Nonparametric Stochastic Analysis of Dynamic Frequency in Power Systems: A Generalized Ito Process Model (2312.10953v1)
Abstract: The large-scale integration of intermittent renewable energy has brought serious challenges to the frequency security of power systems. In this paper, a novel nonparametric stochastic analysis method of system dynamic frequency is proposed to accurately analyze the impact of renewable energy uncertainty on power system frequency security, independent of any parametric distribution assumption. The nonparametric uncertainty of renewable generation disturbance is quantified based on probabilistic forecasting. Then, a novel generalized Ito process is proposed as a linear combination of several Gaussian Ito processes, which can represent any probability distribution. Furthermore, a stochastic model of power system frequency response is constructed by considering virtual synchronization control of wind power. On basis of generalized Ito process, the complex nonlinear stochastic differential equation is transformed into a linear combination of several linear stochastic differential equations to approximate nonparametric probability distribution of the system dynamic frequency. Finally, the validity of the proposed method is verified by the single-machine system and IEEE 39-Bus system.
- C. Seneviratne and C. Ozansoy, “Frequency response due to a large generator loss with the increasing penetration of wind/PV generation-A literature review,” Renew. Sust. Energ. Rev., vol. 57, pp. 659-668, Jan. 2016.
- A. Kalair, N. Abas, and N. Khan, “Comparative study of HVAC and HVDC transmission systems,” Renew. Sust. Energ. Rev., vol. 59, pp. 1653–1675, Jun. 2016.
- C. Wan, J. Lin, and J. Wang, “Direct quantile regression for non-parametric probabilistic forecasting of wind power generation,” IEEE Trans. Power Syst., vol. 32, no. 4, pp. 2767–2778, Jul. 2017.
- Y. C. Chen and A. D. Dominguez-Garcia, “A Method to Study the Effect of Renewable Resource Variability on Power System Dynamics,” IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1978-1989, Nov. 2012.
- J. O’Sullivan, A. Rogers, D. Flynn, P. Smith, and M. O’Malley, “Studying the maximum instantaneous non-synchronous generation in an island system-frequency stability challenges in Ireland,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 2943-2951, Nov. 2014.
- G. Kou, P. Markham, S. Hadley, T. King, and Y. Liu, “Impact of governor dead-band on frequency response of the U.S. Eastern Interconnection,” IEEE Trans. Smart Grid, vol. 7, no. 3, pp. 1368-1377, Jun. 2016.
- C. Fan, X. Wang, Y. Teng, and W. Wu, “Minimum frequency estimation of power system considering governor deadbands,” IET Gener. Transm. Distrib., vol. 211, no. 15, pp. 3814-3822, Sep. 2017.
- P. M. Anderson, and M. Mirheydar, “A low-order system frequency response model,” IEEE Trans. Power Syst., vol. 5, no. 3, pp. 720–729, Aug. 1990.
- M. L. Chan, R. D. Dunlop, and F. Schweppe, “Dynamic equivalents for average system frequency behaviour following major disturbances,” IEEE Trans. Power App. Syst., vol. PAS-91, no. 4, pp. 1637-1642, Jul. 1972.
- Q. Shi, F. Li, and H. Cui, “Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies,” IEEE Trans. Power Syst., vol. 33, no. 6, pp. 6355–6367, Nov. 2018.
- I. Egido, F. Fernández-Bernal, P. Centeno, and L. Rouco, “Maximum frequency deviation calculation in small isolated power systems,” IEEE Trans. Power Syst., vol. 24, no. 4, pp. 1731-1738, Nov. 2009.
- L. Liu, W. Li, and Y. Ba, “An analytical model for frequency nadir prediction following a major disturbance,” IEEE Trans. Power Syst., vol. 32, no. 4, pp. 2527–2536, Dec. 2020.
- C. Wan, Z. Xu, P. Pinson, Z.Y. Dong, and K.P. Wong, “Probabilistic forecasting of wind power generation using extreme learning machine,” IEEE Trans. Power Syst., vol.29, no.3, pp.1033-1044, May 2014.
- A. Baharvandi, J. Aghaei, and T. Niknam, “Bundled generation and transmission planning under demand and wind generation uncertainty based on a combination of robust and stochastic optimization,” IEEE Trans. Sustain. Energy, vol.9, no.3, pp.1477-1486, Jul. 2018.
- H. Bludszuweit, J. A. Domínguez-navarro, A. Llombart, “Statistical analysis of wind power forecast error,” IEEE Trans. Power Syst., vol.23, no.3, pp.983-991, Nov. 2008.
- H. Li, P. Ju, C. Gan, S. You, F. Wu, and Y. Liu, “Analytic analysis for dynamic system frequency in power systems under uncertain variability,” IEEE Trans. Power Syst., vol. 34, no. 2, pp. 982–993, Mar. 2019.
- X. Chen, J. Lin, F. Liu, and Y. Song, “Stochastic assessment of AGC systems under non-Gaussian uncertainty,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 705–717, Jan. 2019.
- W. Xie, P. Zhang, R. Chen and Z. Zhou, “A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 371-379, Jan. 2019.
- D. A. Reynolds, “Gaussian mixture models,” Encyclopedia of biometrics, vol. 741, pp:659-663, May, 2009.
- C. Biernacki, G. Celeux, and G. Govaert, “Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models,” Computational Statistics & Data Analysis, vol. 41, no.3, pp. 561-575, Jul. 2003.
- H. Wu, X. Ruan, D. Yang, X. Chen, W. Zhao, Z. Lv, and Q. Zhong, “Small-Signal Modeling and Parameters Design for Virtual Synchronous Generators,” IEEE Trans. Ind. Electron., vol. 63, no. 7, pp. 4292-4303, Jul. 2016.
- U. Markovic, Z. Chu, P. Aristidou and G. Hug, “LQR-Based Adaptive Virtual Synchronous Machine for Power Systems With High Inverter Penetration,” IEEE Trans. Sustain. Energy., vol. 10, no. 3, pp. 1501-1512, Jul. 2019.
- B. Yuan, M. Zhou, G. Li and X. P. Zhang, “Stochastic small-signal stability of power systems with wind power generation,” IEEE Trans. Power Syst., vol. 30, no. 4, pp. 1680–1689, Jul. 2015.
- Z. Ying, S. H. Jung, and L. J. Wei, “Survival analysis with median regression models,” J. Am. Stat. Assoc., vol.90, no.429, pp.178-184, Mar. 1995.
- “IEEE 39-Bus System,” 2018. [Online]. Available: http://icseg.iti.illinois.edu/ieee-39-bus-system/
- V. M. Panaretos, and Y. Zemel, “Statistical aspects of Wasserstein distances,” Annu. Rev. Stat. Appl., vol.6, no.1, pp.405-431, Mar. 2019.