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Li, S.; Duan, C.; Gao, Y.; Cai, Y. New Power System Stability. Encyclopedia. Available online: https://encyclopedia.pub/entry/54326 (accessed on 01 May 2024).
Li S, Duan C, Gao Y, Cai Y. New Power System Stability. Encyclopedia. Available at: https://encyclopedia.pub/entry/54326. Accessed May 01, 2024.
Li, Sheng, Changhong Duan, Yuan Gao, Yuhao Cai. "New Power System Stability" Encyclopedia, https://encyclopedia.pub/entry/54326 (accessed May 01, 2024).
Li, S., Duan, C., Gao, Y., & Cai, Y. (2024, January 25). New Power System Stability. In Encyclopedia. https://encyclopedia.pub/entry/54326
Li, Sheng, et al. "New Power System Stability." Encyclopedia. Web. 25 January, 2024.
New Power System Stability
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This entry is adapted from the peer-reviewed paper 10.3390/su152416614

Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda.

new power system stability stochastic disturbance factor (SDF) probabilistic stability stochastic stability

1. Introduction

In December 2020, a white paper entitled “China’s Energy Development in the New Era” was issued by the Information Office of China’s State Council. In this white paper, the routes of “carbon peaking” and “carbon neutrality” are proposed, and the proportion of new energy in power systems will be further increased [1]. At the same time, the overall structure of the power grid has become more complex, and power outage accidents occur frequently around the world [2]. The blackout accidents caused by large-scale off-grid renewable energy also occurred [3][4][5], and the consequences are severe. While constructing a new power system, the influences of a large number of stochastic disturbance factors (SDFs), such as wind or photovoltaic (PV) power output with stochastic fluctuations on the power system’s stability, cannot be ignored [6][7][8].
The perceptions of “uncertainty” can be classified into three aspects: stochasticity, fuzziness, and ignorance, with stochasticity being the most widely discussed in power system analysis [9]. The high penetration of renewable energy, such as wind/PV power, is greatly affected by the weather and environment, and their generation performance is characterized by intermittence and volatility. The large number of new energy electric vehicles (EVs) leads to system load display initiatives and complexity. The transmission grid is affected by stochastic load sources, and wide-ranging current fluctuations can also occur. At the same time, the increasingly complex overall grid structure and the mass access of power electronic equipment make the grid change from a deterministic system to a strong stochastic system. Whether the system can maintain stability is the prerequisite for safe and stable operations. The input of SDFs increases the overall or local instability and power outage risks, making the system more prone to extreme development. The traditional grid has a simple topology, low degree of regional interconnection, small unit capacity, and relatively simple equipment types, so the system stability analysis is mainly based on a deterministic method and has obtained quite rich research results [10][11]. However, the traditional deterministic analysis method ignores the SDFs’ influence on system stability, reducing the accuracy of the analysis results. It is necessary to adopt a stochastic analysis method to dissect the stability of a new power system with new energy as the main body.
Power system stability research under the consideration of SDFs has been carried out for nearly half a century, which can be mainly divided into two stages. In the first stage, R. C. Burchett first applied probability theory to describe the stochastic disturbance of power systems and solve the probability distribution of stochastic variables in the form of mathematical modeling [12]. Later, studies were continued based on the probabilistic analysis method. After 2000, the research hotspot of the probabilistic analysis method was shifted to new energy, and until now, there are still many scholars researching the relevant issues in this field [13][14]. In the second stage, scholars considered the SDFs as stochastic processes and integrated them into the stochastic modeling of power systems, using the stochastic differential equation (SDE) theory, crossing the probability theory and the differential equation to carry out the analysis. This approach has pioneered a new field of research in stochastic dynamics within power systems [15][16].

2. Stochastic Disturbance Factors

The specific meaning of the SDFs is the uncertainty with statistical law, which is a form of contingency. The SDFs in the power system exhibit a diverse range of attributes, varying from inconspicuous to prominent and from minor to substantial disturbances. The change rule in the SDFs cannot be accurately grasped, and it is generally portrayed by using statistical methods, such as providing the probability distribution function or fluctuation interval [17][18]. In traditional systems, SDFs also exist; however, due to their relatively insignificant stochastic nature, these SDFs are often overlooked in related studies. The impact of stochasticity becomes non-negligible in new power systems, and the SDFs are complex and diverse and can be classified in terms of the source, variable type, time scale, descriptive equation, and so on.

2.1. Sources of SDFs

The input of SDFs not only makes the power system surge in stochasticity but also greatly increases the complexity of the system, which is mainly reflected in the three links of “source–grid–load”.
In order to promote the overall green transformation and upgrading of energy, on the “source” side, a large amount of new energy power generation is being connected. Unlike traditional power generation, new energy power has a strong stochastic fluctuation, which is easy to impact the system’s stability [8].
Due to the demand for electricity, the power grid’s scale becomes larger. On the “grid” side, the emerging power electronics technology and control technology are commonly used in transmission and distribution links and bring more SDFs with greater intensity [19]. The heightened amplitude of system voltage and frequency fluctuations leads to a higher incidence of stochastic events such as network failure.
On the “load” side, EVs and electric trains increase uncertainty and flexibility. The load property shifts from “rigid” to “soft”; it is easy to increase the probability of the load loss time, which further increases the stochasticity [20].

2.2. Variable Types of SDFs

Statistically, the curves of load changes and new energy power changes are continuous and smooth, so they can be classified as continuous stochastic variables. Discrete events that occur by chance, such as stochastic charge and discharge behaviors of EVs, load shocks, power generation unit switching, system faults, and reclosing actions, can be classified as discrete stochastic variables [21].

2.3. Time Scales of SDFs

From a time-scale perspective, continuously varying stochastic variables caused by loads such as EVs are fast-varying SDFs. The variations in new energy sources, such as wind/PV, are generally between seconds and minutes and can be treated as slow-varying SDFs [22]. Stochastic system failures, as well as the actions of power electronic equipment, are usually performed in a very short period of time with a time scale of microseconds and thus can be considered as sudden-change SDFs [23].

2.4. Descriptive Equations for SDFs

The equations for describing the SDFs in the power system have three categories. The first one is the uncertainty of the initial value. The initial value is not the system equilibrium point before suffering a stochastic disturbance but rather the system value after the last operation. The initial value’s stochasticity is mainly caused by the uncertainty of the equilibrium point, and during the steady-state operation, the equilibrium point will fluctuate stochastically in a very small range. The main reason is that external environmental changes or internal structural changes lead to fluctuations in a small range of the equilibrium point. Such stochastic disturbances are generally analyzed using the probabilistic analysis method. Firstly, it is assumed that the initial value’s stochasticity obeys a certain probability distribution, and the probabilistic algebraic equation model is derived, and then the probability of the whole system stability is analyzed and calculated by using the probabilistic method. The system is judged whether it is stable or not according to the probabilistic results. With the depth of the study, for this type of stochastic disturbance, the contradiction between computation quantity and accuracy is gradually reduced.
The second category is the stochasticity of component parameters and coefficients, which is mainly caused by some internal or external factors that change the parameters or coefficients of lines and equipment. This type of stochastic disturbance is akin to the first type, which is used as a slow variable in dynamic analysis and is also assumed to obey a certain probability distribution, and then the system’s stability probability or the trajectory envelope is calculated [24][25].
The above two types of stochastic disturbances are based on the probability equation model, which is mainly analyzed by the probability theory method and is relatively mature.
The third category is the stochasticity of external excitation, which is different from the first two categories of stochastic disturbances. It is a fast variable in the dynamic process with time-variant properties, such as the intermittence of wind/PV power. When regional interconnected power grids are subject to external stochastic disturbances, probabilistic algebraic equations cannot satisfy the modeling and analysis of this type of stochastic disturbances. Therefore, the SDE and related theories should be introduced [26].

3. Research Framework of New Power System Stability

The research framework of new power system stability is shown in Table 1. System stability is classified into two major categories: probabilistic stability and stochastic stability, according to the processing type of SDFs. It can be sorted out as follows:
Table 1. The research framework of new-type power system stability considering the stochastic disturbance factors (SDFs).
Processing Type of SDF Stability
Category		 Research Content Research
Methodology		 Theory
Stochastic
variable		 Probabilistic small- disturbance stability Based on the deterministic small-disturbance stability analysis, considering the probability models of various stochastic uncertain sources, the small-disturbance stability is determined by the probability distributions of the key eigenvalues and other associated SDFs. Simulation method, approximation method and analytical method based on probabilistic analysis methods Probabilistic algebraic equation theory
Probabilistic transient stability Take the fault factors in the system as stochastic probabilistic events and consider the impact of a limited number of stochastic variables on the transient stability.
Probabilistic voltage stability Introduce the SDFs into the system and consider the possibility of the existence of a certain state together with the voltage stability in that state.
Stochastic
process		 Stochastic small- disturbance stability Establish the model of stochastic small disturbance and introduce it into the system state equations, and study the impact of stochastic excitations on the system’s dynamic processes. Mean value stability and mean square stability Stochastic differential equation theory
Stochastic transient stability Study the system transient stability by considering stochastic disturbances with large intensity, such as stochastic faults superimposed on stochastic excitations. Energy function method, extended equal-area method and analytical method considering stochastic excitations
Stochastic voltage stability Stochastic disturbances are modeled as stochastic excitations to study the system’s stochastic voltage dynamic response. Voltage stability assessment method based on stochastic model
 
(1)
When the SDFs are treated as stochastic variables, the probabilistic stability is analyzed and evaluated based on probabilistic analysis method and probabilistic algebraic equation, including probabilistic small-disturbance stability, probabilistic transient stability, and probabilistic voltage stability.
(2)
If stochastic variables are replaced by stochastic processes considering the persistent disturbances by the SDFs, the stochastic stability is analyzed and evaluated based on the stochastic analysis method and SDE, including stochastic small-disturbance stability, probabilistic transient stability, and probabilistic voltage stability.

References

  1. Li, B.; Chen, M.; Zhong, H.; Ma, Z.; Liu, D.; He, G. A Review of long-term planning of new power systems with large share of renewable energy. Proc. CSEE 2023, 43, 555–580.
  2. Zeng, B.; Ouyang, S.; Zhang, J.; Shi, H.; Wu, G.; Zeng, M. An analysis of previous blackouts in the world: Lessons for China’s power industry. Renew. Sustain. Energy Rev. 2015, 42, 1151–1163.
  3. Wang, W.; Lin, W.; He, G.; Shi, W.; Feng, S. Enlightenment of 2021 Texas blackout to the renewable energy development in China. Proc. CSEE 2021, 41, 4033–4042.
  4. Zeng, H.; Sun, F.; Li, T.; Zhang, Q.; Tang, J.; Zhang, T. Analysis of “9·28” blackout in South Australia and its enlightenment to China. Autom. Electr. Power Syst. 2017, 41, 1–6.
  5. Sun, H.; Xu, T.; Guo, Q.; Li, Y.; Lin, W.; Yi, J.; Li, W. Analysis on blackout in Great Britain power grid on 9 August 2019 and its enlightenment to power grid in China. Proc. CSEE 2019, 39, 6183–6191.
  6. Shu, Y.; Tang, Y. Analysis and recommendations for the adaptability of China’s power system security and stability relevant standards. CSEE J. Power Energy Syst. 2017, 3, 334–339.
  7. Wang, F.; Li, L.; Li, C.; Wu, Q.; Cao, Y.; Zhou, B.; Fang, B. Fractal characteristics analysis of blackouts in interconnected power grid. IEEE Trans. Power Syst. 2018, 33, 1085–1086.
  8. Li, S.; Lu, Y.; Ge, Y. Static voltage stability zoning analysis based on a sensitivity index reflecting the influence degree of photovoltaic power output on voltage stability. Energies 2023, 16, 2808.
  9. Wang, J.; Xie, N.; Wu, W.; Han, D.; Wang, C.; Zhu, B. Resilience enhancement strategy using microgrids in distribution network. Glob. Energy Interconnect. 2018, 1, 537–543.
  10. Sun, H.; Xu, S.; Xu, T.; Bi, J.; Zhao, B.; Guo, Q.; He, J.; Song, R. Research on definition and classification of power system security and stability. Proc. CSEE 2022, 42, 7796–7808.
  11. Xie, X.; He, J.; Mao, H.; Li, H. New issues and classification of power system stability with high shares of renewables and power electronics. Proc. CSEE 2021, 41, 461–474.
  12. Burchett, R.C.; Heydt, G.T. Generalized method for stochastic analysis of the dynamic stability of electric power systems. IEEE Trans. Power Appar. Syst. 1979, 98, 5.
  13. Wang, C.; Wang, X.; Ju, P.; Shao, C.; Li, H. Survey on stochastic analysis methods for power systems. Autom. Electr. Power Syst. 2022, 46, 184–199.
  14. Liu, S.; Liu, P.; Wang, X. Stochastic small–signal stability analysis of grid–connected photovoltaic systems. IEEE Trans. Ind. Electron. 2016, 63, 1027–1038.
  15. Xu, X.; Wang, H.; Yan, Z.; Lu, Z.; Kang, C.; Xie, K. Overview of power system uncertainty and its solutions under energy transition. Autom. Electr. Power Syst. 2021, 45, 2–13.
  16. Ju, P.; Liu, Y.; Xue, Y.; Li, H.; Sun, L.; Zhou, H.Q. Research Prospects for Stochastic Dynamics of power system. Autom. Electr. Power Syst. 2017, 41, 1–8.
  17. Gu, H.; Yan, R.; Saha, T.K.; Muljadi, E. System strengthand inertia constrained optimal generator dispatch underhigh renewable penetration. IEEE Trans. Sustain. Energy 2020, 11, 2392–2406.
  18. Liu, H.; Hervas, J.R.; Ong, Y.S.; Cai, J.; Wang, Y. An adaptive RBF–HDMR modeling approach under limited computational budget. Struct. Multidiscip. Optim. 2018, 57, 1233–1250.
  19. Li, H.; Ju, P.; Gan, C.; You, S.; Wu, F.; Liu, Y. Analytic analysis for dynamic system frequency in power systems under uncertain variability. IEEE Trans. Power Syst. 2018, 34, 982–993.
  20. Ju, P.; Li, H.; Pan, X.; Gan, C.; Liu, Y.; Liu, Y. Stochastic dynamic analysis for power systems under uncertain variability. IEEE Trans. Power Syst. 2017, 33, 3789–3799.
  21. Liu, J.; Yang, Z.; Yu, J.; Huang, J.; Li, W. Coordinated control parameter setting of DFIG wind farms with virtual inertia control. Int. J. Electr. Power Energy Syst. 2020, 122, 106167.
  22. Bian, X.; Geng, Y.; Li, X.; Fu, Y.; Wang, Q. Probabilistic small–signal stability analysis and improvement of power system with wind farm integration. Power Syst. Prot. Control 2015, 43, 1–7.
  23. Oliveira, W.D.; Vieira, J.P.A. Power system security assessment for multiple contingencies using multiway decision tree. Electr. Power Syst. Res. 2017, 148, 264–272.
  24. Zhou, Z.; Shi, L. Risk assessment of power system cascading failure considering wind power uncertainty and system frequency modulation. Proc. CSEE 2021, 41, 3305–3316.
  25. Lian, H.; Zhou, B.; Qin, P.; Wang, T.; Bie, C.H. Probabilistic power flow analytic algorithm based on scenario partition. Power Syst. Technol. 2017, 41, 3153–3160.
  26. Liu, Y.; Ju, P.; Xue, Y.; Wu, F.; Zhang, J. Calculation analysis on power system characteristics under random excitation. Autom. Electr. Power Syst. 2014, 38, 137–142.
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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Sheng Li
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Yuhao Cai
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Update Date: 04 Apr 2024
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