Electric power system toughness improving method and system considering extreme weather events

Abstract

The invention provides a power system toughness improvement method and system considering extreme weather events, and relates to the technical field of power system toughness improvement. When the toughness of the power system is researched, power system interruption caused by an extreme weather event is considered, and the relationship between the extreme weather and a cascade fault is also established; the process and mechanism of the cascade fault caused by the extreme weather are presented by a formula, and the extreme event-cascade fault dual-time-dimension process constraint is introduced, so that the actual condition that the cascade fault possibly occurs in the power system after the current HILP event occurs is met; through mixed integer linear programming solution, an optimal solution corresponds to different toughness levels, and a decision maker can select an optimal configuration plan for improving toughness according to a system toughness level generated by implementing a provided solution, so that line overload during a weather disaster period is effectively relieved, and the probability of cascade faults is reduced; and an actual reference is provided for a path for improving the toughness of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of improving the resilience of power systems, and in particular to a method and system for improving the resilience of power systems taking extreme weather events into consideration. Background Art

[0002] Extreme weather events such as typhoons, blizzards, and hail can pose severe challenges to the safe and stable operation of power systems. Due to the inherent nature of these events, high impact low probability (HILP), they will not only cause power grid failures, but also cause huge losses to the economy and society. Therefore, determining the impact of HILP events on power systems is a very important and urgent task. Traditional reliability methods only evaluate the performance of the power system in supplying power to all connected loads under low impact high probability events, without considering the origin of the event, nor attributing the vulnerability of the power system caused by the event to the HILP event. The power system resilience assessment method focuses on the assessment of the power system's absorption, resistance, and recovery capabilities under HILP events, which is of great significance for mitigating the destructive effects of events such as extreme weather disasters on the power system.

[0003] Before extreme weather events occur, the power system's ability to resist weather disasters can be enhanced by strengthening the power infrastructure and taking resilience-oriented prevention plans in advance. During extreme weather events, the relationship between meteorological elements and the failure rate of the power system needs to be considered. Since the process and state of most weather disasters are dynamic, the intensity of the impact on the power system varies with time and geography in the trajectory area of ​​the weather disaster. Therefore, weather events should be regarded as a dynamic process to conduct resilience simulation analysis on multi-state power systems. After extreme weather events occur, response recovery measures can help the power system return to normal as soon as possible. At the same time, in order to avoid potential cascading failures caused by extreme weather, more attention should be paid to targeted methods to improve the resilience of the power system during extreme weather.

[0004] Improving the resilience of the power system considering extreme weather events means identifying the overload of transmission lines caused by extreme weather events and the cascading failure programs triggered by them, introducing time dimension process constraints to describe the correlation between extreme weather events and power system failures, and then making optimal decisions (including power generation scheduling and load reduction) to improve the power system's ability to resist extreme weather events and the cascading failures caused by them, and to improve the resilience of the power system under extreme weather events.

[0005] The existing technical solutions for power systems to cope with extreme weather events focus more on power outages directly caused by extreme weather events, but rarely involve cascading failures caused by weather disasters, ignoring the dynamic process and the chain reaction of extreme weather events on power system failure points. At the same time, when evaluating the impact of HILP events on system resilience, more attention is paid to the ability of the system to cope with HILP events within a single intensity range, which cannot fully reflect the overall resilience of the system.

[0006] In addition, the resilience assessment of power systems under the influence of extreme weather events is rarely modeled from the perspective of risk assessment measurement. The risk aversion of the worst-case failure scenario is not included in the objective function of the resilience assessment. The risk-taking level of decision makers facing the worst-case scenario is not considered. There is a lack of comprehensive analysis that reflects the resilience of the power system, and the goal of optimizing the resilience of the power system cannot be achieved. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a method and system for improving the resilience of an electric power system taking into account extreme weather events, thereby solving the problem of poor resistance and resilience of the electric power system when extreme weather events occur.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for improving the resilience of a power system considering extreme weather events, the method comprising the following steps:

[0010] S1. Based on the regional power grid wind speed profile and power outage data, the dual time dimension process constraints of extreme events and cascading failures are introduced to construct a probability model between extreme weather events, cascading failures and power system interruptions.

[0011] S2. Monte Carlo method is used to simulate the power system outage process including wind speed and cascading failures and generate fault scenarios. K-means clustering algorithm is used to simplify and optimize the power system outage scenarios under the influence of extreme weather events.

[0012] S3. Combined with the generated fault scenarios, the CVAR index based on the expected value of the objective function is used to evaluate the resilience of the power system;

[0013] S4. Based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model, the best configuration plan to improve resilience is selected.

[0014] Preferably, the S1 comprises the following steps:

[0015] S1.1. Construct a basic model of the impact of extreme weather events on the power system

[0016]

[0017] Where λ[w(t)] represents the probability of power system outage when the wind speed is w(t);

[0018] λ n It represents the probability of power system outage under normal weather conditions;

[0019] α represents the scaling factor;

[0020] w(t) represents the wind speed at time t, which can be obtained from historical data of the meteorological department or simulation;

[0021] w c Indicates critical wind speed;

[0022] S1.2. Determine the probabilistic relationship between cascading failures and power system outages

[0023]

[0024] Among them, π outagr It indicates the probability of power system interruption caused by line overload;

[0025] π 0 represents the initial hidden outage probability of the line;

[0026] p l Indicates the unit line flow;

[0027] K 0 represents the coefficients of the linear function;

[0028] Indicates the maximum power flow of the line;

[0029] S1.3. Construct a probability model between extreme weather events, cascading failures and power system interruptions.

[0030] Preferably, the step S1.3 includes the following steps:

[0031] S1.3.1. Identify the operating status of transmission lines under normal conditions and under the influence of extreme weather events

[0032]

[0033] in, represents the operating status of the transmission line at time t, It means that the transmission line is operating normally at time t. When means the transmission line is in interrupted state at time t;

[0034] Indicates that the transmission line l is at t’ The state at any moment, and They represent the interruption status of transmission lines caused by extreme weather and normal operation respectively;

[0035] represents the state of line l at time t”, and They represent the transmission line interruption status caused by cascading faults and normal operation status respectively;

[0036] S1.3.2. Analyze the probability change process of power transmission line interruption in different states under the influence of extreme weather events

[0037] As extreme weather changes, the power system state and the probability of power line interruption change with time, so the power system changes from the state at time t to the state at time t. ’ The probability of interruption of the state at time can be expressed as:

[0038]

[0039] Among them, Ω L,t+1 It represents the set of lines that may be interrupted at time t+1. represents the set of power system states at time t;

[0040] S1.3.3 Determine the probability of power system interruption due to extreme weather events and cascading failures

[0041]

[0042] in, and Respectively represent the operating status of the transmission line at time t and time t+1;

[0043] Indicates that at t ’ +1 time transmission line failure probability caused by extreme weather;

[0044] represents the failure probability caused by the cascading failure of the transmission line routing at time t”+1.

[0045] Preferably, S2 comprises the following steps:

[0046] S2.1, assuming that all lines are connected before the hurricane;

[0047] S2.2, the total probability of line interruption is determined by the line failure probability in the vulnerability curve and the typhoon occurrence factor in each area;

[0048] S2.3, the number of transmission lines in the power system is represented by a random number n, and n is uniformly distributed in the interval (0,1);

[0049] S2.4, based on S1, the failure probability of each transmission line is obtained and compared with the corresponding random number; the lines with a failure probability less than the corresponding random number will experience a power outage due to the typhoon, while the lines with a failure probability higher than the corresponding random number will remain connected to the grid in the face of the typhoon; by comparing the failure probability with the corresponding random numbers of all its lines, a scenario can be generated, which consists of a set of states estimated for all transmission lines;

[0050] S2.5. When the number of transmission lines is assumed to be n, the number of scenarios that can be developed based on the status of all transmission lines is 2 n ;So through Monte Carlo simulation, a limited number of scenarios can be randomly generated;

[0051] S2.6. Combined with the K-means clustering algorithm, the 10,000 power system fault scenarios generated by the Monte Carlo simulation are simplified and optimized to 50 fault scenarios for analysis to improve computing performance.

[0052] Preferably, S3 comprises the following steps:

[0053] S3.1. Introduce the expected CENS value based on CVaR as an evaluation indicator to conduct snapshot resilience assessment of power system resilience under single intensity events in different scenarios;

[0054] S3.2. Based on the power system resilience under a single intensity event obtained through snapshot resilience assessment, a resilience diagram of the power system under different intensity events is obtained, and the smoothness of the resilience diagram is used as a comprehensive power system resilience assessment method.

[0055] Preferably, the S3.1 comprises the following steps:

[0056] S3.1.1. The SCOPF optimization method based on multi-period risk is used to evaluate the resilience of the power system to typhoons. The time period is set to 24 hours. According to relevant principles, the CENS value under CVaR should be minimized.

[0057]

[0058] V=VoLL1·Ls□1 e,k,t +VoLL2·Ls□2 e,k,t

[0059] Among them, α∈[0,1] represents the risk level assumed by the decision maker;

[0060] e indicates a failure scenario;

[0061] k represents the busbar;

[0062] t represents time;

[0063] β is an auxiliary variable for formulating CVaR, ψ∈(0,1) is the confidence level, the worst expected CENS is (1-ω)×100%, Lsh1 e,k,t is the load shedding of level 1 load in bus k at time t and scenario e, Lsh2 e,k,t is the load shedding of level 2 load in bus k at time t and scenario e;

[0064] S3.1.2, In addition to the general related constraints, the following constraints are also involved in the CVaR model;

[0065]

[0066] Among them, ω(e) is a continuous non-negative variable;

[0067] β is an auxiliary variable, and its optimal value is equal to the CENS value under CVaR;

[0068] S3.1.3. The CENS value under CVaR is obtained through steps S3.1.1 and S3.1.2, and the system resilience is inversely proportional to it, so the index of system resilience snapshot evaluation is obtained.

[0069] Preferably, the S4 comprises the following steps:

[0070] S4.1. In order to construct a power system resilience improvement model to obtain the best results, it is necessary to first determine the state of the line. This process is called optimal transmission switching (OTS). The present invention adopts an improved OTS model to improve the resilience of the power system. Since the OTS model is widely used, the specific solution process will not be repeated. Only the variables, objective functions and constraints are listed for reference:

[0071] Variables: Consider the probability of extreme weather and cascading failures, see S1 for details;

[0072] Objective function: The objective is to minimize the power outage chain and scale in the power system lines. The specific function is consistent with S3.1.1;

[0073] Constraints: including power balance constraints, generator ramp rate constraints, minimum generator operating time and downtime limits, upper and lower limits of generator output, load reduction limits, voltage phase angle limits, absolute value of unit power flow on the line, power flow constraints on the line, dual-time dimension process constraints including extreme weather events and cascading failures, etc.;

[0074] S4.2. The above model is a mixed integer linear programming and can be solved by the CPLEX solver. The OTS optimization problem is implemented according to the above constraints on the optimization model. For any parameter selection, several sets of optimal solutions β can be obtained within the system operation time range, and the optimal solutions correspond to different resilience levels. Decision makers can choose the best configuration plan to improve resilience based on the system resilience performance generated by the implementation of the provided solution.

[0075] A power system resilience enhancement system considering extreme weather events, the enhancement system comprising: a model building module, a simplification optimization module, an evaluation module, and a configuration module;

[0076] The model building module is used to introduce extreme event-cascading failure dual time dimension process constraints based on regional power grid wind speed profile and power outage data, and build a probability model among extreme weather events-cascading failures-power system interruptions;

[0077] The simplified optimization module is used to simulate the power system interruption process including wind speed and cascading failures using the Monte Carlo method and generate fault scenarios, and to simplify and optimize the power system interruption scenarios under the influence of extreme weather events using the K-means clustering algorithm;

[0078] The evaluation module is used to evaluate the resilience of the power system using a CVAR index based on an expected value of an objective function in combination with the generated fault scenario;

[0079] The configuration module is used to select the best configuration plan to improve resilience based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model.

[0080] The present invention provides a method and system for improving the resilience of a power system taking into account extreme weather events. Compared with the prior art, it has the following beneficial effects:

[0081] In the present invention, when studying the resilience of the power system, not only the power system interruptions caused by extreme weather events are considered, but also the relationship between extreme weather and cascading failures is established; not only the process and mechanism of cascading failures caused by extreme weather are presented by formulas, but also the dual time dimension process constraints of extreme events-cascading failures are introduced, which conforms to the reality that cascading failures may occur in the power system after the current HILP event occurs; through mixed integer linear programming solution, the optimal solution is corresponded to different resilience levels, and decision makers can choose the best configuration plan to improve resilience according to the system resilience level generated by implementing the provided solution, thereby effectively alleviating line overload during weather disasters and reducing the probability of cascading failures, providing a practical reference for the path to improving the resilience of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0083] Figure 1 A schematic flow chart of a method for improving power system resilience considering extreme weather events in an embodiment of the present invention;

[0084] Figure 2 This is a schematic diagram of the module structure of a power system resilience enhancement system considering extreme weather events in an embodiment of the present invention;

[0085] Figure 3 The present invention is a flowchart of generating and simplifying a power system line fault scenario considering extreme weather events in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] The embodiments of the present application solve the problem of poor resistance and resilience of the power system when extreme weather events occur by providing a method and system for improving the resilience of the power system taking extreme weather events into consideration.

[0088] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0089] Example:

[0090] like Figure 1 to Figure 3 As shown, the present invention provides a method for improving the resilience of a power system considering extreme weather events, the method comprising the following steps:

[0091] S1. Based on the regional power grid wind speed profile and power outage data, the dual time dimension process constraints of extreme events and cascading failures are introduced to construct a probability model among extreme weather events, cascading failures and power system interruptions.

[0092] S1.1. Construct a basic model of the impact of extreme weather events on the power system

[0093]

[0094] Where λ[w(t)] represents the probability of power system outage when the wind speed is w(t);

[0095] λ n It represents the probability of power system outage under normal weather conditions;

[0096] α represents the scaling factor;

[0097] w(t) represents the wind speed at time t, which can be obtained from historical data of the meteorological department or simulation;

[0098] w c Indicates the critical wind speed.

[0099] S1.2. Determine the probabilistic relationship between cascading failures and power system outages

[0100]

[0101] Among them, π outage It indicates the probability of power system interruption caused by line overload;

[0102] π 0 represents the initial hidden outage probability of the line;

[0103] p l Indicates the unit line flow;

[0104] K 0 represents the coefficients of the linear function;

[0105] Indicates the maximum power flow of the line.

[0106] S1.3. Constructing a probability model between extreme weather events, cascading failures, and power system interruptions

[0107] S1.3.1. Identify the operating status of transmission lines under normal conditions and under the influence of extreme weather events

[0108]

[0109] in, represents the operating status of the transmission line at time t, It means that the transmission line is operating normally at time t. When means the transmission line is in interrupted state at time t;

[0110] Indicates that the transmission line l is at t ’ The state at any moment, and They represent the interruption status of transmission lines caused by extreme weather and normal operation respectively;

[0111] represents the state of line l at time t”, and They represent the transmission line interruption status caused by cascading faults and normal operation status respectively.

[0112] S1.3.2. Analyze the probability change process of power transmission line interruption in different states under the influence of extreme weather events

[0113] As extreme weather changes, the power system state and the probability of power line interruption change with time, so the power system changes from the state at time t to the state at time t. ’ The probability of interruption of the state at time can be expressed as:

[0114]

[0115] Among them, Ω L,t+1 It represents the set of lines that may be interrupted at time t+1. represents the set of power system states at time t.

[0116] S1.3.3 Determine the probability of power system interruption due to extreme weather events and cascading failures

[0117]

[0118] in, and Respectively represent the operating status of the transmission line at time t and time t+1;

[0119] Indicates that at t ’ +1 time transmission line failure probability caused by extreme weather;

[0120] represents the failure probability caused by the cascading failure of the transmission line routing at time t”+1.

[0121] S2. The Monte Carlo method is used to simulate the power system interruption process including wind speed and cascading failures and generate fault scenarios. The K-means clustering algorithm is used to simplify and optimize the power system interruption scenarios under the influence of extreme weather events.

[0122] S2.1. Assume that all lines are connected before the hurricane.

[0123] S2.2. The total probability of line interruption is determined by the line failure probability in the vulnerability curve and the typhoon occurrence factor in each area.

[0124] S2.3. The number of transmission lines in the power system is represented by a random number n, and n is uniformly distributed in the interval (0,1).

[0125] S2.4, based on S1, the failure probability of each transmission line is obtained and compared with the corresponding random number; the lines with a failure probability less than the corresponding random number will experience a power outage due to the typhoon, while the lines with a failure probability higher than the corresponding random number will remain connected to the grid in the face of the typhoon. By comparing the failure probability with the corresponding random numbers of all its lines, a scenario can be generated, which consists of a set of states estimated for all transmission lines.

[0126] S2.5. When the number of transmission lines is assumed to be n, the number of scenarios that can be developed based on the status of all transmission lines is 2 n . Therefore, through Monte Carlo simulation, a finite number of scenarios can be randomly generated.

[0127] S2.6. Combined with the K-means clustering algorithm, the 10,000 power system fault scenarios generated by the Monte Carlo simulation are simplified and optimized to 50 fault scenarios for analysis to improve the computing performance (running time and accuracy).

[0128] S3. Combined with the generated fault scenarios, the CVAR index based on the expected value of the objective function is used to evaluate the resilience of the power system.

[0129] S3.1. Introduce the expected CENS value based on CVaR as an evaluation indicator to conduct snapshot resilience assessment of power system resilience under single intensity events in different scenarios;

[0130] HILP events are often accompanied by the occurrence of load shedding. Therefore, energy not provided (ENS) can be used as a simple indicator to evaluate the resilience of the power system. In addition, since the system load loss value (VoLL) is used to prioritize the system load, the increased cost of the power system should be considered in the case of ENS. Therefore, the present invention uses the expected value of the ENS cost (CENS) under the risk-based CVaR index as the main indicator for evaluating the resilience of the power system.

[0131] S3.1.1. The SCOPF optimization method based on multi-period risk is used to evaluate the resilience of the power system to typhoons, with a time limit of 24 hours. According to relevant principles, the CENS value under CVaR should be minimized.

[0132]

[0133] V=VoLL1·Lsh1 e,k,t +VoLL2·Lsh2 e,k,t

[0134] Among them, α∈[0,1] represents the risk level assumed by the decision maker;

[0135] e indicates a failure scenario;

[0136] k represents the busbar;

[0137] t represents time;

[0138] β is an auxiliary variable for formulating CVaR, ψ∈(0,1) is the confidence level, the worst expected CENS is (1-ω)×100%, Lsh1 e,k,t is the load shedding of level 1 load in bus k at time t and scenario e, Lsh2 e,k,t is the load shedding of level 2 load in bus k at time t and scenario e.

[0139] S3.1.2. In addition to the general related constraints, the following constraints are also involved in the CVaR model.

[0140]

[0141] Among them, ω(e) is a continuous non-negative variable;

[0142] β is an auxiliary variable, and its optimal value is equal to the CENS value under CVaR.

[0143] S3.1.3. The CENS value under CVaR is obtained through steps S3.1.1 and S3.1.2, and the system resilience is inversely proportional to it, so the index of system resilience snapshot evaluation is obtained:

[0144] Power System Resilience∝1 / CENS of CVaR

[0145] S3.2. Based on the power system resilience under a single intensity event obtained by snapshot resilience assessment, a resilience map of the power system under different intensity events is obtained, and the smoothness of the resilience map is used as a comprehensive power system resilience assessment method;

[0146] A comprehensive method is introduced to evaluate the resilience of the power system under a series of intensities of HILP events and form a corresponding resilience graph; the horizontal axis of the resilience graph represents the typhoon speed, and the vertical axis represents the snapshot resilience index value corresponding to each intensity under the HILP event. The rate of change of its derivative is inversely proportional to the resilience of the system, and the stronger the system resilience, the smoother its graph; the smoothness criterion of the resilience graph can be used as an indicator for comprehensive resilience evaluation; the following is the calculation formula for smoothness:

[0147] Smoothness Index=1 / Max(Slope)-Min(Slope)

[0148] Among them, Max(Slope) is the maximum rate of change of the derivative of a certain point of the system representative curve in the toughness diagram;

[0149] Min(Slope) is the minimum rate of change of the derivative of a point on the system representative curve in the toughness diagram.

[0150] S4. Based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model, the best configuration plan to improve resilience is selected.

[0151] S4.1. In order to construct a power system resilience improvement model to obtain the best results, it is necessary to first determine the state of the line. This process is called optimal transmission switching (OTS). The present invention uses an improved OTS model to improve the resilience of the power system. Since the OTS model is widely used, the specific solution process will not be repeated. Only the variables, objective functions and constraints are listed for reference:

[0152] Variables: Consider the probability of extreme weather and cascading failures, see S1 for details.

[0153] Objective function: The objective is to minimize the power outage chain and scale in the power system lines. The specific function is consistent with S3.1.1.

[0154] Constraints: including power balance constraints, generator ramp rate constraints, minimum generator operating time and downtime limits, upper and lower limits of generator output, load reduction limits, voltage phase angle limits, absolute value of unit power flow on the line, power flow constraints on the line, dual-time dimension process constraints including extreme weather events and cascading failures, etc.

[0155] S4.2. The above model is a mixed integer linear programming and can be solved by CPLEX solver. According to the above constraints on the optimization model, the OTS optimization problem is realized. For any parameter selection, several sets of optimal solutions β can be obtained within the system operation time range, and the optimal solutions correspond to different resilience levels. Decision makers can choose the best configuration plan to improve resilience based on the system resilience performance generated by implementing the provided solution.

[0156] The present invention provides a power system resilience enhancement system considering extreme weather events, the enhancement system comprising: a model building module, a simplification optimization module, an evaluation module, and a configuration module;

[0157] The model building module is used to introduce extreme event-cascading failure dual time dimension process constraints based on regional power grid wind speed profile and power outage data, and build a probability model among extreme weather events-cascading failures-power system interruptions;

[0158] The simplified optimization module is used to simulate the power system interruption process including wind speed and cascading failures using the Monte Carlo method and generate fault scenarios, and to simplify and optimize the power system interruption scenarios under the influence of extreme weather events using the K-means clustering algorithm;

[0159] The evaluation module is used to evaluate the resilience of the power system using a CVAR index based on an expected value of an objective function in combination with the generated fault scenario;

[0160] The configuration module is used to select the best configuration plan to improve resilience based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model.

[0161] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0162] 1. In the embodiment of the present invention, when studying the resilience of the power system, not only the power system interruption caused by extreme weather events is considered, but also the relationship between extreme weather and cascading failures is established; not only the process and mechanism of extreme weather-induced cascading failures are presented by formulas, but also the dual time dimension process constraints of extreme events-cascading failures are introduced, which conforms to the reality that cascading failures may occur in the power system after the current HILP event occurs; through mixed integer linear programming solution, the optimal solution is matched with different resilience levels, and decision makers can choose the best configuration plan to improve resilience according to the system resilience level generated by implementing the provided solution, thereby effectively alleviating line overload during weather disasters and reducing the probability of cascading failures, providing a practical reference for the path to improve the resilience of the power system.

[0163] 2. In an embodiment of the present invention, when determining the resilience evaluation index of the power system, the risk to the power system caused by the HILP event is taken into account, a resilience evaluation index based on the CENS value of CVaR is formed, and it is included in the objective function of the resilience evaluation problem, which is helpful for a comprehensive evaluation of the resilience of the power system.

[0164] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the resilience of a power system considering extreme weather events, characterized in that: The method comprises the following steps: S1. Based on the regional power grid wind speed profile and power outage data, the dual time dimension process constraints of extreme events and cascading failures are introduced to construct a probability model between extreme weather events, cascading failures and power system interruptions. S2. Monte Carlo method is used to simulate the power system outage process including wind speed and cascading failures and generate fault scenarios. K-means clustering algorithm is used to simplify and optimize the power system outage scenarios under the influence of extreme weather events. S3. Combined with the generated fault scenarios, the CVAR index based on the expected value of the objective function is used to evaluate the resilience of the power system; S4. Based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model, the best configuration plan to improve resilience is selected.

2. The method for improving power system resilience considering extreme weather events as claimed in claim 1, characterized in that: The S1 comprises the following steps: S1.

1. Construct a basic model of the impact of extreme weather events on the power system Where λ[w(t)] represents the probability of power system outage when the wind speed is w(t); λ n It represents the probability of power system outage under normal weather conditions; α represents the scaling factor; w(t) represents the wind speed at time t, which can be obtained from historical data of the meteorological department or simulation; w c Indicates critical wind speed; S1.

2. Determine the probabilistic relationship between cascading failures and power system outages Among them, π outage It indicates the probability of power system interruption caused by line overload; π0 represents the initial hidden outage probability of the line; p l Indicates the unit line flow; K0 represents the coefficient of the linear function; Indicates the maximum power flow of the line; S1.

3. Construct a probability model between extreme weather events, cascading failures and power system interruptions.

3. The method for improving the resilience of a power system considering extreme weather events as claimed in claim 2, characterized in that: The S1.3 comprises the following steps: S1.3.

1. Identify the operating status of transmission lines under normal conditions and under the influence of extreme weather events in, represents the operating status of the transmission line at time t, It means that the transmission line is operating normally at time t. When indicates that the transmission line is in interrupted state at time t; Indicates that the transmission line l is at t ’ The state at any moment, and They represent the interruption status of transmission lines caused by extreme weather and normal operation respectively; represents the state of line l at time t”, and They represent the transmission line interruption status caused by cascading faults and normal operation status respectively; S1.3.

2. Analyze the probability change process of power transmission line interruption in different states under the influence of extreme weather events As extreme weather changes, the power system state and the probability of power line interruption change with time, so the power system changes from the state at time t to the state at time t. ’ The probability of interruption of the state at time can be expressed as: Among them, Ω L,t+1 It represents the set of lines that may be interrupted at time t+1. represents the set of power system states at time t; S1.3.3 Determine the probability of power system interruption due to extreme weather events and cascading failures in, and Respectively represent the operating status of the transmission line at time t and time t+1; Indicates that at t ’ +1 time transmission line failure probability caused by extreme weather; represents the failure probability caused by the cascading failure of the transmission line routing at time t”+1.

4. The method for improving the resilience of a power system considering extreme weather events as claimed in claim 1, characterized in that: The S2 comprises the following steps: S2.1, assuming that all lines are connected before the hurricane; S2.2, the total probability of line interruption is determined by the line failure probability in the vulnerability curve and the typhoon occurrence factor in each area; S2.3, the number of transmission lines in the power system is represented by a random number n, and n is uniformly distributed in the interval (0,1); S2.4, based on S1, obtain the failure probability of each transmission line and compare it with the corresponding random number; Lines with a failure probability less than the corresponding random number will experience a power outage due to the typhoon, while lines with a failure probability higher than the corresponding random number will remain connected to the grid in the face of the typhoon; By comparing the failure probability with the corresponding random numbers of all its lines, a scenario can be generated, which consists of a set of states estimated for all transmission lines; S2.

5. When the number of transmission lines is assumed to be n, the number of scenarios that can be developed based on the status of all transmission lines is 2 n ;So through Monte Carlo simulation, a limited number of scenarios can be randomly generated; S2.

6. Combined with the K-means clustering algorithm, the 10,000 power system fault scenarios generated by the Monte Carlo simulation are simplified and optimized to 50 fault scenarios for analysis to improve computing performance.

5. The method for improving the resilience of a power system considering extreme weather events as claimed in claim 1, characterized in that: The S3 comprises the following steps: S3.

1. Introduce the expected CENS value based on CVaR as an evaluation indicator to conduct snapshot resilience assessment of power system resilience under single intensity events in different scenarios; S3.

2. Based on the power system resilience under a single intensity event obtained through snapshot resilience assessment, a resilience diagram of the power system under different intensity events is obtained, and the smoothness of the resilience diagram is used as a comprehensive power system resilience assessment method.

6. The method for improving the resilience of a power system considering extreme weather events as claimed in claim 5, characterized in that: The S3.1 comprises the following steps: S3.1.

1. The SCOPF optimization method based on multi-period risk is used to evaluate the resilience of the power system to typhoons. The time period is set to 24 hours. According to relevant principles, the CENS value under CVaR should be minimized. V=VoLL1·Lsh1 e,k,t +VoLL2·Lsh2 e,k,t Among them, α∈[0,1] represents the risk level assumed by the decision maker; e indicates a failure scenario; k represents the busbar; t represents time; β is an auxiliary variable for formulating CVaR, ψ∈(0,1) is the confidence level, the worst expected CENS is (1-ω)×100%, Lsh1 e,k,t is the load shedding of level 1 load in bus k at time t and scenario e, Lsh2 e,k,t is the load shedding of level 2 load in bus k at time t and scenario e; S3.1.2, In addition to the general related constraints, the following constraints are also involved in the CVaR model; Among them, ω(e) is a continuous non-negative variable; β is an auxiliary variable, and its optimal value is equal to the CENS value under CVaR; S3.1.

3. The CENS value under CVaR is obtained through steps S3.1.1 and S3.1.2, and the system resilience is inversely proportional to it, so the index of system resilience snapshot evaluation is obtained.

7. The method for improving power system resilience considering extreme weather events as claimed in claim 1, characterized in that: The S4 comprises the following steps: S4.

1. In order to construct a power system resilience improvement model to obtain the best results, it is necessary to first determine the state of the line. This process is called optimal transmission switching (OTS). The present invention adopts an improved OTS model to improve the resilience of the power system. Since the OTS model is widely used, the specific solution process will not be repeated. Only the variables, objective functions and constraints are listed for reference: Variables: Consider the probability of extreme weather and cascading failures, see S1 for details; Objective function: The objective is to minimize the power outage chain and scale in the power system lines. The specific function is consistent with S3.1.1; Constraints: including power balance constraints, generator ramp rate constraints, minimum generator operating time and downtime limits, upper and lower limits of generator output, load reduction limits, voltage phase angle limits, absolute value of unit power flow on the line, power flow constraints on the line, dual-time dimension process constraints including extreme weather events and cascading failures, etc.; S4.

2. The above model is a mixed integer linear programming and can be solved by the CPLEX solver. The OTS optimization problem is implemented according to the above constraints on the optimization model. For any parameter selection, several sets of optimal solutions β can be obtained within the system operation time range, and the optimal solutions correspond to different resilience levels. Decision makers can choose the best configuration plan to improve resilience based on the system resilience performance generated by the implementation of the provided solution.

8. A power system resilience enhancement system considering extreme weather events, characterized in that: The improvement system includes: a model building module, a simplification and optimization module, an evaluation module, and a configuration module; The model building module is used to introduce extreme event-cascading failure dual time dimension process constraints based on regional power grid wind speed profile and power outage data, and build a probability model among extreme weather events-cascading failures-power system interruptions; The simplified optimization module is used to simulate the power system interruption process including wind speed and cascading failures using the Monte Carlo method and generate fault scenarios, and to simplify and optimize the power system interruption scenarios under the influence of extreme weather events using the K-means clustering algorithm; The evaluation module is used to evaluate the resilience of the power system using a CVAR index based on an expected value of an objective function in combination with the generated fault scenario; The configuration module is used to select the best configuration plan to improve resilience based on the obtained power system resilience level and the optimal solution obtained by the mixed integer linear programming corresponding to the wind speed-cascading failure-power system interruption probability model.

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