Power distribution network restoring force evaluation method considering fault randomness

Abstract

The invention discloses a power distribution network restoring force evaluation method considering fault randomness, and the method comprises the following steps: collecting power distribution network fault feature information caused by extreme weather, and obtaining a restoring force set of a node i in q times of random faults; based on the node restoring force indexes, establishing a power distribution system restoring force evaluation matrix X; carrying out linear fitting on the restoring force Xi of the i node based on the reserved multi-time random fault data information features, and carrying out correlation quantitative description by adopting a multiple correlation coefficient # imgabs0 #; determining a node resilience score set weight Wi; overall evaluation is carried out on the power distribution network restoring force; obtaining an average value # imgabs1 # of the restoring force set based on the obtained node i restoring force set, and obtaining the overall restoring force XNDN of the power distribution network in combination with the weight Wi of each node; according to the method, the correlation between the node restoring force sets under multiple random faults is quantified, the weight of each node restoring force set is determined, and the purpose of accurately evaluating the overall restoring force of the power distribution network is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of resilience assessment of distribution networks, and particularly relates to a method for assessing the resilience of a distribution network considering the randomness of faults. Background Art

[0002] Due to the uncertainties of factors such as extreme weather, load fluctuations, and equipment failures, the inducements of random faults in the distribution network have increased significantly, posing a severe challenge to the safe operation of the distribution network. Nodes at different positions in the distribution network will have different degrees of losses under different random faults. Therefore, the assessment of the overall resilience of the distribution network is not a simple summation of the resilience scores of each node.

[0003] Therefore, to solve the above problems, it is necessary to develop a method for assessing the resilience of a distribution network considering the randomness of faults. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for assessing the resilience of a distribution network considering the randomness of faults. By quantifying the correlation between the resilience sets of nodes under multiple random faults, the weights of the resilience sets of each node are determined, so as to achieve the purpose of accurately assessing the overall resilience of the distribution network.

[0005] The purpose of the present invention is achieved as follows: A method for assessing the resilience of a distribution network considering the randomness of faults includes the following steps:

[0006] S1. Collect the fault characteristic information of the distribution network caused by extreme weather, and obtain the resilience set X of node i under q random faults i ={x i1 ,x i2 ,…,x ij ,…,x iq};

[0007] S2. Based on the node resilience index, establish a resilience evaluation matrix X for p nodes of the distribution system and q random faults of the distribution system;

[0008]

[0009] S3. Based on the characteristics of the retained multiple random fault data information, use the resilience of all nodes except node i to linearly fit the resilience X i of node i, and obtain the fitting value and use the multiple correlation coefficient to quantitatively describe the correlation between X i and ;

[0010] S4. Determine the weight W i of the node resilience score set;

[0011] S5. Conduct an overall assessment of the resilience of the distribution network; based on the obtained resilience set X of node i i ={x i1 ,x i2 ,…,x ij ,…,x iq}, obtain the average value of the resilience set Combine the weights W of each node i to obtain the overall resilience X of the distribution network NDN :

[0012]

[0013] Furthermore, the node resilience index in step S2 includes the complete power supply restoration duration T i,RT , the intermittent power supply duration T i,OT of the node, and the percentage K of energy loss of the node i,ELP .

[0014] Furthermore, the complete power supply restoration duration T i,RT is expressed as:

[0015]

[0016] In the formula, t0 is the fault occurrence time, t5 is the time when the system resumes normal power supply, η non-FR is the set of nodes without flexible supply-demand resource access, t4 is the time when the load of the nodes with flexible supply-demand resource access resumes to the normal level, η FR is the set of nodes with flexible supply-demand resource access; convert this power supply restoration duration into a maximization index and perform normalization processing to obtain:

[0017] M i,RT =1 - T i,RT / λ, T i,RT ≤λ

[0018] In the formula, λ is the upper limit of the complete power supply restoration duration of the node.

[0019] Furthermore, the intermittent power supply duration T i,OT of the node is expressed as:

[0020]

[0021] In the formula, t0 is the fault occurrence time, t4 is the time when the load of the nodes with flexible supply-demand resource access resumes to the normal level, f(p) is the intermittent power supply counting function, and f(p) is expressed as:

[0022]

[0023] In the formula, is the maximum power that can be provided by the flexible supply and demand resources configured for node i, and p i (t) is the power of node i; after normalizing the intermittent power supply duration, we get:

[0024] M i,OT = T i,OT / γ

[0025] , where γ is the upper limit of the intermittent power supply duration of the node.

[0026] Furthermore, the node energy loss percentage K i,ELP is expressed as:

[0027]

[0028] In the formula, t0 is the fault occurrence time, t1, t2, and t3 are the times when the load power of node i is equal to the maximum power that can be provided by the flexible supply and demand resources, t4 is the time when the node load connected to the flexible supply and demand resources returns to the normal level, and p i (t) is the power of node i, is the maximum power that can be provided by the flexible supply and demand resources configured for node i; by transforming the energy loss percentage into an extremely large index, we get:

[0029] M i,ELP = 1 - K i,ELP

[0031] Furthermore, the fitting value in step S3 is expressed as:

[0032]

[0033] In the formula, β0, β1,...β i-1 ,...β p-1 are constant coefficients.

[0034] Furthermore, the multiple correlation coefficient in step S3 is expressed as:

[0035]

[0036] In the formula, E is the expected value.

[0037] Furthermore, W i in step S4 is represented by the proportion of the reciprocal of the multiple correlation coefficient of node i in the sum of the reciprocals of the multiple correlation coefficients of all p nodes, and is specifically expressed as:

[0038]

[0039] In the formula, is the reciprocal of the complex correlation coefficient of node i, is the sum of the reciprocals of the complex correlation coefficients of all p nodes included in the distribution network.

[0040] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows: For q random failures caused by small-probability extreme weather, a resilience evaluation matrix X of q random failures is established. Based on the retained characteristic information of multiple random failure data, by quantifying the correlation between the node resilience sets under multiple random failures, the weight of the node resilience score set considering the influence of failure randomness is determined, and an overall evaluation of the resilience of the new distribution network is carried out. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is the flowchart of the present invention.

[0042] Figure 2 is the analysis diagram of the fault recovery process of the flexible supply and demand resource access node i in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] The technical solution of the present invention will be further specifically described below through embodiments and in combination with the drawings.

[0044] As Figure 1 , Figure 2 shown, a method for evaluating the resilience of a distribution network considering failure randomness includes the following steps:

[0045] Step S1, collect the fault characteristic information of the distribution network caused by extreme weather, and obtain the resilience set X of node i under q random failures i ={x i1 , x i2 , …, x ij , …, x iq}.

[0046] Step S2, based on the node resilience index, establish a resilience evaluation matrix X of p nodes of the distribution system and q random failures of the distribution system;

[0047]

[0048] The node resilience index in step S2 includes the complete power supply restoration duration T i,RT , the intermittent power supply duration T i,OT of the node, and the node energy loss percentage K i,ELP .

[0049] Specifically, as Figure 2As shown in the figure, considering the flexibility of the source-grid-load-storage, the fault recovery process of the access node i of the flexibility supply-demand resources is analyzed. The set of access nodes of the flexibility supply-demand resources in the distribution network is η FR , and the set of nodes without access to flexibility supply-demand resources is η non-FR ; The load power change of a certain node i in the distribution network is as Figure 2 shown. p i (t) is the power of node i, is the maximum power that the configured flexibility supply-demand resources of node i can provide. t0 is the moment when the fault occurs, and the flexibility supply-demand resources start to supply power; at the moment t1, the load power of node i is equal to the maximum power that the flexibility supply-demand resources can provide. During the period from t0 to t1, the load power of node i is greater than the maximum power that the flexibility supply-demand resources can provide. During the period from t1 to t2, the load power of node i is less than the maximum power that the flexibility supply-demand resources can provide; at the moment t2, the load power of node i is equal to the maximum power that the flexibility supply-demand resources can provide. During the period from t2 to t3, the load power of node i is greater than the maximum power that the flexibility supply-demand resources can provide; at the moment t3, the load power of node i is equal to the maximum power that the flexibility supply-demand resources can provide. During the period from t3 to t4, the load power of node i is less than the maximum power that the flexibility supply-demand resources can provide; t4 is the moment when the load of the node with access to flexibility supply-demand resources returns to the normal level, and t5 is the moment when the system resumes normal power supply.

[0050] Specifically, the full power restoration duration T i,RT is expressed as:

[0051]

[0052] , where t0 is the moment when the fault occurs, t5 is the moment when the system resumes normal power supply, η non-FR is the set of nodes without access to flexibility supply-demand resources, t4 is the moment when the load of the node with access to flexibility supply-demand resources returns to the normal level, and η FR is the set of nodes with access to flexibility supply-demand resources; The shorter the full power restoration duration of the node, the stronger the elastic restoration ability of the power grid; Convert this power restoration duration into a maximization index and perform normalization processing to obtain:

[0053] M i,RT = 1 - T i,RT / λ, T i,RT ≤λ

[0054] where λ is the upper limit of the full power restoration duration of the node. When the node is an access node of flexibility supply-demand resources, λ = λ FR , and when the node is a non-access node of flexibility supply-demand resources, λ = λ others .

[0055] Specifically, the intermittent power supply duration T of the node i,OT is expressed as:

[0056]

[0057] In the formula, t0 is the moment when the fault occurs, t4 is the moment when the node load connected to the flexible supply-demand resources resumes to the normal level, f(p) is the intermittent power supply counting function, and f(p) is expressed as:

[0058]

[0059] In the formula, is the maximum power that can be provided by the flexible supply-demand resources configured for node i, and p i (t) is the power of node i. As Figure 2 shown, in the time periods of t1 - t2 and t3 - t4, f(p) is 1, and in the time periods of t0 - t1 and t2 - t3, f(p) is 0; the longer the intermittent power supply duration of the flexible supply-demand resources of the source-network-load-storage, the stronger the ability to supply power to critical loads during the recovery stage; the intermittent power supply duration is normalized to obtain:

[0060] M i,OT = T i,OT / γ

[0061] In the formula, γ is the upper limit of the intermittent power supply duration of the node; when the node is a node connected to the flexible supply-demand resources, γ = γ FR , and when the node is a node intervened by non-flexible supply-demand resources, γ = γ others .

[0062] Specifically, the node energy loss percentage K i,ELP is expressed as:

[0063]

[0064] In the formula, t0 is the moment when the fault occurs, t1, t2, and t3 are respectively the moments when the load power of node i is equal to the maximum power that can be provided by the flexible supply-demand resources, t4 is the moment when the node load connected to the flexible supply-demand resources resumes to the normal level, p i (t) is the power of node i, is the maximum power that can be provided by the flexible supply-demand resources configured for node i, as Figure 2As shown in the figure, during the time periods of t0 - t1 and t2 - t3, the maximum power that the flexibility supply - demand resources of the source - grid - load - storage can provide cannot meet all load demands. At this time, the flexibility supply - demand resources of the source - grid - load - storage output at the maximum power to support critical loads. While during the time periods of t1 - t2 and t3 - t4, the power of the flexibility supply - demand resources can meet the load demands; the smaller the percentage of energy loss, the stronger the ability to restore the power supply of critical loads; converting the percentage of energy loss into an extremely large - type index, we get:

[0065] M i,ELP = 1 - K i,ELP

[0067] Step S3: Based on the characteristics of the multiple random - fault data information retained, use the resilience of all nodes except node i to linearly fit the resilience Xi i to obtain the fitting value and use the multiple - correlation coefficient to quantitatively describe the correlation between Xi i and .

[0068] Specifically, in step S3, the fitting value is expressed as:

[0069]

[0070] In the formula, β0, β1, …β i-1 , …β p-1 are constant coefficients.

[0071] Specifically, in step S3, the multiple - correlation coefficient is expressed as:

[0072]

[0073] In the formula, E is the expected value; The larger the value, the stronger the correlation of the node - resilience data, the greater the degree of data overlap, and the smaller its weight should be; the smaller the multiple - correlation coefficient, the smaller the degree of data overlap, and the greater its weight should be.

[0074] Step S4: Determine the weight Wi of the node - resilience scoring set i .

[0075] Specifically, in step S4, Wi i is represented by the proportion of the reciprocal of the multiple - correlation coefficient of node i in the sum of the reciprocals of the multiple - correlation coefficients of all p nodes, and is specifically expressed as:

[0076]

[0077] In the formula, is the reciprocal of the complex correlation coefficient of node i, is the sum of the reciprocals of the complex correlation coefficients of all p nodes included in the distribution network.

[0078] Step S5: Conduct an overall assessment of the resilience of the distribution network; based on the obtained resilience set X of node i i ={x i1 , x i2 ,…, x ij ,…, x iq}, obtain the average value of the resilience set Combine the weights W of each node i to obtain the overall resilience X of the distribution network NDN :

[0079]

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the scope of the claims of the present invention.

Claims

1. A resilience evaluation method for distribution networks considering the randomness of faults, characterized in that: Including the following steps: S1. Collect the fault characteristic information of the distribution network caused by extreme weather, and obtain the resilience set X of node i in q random faults i ={x i1 ,x i2 ,…,x ij ,…,x iq}; S2. Based on the node resilience index, establish a resilience evaluation matrix X for p nodes of the distribution system and q times of random faults of the distribution system; S3. Based on the characteristics of the retained multiple random fault data information, use the resilience of all nodes except node i to linearly fit the resilience X of node i i to obtain the fitted value and use the multiple correlation coefficient to quantitatively describe the correlation between X i and ; S4. Determine the weight W of the node resilience score set i ; S5. Conduct an overall assessment of the resilience of the distribution network; based on the obtained resilience set X of node i i ={x i1 , x i2 , …, x ij , …, x iq}, and obtain the average value of the resilience set Combine the weights W of each node i to obtain the overall resilience X of the distribution network NDN :

2. The resilience evaluation method for a distribution network considering the randomness of faults according to claim 1, characterized in that: In the step S2, the node resilience index includes the complete power supply restoration duration T i,RT , the node intermittent power supply duration T i,OT , and the node energy loss percentage K i,ELP .

3. A resilience evaluation method for a distribution network considering fault randomness according to claim 2, characterized in that: The full power restoration duration T i,RT is expressed as: Where, \(t_0\) is the fault occurrence time, \(t_5\) is the time when the system resumes normal power supply, and \(\eta\) non-FR is the set of nodes without flexible supply-demand resource access, \(t_4\) is the time when the node load with flexible supply-demand resource access resumes to the normal level, and \(\eta\) FR is the set of nodes with flexible supply-demand resource access; convert this power supply restoration duration into an extremely large index and perform normalization to obtain: M i,RT = 1 - T i,RT / λ, T i,RT ≤ λ In the formula, λ is the upper limit of the complete power supply restoration duration of the node.

4. A resilience evaluation method for a distribution network considering the randomness of faults according to claim 2, characterized in that: The intermittent power supply duration T of the node i,OT is expressed as: In the formula, t0 is the fault occurrence time, t4 is the time when the node load connected with flexible supply and demand resources resumes to the normal level, and f(p) is the discontinuous power supply counting function, and f(p) is expressed as: wherein is the maximum power that can be provided by the flexible supply and demand resources configured for node i, and p i (t) is the power of node i; after normalizing the duration of intermittent power supply, we get: M i,OT = T i,OT / γ In the formula, γ is the upper limit of the discontinuous power supply duration of the node.

5. The resilience assessment method of a distribution network considering the randomness of faults according to claim 2, characterized in that: The node energy loss percentage K i,ELP is expressed as: where \(t_0\) is the fault occurrence time, \(t_1\), \(t_2\), and \(t_3\) are the times when the load power of node \(i\) is equal to the maximum power that the flexibility supply-demand resources can provide, \(t_4\) is the time when the load of the node where the flexibility supply-demand resources are connected returns to the normal level, and \(p i (t)\) is the power of node \(i\), is the maximum power that the flexibility supply-demand resources configured for node \(i\) can provide; converting the energy loss percentage into an extremely large index, we get: M i,ELP = 1 - K i,ELP .

6. The resilience evaluation method of a distribution network considering the randomness of faults according to claim 1, characterized in that: The fitted value in step S3 is expressed as: where β0, β1, … β i-1 , … β p-1 are constant coefficients.

7. A resilience evaluation method for a distribution network considering fault randomness according to claim 1, characterized in that: The complex correlation coefficient in the step S3 is expressed as: In the formula, E is the expected value.

8. A resilience evaluation method for a distribution network considering the randomness of faults according to claim 1, characterized in that: In step S4, W i is represented by the ratio of the reciprocal of the complex correlation coefficient of node i to the sum of the reciprocals of the complex correlation coefficients of all p nodes, and is specifically expressed as: Wherein, is the reciprocal of the complex correlation coefficient of node i, is the sum of the reciprocals of the complex correlation coefficients of all p nodes included in the distribution network.

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