access icon free Parallel deep reinforcement learning-based power flow state adjustment considering static stability constraint

To solve the problem of manpower and time consumption caused by power flow state adjustment in a large-scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision-making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi-sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39-bus standard system and actual power grid are used to verify the effectiveness of the method.

Inspec keywords: power system stability; power grids; learning (artificial intelligence); load flow; power system security; decision making; Markov processes

Other keywords: power flow adjustment; reinforcement learning strategy; adjustment process; parallel deep reinforcement learning model; realises parallel adjustment; parallel deep reinforcement learning-based power flow state adjustment; power system operation state adjustment method; large-scale power grid; power flow state adjustment strategy; actual power grid; static stability constraint; deep learning network; adjustment target; Markov decision-making process

Subjects: Optimisation techniques; Control of electric power systems; Power system management, operation and economics; Power engineering computing; Power system control; Knowledge engineering techniques

References

    1. 1)
      • 7. Noroozian, M., Angquist, L., Ghandhari, M., et al: ‘Use of UPFC for optimal power flow control’, IEEE Trans. Power Deliv., 1997, 12, (4), pp. 16291634.
    2. 2)
      • 22. Jiang, Q., Xu, K.: ‘A novel iterative contingency filtering approach to corrective security-constrained optimal power flow’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 10991109.
    3. 3)
      • 4. Wang, Y., Zhong, H., Xia, Q., et al: ‘An approach for integrated generation and transmission maintenance scheduling considering N−1 contingencies’, IEEE Trans. Power Syst., 2016, 31, (3), pp. 22252233.
    4. 4)
      • 16. Chen, J., Chen, Y., Tian, F., et al: ‘The research on the user behavior of adjustment power flow based on deep learning algorithm’. Proc. Int. Electricity Distribution, Tianjin China, September 2018.
    5. 5)
      • 12. Yan, Z., Xu, Y.: ‘Data-driven load frequency control for stochastic power systems: a deep reinforcement learning method with continuous action search’, IEEE Trans. Power Syst., 2018, 34, (2), pp. 16531656.
    6. 6)
      • 10. Xiaobin, L., Qiaolin, D., Xianghong, T., et al: ‘Active power flow adjustment based on sensitivity analysis of DC load flow model’. Proc. Int. Conf. Power Engineering and Automation, Wuhan China, September 2012.
    7. 7)
      • 8. Ding, T., Bo, R., Bie, Z., et al: ‘Optimal selection of phase shifting trans-former adjustment in optimal power flow’, IEEE Trans. Power Syst., 2016, 32, (3), pp. 24642465.
    8. 8)
      • 1. Ding, T., Bo, R., Bie, Z., et al: ‘Optimal selection of phase shifting transformer adjustment in optimal power flow’, IEEE Trans. Power Syst., 2017, 32, (3), pp. 24642465.
    9. 9)
      • 13. Vlachogiannis, J.G., Hatziargyriou, N.D.: ‘Reinforcement learning for reactive power control’, IEEE Trans. Power Syst., 2004, 19, (3), pp. 13171325.
    10. 10)
      • 15. King, J.E., Jupe, S.C.E., Taylor, P.C.: ‘Network state-based algorithm selection for power flow management using machine learning’, IEEE Trans. Power Syst., 2014, 30, (5), pp. 26572664.
    11. 11)
      • 11. Nakatani, F., Mori, Y., Ito, T., et al: ‘Economic power flow adjustment technique using series capacitor and generator output adjustment for generation configuration change’. Proc. Int. Conf. IEEE Innovative Smart Grid Technologies – Asia, Bangkok Thailand, January 2016.
    12. 12)
      • 18. Ahmad, S., Albatsh, F.M., Mekhilef, S., et al: ‘Fuzzy based controller for dynamic unified power flow controller to enhance power transfer capability’, Energy Convers. Manage., 2014, 79, (3), pp. 652665.
    13. 13)
    14. 14)
      • 21. IEEE 39-Bus Test Case Archive’, https://icseg.iti.illinois.edu/ieee-39-bus-system/, (Accessed December 17 2018).
    15. 15)
      • 6. Hug-Glanzmann, G., Andersson, G.: ‘N−1 security in optimal power flow control applied to limited areas’. IET Gener. Transm. Distrib., 2009, 3, (2), pp. 206215.
    16. 16)
    17. 17)
      • 3. Liu, L., Zhu, P., Kang, Y., et al: ‘Power-flow control performance analysis of a unified power-flow controller in a novel control scheme’, IEEE Trans. Power Deliv., 2007, 22, (3), pp. 16131619.
    18. 18)
      • 2. Dong, X., Sun, H., Wang, C., et al: ‘Power flow analysis considering automatic generation control for multi-area interconnection power networks’, IEEE Trans. Ind. Appl., 2017, 53, (6), pp. 52005208.
    19. 19)
      • 14. Le, T.L., Negnevitsky, M., Piekutowski, M.: ‘Network equivalents and expert system application for voltage and VAR control in large-scale power system’, IEEE Trans. Power Syst., 1997, 12, (4), pp. 14401445.
    20. 20)
      • 17. Sutton, R.S., Barto, A.G.: ‘Reinforcement learning: an introduction’ (MIT Press, America, 2018, 2nd Edn.), Chapter 1–3.
    21. 21)
      • 5. Bai, L., Li, F., Jiang, T., et al: ‘Robust scheduling for wind integrated energy systems considering gas pipeline and power transmission N–1 contingencies’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 15821584.
    22. 22)
      • 9. Harsan, H., Hadjsaid, N.: ‘Cyclic security analysis for security constrained optimal power flow’, IEEE Trans. Power Syst., 1997, 12, (2), pp. 948953.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2020.1377
Loading

Related content

content/journals/10.1049/iet-gtd.2020.1377
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading