Electric power system risk assessment method and system influenced by extreme meteorology

Abstract

The invention provides a power system risk assessment method influenced by extreme meteorology, which comprises the following steps: acquiring statistical data of extreme meteorology of a place where a to-be-assessed power system is located, and analyzing to obtain extreme meteorological variables; establishing a probability distribution model of extreme meteorological variables, and calculating complete probability distribution of extreme meteorology; analyzing operation data of the to-be-evaluated power system to obtain a loss segmentation point; substituting the loss segmentation points into the complete probability distribution to obtain a risk function; and according to the risk function, evaluating the loss probability of the to-be-evaluated power system under the influence of the extreme weather. Therefore, according to the method, the statistical data of the extreme weather of the location of the to-be-evaluated power system is summarized, the loss expectation caused by the extreme weather is calculated, and the corresponding probability of different losses caused by the extreme weather is determined by combining the loss segmentation points, so that the reliability of evaluating the influence of the extreme weather on the power system is remarkably improved; therefore, related workers can take appropriate measures before and after disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for assessing the risks of power systems affected by extreme weather. Background Art

[0002] In recent years, global climate change and frequent extreme weather have caused nearly a thousand major power outages in power systems at home and abroad caused by natural disasters, such as the 2011 Fukushima earthquake and tsunami in Japan, the 2008 ice disaster in southern China, and the 2012 Typhoon Sandy. In 2023 and 2024, floods caused by rainfall in many places have caused serious damage to the power system, causing large-scale and long-term power outages, seriously affecting residents' lives and the power supply of loads. According to incomplete statistics, since 2002, 58% of major power outages and 87% of power outages affecting more than 50,000 households have been caused by extreme climate, causing huge economic losses.

[0003] As an important infrastructure related to national security and the lifeline of the national economy, the power system has been severely damaged by many extreme natural disasters that have occurred around the world in recent years, causing large-scale and long-term power outages, seriously affecting residents' lives and the power supply of loads. The reason is that the currently widely used reliability indicators, including the system average interruption duration (SAIDI), often do not take into account major power outages caused by extreme events.

[0004] Different types of disasters have different impact mechanisms on power systems, and changes in system performance are difficult to quantitatively characterize. Changes in renewable energy output and load energy consumption behavior under disaster conditions are difficult to predict. There are few studies on the impact of typical extreme climate events and various climate disasters on power systems.

[0005] In summary, how to improve the reliability of assessing the impact of extreme weather on power systems is a technical problem that technical personnel in this field currently need to solve. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a method and system for assessing the risk of power systems affected by extreme weather, which can significantly improve the reliability of assessing the impact of extreme weather on power systems. The specific scheme is as follows:

[0007] A method for risk assessment of power system affected by extreme weather conditions, comprising:

[0008] Obtaining statistical data of extreme weather at the location of the power system to be evaluated, and analyzing to obtain extreme weather variables, wherein the extreme weather includes rainstorms, floods, thunderstorms, and typhoons;

[0009] Establishing a probability distribution model of the extreme meteorological variables and calculating the complete probability distribution of the occurrence of the extreme meteorological variables;

[0010] Analyze the operation data of the power system to be evaluated to obtain a loss segmentation point;

[0011] Substituting the loss split point into the complete probability distribution to obtain a risk function;

[0012] The loss probability of the power system to be evaluated under the influence of the extreme weather is evaluated according to the risk function.

[0013] Preferably, calculating the complete probability distribution of the occurrence of the extreme weather comprises:

[0014] The probability space of the extreme weather is divided into m, and the F m is the variable of the occurrence of the mth extreme weather, and the probability is from large to small and has an inclusive relationship, namely F0, F1, ..., F m-1 、F m , forming a series of subsets with inclusion relations, whose relations are:

[0015]

[0016] Then the complete probability distribution of extreme weather is the product of the corresponding conditional probabilities of m kinds of extreme weather:

[0017]

[0018]

[0019] Among them, the P F is the complete probability distribution of m kinds of extreme weather, where P(F m ) is the probability of the occurrence of the mth extreme weather, where m is a positive integer greater than or equal to 0, and i is a positive integer greater than or equal to 0 and less than or equal to m.

[0020] Preferably, analyzing the operating data of the power system to be evaluated to obtain the loss segmentation point includes:

[0021] The routine maintenance data, disaster emergency data, and disaster repair data of the power system to be evaluated are summarized to obtain the loss segmentation point, wherein the routine maintenance data is the operating cost of the power system to be evaluated when it is not affected by the extreme weather, the disaster emergency data is the operating cost of the power system to be evaluated when the emergency plan is activated during the period when it is affected by the extreme weather, and the disaster repair data is the operating cost of the power system to be evaluated after it is affected by the extreme weather.

[0022] Preferably, if the extreme weather is rainstorm and flooding, the precipitation data and hydrological map of the location of the power system to be evaluated are obtained, and the flood points are identified based on the precipitation intensity, duration and frequency in the precipitation data in combination with the hydrological map. Based on the disaster-stricken failure losses, outage losses and maintenance losses at the flood points in the power system to be evaluated, the expected damage caused by rainstorm and flooding to the power system to be evaluated is calculated to obtain the loss segmentation point.

[0023] If the extreme weather is a thunderstorm, then the thunderstorm data of the location of the power system to be evaluated is obtained, and based on the thunderstorm data and the disaster failure losses, outage losses and maintenance losses after the thunderstorm in the power system to be evaluated, the thunderstorm disaster expectation of the power system to be evaluated is calculated to obtain the loss segmentation point.

[0024] If the extreme weather is a typhoon, the typhoon damage data of the location of the power system to be evaluated is obtained, the typhoon damage expectation of the power system to be evaluated is calculated, and the loss segmentation point is obtained.

[0025] Preferably, the number of the loss split points is greater than or equal to 1, wherein, when the number of the loss split points is greater than or equal to 2, the loss split points are substituted into the complete probability distribution to obtain the risk function,

[0026]

[0027] Among them, β1 and β2 are two adjacent segmentation points in the loss segmentation point, and 0<β1<

[0028] β2, R is the expectation that the loss caused by the extreme weather will be between β1 and β2.

[0029] Preferably, the loss split point includes a low / medium loss split point β L and the medium / high loss split point β H , then the loss probability of the power system to be evaluated under the influence of the extreme weather includes low loss / high probability risk loss probability, medium loss / medium probability risk loss probability and high loss / low probability risk loss probability, wherein,

[0030] The low loss / high probability risk loss probability P L The corresponding calculation formula is:

[0031]

[0032] The medium loss / medium probability risk loss probability P M The corresponding calculation formula is:

[0033]

[0034] The high loss / low probability risk loss probability P H The corresponding calculation formula is:

[0035]

[0036] Preferably, the risk assessment method for power systems affected by extreme weather also includes locating locations prone to failure during periods of extreme weather based on the low loss / high probability risk loss probability, the medium loss / medium probability risk loss probability and the high loss / low probability risk loss probability, in combination with the corresponding topology map of the power system to be assessed, thereby determining the priority of line repair for reconstructing the power supply network during a disaster.

[0037] Preferably, the risk assessment method for power systems affected by extreme weather also includes optimizing the line priority for post-disaster power restoration and reconstruction based on the low loss / high probability risk loss probability, the medium loss / medium probability risk loss probability and the high loss / low probability risk loss probability, in combination with the corresponding topology map of the power system to be assessed.

[0038] Preferably, when the risk assessment method is applied to a high-proportion renewable energy power system, it also includes analyzing the degree of correlation between the actual losses caused by the high-proportion renewable energy power system when the extreme weather actually occurs and the loss probability, and forming a risk assessment report summary based on the degree of correlation.

[0039] The present invention also discloses a risk assessment system for a power system affected by extreme weather, comprising:

[0040] An extreme meteorological variable acquisition unit is used to acquire statistical data of extreme meteorology at the location of the power system to be evaluated, and analyze and obtain extreme meteorological variables;

[0041] A probability distribution model calculation unit, used to establish a probability distribution model of the extreme weather variables and calculate the complete probability distribution of the occurrence of the extreme weather;

[0042] A loss split point acquisition unit, used for analyzing the operation data of the power system to be evaluated to obtain a loss split point;

[0043] A risk function calculation unit, used for substituting the loss split point into the complete probability distribution to obtain a risk function;

[0044] The loss probability assessment unit assesses the loss probability of the power system to be assessed under the influence of the extreme weather according to the risk function.

[0045] The present invention provides a method for assessing the risk of an electric power system affected by extreme weather, including: obtaining statistical data of extreme weather at the location of the electric power system to be assessed, and analyzing to obtain extreme weather variables; establishing a probability distribution model of the extreme weather variables, and calculating the complete probability distribution of the occurrence of the extreme weather; analyzing the operating data of the electric power system to be assessed, and obtaining a loss segmentation point; substituting the loss segmentation point into the complete probability distribution to obtain a risk function; and assessing the loss probability of the electric power system to be assessed under the influence of the extreme weather according to the risk function. It can be seen that in the present invention, by summarizing the statistical data of extreme weather at the location of the electric power system to be assessed, calculating the loss expectation caused by the occurrence of extreme weather, and combining the loss segmentation point to determine the corresponding probability of different losses caused by extreme climate, the reliability of assessing the impact of extreme weather on the electric power system is significantly improved, so that relevant personnel can take appropriate response measures before, during, and after disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the implementation scheme of the present invention or the technical scheme in the prior art, the drawings required for use in the implementation scheme or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only the implementation scheme of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0047] Figure 1 A schematic flow chart of a method for risk assessment of a power system affected by extreme weather provided by an embodiment of the present invention;

[0048] Figure 2 A simplified schematic diagram of four conditional probability distributions in a two-dimensional plane in a specific method for risk assessment of a power system affected by extreme weather provided by an embodiment of the present invention;

[0049] Figure 3 A specific risk assessment method for a power system affected by extreme weather provided by the embodiment of the present invention has a low / medium loss split point β L and the medium / high loss split point β H Schematic diagram of risk analysis of loss probability function;

[0050] Figure 4 A schematic diagram of the structure of a risk assessment system for a power system affected by extreme weather is provided for an implementation scheme of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0052] The embodiment of the present invention provides a method for risk assessment of power systems affected by extreme weather, which is applicable to various power systems, especially to power systems with a high proportion of renewable energy, such as Figure 1 As shown, including:

[0053] Step S1: Obtain statistical data of extreme weather in the location of the power system to be evaluated, and analyze to obtain extreme weather variables.

[0054] In practical applications, extreme weather includes heavy rain, floods, thunderstorms and typhoons.

[0055] The present invention provides a specific implementation scheme, which designs the rainstorm amount according to the urban rainstorm intensity formula to obtain the relationship between the urban rainstorm intensity and the return period and rainfall duration. The rainstorm type formula for the central my country region is selected to calculate the rainfall intensity data of each time step before and after the rain peak, which is used as the rainfall intensity time series under the return period scenario for modeling the subsequent rainstorm and flood disasters. Urban waterlogging is simulated according to the runoff generation and confluence model, and the net rainfall is calculated by using the runoff coefficient to deduct the surface infiltration loss from the rainfall for the runoff generation simulation process. The local equal volume method is used to complete the confluence simulation of the study area, and the spatial distribution of water accumulation in the study area is obtained by summarizing.

[0056] The rainfall intensity formula is:

[0057]

[0058] The formula for heavy rain type is:

[0059]

[0060]

[0061] Among them, q is the average rainfall intensity, P is the recurrence period, t is the rainfall duration, i is the instantaneous rainfall intensity (mm / min), t1 is the pre-peak time (min), t2 is the post-peak time (min), A, c, b, n are the parameters of the local rainstorm intensity formula, and r is the rain peak position coefficient, which is calculated and determined according to statistical methods and can be obtained from meteorological or address departments.

[0062] Step S2: Establish a probability distribution model for the above extreme weather variables and calculate the complete probability distribution of the above extreme weather.

[0063] Calculating the complete probability distribution of the above extreme weather events involves:

[0064] The probability space of the above extreme weather is divided into m parts. m is the variable of the occurrence of the mth extreme weather, and the probability is from large to small and has an inclusive relationship, namely F0, F1, ..., F m-1 、F m , forming a series of subsets with inclusion relations, whose relations are:

[0065]

[0066] The complete probability distribution of the above extreme weather is the product of the corresponding conditional probabilities of m kinds of extreme weather:

[0067]

[0068] Among them, the above P F is the complete probability distribution of m kinds of extreme weather. m ) is the probability of the occurrence of the mth extreme weather, the above m is a positive integer greater than or equal to 0, and the above i is a positive integer greater than or equal to 0 and less than or equal to m.

[0069] The present invention provides a specific implementation scheme, assuming that m=4, P F =P(F4)~10 -4 ,like Figure 2 As shown, F=F4, F3=P(F3|F2)~10 -1 , F2=P(F2|F1)~10 -1 , F1=P(F1)~10 -1 Through subset simulation, a small probability can be converted into the product of a larger conditional probability. The probability of occurrence corresponding to this series of conditional spaces continues to decrease, which means that the conditional probability space sampled later corresponds to the set of small probability and high loss events. Therefore, this method can accurately and efficiently capture the set of small probability and high loss events.

[0070] Step S3: Analyze the operating data of the power system to be evaluated to obtain the loss segmentation point.

[0071] Analyze the operating data of the power system to be evaluated, including summarizing and analyzing the routine maintenance data, disaster emergency data, and disaster repair data of the power system to be evaluated, and obtain the above loss segmentation points. Usually, the number of loss segmentation points is greater than or equal to 1. When the number of loss segmentation points is 1, it is divided into high loss and low loss. In practical applications, the number of loss segmentation points can be determined based on the power system budget and maintenance cost. The more loss segmentation points there are, the finer the loss division, and the more detailed the corresponding analysis.

[0072] The routine maintenance data is the operating cost of the power system to be evaluated when it is not affected by extreme weather. The routine maintenance data is the operating cost of the power system to be evaluated when it is not affected by extreme weather, including the operating costs incurred in routine operation guarantee and the operating costs of reinforcing vulnerable components and deploying emergency resources before extreme weather occurs.

[0073] The disaster emergency data is the operating cost of the emergency plan for the power system to be evaluated during the period of extreme weather. The disaster emergency data is the operating cost of the emergency plan for the power system to be evaluated during the period of extreme weather, including the cost of manpower and material resources, emergency control costs such as emergency load reduction of equipment and active island operation, and the cost of shutdown measures such as active shutdown of equipment and abandonment of lines and poles.

[0074] The disaster-affected maintenance data is the operating cost of the above-mentioned power system to be evaluated after being affected by the above-mentioned extreme weather. The disaster-affected maintenance data is the operating cost of the above-mentioned power system to be evaluated after being affected by the above-mentioned extreme weather, including the corresponding operating costs of emergency repair and power restoration.

[0075] In a specific implementation scheme, if the above-mentioned extreme weather is rainstorm and floods, the precipitation data and hydrological map of the location of the above-mentioned power system to be evaluated are obtained, and the flood points are identified based on the precipitation intensity, duration and frequency in the above-mentioned precipitation data in combination with the above-mentioned hydrological map. Based on the disaster-stricken failure losses, shutdown losses and maintenance losses of the above-mentioned flood points in the above-mentioned power system to be evaluated, the expected disaster impact of rainstorm and floods on the above-mentioned power system to be evaluated is calculated to obtain the above-mentioned loss segmentation point.

[0076] In practical applications, the probability and degree of the occurrence of the above-mentioned flood points under the influence of different rainstorms and floods require a topological analysis of the power system. The urban waterlogging model can be used to match the spatial position of the power system and determine the depth of water accumulation in the areas where the equipment and components are located, so as to more accurately judge the loss segmentation point.

[0077] In practical applications, taking into account the fault chain problem and line fault repair time existing in the actual power system, that is, the line fault is considered to have a timing characteristic, and the network is repartitioned according to the locations where the line fault occurs at different stages corresponding to the specific circuit topology diagram, so as to more accurately determine the loss segmentation point.

[0078] In a specific implementation scheme, if the above-mentioned extreme weather is a thunderstorm, then the thunderstorm data occurring in the location of the above-mentioned power system to be evaluated is obtained, and based on the above-mentioned thunderstorm data and the disaster failure losses, outage losses and maintenance losses after the thunderstorm occurred in the above-mentioned power system to be evaluated, the thunderstorm disaster expectation of the above-mentioned power system to be evaluated is calculated to obtain the above-mentioned loss segmentation point.

[0079] In actual applications, the power system is usually equipped with grounding systems, lightning rod devices, lightning protection grounding protection and other protective devices in accordance with the technical specifications of the field, which can generally avoid equipment damage caused by direct lightning strikes. In addition, the use of electrostatic rings, lightning arresters, capacitors and other protective devices on key equipment such as transformers and generators can also limit the amplitude and steepness of lightning waves. However, the electromagnetic induction generated by thunderstorms will induce high voltages near power facilities, causing damage or failure of equipment. Although this impact may not be immediately apparent, it will gradually aggravate the damage or failure of equipment. Therefore, the technical solution disclosed in the present invention comprehensively calculates the impact of thunderstorms by summarizing the above-mentioned evaluation of the power system's damage, failure losses, downtime losses and maintenance losses over a period of time after a thunderstorm.

[0080] It is particularly important to note that the period of time after a thunderstorm needs to be determined based on thunderstorm data at the location of the power system to be evaluated and the power grid equipment of the power system to be evaluated itself.

[0081] In a specific implementation scheme, if the extreme weather is a typhoon, the typhoon damage data of the location of the power system to be evaluated is obtained, the typhoon damage expectation of the power system to be evaluated is calculated, and the loss segmentation point is obtained.

[0082] It should be noted that typhoons usually include strong winds and heavy rainfall, among which the heavy rainfall scenario can be calculated by the extreme weather conditions of rainstorms and floods mentioned above. Here, we mainly discuss and analyze the impact of strong winds on the power system to be evaluated.

[0083] In practical applications, in terms of the disaster losses caused by typhoons, the focus is on extreme weather and the structure of the disaster-bearing body itself, such as analyzing extreme strong winds, building materials, and the vulnerability of structures; in typhoon-related fields, whether it is using detection aircraft, satellite images and remote sensing data (mainly visible light, infrared, and water vapor channel data, supplemented by SSM / I, TRMM, QuikSCAT and other data) to establish an assessment model, or meteorological researchers using local tropical cyclone wind and rain data, disaster data and other data, using typical correlation analysis, historical disaster inversion and other methods to establish an assessment model, all of which are quite mature, and with the development of AI big data, the accuracy of typhoon simulation evaluation and typhoon path calculation has been further improved, which can provide effective and practical guidance and reference for predicting the severity of typhoon disasters. Therefore, when the present invention is aimed at extreme weather for typhoons, the calculation of typhoon disaster expectations can be calculated through typhoon data and forecasts from third-party meteorological departments, which will not be repeated here.

[0084] Step S4: Substitute the above loss split point into the above complete probability distribution to obtain the risk function.

[0085] When the number of loss split points is greater than or equal to 2, substitute the above loss split points into the above complete probability distribution to obtain the above risk function,

[0086]

[0087] Among them, β1 and β2 are two adjacent division points among the above loss division points, and 0<β1<β2, and R is the expectation that the loss caused by the above extreme weather is between β1 and β2.

[0088] The present invention provides a specific implementation scheme, including two loss split points, namely, low / medium loss split points β L and the medium / high loss split point β H , and the above risk function is

[0089]

[0090] Step S5: Based on the risk function, the loss probability of the power system to be evaluated under the influence of the extreme weather is evaluated.

[0091] Take the technical implementation scheme of the above two loss segmentation points as an example. Figure 3 As shown, the above-mentioned loss probability specifically includes low loss / high probability risk loss probability, medium loss / medium probability risk loss probability and high loss / low probability risk loss probability, among which,

[0092] The above low loss / high probability risk loss probability P L The corresponding calculation formula is:

[0093]

[0094] The above medium loss / medium probability risk loss probability P M The corresponding calculation formula is:

[0095]

[0096] The above high loss / low probability risk loss probability P H The corresponding calculation formula is:

[0097]

[0098] In practical applications, by calculating different risk losses, decision makers can weigh the conditional expected risks of high system losses, medium losses and low losses, so as to have a comprehensive understanding of the risk status of the power system, enabling system risk decision makers to better analyze the actual risk level of the system and make final decisions based on their own risk preferences.

[0099] In order to improve the application effect of power system assessment, the present invention also provides a technical implementation plan, according to the above-mentioned low loss / high probability risk loss probability, the above-mentioned medium loss / medium probability risk loss probability and the above-mentioned high loss / low probability risk loss probability, combined with the above-mentioned corresponding topology map of the power system to be assessed, the locations prone to failures during the above-mentioned extreme weather periods are located, thereby determining the line repair priority for reconstructing the power supply network during the disaster.

[0100] In practical applications, during extreme weather events, in a high-proportion renewable energy power system, in all distribution lines including interconnecting branches, as long as the power system topology is connected, a radial high-proportion renewable energy power system reconstruction solution can be found. Therefore, in the simulation of load transfer during a disaster, as long as the graph composed of all non-faulty lines in the high-proportion renewable energy power system is connected, it is considered that the high-proportion renewable energy power system can be reconstructed and operated, and the load loss nodes in the system can be found through breadth-first traversal. If a certain topology graph cannot be reconstructed and operated, it means that the necessary lines for reconstructing the power supply network are interrupted, thereby determining the priority of line repair for reconstructing the power supply network during a disaster.

[0101] After a fault occurs, usually, due to the limited power supply capacity of a high proportion of renewable energy power system, the system needs to disconnect some branches, making nodes with lower importance become power-off load nodes. In order to improve the power supply capacity and avoid forming a ring network, the energy storage batteries and gas turbines inside the power system are connected to the grid to form an island operation. At this time, if the power of individual important nodes is tight, the priority of the node will be increased to ensure that emergency repair personnel can repair it in time.

[0102] In order to further improve the application effect of power system assessment, the present invention also provides a technical implementation plan, which also includes optimizing the line priority of post-disaster power restoration and reconstruction based on the above-mentioned low loss / high probability risk loss probability, the above-mentioned medium loss / medium probability risk loss probability and the above-mentioned high loss / low probability risk loss probability, combined with the corresponding topology map of the above-mentioned power system to be assessed.

[0103] In the post-disaster optimization dispatch, the repair points with the highest priority and the repair points with the highest probability of failure are determined according to the loss probability and the corresponding topology of the power system to be evaluated. At the same time, a greedy algorithm that ensures single-step optimality is adopted. When a repair team completes the repair of a component and becomes idle, the damaged lines that have not yet been scheduled for repair will be ranked in importance, and the repair team will be assigned the most important repair line at the moment. The importance of the unrepaired damaged lines is ranked according to the amount of load recovery that can be brought about after the line is repaired. The greater the amount of load recovery that can be brought about after the repair of a suspended line, the more important the damaged line is at the current moment. Among them, the amount of load recovery that can be brought about after the line is repaired can still be calculated through breadth-first traversal.

[0104] In order to further improve the risk assessment method disclosed in the present invention and improve the decision-making accuracy of risk decision makers when dealing with the next extreme weather disaster, the technical solution of the present invention also discloses a specific implementation plan, which is applied to a high-proportion renewable energy power system or other power system, and also includes analyzing the correlation between the actual losses caused by the above-mentioned extreme weather in the above-mentioned high-proportion renewable energy power system and the above-mentioned loss probability, and forming a risk assessment report summary based on the above-mentioned correlation degree.

[0105] The present invention also discloses a risk assessment system for power systems affected by extreme weather, such as Figure 4 As shown, including:

[0106] The extreme meteorological variable acquisition unit 1 is used to acquire the statistical data of extreme meteorology at the location of the power system to be evaluated, and analyze and obtain extreme meteorological variables;

[0107] The probability distribution model calculation unit 2 is used to establish the probability distribution model of the above extreme weather variables and calculate the complete probability distribution of the above extreme weather;

[0108] A loss split point acquisition unit 3 is used to analyze the operation data of the power system to be evaluated to obtain a loss split point;

[0109] The risk function calculation unit 4 is used to substitute the above loss split point into the above complete probability distribution to obtain the risk function;

[0110] The loss probability assessment unit 5 assesses the loss probability of the power system to be assessed under the influence of the extreme weather according to the risk function.

[0111] The risk assessment system for power systems affected by extreme weather has the same technical effect as any of the above-mentioned risk assessment methods for power systems affected by extreme weather, and will not be repeated here.

[0112] Finally, 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 presence of other identical elements in the process, method, article or device including the elements.

[0113] The above is a detailed introduction to a method and system for assessing the risk of power systems affected by extreme weather provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above implementation plan is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for risk assessment of power systems affected by extreme weather, characterized in that: include: Obtaining statistical data of extreme weather at the location of the power system to be evaluated, and analyzing to obtain extreme weather variables, wherein the extreme weather includes rainstorms, floods, thunderstorms, and typhoons; Establishing a probability distribution model of the extreme meteorological variables and calculating the complete probability distribution of the occurrence of the extreme meteorological variables; Analyze the operation data of the power system to be evaluated to obtain a loss segmentation point; Substituting the loss split point into the complete probability distribution to obtain a risk function; The loss probability of the power system to be evaluated under the influence of the extreme weather is evaluated according to the risk function.

2. The method for risk assessment of power system affected by extreme weather according to claim 1, characterized in that: Calculating the complete probability distribution of the occurrence of the extreme weather in question involves: The probability space of the extreme weather is divided into m, and the F m is the variable of the occurrence of the mth extreme weather, and the probability is from large to small and has an inclusive relationship, namely F0, F1, ..., F m-1 、F m , forming a series of subsets with inclusion relations, whose relations are: Then the complete probability distribution of extreme weather is the product of the corresponding conditional probabilities of m kinds of extreme weather: Among them, the P F is the complete probability distribution of m kinds of extreme weather, where P(F m ) is the probability of the occurrence of the mth extreme weather, where m is a positive integer greater than or equal to 0, and i is a positive integer greater than or equal to 0 and less than or equal to m.

3. The method for risk assessment of power system affected by extreme weather according to claim 2, characterized in that: Analyzing the operating data of the power system to be evaluated to obtain the loss segmentation point includes: The routine maintenance data, disaster emergency data, and disaster repair data of the power system to be evaluated are summarized to obtain the loss segmentation point, wherein the routine maintenance data is the operating cost of the power system to be evaluated when it is not affected by the extreme weather, the disaster emergency data is the operating cost of the power system to be evaluated when the emergency plan is activated during the period when it is affected by the extreme weather, and the disaster repair data is the operating cost of the power system to be evaluated after it is affected by the extreme weather.

4. The method for risk assessment of power system affected by extreme weather according to claim 3, characterized in that: If the extreme weather is rainstorm and flood, then the precipitation data and hydrological map of the location of the power system to be evaluated are obtained, and the flood points are identified based on the precipitation intensity, duration and frequency in the precipitation data in combination with the hydrological map. Based on the disaster-stricken failure losses, shutdown losses and maintenance losses at the flood points in the power system to be evaluated, the expected damage caused by rainstorm and floods to the power system to be evaluated is calculated to obtain the loss segmentation point. If the extreme weather is a thunderstorm, then the thunderstorm data of the location of the power system to be evaluated is obtained, and based on the thunderstorm data and the disaster failure losses, outage losses and maintenance losses after the thunderstorm in the power system to be evaluated, the thunderstorm disaster expectation of the power system to be evaluated is calculated to obtain the loss segmentation point. If the extreme weather is a typhoon, the typhoon damage data of the location of the power system to be evaluated is obtained, the typhoon damage expectation of the power system to be evaluated is calculated, and the loss segmentation point is obtained.

5. The method for risk assessment of power system affected by extreme weather according to claim 4, characterized in that: The number of the loss split points is greater than or equal to 1, wherein when the number of the loss split points is greater than or equal to 2, the loss split points are substituted into the complete probability distribution to obtain the risk function, Among them, β1 and β2 are two adjacent division points among the loss division points, and 0<β1<β2, and R is the expectation that the loss caused by the extreme weather is between β1 and β2.

6. The method for risk assessment of power system affected by extreme weather according to claim 5, characterized in that: The loss split point includes a low / medium loss split point β L and the medium / high loss split point β H , then the loss probability of the power system to be evaluated under the influence of the extreme weather includes low loss / high probability risk loss probability, medium loss / medium probability risk loss probability and high loss / low probability risk loss probability, wherein, The low loss / high probability risk loss probability P L The corresponding calculation formula is: The medium loss / medium probability risk loss probability P M The corresponding calculation formula is: The high loss / low probability risk loss probability P H The corresponding calculation formula is:

7. The method for risk assessment of power system affected by extreme weather according to claim 6, characterized in that: It also includes locating locations that are prone to failure during the extreme weather period based on the low loss / high probability risk loss probability, the medium loss / medium probability risk loss probability and the high loss / low probability risk loss probability, in combination with the corresponding topology map of the power system to be evaluated, so as to determine the priority of line repair for reconstructing the power supply network during the disaster.

8. The method for risk assessment of power system affected by extreme weather according to claim 7, characterized in that: It also includes optimizing the line priority of post-disaster power restoration and reconstruction based on the low loss / high probability risk loss probability, the medium loss / medium probability risk loss probability and the high loss / low probability risk loss probability, in combination with the corresponding topology diagram of the power system to be evaluated.

9. The method for risk assessment of power system affected by extreme weather according to claim 8, characterized in that: When the risk assessment method is applied to a high-proportion renewable energy power system, it also includes analyzing the degree of correlation between the actual losses caused by the high-proportion renewable energy power system when the extreme weather actually occurs and the loss probability, and forming a risk assessment report summary based on the degree of correlation.

10. The present invention also discloses a risk assessment system for power systems affected by extreme weather, characterized in that: include: An extreme meteorological variable acquisition unit is used to acquire statistical data of extreme meteorology at the location of the power system to be evaluated, and analyze and obtain extreme meteorological variables; A probability distribution model calculation unit, used to establish a probability distribution model of the extreme weather variables and calculate the complete probability distribution of the occurrence of the extreme weather; A loss split point acquisition unit, used for analyzing the operation data of the power system to be evaluated to obtain a loss split point; A risk function calculation unit, used for substituting the loss split point into the complete probability distribution to obtain a risk function; The loss probability assessment unit assesses the loss probability of the power system to be assessed under the influence of the extreme weather according to the risk function.

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