Power system comprehensive evaluation method based on time sequence production simulation

Abstract

The invention discloses a power system comprehensive evaluation method based on time sequence production simulation, and the method is characterized in that the method comprises the following steps: a data collection and preprocessing step, a time sequence production simulation step, a safety evaluation step, a stability evaluation step, and a reliability evaluation step. Compared with the prior art, by integrating data collection, preprocessing, dynamic simulation and multi-aspect evaluation, large-scale and real-time data can be processed and analyzed, and more accurate and comprehensive system performance evaluation can be provided, especially under the condition of large-scale access of new energy. The method is expected to significantly improve the management efficiency of the power system and the ability to cope with new energy challenges.

Description

Technical Field

[0001] The present invention relates to power system analysis technology, and in particular to a time-series production simulation method for evaluating the safety, reliability and stability of a power system when facing large-scale access to new energy. Background Art

[0002] With the transformation of the global energy structure and the increasingly severe climate change problem, the large-scale access of renewable energy, especially wind power and solar energy, has become an important trend in the development of the power system. This energy transformation has brought new challenges to the operational safety, stability and reliability of the power system. The high intermittent and unpredictable nature of renewable energy, as well as the volatility of the electricity it generates, have put forward higher requirements for the dispatch and management of the power system.

[0003] Traditional power system assessment methods mostly rely on static analysis, mainly evaluating the performance of the system under specific conditions, which is difficult to fully reflect the response of the system to new energy fluctuations and other dynamic changes in actual operation. In addition, traditional methods have limitations in processing large-scale data and real-time data streams, which is insufficient to support the needs of modern power systems for rapid decision-making and real-time adjustments. Summary of the invention

[0004] This invention proposes a comprehensive evaluation method for power systems based on time series production simulation, which can comprehensively consider the dynamic changes of time series and realize a comprehensive dynamic evaluation of power systems under the access of new energy. This method should be able to simulate and analyze the performance of power systems under various operating conditions and emergencies, and timely evaluate the safety, stability and reliability of the system to support the optimal operation and decision-making of the power system.

[0005] The present invention adopts the following technical means to achieve: a comprehensive evaluation method of a power system based on time series production simulation, comprising the following steps:

[0006] Data collection and preprocessing steps: collect meteorological, load and power generation data, and clean, cluster and extract and standardize these data;

[0007] Sequential production simulation steps: using the preprocessed data to simulate the operation status of the power system within a set time period through the sequential production simulation engine;

[0008] Safety assessment steps: Use simulation results to conduct N-1 analysis to assess the safety margin of the system when key equipment fails;

[0009] Stability assessment steps: Evaluate the stability of the system under renewable energy output fluctuations and other dynamic conditions through transient and small signal stability analysis;

[0010] Reliability evaluation steps: Based on the simulation results and historical fault data, calculate the system reliability index, and select the system average interruption duration SAIDI and the system average interruption frequency index SAIFI.

[0011] The data collection and preprocessing steps include:

[0012] Using automated tools to collect data from multiple data sources, including weather stations, load monitoring systems, and power plants;

[0013] Perform outlier detection and removal on the collected data, using standard deviation and quartile methods to identify and process abnormal data, including:

[0014]

[0015] IQR=Q3-Q1

[0016] Among them, X is the observed value; μ is the mean of the data; σ is the standard deviation of the data; Z is the standardized variable value. When it is greater than a certain threshold, the data is considered to be an outlier; IQR represents the quartile range, and Q3 and Q1 are the upper quartile and lower quartile of the data respectively;

[0017] Perform time series interpolation on the data to fill in the missing time points and ensure data integrity, including:

[0018]

[0019] Among them, (x0, y0) and (x1, y1) are known data values ​​and their time points, x is the time point of missing data, and y is the interpolation result of linear interpolation.

[0020] The time-series production simulation engine is used to process and integrate meteorological data, load data and power generation data, use advanced algorithms to simulate the dynamic response of the power system, and apply various operation strategies and emergency measures during the simulation process to evaluate the impact of dynamic response on system stability and security;

[0021] The advanced algorithms include load flow algorithm, dynamic stability analysis algorithm and fault propagation analysis algorithm.

[0022] The safety assessment step features include:

[0023] Conduct N-1 analysis, including failure simulation for each critical component;

[0024] Analyze the response and recovery capabilities of the system after a failure and evaluate the safety margin of the system in actual operation;

[0025] Use simulation results to optimize system design and enhance system tolerance to single point failures.

[0026] The stability assessment step is characterized by:

[0027] Use transient stability analysis to evaluate the system response to short-duration, large-amplitude disturbances, such as fault clearing or sudden load changes;

[0028] Conduct small signal stability analysis to evaluate the system's performance under small-amplitude continuous disturbances and ensure the system's long-term stability;

[0029] Combined with the timing analysis results, strategies and measures are proposed to enhance system stability.

[0030] Calculate SAIDI and SAIFI indicators, analyze the average system interruption time and frequency, and provide quantitative evaluation of system performance;

[0031] Analyze historical fault data and simulate fault conditions to identify system weaknesses and provide a basis for system upgrades and maintenance;

[0032] Reliability assessment results are used to guide the optimal configuration of the power system and the formulation of emergency plans.

[0033] The load flow algorithm characterizes and calculates the voltage, power and other parameters of each node in the system to help evaluate the stability and operating status of the system under given load conditions, including:

[0034]

[0035] Where P i With Q i are the active and reactive power of node i respectively; i and j are the node numbers at both ends of the line respectively, and the total number of nodes is N; V i and V j is the node voltage; G ij and B ij is the admittance matrix of line ij; θ ij is the phase angle between nodes i and j.

[0036] The dynamic stability analysis algorithm is used to characterize the state equations of the power system and its components, including:

[0037]

[0038] Where J is the moment of inertia; θ is the rotor angle; T e is the electromagnetic torque; T m is the mechanical torque; D is the damping coefficient; t is the time; P is the power; E a and E bis the generator terminal voltage; X is the generator synchronous reactance.

[0039] The fault propagation analysis algorithm described above affects the voltage and power in the fault propagation area, including:

[0040]

[0041] Among them, V fault is the fault voltage; R is the equivalent impedance of the system; τ is the propagation time constant.

[0042] In the fault simulation method in the N-1 analysis, the key components include a substation, a transmission line and a generator set, and the fault simulation method includes:

[0043] The power flow changes of the system after the fault occurs. The transmission line fault and the substation and generator set fault respectively use load flow analysis to evaluate the power distribution of the power grid after the fault, and simulate the response of the system by removing the faulty equipment or adjusting the power supply.

[0044] Fault recovery model construction, including automatic fault removal, load transfer, backup generator activation, etc. Restore system stability by reconfiguring the power grid.

[0045] The system safety margin assessment method is to conduct a comprehensive assessment of the critical power, maximum load tolerance and grid reconstruction capability of the power system, including:

[0046]

[0047] L max =P critical ·Z factor

[0048] Among them, P critical is the critical power; V max and I max are the maximum voltage and current of the system respectively; X is the grid impedance; L max is the maximum load bearing capacity of the system after a single fault occurs; Z factor It is the load factor, which means the ratio of actual load to critical power.

[0049] The SAIDI and SAIFI are specifically calculated as follows:

[0050]

[0051] Compared with the existing technology, the present invention can not only process and analyze large-scale and real-time data, but also provide more accurate and comprehensive system performance evaluation, especially in the case of large-scale access to new energy, by integrating data collection, preprocessing, dynamic simulation and multi-faceted evaluation. This method is expected to significantly improve the management efficiency of the power system and its ability to cope with new energy challenges. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the overall flow chart of the method of the present invention;

[0053] Figure 2 This is a flow chart of data collection and preprocessing of the present invention;

[0054] Figure 3 A framework diagram of a timing production simulation engine for the present invention;

[0055] Figure 4 This is a schematic diagram of the safety assessment module of the present invention;

[0056] Figure 5 This is a framework diagram of the stability assessment module of the present invention;

[0057] Figure 6 This is a calculation diagram of the reliability assessment module of the present invention. DETAILED DESCRIPTION

[0058] The present application is further described below in conjunction with the accompanying drawings:

[0059] The present invention includes a comprehensive evaluation method of a power system based on time-series production simulation, comprising the following steps:

[0060] Data collection and preprocessing steps: collect meteorological, load and power generation data, and clean, cluster and extract and standardize these data;

[0061] Sequential production simulation steps: using the preprocessed data to simulate the operation status of the power system within a set time period through the sequential production simulation engine;

[0062] Safety assessment steps: Use simulation results to conduct N-1 analysis to assess the safety margin of the system when key equipment fails;

[0063] Stability assessment steps: Evaluate the stability of the system under renewable energy output fluctuations and other dynamic conditions through transient and small signal stability analysis;

[0064] Reliability evaluation steps: Based on the simulation results and historical fault data, calculate the system reliability index, and select the system average interruption duration SAIDI and the system average interruption frequency index SAIFI.

[0065] Specifically, the data collection and preprocessing steps include:

[0066] Using automated tools to collect data from multiple data sources, including weather stations, load monitoring systems, and power plants;

[0067] Perform outlier detection and removal on the collected data, using standard deviation and quartile methods to identify and process abnormal data, including:

[0068]

[0069] IQR=Q3-Q1

[0070] Among them, X is the observed value; μ is the mean of the data; σ is the standard deviation of the data; Z is the standardized variable value. When it is greater than a certain threshold, the data is considered to be an outlier; IQR represents the quartile range, and Q3 and Q1 are the upper quartile and lower quartile of the data respectively;

[0071] Perform time series interpolation on the data to fill in the missing time points and ensure data integrity, including:

[0072]

[0073] Among them, (x0, y0) and (x1, y1) are known data values ​​and their time points, x is the time point of missing data, and y is the interpolation result of linear interpolation.

[0074] Furthermore, the time-series production simulation engine is used to process and integrate meteorological data, load data and power generation data, use advanced algorithms to simulate the dynamic response of the power system, and apply various operation strategies and emergency measures during the simulation process to evaluate the impact of the dynamic response on system stability and security;

[0075] The advanced algorithms include load flow algorithm, dynamic stability analysis algorithm and fault propagation analysis algorithm.

[0076] The safety assessment step features include:

[0077] Conduct N-1 analysis, including failure simulation for each critical component;

[0078] Analyze the response and recovery capabilities of the system after a failure and evaluate the safety margin of the system in actual operation;

[0079] Use simulation results to optimize system design and enhance system tolerance to single point failures.

[0080] The stability assessment steps are:

[0081] Use transient stability analysis to evaluate the system response to short-duration, large-amplitude disturbances, such as fault clearing or sudden load changes;

[0082] Conduct small signal stability analysis to evaluate the system's performance under small-amplitude continuous disturbances and ensure the system's long-term stability;

[0083] Combined with the timing analysis results, strategies and measures are proposed to enhance system stability.

[0084] Calculate SAIDI and SAIFI indicators, analyze the average system interruption time and frequency, and provide quantitative evaluation of system performance;

[0085] Analyze historical fault data and simulate fault conditions to identify system weaknesses and provide a basis for system upgrades and maintenance;

[0086] Reliability assessment results are used to guide the optimal configuration of the power system and the formulation of emergency plans.

[0087] The load flow algorithm characterizes and calculates the voltage, power and other parameters of each node in the system to help evaluate the stability and operating status of the system under given load conditions, including:

[0088]

[0089] Where P i With Q i are the active and reactive power of node i respectively; i and j are the node numbers at both ends of the line respectively, and the total number of nodes is N; V i and V j is the node voltage; G ij and B ij is the admittance matrix of line ij; θ ij is the phase angle between nodes i and j.

[0090] The dynamic stability analysis algorithm is used to characterize the state equations of the power system and its components, including:

[0091]

[0092] Where J is the moment of inertia; θ is the rotor angle; T e is the electromagnetic torque; T m is the mechanical torque; D is the damping coefficient; t is the time; P is the power; E a and E b is the generator terminal voltage; X is the generator synchronous reactance.

[0093] The fault propagation analysis algorithm described above affects the voltage and power in the fault propagation area, including:

[0094]

[0095] Among them, V fault is the fault voltage; R is the equivalent impedance of the system; τ is the propagation time constant.

[0096] In the fault simulation method in the N-1 analysis, the key components include a substation, a transmission line and a generator set, and the fault simulation method includes:

[0097] The power flow changes of the system after the fault occurs. The transmission line fault and the substation and generator set fault respectively use load flow analysis to evaluate the power distribution of the power grid after the fault, and simulate the response of the system by removing the faulty equipment or adjusting the power supply.

[0098] Fault recovery model construction, including automatic fault removal, load transfer, backup generator activation, etc. Restore system stability by reconfiguring the power grid.

[0099] The system safety margin assessment method is to conduct a comprehensive assessment of the critical power, maximum load tolerance and grid reconstruction capability of the power system, including:

[0100]

[0101] L max =P critical ·Z factor

[0102] Among them, P critical is the critical power; V max and I max are the maximum voltage and current of the system respectively; X is the grid impedance; L max is the maximum load bearing capacity of the system after a single fault occurs; Z factor It is the load factor, which means the ratio of actual load to critical power.

[0103] The system reliability evaluation method is to construct an evaluation system including SAIDI and SAIFI indicators, analyze the average interruption time and frequency of the system, and provide a quantitative evaluation of system performance, including:

[0104]

[0105] Among them, SAIDI represents the average interruption duration of each user in the system during the statistical period; SAIFI represents the average interruption frequency index of each user in the system during the statistical period.

Claims

1. A comprehensive evaluation method for a power system based on time series production simulation, characterized in that: The following steps are involved: Data collection and preprocessing steps: collect meteorological, load and power generation data, and clean, cluster and extract and standardize these data; Sequential production simulation steps: using the preprocessed data to simulate the operation status of the power system within a set time period through the sequential production simulation engine; Safety assessment steps: Use simulation results to conduct N-1 analysis to assess the safety margin of the system when key equipment fails; Stability assessment steps: Evaluate the stability of the system under renewable energy output fluctuations and other dynamic conditions through transient and small signal stability analysis; Reliability evaluation steps: Based on the simulation results and historical fault data, calculate the system reliability index, and select the system average interruption duration SAIDI and the system average interruption frequency index SAIFI.

2. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 1, characterized in that: The data collection and preprocessing steps include: Using automated tools to collect data from multiple data sources, including weather stations, load monitoring systems, and power plants; Perform outlier detection and removal on the collected data, using standard deviation and quartile methods to identify and process abnormal data, including: IQR=Q3-Q1 Among them, X is the observed value; μ is the mean of the data; σ is the standard deviation of the data; Z is the standardized variable value. When it is greater than a certain threshold, the data is considered to be an outlier; IQR represents the quartile range, and Q3 and Q1 are the upper quartile and lower quartile of the data respectively; Perform time series interpolation on the data to fill in the missing time points and ensure data integrity, including: Among them, (x0, y0) and (x1, y1) are known data values ​​and their time points, x is the time point of missing data, and y is the interpolation result of linear interpolation.

3. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 2, characterized in that: The time-series production simulation engine is used to process and integrate meteorological data, load data and power generation data, use advanced algorithms to simulate the dynamic response of the power system, and apply various operation strategies and emergency measures during the simulation process to evaluate the impact of dynamic response on system stability and security; The advanced algorithms include load flow algorithms, dynamic stability analysis algorithms and fault propagation analysis algorithms.

4. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 1, characterized in that: The safety assessment step features include: Conduct N-1 analysis, including failure simulation for each critical component; Analyze the response and recovery capabilities of the system after a failure and evaluate the safety margin of the system in actual operation; Use simulation results to optimize system design and enhance system tolerance to single point failures.

5. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 1, characterized in that: The stability assessment step is characterized by: Use transient stability analysis to evaluate the system response to short-duration, large-amplitude disturbances, such as fault clearing or sudden load changes; Conduct small signal stability analysis to evaluate the system's performance under small-amplitude continuous disturbances and ensure the system's long-term stability; Combined with the timing analysis results, strategies and measures are proposed to enhance system stability.

6. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 1, characterized in that: The reliability assessment steps include: Calculate SAIDI and SAIFI indicators, analyze the average system interruption time and frequency, and provide quantitative evaluation of system performance; Analyze historical fault data and simulate fault conditions to identify system weaknesses and provide a basis for system upgrades and maintenance; Reliability assessment results are used to guide the optimal configuration of the power system and the formulation of emergency plans.

7. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 3, characterized in that: The load flow algorithm characterizes and calculates the voltage, power and other parameters of each node in the system to help evaluate the stability and operating status of the system under given load conditions, including: Where P i With Q i are the active and reactive power of node i respectively; i and j are the node numbers at both ends of the line respectively, and the total number of nodes is N; V i and V j is the node voltage; G ij and B ij is the admittance matrix of line ij; θ ij is the phase angle between nodes i and j.

8. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 3, characterized in that: The dynamic stability analysis algorithm is used to characterize the state equations of the power system and its components, including: Where J is the moment of inertia; θ is the rotor angle; T e is the electromagnetic torque; T m is the mechanical torque; D is the damping coefficient; t is the time; P is the power; E a and E b is the generator terminal voltage; X is the generator synchronous reactance.

9. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 3, characterized in that: The fault propagation analysis algorithm affects the voltage and power of the fault propagation area, including: Among them, V fault is the fault voltage; R is the equivalent impedance of the system; τ is the propagation time constant.

10. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 4, characterized in that: In the fault simulation method in the N-1 analysis, the key components include a substation, a transmission line and a generator set, and the fault simulation method includes: The power flow changes of the system after the fault occurs. The transmission line fault and the substation and generator set fault respectively use load flow analysis to evaluate the power distribution of the power grid after the fault, and simulate the response of the system by removing the faulty equipment or adjusting the power supply. Fault recovery model construction, including automatic fault removal, load transfer, backup generator activation, etc. Restore system stability by reconfiguring the power grid.

11. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 4, characterized in that: The system safety margin assessment method is to conduct a comprehensive assessment of the critical power, maximum load tolerance and grid reconstruction capability of the power system, including: L max =P critical ·Z factor Among them, P critical is the critical power; V max and I max are the maximum voltage and current of the system respectively; X is the grid impedance; L max is the maximum load bearing capacity of the system after a single fault occurs; Z factor is the load factor, which represents the ratio of actual load to critical power.

12. A method for comprehensive evaluation of a power system based on time series production simulation as claimed in claim 6, characterized in that: The SAIDI and SAIFI index evaluation system is specifically calculated as follows:

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