Method and system for on-line dynamical screening of electric power system

• US 20030200010A1
• Filed: 04/22/2003
• Published: 10/23/2003
• Est. Priority Date: 04/22/2002
• Status: Active Grant

• Current Assignee: Hsiao-Dong Chiang, Tokyo Electric Power Company Holdings, Incorporated (Nuclear Damage Compensation Decommissioning Facilitation Corp.)
• Original Assignee: Korea Electric Power Corporation (Government of South Korea), Hsiao-Dong Chiang
• Inventors: Koyanagi, Kaoru, Tada, Yasuyuki, Chiang, Hsiao-Dong, Kurita, Atsushi, Zhou, Yicheng, Okamoto, Hiroshi, Tanabe, Ryuya
• Granted Patent: US 6,868,311 B2
• Time in Patent Office: Days
• US Class Current: 700/286
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/0012 Contingency detection
• CPC Class Codes: H02J 3/24 Arrangements for preventing or reducing oscillations of power in networks by control effected upon a single generator H02P9/00
• CPC Class Codes: H02J 3/388 Islanding, i.e. disconnection of local power supply from the network
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A system for on-line dynamic screening of contingencies comprising postulated disturbances which an electric power system may experience, the system comprising a dynamic contingency screening program for evaluating a plurality of contingencies with a plurality of contingency classifiers based on the method of finding the controlling unstable equilibrium point of the power system known as the boundary of stability region based controlling unstable equilibrium point method by sequentially applying the contingencies to a network islanding problem classifier, S.E.P problem classifier, a large stability regions classifier, an exist point problem classifier, a ray adjustment problem classifier, an energy function problem classifier, a CUEP convergence problem classifier, and a controlling UEP (unstable equilibrium point) classifier to form a first group of stable contingencies and a second group of unstable or undecided contingencies, and a time-domain simulation program for determining which of the second group of contingencies are unstable.

Claims

1. A method for on-line dynamic screening of contingencies comprising postulated disturbances which an electric power system may experience, said method comprising the steps of:

a) evaluating a plurality of contingencies with a plurality of contingency classifiers based on the method of finding the controlling unstable equilibrium point of said power system known as the boundary of stability region based controlling unstable equilibrium point method by sequentially applying said contingencies to a network islanding problem classifier, a S.E.P problem classifier, a large stability regions classifier, an exist point problem classifier, a ray adjustment problem classifier, an energy function problem classifier, a CUEP convergence problem classifier, and a controlling UEP (unstable equilibrium point) classifier to form a first group of stable contingencies and a second group of unstable or undecided contingencies; and

b) determining which of said second group of contingencies are unstable.

2. The method of claim 1, wherein said step of applying said network islanding problem classifier further comprises screening out highly unstable contingencies which result in a network islanding problem.

3. The method of claim 1, wherein said step of applying said S.E.P problem classifier further comprises computing the post-fault stable equilibrium point (SEP) starting from the pre-fault SEP, so as to detect potentially unstable contingencies.

4. The method of claim 1, wherein said step of applying said large stability regions classifier further comprises screening out highly stable contingencies which result in a large stability region of the underlying post-fault SEP.

5. The method of claim 1, wherein said step of applying said exist point problem classifier further comprises screening out potentially unstable contingencies which result in the so-called exit point problem employing some dynamic information during the exit point search.

6. The method of claim 1, wherein said step of applying said ray adjustment problem classifier further comprises screening out potentially unstable contingencies based on some dynamic information during the minimum gradient point search.

7. The method of claim 1, wherein said step of applying said energy function problem classifier further comprises an index using the property that energy functions decrease along method trajectories, when the potential energy at the minimum gradient point is greater than that at the exit point, the corresponding contingency is identified as causing the energy function problem and is classified as unstable.

8. The method of claim 1, wherein said step of applying said CUEP convergence problem classifier further comprises detecting the following UEP convergence problem, when a numerical method is applied to compute the controlling u.e.p. starting from the MGP.

9. The method of claim 1, wherein said step of applying said controlling UEP classifier further comprises the energy value at the CUEP as the critical energy, classifying each remaining contingency into (definitely) stable or (potentially) unstable.

10. The method of claim 1, wherein

said step of applying said network islanding problem classifier further comprises screening out highly unstable contingencies which result in a network islanding problem;

said step of applying said S.E.P problem classifier further comprises computing the post-fault stable equilibrium point (SEP) starting from the pre-fault SEP, so as to detect potentially unstable contingencies;

said step of applying said large stability regions classifier further comprises screening out highly stable contingencies which result in a large stability region of the underlying post-fault SEP;

said step of applying said exist point problem classifier further comprises screening out potentially unstable contingencies which result in the so-called exit point problem employing some dynamic information during the exit point search;

said step of applying said ray adjustment problem classifier further comprises screening out potentially unstable contingencies based on some dynamic information during the minimum gradient point search;

said step of applying said energy function problem classifier further comprises an index using the property that energy functions decrease along method trajectories, when the potential energy at the minimum gradient point is greater than that at the exit point, the corresponding contingency is identified as causing the energy function problem and is classified as unstable;

said step of applying said CUEP convergence problem classifier further comprises detecting the following UEP convergence problem, when a numerical method is applied to compute the controlling u.e.p. starting from the MGP; and

said step of applying said controlling UEP classifier further comprises the energy value at the CUEP as the critical energy, classifying each remaining contingency into (definitely) stable or (potentially) unstable.

21. An on-line dynamic security assessment system to be adapted an electric power system may experience, said assessment system comprising the method of any one of claims 1-10.

23. An energy management system to be adapted an electric power system may experience, said management system comprising the method of any one of claims 1-10.

29. The method of claim 1, wherein said step of applying said ray adjustment problem classifier comprising the step of performing the process without a decision the stable and the unstable of the electric power system,

computing the minimum gradient point;

estimating to set the critical energy to be the energy value at the exit point V_cr=V_ep, and find the corresponding fault-on time t_ep from the fault-on trajectory, when the minimum gradient point is not found;

verifying to perform a time domain simulation with

t_ep being the fault clearing time, when the post-fault system is stable and setting V_ep to be V_cr;

performing a time-domain simulation of the post-fault system with the state at t_cl as the initial condition, when the post-fault system is not stable;

setting t₀=t_cl and t₁=t_mgp, when the post-fault system is stable, and setting t₀=0 and t₁=t_cl, when the post-fault system is not stable;

interpolating to make an interpolation between (t₀, t₁) using the Golden bisection-based interpolation method to find an instant, denoted as t⁽⁰⁾; and

verifying to perform a time domain simulation with t⁽⁰⁾ being the fault clearing time, if the post-fault system is stable, then treat t⁽⁰⁾ as the critical clearing time and the energy value at the corresponding state as the critical energy and stop the process, otherwise set t₁=t⁽⁰⁾ and go to interpolating step is conducted between the interval (t₀, t⁽⁰⁾).

30. The method of claim 1, wherein said step of applying said energy function problem classifier comprising the step of performing the process without a decision the stable and the unstable of the electric power system,

computing the minimum gradient point;

performing a time-domain simulation of the post-fault system with the state at t_cl as the initial condition, when the post-fault system is not stable;

setting t₀=t_cl and t₁=t_mgp, when the post-fault system is stable, and setting t₀=0 and t₁=t_cl, when the post-fault system is not stable;

interpolating to make an interpolation between (t₀, t₁) using the Golden bisection-based interpolation method to find an instant, denoted as t⁽⁰⁾; and

verifying to perform a time domain simulation with t⁽⁰⁾ being the fault clearing time, if the post-fault system is stable, then treat t⁽⁰⁾ as the critical clearing time and the energy value at the corresponding state as the critical energy and stop the process, otherwise set t₁=t⁽⁰⁾ and go to interpolating step is conducted between the interval (t₀, t⁽⁰⁾).

31. The method of claim 1, wherein said step of applying said CUEP convergence problem classifier, comprising the step of performing the process without a decision the stable and the unstable of the electric power system,

computing controlling un stable equilibrium point,

estimating to set the critical energy to be the energy value at the minimum gradient point V_cr=V_mgp, and find the corresponding fault-on time t_mgp from the fault-on trajectory, when CUEP is not found;

verifying to perform a time domain simulation with t_mgp being the fault clearing time, and the post-fault system is stable, then set V_mgp as the critical energy and stop the process, otherwise, go to following step;

Performing a time-domain simulation of the post-fault system with the state at t_cl as the initial condition, the post-fault system is stable, then set t₀=t_cl and t₁=t_mgp, otherwise, set t₀=0 and t₁ =t_cl, go to following step;

interpolating to make an interpolation between (t₀, t₁) using the Golden bisection-based interpolation method to find an instant, denoted as t⁽⁰⁾; and

verifying to perform a time domain simulation with t⁽⁰⁾ being the fault clearing time, if the post-fault system is stable, then treat t⁽⁰⁾ as the critical clearing time and the energy value at the corresponding state as the critical energy and stop the process, otherwise set t₁=t⁽⁰⁾ and go to interpolating step is conducted between the interval (t₀, t⁽⁰⁾).

32. A system for planning the electric power system, the system comprising;

a provider for providing construction plans with an electric power system and a contingency list of the electric power system;

a BCU-DSA system configured to perform the method of any one of claims 29-31 accordance with any one of the construction plans and the contingency list;

a detailed simulation system for performing a detailed simulation accordance with a operation result of the BCU-DSA system.

33. A system for analysing the electric power system, the system comprising;

an acquisition system for acquiring an information of an electric power system;

an energy management system for performing an energy management of the electric power system and estimating an energy flow of the electric power system;

a database for storing the energy flow estimated by the energy management system; and

a BCU-DSA system configured to perform the method of any one of claims 29-31 accordance with the energy flow stored by the database and a contingency list, so. as to calculate an energy margin index of the electric power system.

34. A system for operating the electric power system, the system comprising;

an acquisition system for acquiring an information of an electric power system;

an energy management system for performing an energy management of the electric power system and estimating an energy flow of the electric power system;

a BCU-DSA system associated to the energy management system, configured to perform the method of any one of claims 29-31 accordance with the energy flow calculated by the energy management system and a contingency list, so as to calculate an energy margin index of the electric power system which utilizes a redistributing instruction of generator output of the electric power system.

35. An information system for a market of the electric power, the information system comprising;

an acquisition system for acquiring an information of an electric power system;

an energy management system for performing an energy management of the electric power system and estimating an energy flow of the electric power system;

a BCU-DSA system associated to the energy management system, configured to perform the method of any one of claims 29-31 accordance with the energy flow calculated by the energy management system and a contingency list, so as to calculate an energy margin index of the electric power system which utilizes a market of a electric power and a redistributing instruction of generator output of the electric power system.

11. A system for on-line dynamic screening of contingencies comprising postulated disturbances which an electric power system may experience, said system comprising:

a) a dynamic contingency screening program for evaluating a plurality of contingencies with a plurality of contingency classifiers based on the method of finding the controlling unstable equilibrium point of said power system known as the boundary of stability region based controlling unstable equilibrium point method by sequentially applying said contingencies to a network islanding problem classifier, a S.E.P problem classifier, a large stability regions classifier, an exist point problem classifier, a ray adjustment problem classifier, an energy function problem classifier, a CUEP convergence problem classifier, and a controlling UEP (unstable equilibrium point) classifier to form a first group of stable contingencies and a second group of unstable or undecided contingencies; and

b) a time-domain simulation program for determining which of said second group of contingencies are unstable.

12. The system of claim 11, wherein said network islanding problem classifier further comprises means for screening out highly unstable contingencies which result in a network islanding problem.

13. The system of claim 11, wherein said S.E.P problem classifier further comprises means for computing the post-fault stable equilibrium point (SEP) starting from the pre-fault SEP, so as to detect potentially unstable contingencies.

14. The system of claim 11, wherein said large stability regions classifier further comprises means for screening out highly stable contingencies which result in a large stability region of the underlying post-fault SEP.

15. The system of claim 11, wherein said exist point problem classifier further comprises means for screening out potentially unstable contingencies which result in the so-called exit point problem employing some dynamic information during the exit point search.

16. The system of claim 11, wherein said ray adjustment problem classifier further comprises means for screening out potentially unstable contingencies based on some dynamic information during the minimum gradient point search.

17. The system of claim 11, wherein said energy function problem classifier further comprises means applied an index using the property that energy functions decrease along system trajectories, when the potential energy at the minimum gradient point is greater than that at the exit point, the corresponding contingency is identified as causing the energy function problem and is classified as unstable.

18. The system of claim 11, wherein said CUEP convergence problem classifier further comprises means for detecting the following UEP convergence problem, when a numerical method is applied to compute the controlling u.e.p. starting from the MGP.

19. The system of claim 11, wherein said controlling UEP classifier further comprises means applied the energy value at the CUEP as the critical energy, for classifying each remaining contingency into (definitely) stable or (potentially) unstable.

20. The system of claim 11, wherein

said network islanding problem classifier further comprises means for screening out highly unstable contingencies which result in a network islanding problem;

said S.E.P problem classifier further comprises means for computing the post-fault stable equilibrium point (SEP) starting from the pre-fault SEP, so as to detect potentially unstable contingencies;

said large stability regions classifier further comprises means for screening out highly stable contingencies which result in a large stability region of the underlying post-fault SEP;

said exist point problem classifier further comprises means for screening out potentially unstable contingencies which result in the so-called exit point problem employing some dynamic information during the exit point search;

said ray adjustment problem classifier further comprises means for screening out potentially unstable contingencies based on some dynamic information during the minimum gradient point search;

said energy function problem classifier further comprises means applied an index using the property that energy functions decrease along system trajectories, when the potential energy at the minimum gradient point is greater than that at the exit point, the corresponding contingency is identified as causing the energy function problem and is classified as unstable;

said CUEP convergence problem classifier further comprises means for detecting the following UEP convergence problem, when a numerical method is applied to compute the controlling u.e.p. starting from the MGP; and

said controlling UEP classifier further comprises means applied the energy value at the CUEP as the critical energy, for classifying each remaining contingency into (definitely) stable or (potentially) unstable.

22. An on-line dynamic security assessment system to be adapted an electric power system may experience, said assessment system comprising the system of any one of claims 11-20.

24. An energy management system to be adapted an electric power system may experience, said management system comprising the system of any one of claims 11-20.

25. A BCU guided time-domain method which inputs a power system with related data for dynamic security assessment and a contingency and outputs stability assessment and energy margin value for the contingency on the power system, comprising the steps of;

applying a boundary of stability region based controlling unstable equilibrium point method to compute the exit point, and

declaring to be highly stable and the energy margin by a post-fault system, when the exit point not be found with a certain period.

26. The method of claim 25, further comprising the steps of;

computing the minimum gradient point;

estimating to set the critical energy to be the energy value at the exit point V_cr=V_ep, and find the corresponding fault-on time t_ep from the fault-on trajectory, when the minimum gradient point is not found;

verifying to perform a time domain simulation with t_ep being the fault clearing time, when the post-fault system is stable and setting V_ep to be V_cr;

performing a time-domain simulation of the post-fault system with the state at t_cl as the initial condition, when the post-fault system is not stable;

setting t₀=t_cl and t₁=t_mgp, when the post-fault system is stable, and setting t₀=0 and t₁=t_cl, when the post-fault system is not stable;

interpolating to make an interpolation between (₀, t₁) using the Golden bisection-based interpolation method to find an instant, denoted as t⁽⁰⁾; and

verifying to perform a time domain simulation with t⁽⁰⁾ being the fault clearing time, if the post-fault system is stable, then treat t⁽⁰⁾ as the critical clearing time and the energy value at the corresponding state as the critical energy and stop the process, otherwise set t₁=t⁽⁰⁾ and go to interpolating step is conducted between the interval (t₀, t⁽⁰⁾).

27. The method of claim 26, further comprising the steps of;

computing controlling un stable equilibrium point,

estimating to set the critical energy to be the energy value at the minimum gradient point V_cr=V_mgp, and find the corresponding fault-on time t_mgp from the fault-on trajectory, when CUEP is not found;

verifying to perform a time domain simulation with t_mgp being the fault clearing time, and the post-fault system is stable, then set V_mgp as the critical energy and stop the process, otherwise, go to following step;

Performing a time-domain simulation of the post-fault system with the state at t_cl as the initial condition, the post-fault system is stable, then set t₀=t_cl and t₁=t_mgp, otherwise, set t₀=0 and t₁=t_cl, go to following step;

interpolating to make an interpolation between (t₀, t₁) using the Golden bisection-based interpolation method to find an instant, denoted as t⁽⁰⁾; and

verifying to perform a time domain simulation with t⁽⁰⁾ being the fault clearing time, if the post-fault system is stable, then treat t⁽⁰⁾ as the critical clearing time and the energy value at the corresponding state as the critical energy and stop the process, otherwise set t₁=t⁽⁰⁾ and go to interpolating step is conducted between the interval (t₀, t⁽⁰⁾).

28. The method of claims 26 or 27, wherein the golden bisection-based interpolation method comprising the steps of;

using the golden bisection method to calculate two fault clearing time instants from the interval [t₁, t₂]

t₀⁽¹⁾=0.168t₁+0.382t₂ t₀⁽²⁾=0.168t₂+0.382t₁

Performing a time-domain stability analysis for the contingency with the fault clearing time t₀⁽¹⁾, if the post-faulty system is unstable, then set t₂=t₀⁽¹⁾ and go to a following step, otherwise set t₁=t₀⁽¹⁾ and perform a time-domain stability analysis of the contingency with the fault clearing time t₀⁽²⁾, if the post-fault system is stable, set t₁=t₀⁽²⁾, otherwise set t₂=t₀⁽²⁾;

checking convergence, if ∥

t₁−

t₂∥

≦

ε

, go to a following step, otherwise go to the using step; and

providing the critical clearing time is set as t₁ and the system energy at this critical clearing time is set as the critical energy.