Power system risk identification and evaluation method and system considering source load uncertainty

Abstract

The invention discloses a power system risk identification and evaluation method and system considering source load uncertainty, and the method comprises the steps: constructing a plurality of risk identification and evaluation indexes considering the source load uncertainty based on the scene probability distribution of a power system, carrying out the fuzzification of the plurality of risk identification and evaluation indexes through employing a self-adaptive bee colony algorithm, and obtaining the risk identification and evaluation result of the power system. Obtaining fuzzification threshold values, corresponding to different operation risk levels, of the risk identification evaluation indexes; and obtaining a plurality of risk identification evaluation indexes corresponding to the current operation data of the power system and corresponding operation risk levels, and comprehensively evaluating the operation risk level corresponding to each risk identification evaluation index to obtain a probabilistic power system risk identification evaluation scheme. According to the method, the plurality of risk identification and evaluation indexes are fuzzified, and the fuzzification threshold values of the risk identification and evaluation indexes corresponding to different operation risk levels are obtained, so that the effectiveness of the risk identification and evaluation indexes of the power system is improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system risk assessment, and in particular relates to a power system risk identification and assessment method and system taking into account source and load uncertainty. Background Art

[0002] With the transformation of the global energy structure, the widespread application of new energy has brought unprecedented challenges to the stable operation of the power system. In particular, the output of renewable energy such as wind and solar energy is highly volatile and uncertain, which makes the source-load balance of the power system more complicated. At the same time, the uncertainty on the load side, such as the increase in emerging load forms such as electric vehicle charging, distributed generation and demand response, has further aggravated the uncertainty of the power system. In this context, in order to ensure the safe, reliable and economical operation of the power system, it is of great theoretical significance and practical application value to carry out risk identification and assessment of the power system under source-load uncertainty. It not only helps power system planners and operators to discover potential risks in a timely manner, but also provides a scientific basis for formulating corresponding risk management measures.

[0003] Although a variety of power system risk assessment methods have been proposed and applied, such as probability theory methods and fuzzy theory methods, these methods have certain limitations in dealing with source and load uncertainty. Traditional probability theory methods require accurate probability distribution information, but in actual power systems, it is difficult to obtain accurate probability distribution due to the scarcity of data and the complex system environment. In addition, although fuzzy theory methods can handle imprecise information, their expression ability and accuracy are still limited when faced with complex systems and multiple sources of uncertainty. These methods often cannot fully reflect the true risk status of the power system under the influence of source and load uncertainty, resulting in doubts about the reliability and effectiveness of the assessment results. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a method and system for identifying and evaluating power system risks taking into account source and load uncertainty.

[0005] The technical solution of the present invention is as follows:

[0006] A power system risk identification and assessment method considering source and load uncertainty includes:

[0007] Based on the historical operation data of the power system, the scenario probability distribution of the power system is obtained, and several risk identification and evaluation indicators considering the uncertainty of source and load are constructed;

[0008] Adopting an adaptive bee colony algorithm to fuzzify the risk identification and evaluation indicators, and obtaining fuzzy thresholds of the risk identification and evaluation indicators corresponding to different operation risk levels;

[0009] Several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system are obtained. According to the DS evidence fusion framework, the operation risk level corresponding to each risk identification and assessment indicator is comprehensively evaluated to obtain a probabilistic power system risk identification and assessment scheme.

[0010] Furthermore, the specific method for obtaining the scenario probability distribution of the power system based on the historical operation data of the power system includes:

[0011] Based on the historical operation data of the power system, several typical scenarios are obtained, and the scenario probability distribution function of the power system is obtained as follows:

[0012]

[0013] Where F(x) is the scenario probability distribution function corresponding to the power system characteristic variable x; f(x) is the probability distribution function of the power system characteristic variable x in the i-th typical scenario scene i The corresponding probability distribution function, the power system characteristic variable x includes the node voltage V, the flow upper limit threshold P max ; x1, x2 are the upper and lower limits of the cumulative probability distribution interval respectively.

[0014] Furthermore, the several risk identification and evaluation indicators that consider source-load uncertainty include a node voltage exceeding an upper limit risk indicator, a node voltage exceeding a lower limit risk indicator, and a branch power flow exceeding a limit risk indicator.

[0015] Furthermore, the node voltage exceeds the upper limit risk index:

[0016]

[0017] Where V is the node voltage; R i max is the maximum value of the node voltage exceeding the upper limit risk of node i; V i,max is the voltage corresponding to F(V)=1; F(V) is the scenario probability distribution function corresponding to the node voltage V;

[0018] The node voltage lower limit risk index can be expressed as:

[0019]

[0020] in, is the risk of the node voltage of node i exceeding the lower limit; V i,min is the minimum possible value of the voltage at node i;

[0021] The branch power flow over-limit risk index can be expressed as

[0022]

[0023] Among them, R l is the risk of branch l current exceeding the limit; P max is the upper threshold of the power flow; F(P max ) is the upper threshold value of the power flow P max The corresponding scene probability distribution function; P Lmax is the maximum value that may occur in the branch flow;

[0024] Furthermore, the specific steps of using the adaptive bee colony algorithm to fuzzify the risk identification and evaluation indicators to obtain fuzzy thresholds of each risk identification and evaluation indicator corresponding to different operation risk levels include:

[0025] S201, construct the fuzzy set X of identification evaluation indicators u ={x1,x2,x3,x4,x5}; where X u is the fuzzy set of identification and evaluation indicators; x1, x2, x3, x4, x5 are the fuzzy thresholds of power system risk assessment indicators, which are used to discretely evaluate the operation risk of the power system; the operation risk levels corresponding to x1, x2, x3, x4, x5 are {completely safe, basically safe, with certain risks, with great risks, very dangerous};

[0026] S202, optimizing the fuzzy threshold of the power system risk assessment indicator based on the adaptive bee colony algorithm.

[0027] Furthermore, the specific method for optimizing the fuzzy threshold of the power system risk assessment index based on the adaptive bee colony algorithm includes:

[0028] S20201, initializing the parameters of the adaptive bee colony algorithm, including: the number of bees in the adaptive bee colony algorithm, the maximum number of times the bees search for nectar sources, and the initial nectar source location; wherein the initial nectar source location is the fuzzy thresholds x1, x2, x3, x4, x5 of the power system risk assessment indicator to be optimized;

[0029] S20202, based on the adaptive bee colony algorithm, the honey content at the honey source location is obtained. The honey content at the honey source location is the evaluation effect of the fuzzy threshold of the current power system risk assessment index on the power system operation risk, specifically:

[0030]

[0031] Among them, F b The effect of assessing the operation risk of the power system; N index The number of risk assessment indicators for power systems; is the assessment value of the power system risk corresponding to the i-th assessment indicator; is the actual value of the power system risk corresponding to the i-th evaluation indicator;

[0032] S20203, searching for a new honey source location in the neighborhood of the current honey source location, thereby obtaining a new fuzzy threshold set of power system risk assessment indicators, specifically:

[0033]

[0034] in, is the location of the neighborhood honey source; u(t) is the adaptive adjustment coefficient of the adaptive bee colony algorithm; k and j are the honey source locations different from i, corresponding to the fuzzy threshold set of power system risk assessment indicators, the value range of i, j, k is [1, N], and N is the number of bees; is the current honey source location, corresponding to the fuzzy threshold set of current power system risk assessment indicators; are two arbitrary nectar source locations; The location of the nectar source with the highest nectar content found by the current adaptive bee colony algorithm;

[0035] S20204, determine whether the number of times the adaptive bee swarm algorithm searches for the nectar source has reached the maximum number; if reached, output the currently selected nectar source location, that is, the current power system risk assessment indicator fuzzy threshold set; if not reached, return to step S20202.

[0036] Furthermore, the specific steps of comprehensively evaluating the operation risk level corresponding to each risk identification and evaluation indicator according to the DS evidence fusion framework to obtain a probabilistic power system risk identification and evaluation scheme include:

[0037] S301, define the evidence of the DS evidence fusion framework, that is, define the i-th fuzzy evaluation index as the power system risk identification and evaluation evidence s i , and define the jth fuzzy evaluation index as the power system risk identification and evaluation evidence s j ;

[0038] S302, obtaining characteristic distances between power system risk identification and assessment evidences, specifically:

[0039]

[0040] Among them, F ds (·,·) is the characteristic distance calculation function between the evidences for power system risk identification and assessment;

[0041] S303, judging the similarity between the evidences based on the characteristic distance between the power system risk identification and assessment evidences, specifically:

[0042]

[0043] in, A similarity calculation function between evidences for power system risk identification assessment;

[0044] S304, based on the similarity between the power system risk identification and assessment evidence, the support level of evidence i is determined, specifically:

[0045]

[0046] in, is the degree of support for evidence i; n is the number of evidences;

[0047] S305: Based on the support level of evidence i, the assessment weight of evidence i on the power system risk is determined, specifically:

[0048]

[0049] Among them, ξ i Calculate function for evaluation weights;

[0050] S306, based on the evidence-based assessment weights for power system risks, obtain a comprehensive assessment of the operational risk levels corresponding to each risk identification and assessment indicator, and obtain a probabilistic power system risk identification and assessment solution.

[0051] A power system risk identification and assessment system considering source and load uncertainty, comprising an indicator construction module, a fuzzification module, and a comprehensive assessment module;

[0052] The indicator construction module is used to obtain the scenario probability distribution of the power system based on the historical operation data of the power system, and to construct a number of risk identification and evaluation indicators that take into account the uncertainty of source and load;

[0053] The fuzzification module is used to fuzzify the risk identification and evaluation indicators using an adaptive bee colony algorithm to obtain fuzzification threshold ranges of the risk identification and evaluation indicators corresponding to different operation risk levels;

[0054] The comprehensive assessment module is used to obtain several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system, and comprehensively evaluate the operation risk level corresponding to each risk identification and assessment indicator according to the DS evidence fusion framework to obtain a probabilistic power system risk identification and assessment plan.

[0055] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0056] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention proposes a method and system for risk identification and assessment of an electric power system taking into account source-load uncertainty. The method constructs a number of risk identification and assessment indicators taking into account source-load uncertainty based on the scenario probability distribution of the electric power system. These risk identification and assessment indicators can effectively incorporate source-load uncertainty characteristics into the risk identification and assessment of the electric power system, thereby obtaining an accurate and reliable assessment scheme.

[0059] The method of the present invention utilizes an adaptive bee colony algorithm to fuzzify the risk identification and evaluation indicators, and obtains fuzzy thresholds of the risk identification and evaluation indicators corresponding to different operating risk levels, thereby improving the effectiveness of the risk identification and evaluation indicators of the power system.

[0060] The method of the present invention comprehensively evaluates the operating risk level corresponding to each risk identification and evaluation indicator according to the DS evidence fusion framework, and obtains a probabilistic power system risk identification and evaluation scheme. The evaluation scheme combines the source and load uncertainty characteristics of the power system, and comprehensively processes the power system risk identification and evaluation indicators, thereby realizing an effective evaluation of the power system operation risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a power system risk identification and assessment method considering source and load uncertainty in an embodiment;

[0062] Figure 2 It is a traditional bee colony algorithm iterative convergence diagram in the embodiment;

[0063] Figure 3 It is an iterative convergence diagram of the adaptive bee colony algorithm in the embodiment;

[0064] Figure 4 Schematic diagram of power system risk identification effect in different scenarios in the embodiment. DETAILED DESCRIPTION

[0065] Embodiment 1:

[0066] The present invention provides a power system risk identification and assessment method considering source and load uncertainty, such as Figure 1 As shown, the specific steps are as follows:

[0067] Based on the historical operation data of the power system, the scenario probability distribution of the power system is obtained, and several risk identification and evaluation indicators considering the uncertainty of source and load are constructed;

[0068] Adopting adaptive bee colony algorithm to fuzzify several risk identification and evaluation indicators, and obtain the fuzzy thresholds of each risk identification and evaluation indicator corresponding to different operation risk levels;

[0069] Several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system are obtained. According to the DS evidence fusion framework, the operation risk level corresponding to each risk identification and assessment indicator is comprehensively evaluated to obtain a probabilistic power system risk identification and assessment scheme.

[0070] Embodiment 2:

[0071] This example is further designed on the basis of the first embodiment in that, in this example, the specific method for obtaining the scenario probability distribution of the power system according to the historical operation data of the power system includes:

[0072] Based on the historical operation data of the power system, several typical scenarios are obtained, and the scenario probability distribution function of the power system is obtained as follows:

[0073]

[0074] Where F(x) is the scenario probability distribution function corresponding to the power system characteristic variable x; f(x) is the probability distribution function of the power system characteristic variable x in the i-th typical scenario scene i The corresponding probability distribution function; x1, x2 are the upper and lower limits of the cumulative probability distribution interval. The power system characteristic variable x includes the node voltage V, the flow upper limit threshold P max The typical scenario can be obtained by clustering the historical operation data of the power system, and the clustering can adopt the traditional k-means clustering algorithm.

[0075] Embodiment three:

[0076] This example is further designed on the basis of the first embodiment in that several risk identification and evaluation indicators that consider source-load uncertainty in this example include a node voltage exceeding an upper limit risk indicator, a node voltage exceeding a lower limit risk indicator, and a branch current exceeding a limit risk indicator.

[0077] Embodiment 4:

[0078] This example is further designed on the basis of the first embodiment in that the node voltage exceeds the upper limit risk index in this example is

[0079]

[0080] Where V is the node voltage; is the maximum value of the node voltage exceeding the upper limit risk of node i; V i,maxis the voltage corresponding to F(V)=1; F(V) is the scenario probability distribution function corresponding to the node voltage V;

[0081] The node voltage lower limit risk index can be expressed as

[0082]

[0083] in, is the risk of the node voltage of node i exceeding the lower limit; V i,min is the minimum possible value of the voltage at node i;

[0084] The branch flow over-limit risk index can be expressed as

[0085]

[0086] Among them, R l is the risk of branch l current exceeding the limit; P max is the upper threshold of the power flow; F(P max ) is the upper threshold value of the power flow P max The corresponding scene probability distribution function; P Lmax is the maximum value that may occur in the branch flow;

[0087] Embodiment five:

[0088] This example is further designed on the basis of the first embodiment in that an adaptive bee colony algorithm is used in this example to fuzzify a number of risk identification and evaluation indicators. The specific steps of obtaining fuzzy thresholds of each risk identification and evaluation indicator corresponding to different operation risk levels include:

[0089] S201, construct the fuzzy set X of identification evaluation indicators suitable for DS evidence fusion framework u ={x1,x2,x3,x4,x5}; where X u is the fuzzy set of identification and evaluation indicators; x1, x2, x3, x4, x5 are the fuzzy thresholds of power system risk assessment indicators, which are used to discretely evaluate the operation risk of the power system; the operation risk levels corresponding to x1, x2, x3, x4, x5 are {completely safe, basically safe, with certain risks, with great risks, very dangerous};

[0090] S202, optimizing the fuzzy threshold of the power system risk assessment indicator based on the adaptive bee colony algorithm.

[0091] The following example illustrates the relationship between the operation risk level and the fuzzy threshold. In this example, the fuzzy thresholds are 0.04, 0.06, 0.12, 0.18, and 0.2, respectively. The corresponding operation risk levels are {completely safe, basically safe, with certain risks, with great risks, and very dangerous}. The above correspondence shows that:

[0092] If the calculated value of the identification evaluation index falls within the interval [0-0.04), the corresponding operation risk level is completely safe;

[0093] If the calculated value of the identification evaluation index falls within the interval [0.04-0.06), the corresponding operation risk level is basically safe;

[0094] If the calculated value of the identification and evaluation index falls within the interval [0.06-0.12), the corresponding operation risk level is that there is a certain risk;

[0095] If the calculated value of the identification and evaluation index falls within the interval [0.12-0.18), the corresponding operation risk level is a high risk;

[0096] If the calculated value of the identification and evaluation index falls within the interval [0.18-0.2], the corresponding operation risk level is very dangerous.

[0097] Embodiment six:

[0098] This example is further designed on the basis of the fifth embodiment in that the specific method for optimizing the fuzzy threshold of the power system risk assessment index based on the adaptive bee colony algorithm in this example includes:

[0099] S20201, initializing the parameters of the adaptive bee colony algorithm, including: the number of bees in the adaptive bee colony algorithm, the maximum number of times the bees search for nectar sources, and the initial nectar source location; wherein the initial nectar source location is the fuzzy thresholds x1, x2, x3, x4, x5 of the power system risk assessment indicator to be optimized;

[0100] S20202, based on the adaptive bee colony algorithm, the honey content at the honey source location is obtained. The honey content at the honey source location is the evaluation effect of the fuzzy threshold of the current power system risk assessment index on the power system operation risk, specifically:

[0101]

[0102] Among them, F b The effect of assessing the operation risk of the power system; N index The number of risk assessment indicators for power systems; is the assessment value of the power system risk corresponding to the i-th assessment indicator; is the actual value of the power system risk corresponding to the i-th evaluation index; |·| represents the absolute value operation;

[0103] S20203, searching for a new honey source location in the neighborhood of the current honey source location, thereby obtaining a new fuzzy threshold set of power system risk assessment indicators, specifically:

[0104]

[0105] in, is the location of the neighborhood honey source; u(t) is the adaptive adjustment coefficient of the adaptive bee colony algorithm; k and j are the honey source locations different from i, corresponding to the fuzzy threshold set of power system risk assessment indicators, the value range of i, j, k is [1, N], and N is the number of bees; is the current honey source location, corresponding to the fuzzy threshold set of current power system risk assessment indicators; are two arbitrary nectar source locations; The location of the nectar source with the highest nectar content found by the current adaptive bee colony algorithm;

[0106] S20204, determine whether the number of times the adaptive bee swarm algorithm searches for the nectar source has reached the maximum number; if reached, output the currently selected nectar source location, that is, the current power system risk assessment indicator fuzzy threshold set; if not reached, return to step S20202.

[0107] Embodiment seven:

[0108] This example is further designed on the basis of the first embodiment. In this example, according to the DS evidence fusion framework, the operation risk level corresponding to each risk identification and evaluation indicator is comprehensively evaluated to obtain a probabilistic power system risk identification and evaluation scheme. The specific steps include:

[0109] S301, define the evidence of the DS evidence fusion framework, that is, define the i-th fuzzy evaluation index as the power system risk identification and evaluation evidence s i , and define the jth fuzzy evaluation index as the power system risk identification and evaluation evidence s j ;

[0110] S302, obtaining characteristic distances between power system risk identification and assessment evidences, specifically:

[0111]

[0112] Among them, F ds (·,·) is the characteristic distance calculation function between the evidences of power system risk identification and assessment; ||·|| represents the vector length operation; <·,·> represents the product operation between vectors.

[0113] S303, judging the similarity between the evidences based on the characteristic distance between the power system risk identification and assessment evidences, specifically:

[0114]

[0115] in, A similarity calculation function between evidences for power system risk identification assessment;

[0116] S304, based on the similarity between the power system risk identification and assessment evidence, the support level of evidence i is determined, specifically:

[0117]

[0118] in, is the degree of support for evidence i; n is the number of evidences;

[0119] S305: Based on the support level of evidence i, the assessment weight of evidence i on the power system risk is determined, specifically:

[0120]

[0121] Among them, ξ i Calculate function for evaluation weights;

[0122] S306, based on the evidence-based assessment weights for power system risks, obtain a comprehensive assessment of the operational risk levels corresponding to each risk identification and assessment indicator, and obtain a probabilistic power system risk identification and assessment solution.

[0123] The following is an example to illustrate the probabilistic power system risk identification and assessment scheme. In this example, there are three risk identification and assessment indicators, namely, the node voltage exceeding the upper limit risk indicator, the node voltage exceeding the lower limit risk indicator and the branch flow exceeding the limit risk indicator, which are indicator 1, indicator 2 and indicator 3 respectively.

[0124] According to the above method, the operation risk level and evaluation weight corresponding to each indicator are obtained. In this example, the operation risk level and evaluation weight of indicator 1 are "basic safety" and 30% respectively; the operation risk level and evaluation weight of indicator 2 are "basic safety" and 30% respectively; the operation risk level and evaluation weight of indicator 3 are "some risk" and 40% respectively;

[0125] The probabilistic power system risk identification and assessment scheme obtained is: the power system currently has a 40% probability of "having certain risks" and a 60% probability of "basically safe". The multivariate probability distribution of the assessment results reflects the impact of system uncertainty on the assessment results.

[0126] Embodiment eight:

[0127] The invention discloses a power system risk identification and evaluation system considering source and load uncertainty, comprising an indicator construction module, a fuzzification module, and a comprehensive evaluation module;

[0128] The indicator construction module is used to obtain the scenario probability distribution of the power system based on the historical operation data of the power system, and to construct several risk identification and evaluation indicators that take into account the uncertainty of source and load;

[0129] A fuzzification module is used to fuzzify the risk identification and evaluation indicators using an adaptive bee colony algorithm to obtain the fuzzification threshold range of the risk identification and evaluation indicators corresponding to different operation risk levels;

[0130] The comprehensive assessment module is used to obtain several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system. According to the DS evidence fusion framework, the operation risk level corresponding to each risk identification and assessment indicator is comprehensively evaluated to obtain a probabilistic power system risk identification and assessment plan.

[0131] Embodiment nine:

[0132] An electronic device of the present invention includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0133] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0134] Application examples:

[0135] This example uses the power system risk identification and assessment method considering source and load uncertainty of the present invention to conduct simulation experiments. This example selects the IEEE standard 10-machine 39-node system as the implementation object of the simulation experiment, that is, the target power system in the method. The power system contains 10 nodes and 46 branches. The number of bees in the adaptive bee colony algorithm used in this example is 100, and the maximum number of iterations is set to 200 times.

[0136] This example compares the optimization effects of the traditional bee colony algorithm and the adaptive bee colony algorithm of the present invention. The risk assessment indicator fuzzy threshold optimization method based on the traditional bee colony algorithm is used as method 1, and the method of the present invention is used as method 2. Figure 2 and Figure 3The convergence of the risk assessment index fuzzy threshold optimization model based on the two methods is shown respectively. It can be seen that the adaptive bee colony algorithm proposed in the present invention can converge to a better effect faster than the traditional method. This is because the adaptive bee colony algorithm proposed in the present invention can adaptively adjust the location of the bee colony to find the nectar source, focusing on the exploration of the global solution in the early stage of optimization, and focusing on the development of local solutions in the later stage of optimization.

[0137] This example also uses the method of the present invention to assess power system risks in different scenarios. Figure 4 Scenario 1 (Low) corresponds to the scenario with low source load uncertainty, Scenario 2 (Mid) corresponds to the scenario with medium source load uncertainty, and Scenario 3 (High) corresponds to the scenario with high source load uncertainty. Figure 4 It can be seen that the method of the present invention can effectively evaluate the operation risk of the power system. In the scenario of low source-load uncertainty, the risks of each node and branch of the power system are generally low; in the scenario of medium source-load uncertainty, the risks of each node and branch of the power system generally increase; in the scenario of high source-load uncertainty, the risks of some nodes are relatively high. This is because a large amount of new energy is connected to this point, and its uncertainty leads to a higher operation risk at this point.

[0138] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for identifying and assessing power system risks considering source and load uncertainty, characterized in that: include: Based on the historical operation data of the power system, the scenario probability distribution of the power system is obtained, and several risk identification and evaluation indicators considering the uncertainty of source and load are constructed; Adopting an adaptive bee colony algorithm to fuzzify the risk identification and evaluation indicators, and obtaining fuzzy thresholds of the risk identification and evaluation indicators corresponding to different operation risk levels; Several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system are obtained. According to the DS evidence fusion framework, the operation risk level corresponding to each risk identification and assessment indicator is comprehensively evaluated to obtain a probabilistic power system risk identification and assessment scheme.

2. The power system risk identification and assessment method considering source and load uncertainty according to claim 1 is characterized in that: The specific method of obtaining the scenario probability distribution of the power system based on the historical operation data of the power system includes: Based on the historical operation data of the power system, several typical scenarios are obtained, and the scenario probability distribution function of the power system is obtained as follows: Where F(x) is the scenario probability distribution function corresponding to the power system characteristic variable x; f(x) is the probability distribution function of the power system characteristic variable x in the i-th typical scenario scene i The corresponding probability distribution function, the power system characteristic variable x includes the node voltage V, the flow upper limit threshold P max ; x1, x2 are the upper and lower limits of the cumulative probability distribution interval respectively.

3. The power system risk identification and assessment method considering source and load uncertainty according to claim 1 is characterized in that: The risk identification and evaluation indicators that take into account the uncertainty of source and load include a node voltage exceeding an upper limit risk indicator, a node voltage exceeding a lower limit risk indicator, and a branch power flow exceeding a limit risk indicator.

4. The power system risk identification and assessment method considering source and load uncertainty according to claim 3 is characterized in that: The node voltage exceeding the upper limit risk index is: Where V is the node voltage; is the maximum value of the node voltage exceeding the upper limit risk of node i; V i,max is the voltage corresponding to F(V)=1; F(V) is the scenario probability distribution function corresponding to the node voltage V; The node voltage lower limit risk index can be expressed as: in, is the risk of the node voltage of node i exceeding the lower limit; V i,min is the minimum possible value of the voltage at node i; The branch power flow over-limit risk index can be expressed as Among them, R l is the risk of branch l current exceeding the limit; P max is the upper threshold of the power flow; F(P max ) is the upper threshold value of the power flow P max The corresponding scene probability distribution function; P Lmax is the maximum value that may occur in the branch flow.

5. The power system risk identification and assessment method considering source and load uncertainty according to claim 1 is characterized in that: The specific steps of using the adaptive bee colony algorithm to fuzzify the risk identification and evaluation indicators to obtain the fuzzy thresholds of each risk identification and evaluation indicator corresponding to different operation risk levels include: S201, construct the fuzzy set X of identification evaluation indicators u ={x1,x2,x3,x4,x5}; where X u is the fuzzy set of identification and evaluation indicators; x1, x2, x3, x4, x5 are the fuzzy thresholds of power system risk assessment indicators, which are used to discretely evaluate the operation risk of the power system; the operation risk levels corresponding to x1, x2, x3, x4, x5 are {completely safe, basically safe, with certain risks, with great risks, very dangerous}; S202, optimizing the fuzzy threshold of the power system risk assessment indicator based on the adaptive bee colony algorithm.

6. The power system risk identification and assessment method considering source and load uncertainty according to claim 5 is characterized in that: The specific method for optimizing the fuzzy threshold of power system risk assessment indicators based on the adaptive bee colony algorithm includes: S20201, initializing the parameters of the adaptive bee colony algorithm, including: the number of bees in the adaptive bee colony algorithm, the maximum number of times the bees search for nectar sources, and the initial nectar source location; wherein the initial nectar source location is the fuzzy thresholds x1, x2, x3, x4, x5 of the power system risk assessment indicator to be optimized; S20202, based on the adaptive bee colony algorithm, the honey content at the honey source location is obtained. The honey content at the honey source location is the evaluation effect of the fuzzy threshold of the current power system risk assessment index on the power system operation risk, specifically: Among them, F b The effect of assessing the operation risk of the power system; N index The number of risk assessment indicators for power systems; is the assessment value of the power system risk corresponding to the i-th assessment indicator; is the actual value of the power system risk corresponding to the i-th evaluation index; |·| represents the absolute value operation; S20203, searching for a new honey source location in the neighborhood of the current honey source location, thereby obtaining a new fuzzy threshold set of power system risk assessment indicators, specifically: in, is the location of the neighborhood honey source; u(t) is the adaptive adjustment coefficient of the adaptive bee colony algorithm; k and j are the honey source locations different from i, corresponding to the fuzzy threshold set of power system risk assessment indicators, the value range of i, j, k is [1, N], and N is the number of bees; is the current honey source location, corresponding to the fuzzy threshold set of current power system risk assessment indicators; are two arbitrary nectar source locations; The location of the nectar source with the highest nectar content found by the current adaptive bee colony algorithm; S20204, determine whether the number of times the adaptive bee swarm algorithm searches for the nectar source has reached the maximum number; if reached, output the currently selected nectar source location, that is, the current power system risk assessment indicator fuzzy threshold set; if not reached, return to step S20202.

7. The power system risk identification and assessment method considering source and load uncertainty according to claim 1 is characterized in that: The specific steps of comprehensively evaluating the operation risk level corresponding to each risk identification and evaluation indicator according to the DS evidence fusion framework to obtain a probabilistic power system risk identification and evaluation scheme include: S301, define the evidence of the DS evidence fusion framework, that is, define the i-th fuzzy evaluation index as the power system risk identification and evaluation evidence s i , and define the jth fuzzy evaluation index as the power system risk identification and evaluation evidence s j ; S302, obtaining characteristic distances between power system risk identification and assessment evidences, specifically: Among them, F ds (·,·) is the characteristic distance calculation function between the evidences of power system risk identification and assessment; ||·|| represents the vector length operation; <·,·> represents the product operation between vectors; S303, judging the similarity between the evidences based on the characteristic distance between the power system risk identification and assessment evidences, specifically: in, A similarity calculation function between evidences for power system risk identification assessment; S304, based on the similarity between the power system risk identification and assessment evidence, the support level of evidence i is determined, specifically: in, is the degree of support for evidence i; n is the number of evidences; S305: Based on the support level of evidence i, the assessment weight of evidence i on the power system risk is determined, specifically: Among them, ξ i Calculate function for evaluation weights; S306, based on the evidence-based assessment weights for power system risks, obtain a comprehensive assessment of the operational risk levels corresponding to each risk identification and assessment indicator, and obtain a probabilistic power system risk identification and assessment solution.

8. A power system risk identification and assessment system considering source and load uncertainty, characterized in that: It includes indicator construction module, fuzzification module and comprehensive evaluation module; The indicator construction module is used to obtain the scenario probability distribution of the power system based on the historical operation data of the power system, and to construct a number of risk identification and evaluation indicators that take into account the uncertainty of source and load; The fuzzification module is used to fuzzify the risk identification and evaluation indicators using an adaptive bee colony algorithm to obtain fuzzification threshold ranges of the risk identification and evaluation indicators corresponding to different operation risk levels; The comprehensive assessment module is used to obtain several risk identification and assessment indicators and their corresponding operation risk levels of the current operation data of the corresponding power system, and comprehensively evaluate the operation risk level corresponding to each risk identification and assessment indicator according to the DS evidence fusion framework to obtain a probabilistic power system risk identification and assessment plan.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

Images