xu 1 ; Zhao Xu 1 ; Rui Zhang 2 ; Songjian Chai 1 ; Jiayong Li 3
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  • Source: Volume 14, Issue 24, 18 December 2020, p. 5720 – 5727
    DOI:  10.1049/iet-gtd.2020.0606 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 31/03/2020, Accepted 20/07/2020, Revised 03/07/2020, Published 30/07/2020

In this study, a novel data-driven based dynamic pricing framework is proposed for sharing rooftop photovoltaic (PV) energy in a single apartment building. In this framework, the input includes the load data, electricity price data, and PV power data and the output includes the pricing strategy for local PV generations. Specifically, the building energy management system operator is responsible for setting internal uniform prices of their own rooftop PV productions to facilitate the local PV energy sharing with apartment building users, aiming to maximise the economic profits. To protect the privacy of apartment building users and meanwhile improve the computational efficiency, a neural network is designed for simulating their demand response (DR) behaviours. Besides, the uncertain rooftop PV generations can be duly addressed by using a well-trained long short-term memory network, which can capture the future trends of rooftop PV generations in a rolling-horizon manner. With the information on PV predictions and DR results, a model-free reinforcement learning method is developed for finding the near-optimal dynamic pricing strategy. The simulation results verify the effectiveness of the proposed framework in making dynamic pricing strategies with partial or uncertain information.

Inspec keywords: energy management systems; building integrated photovoltaics; learning (artificial intelligence); power markets; building management systems; photovoltaic power systems; pricing; neural nets

Other keywords: rooftop PV productions; local PV generations; PV power data; single apartment building; dynamic pricing framework; novel data-driven; rooftop photovoltaic energy; PV predictions; uncertain rooftop PV generations; data-driven-based dynamic pricing method; dynamic pricing strategies; building energy management system operator; model-free reinforcement learning method; electricity price data; internal uniform prices; load data; near-optimal dynamic pricing strategy; local PV energy; apartment building users; short-term memory network

Subjects: Knowledge engineering techniques; Optimisation techniques; Power system management, operation and economics; Other topics in statistics; Solar power stations and photovoltaic power systems

References

    1. 1)
      • 8. Lu, R., Hong, S.H., Zhang, X.: ‘A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach’, Appl. Energy, 2018, 220, pp. 220–230.
    2. 2)
      • 18. Sutton, R.S., Barto, A.G.: ‘Reinforcement learning: an Introduction’ (MIT Press, USA, 2018).
    3. 3)
      • 19. ‘Austrian federal chancellery. Federal act providing new rules for the organisation of the electricity sector: Elwog’, 2010.
    4. 4)
      • 33. Wang, Z., Bovik, A.C.: ‘Mean squared error: love it or leave it? A new Look at signal fidelity measures’, IEEE Signal Process. Mag., 2009, 26, (1), pp. 98–117.
    5. 5)
      • 21. Wang, J., Feng, C., Xu, Y., et al: ‘Stackelberg game-based energy management for a microgrid with commercial buildings considering correlated weather uncertainties’, IET Gener. Transm. Distrib., 2018, 13, (11), pp. 2102–2111.
    6. 6)
      • 2. Sun, Q., Yang, L.-X., Zhang, H.: ‘Smart energy—applications and prospects of artificial intelligence technology in power system’, Control Decis., 2018, 33, (5), pp. 938–949.
    7. 7)
      • 29. Gurobi Optimization Inc.: ‘Gurobi optimizer reference manual’, 2015’. Available at http://www.gurobi.com, 2014.
    8. 8)
      • 31. Li, Y., Yuan, Y.: ‘Convergence analysis of two-layer neural networks with relu activation’. Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017.
    9. 9)
      • 38. Fan, E.: ‘Extended tanh-function method and its applications to nonlinear equations’, Phys. Lett. A, 2000, 277, (4–5), pp. 212–218.
    10. 10)
      • 1. Laughton, M.: ‘Artificial intelligence techniques in power systems’, 1997.
    11. 11)
      • 40. Nagy, G.I., Barta, G., Kazi, S., et al: ‘Gefcom2014: probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach’, Int. J. Forcast., 2016, 32, (3), pp. 1087–1093.
    12. 12)
      • 15. Marzband, M., Javadi, M., Domínguez-García, J.L., et al: ‘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), pp. 2999–3009.
    13. 13)
      • 14. Wang, Y., Wu, X., Li, Y., et al: ‘Autonomous energy community based on energy contract’, IET Gener. Transm. Distrib., 2019, 14, (4), pp. 682–689.
    14. 14)
      • 5. Luo, C., Huang, Y.-F., Gupta, V.: ‘Stochastic dynamic pricing for EV charging stations with renewable integration and energy storage’, IEEE Trans. Smart Grid, 2017, 9, (2), pp. 1494–1505.
    15. 15)
      • 22. Luo, Z.-Q., Pang, J.-S., Ralph, D.: ‘Mathematical programs with equilibrium constraints’ (Cambridge University Press, UK, 1996).
    16. 16)
      • 32. Hameed, A.A., Karlik, B., Salman, M.S.: ‘Back-propagation algorithm with Variable adaptive momentum’, Knowl.-Based Syst., 2016, 114, pp. 79–87.
    17. 17)
      • 27. Wazwaz, A.-M.: ‘The tanh method for traveling wave solutions of nonlinear equations’, Appl. Math. Comput., 2004, 154, (3), pp. 713–723.
    18. 18)
      • 17. Goodfellow, I., Bengio, Y., Courville, A.: ‘Deep learning’ (MIT Press, USA, 2016).
    19. 19)
      • 20. Missaoui, R., Joumaa, H., Ploix, S., et al: ‘Managing energy smart homes according to energy prices: analysis of a building energy management system’, Energy Build., 2014, 71, pp. 155–167.
    20. 20)
      • 35. Wei, Q., Lewis, F.L., Sun, Q., et al: ‘Discrete-time deterministic Q-learning: a novel convergence analysis’, IEEE Trans. Cybern., 2016, 47, (5), pp. 1224–1237.
    21. 21)
      • 9. Akter, M.N., Mahmud, M.A., Haque, M.E., et al: ‘Transactive energy coordination mechanism for community microgrids supplying multi-dwelling residential apartments’, IET Gener. Transm. Distrib., 2020, 14, (7), pp. 1207–1213.
    22. 22)
      • 34. Ouyang, Y., Gagrani, M., Nayyar, A., et al: ‘Learning unknown Markov decision processes: a Thompson sampling approach’. Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017.
    23. 23)
      • 36. Kappen, H.J.: ‘Optimal control theory and the linear Bellman equation’, 2011.
    24. 24)
      • 24. Sak, H., Senior, A., Beaufays, F.: ‘Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition’, arXiv preprint arXiv:, 2014.
    25. 25)
      • 23. Xingjian, S., Chen, Z., Wang, H., et al: ‘Convolutional LSTM network: a machine learning approach for precipitation nowcasting’. Advances in Neural Information Processing Systems, Montreal, Canada, 2015.
    26. 26)
      • 28. CPLEX, I.I.: ‘V12. 1: user's manual for CPLEX’, Int. Bus Mach. Corp., 2009, 46, (53), p. 157.
    27. 27)
      • 26. Hochreiter, S.: ‘The vanishing gradient problem during learning recurrent neural nets and problem solutions’, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 1998, 6, (2), pp. 107–116.
    28. 28)
      • 11. Liu, N., Yu, X., Wang, C., et al: ‘Energy sharing management for microgrids with PV prosumers: a Stackelberg game approach’, IEEE Trans. Ind. Inf., 2017, 13, (3), pp. 1088–1098.
    29. 29)
      • 16. Ma, Z., Zhang, C., Qian, C.: ‘Review of machine learning in power system’. 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), Chengdu, China, 2019.
    30. 30)
      • 37. Tokic, M.: ‘Adaptive Ε-greedy exploration in reinforcement learning based on value differences’. Annual Conf. on Artificial Intelligence, San Diego, CA, USA, 2010.
    31. 31)
      • 25. Elfwing, S., Uchibe, E., Doya, K.: ‘Sigmoid-weighted linear units for neural network function approximation in reinforcement learning’, Neural Netw., 2018, 107, pp. 3–11.
    32. 32)
      • 10. Cui, S., Wang, Y.-W., Xiao, J.-W., et al: ‘A two-stage robust energy sharing management for prosumer microgrid’, IEEE Trans. Ind. Inf., 2018, 15, (5), pp. 2741–2752.
    33. 33)
      • 13. Liu, N., Yu, X., Fan, W., et al: ‘Online energy sharing for nanogrid clusters: a Lyapunov optimization approach’, IEEE Trans. Smart Grid, 2017, 9, (5), pp. 4624–4636.
    34. 34)
      • 39. Gulli, A., Pal, S.: ‘Deep learning with Keras’ (Packt Publishing Ltd, Birmingham, UK, 2017).
    35. 35)
      • 4. Kim, B.-G., Zhang, Y., Van Der Schaar, M., et al: ‘Dynamic pricing and energy consumption scheduling with reinforcement learning’, IEEE Trans. Smart Grid, 2015, 7, (5), pp. 2187–2198.
    36. 36)
      • 3. Pan, Y.: ‘Research on application of artificial intelligence in power system’. 2017 5th Int. Conf. on Machinery, Materials and Computing Technology (ICMMCT 2017), Beijing, China, 2017.
    37. 37)
      • 7. Harsha, P., Dahleh, M.: ‘Optimal management and sizing of energy storage under dynamic pricing for the efficient integration of renewable energy’, IEEE Trans. Power Syst., 2014, 30, (3), pp. 1164–1181.
    38. 38)
      • 12. Cui, S., Wang, Y.-W., Liu, N.: ‘Distributed game-based pricing strategy for energy sharing in microgrid with PV prosumers’, IET Renew. Power Gener., 2017, 12, (3), pp. 380–388.
    39. 39)
      • 30. Abubakar, I., Khalid, S., Mustafa, M., et al: ‘Application of load monitoring in appliances’ energy management–a review’, Renew. Sustain. Energy Rev., 2017, 67, pp. 235–245.
    40. 40)
      • 6. Jia, L., Tong, L.: ‘Dynamic pricing and distributed energy management for demand response’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 1128–1136.
    41. 41)
      • 41. Xi, F., An, Y., Wei, Q., et al: ‘Study on economic dispatching of wind power consumption considering dynamic game time-of-use price’. 2017 Global Conf. on Mechanics and Civil Engineering (GCMCE 2017), Guangzhou, China, 2017.
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