Power distribution network multi-dimensional elastic evaluation method considering flexible resource uncertainty

Abstract

The invention discloses a power distribution network multi-dimensional elastic evaluation method considering flexible resource uncertainty. The method comprises the following evaluation steps: constructing an elastic evaluation index in a fault defense stage; elastic evaluation indexes of the fault duration stage are constructed; calculating the subjective weight by adopting an analytic hierarchy process; calculating the objective weight by adopting an anti-entropy weight method; integrating the subjective weight and the objective weight to obtain an optimal weight; based on positive and negative correlation index analysis, calculating a comprehensive elastic index value; according to the method, the elastic evaluation indexes are constructed in stages based on the operation state of the power distribution network, the optimal weight is determined by adopting the optimal weighting method integrating subjective and objective weights, the influence of the indexes on the elastic evaluation of the power distribution network is comprehensively considered, and the emergency power supply capacity and the elastic performance of the power distribution network are displayed in multiple aspects and multiple dimensions.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network elasticity assessment, and in particular relates to a multi-dimensional elasticity assessment method for distribution networks taking into account the uncertainty of flexible resources. Background Art

[0002] The distribution network is the basic infrastructure to ensure social and economic development and life. It not only needs to meet the requirements of reliable operation under normal conditions, but also needs to maintain necessary operating functions when extreme disturbance events occur, control the impact range of extreme disturbance events to the minimum, flexibly adapt to environmental changes and quickly restore the power supply capacity of the distribution network.

[0003] As distribution networks are widely connected to distributed resources such as renewable energy generation and energy storage, the high volatility and diversity of these distributed resources have also brought certain complexities to the regulation and optimization of distribution networks. Since these highly flexible distributed resources have a wide range of temporal and spatial distribution and uncertain response characteristics, traditional single elasticity assessment methods are difficult to effectively quantify their contribution.

[0004] Therefore, in order to solve the above problems, it is necessary to develop a multi-dimensional elasticity evaluation method for distribution networks that considers the uncertainty of flexible resources. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a multi-dimensional elasticity evaluation method for distribution networks that takes into account the uncertainty of flexible resources, and to demonstrate the emergency power supply capability and elastic performance of the distribution network in multiple aspects and dimensions.

[0006] The object of the present invention is achieved by: a method for evaluating the multidimensional elasticity of a distribution network considering the uncertainty of flexible resources, comprising the following evaluation steps:

[0007] S1. Construction of resilience evaluation index in fault defense stage; using grid infrastructure density I 11 、Distributed power supply ratio I 12 、Ratio of energy storage device I 13 , load loss rate I 14 As a resilience evaluation indicator in the fault prevention phase;

[0008] S2, construction of elasticity evaluation index in fault duration stage; using load recovery rate I 21 , voltage offset I 22 、Load loss I 23 As a resilience assessment indicator during the fault persistence phase;

[0009] S3. Use the analytic hierarchy process to calculate the subjective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. s,i Perform calculations;

[0010] S4. Use the anti-entropy weight method to calculate the objective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. o,i Perform calculations;

[0011] S5. Comprehensive subjective and objective weights to obtain the optimal weight ω i ;

[0012] S6. Calculate the comprehensive elasticity index value I based on the analysis of positive and negative correlation indicators F ; The positive and negative correlations between the resilience evaluation indicators and the system in the fault prevention stage and the fault persistence stage are analyzed respectively, the original values ​​of the negatively correlated indicators are complemented, and then added to the positively correlated indicators to obtain the final distribution network comprehensive resilience index value I F .

[0013] Furthermore, in step S1, the grid infrastructure density I 11 It is expressed as the number of elastic power sources per unit area:

[0014]

[0015] Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; S DN The geographical area covered by the distribution network;

[0016] The distributed power supply ratio I 12 It is expressed as the ratio of the distributed power capacity connected to the distribution network to the total power capacity:

[0017]

[0018] Where: is the configuration capacity of the kth type of elastic support power source connected to the distribution network; S0 is the capacity of the thermal power unit connected to the distribution network;

[0019] The energy storage device ratio I 13 Expressed as:

[0020]

[0021] Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; N E is the number of energy storage power stations in the distribution network; N T is the number of thermal power units in the distribution network;

[0022] The load loss rate I 14It is expressed by the average speed at which the distribution network switches from normal operation to load reduction operation:

[0023]

[0024] Where: t1 is the time when the disaster occurs; t2 is the time when the system first takes load shedding measures; L R (t) represents the system disaster status curve.

[0025] Furthermore, in step S2, the load recovery rate I 21 It is expressed as the average speed at which the distribution network recovers from a fault state to a normal operating state after recovery measures are taken:

[0026]

[0027] Where: t3 is the time when recovery measures are started; t4 is the time when the system returns to normal operation; L R (t) represents the system disaster state curve;

[0028] The voltage offset I 22 It is expressed as the sum of the differences between the per-unit voltage value of each node and the reference value during the fault period:

[0029]

[0030] Where: Ω t is the set of distribution network fault periods; Ω n is the set of all nodes in the distribution network; U i,t is the per-unit voltage value of node i in period t; U0 is the voltage reference value;

[0031] The load loss amount I 23 Use the missing area S of the load curve L To express:

[0032]

[0033] Where: L T (t) represents the system fault-free state curve, that is, the target load curve; L R (t) represents the system disaster state curve; T0 is the time when the distribution network is affected by the disaster, T0 = t4-t1, t4 is the time when the system recovers to normal operation, and t1 is the time when the disaster occurs.

[0034] Furthermore, in step S3, the subjective weight ω is calculated using the hierarchical analysis method. s,i Specifically include: using the 0.1-0.9 scaling method to construct the judgment matrix and calculate the weight ω s,i, use the square root method to take the square root of the product of each row of elements in the judgment matrix, solve its geometric mean, obtain the weight after normalization, and calculate the maximum eigenvalue λ of the judgment matrix max , consistency test is performed through consistency ratio CR, where CR = CI / RI, where CI = λ max -n / n-1, n is the order of the judgment matrix, RI is the average random consistency index, and when CR<0.1, the judgment matrix has satisfactory consistency.

[0035] Furthermore, in step S4, the objective weight ω is calculated using the anti-entropy weight method. o,i Expressed as:

[0036]

[0037] Where: h i is the anti-entropy of an evaluation index, x ij (i=1,2,…,k;j=1,2,…m) are the index values ​​of m evaluation objects and n evaluation indicators, and the evaluation matrix is ​​X=(x ij ) n×m , x ij It can be obtained by multiple experts scoring the weight of each evaluation indicator.

[0038] Furthermore, the importance coefficient α of the subjective weight and the objective weight in step S5 is i and β i The calculation formula is:

[0039]

[0040] Then the optimal weight ω i Expressed as:

[0041]

[0042] Furthermore, the final distribution network comprehensive elasticity index value in step S6 is calculated as follows:

[0043]

[0044] Where: A and B are the number of positive and negative correlation indicators respectively; ω a and ω b are the optimal weights of positive and negative correlation indicators respectively; I a and I b are the original index values ​​of positive and negative correlation indicators, respectively.

[0045] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: constructing elasticity evaluation indicators in stages based on the operating status of the distribution network, and determining the optimal weights by adopting the optimal weighting method that integrates subjective and objective weights, comprehensively considering the impact of individual indicators on the elasticity evaluation of the distribution network, and displaying the emergency power supply capacity and elastic performance of the distribution network in multiple aspects and dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the present invention.

[0047] Figure 2 It is a load curve diagram of the distribution network in the process of being affected by a disaster in the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0049] like Figure 1 、 Figure 2 As shown in Figure 1, a multi-dimensional elasticity evaluation method for distribution networks considering the uncertainty of flexible resources includes the following evaluation steps:

[0050] S1. Construction of resilience evaluation index in fault defense stage; using grid infrastructure density I 11 、Distributed power supply ratio I 12 、Ratio of energy storage device I 13 , load loss rate I 14 As a resilience evaluation indicator in the fault prevention phase.

[0051] Specifically, the grid infrastructure density I 11 It is expressed as the number of elastic power sources per unit area:

[0052]

[0053] Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; S DN is the geographical area covered by the distribution network.

[0054] Specifically, the distributed power supply ratio I 12 It is expressed as the ratio of the distributed power capacity connected to the distribution network to the total power capacity:

[0055]

[0056] Where: is the configuration capacity of the kth type of elastic supporting power source connected to the distribution network; S0 is the capacity of the thermal power unit connected to the distribution network.

[0057] Specifically, the energy storage device ratio I 13 Expressed as:

[0058]

[0059] Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; N E is the number of energy storage power stations in the distribution network; N T is the number of thermal power units in the distribution network.

[0060] Specifically, the load loss rate I 14 It is expressed by the average speed at which the distribution network switches from normal operation to load reduction operation:

[0061]

[0062] Where: t1 is the time when the disaster occurs; t2 is the time when the system first takes load shedding measures; L R (t) represents the system disaster status curve.

[0063] S2, construction of elasticity evaluation index in fault duration stage; using load recovery rate I 21 , voltage offset I 22 、Load loss I 23 As a resilience evaluation indicator during the fault persistence phase.

[0064] Specifically, the load recovery rate I 21 It is expressed as the average speed at which the distribution network recovers from a fault state to a normal operating state after recovery measures are taken:

[0065]

[0066] Where: t3 is the time when recovery measures are started; t4 is the time when the system returns to normal operation; L R (t) represents the system disaster status curve.

[0067] Specifically, the voltage offset I 22 It is expressed as the sum of the differences between the per-unit voltage value of each node and the reference value during the fault period:

[0068]

[0069] Where: Ω t is the set of distribution network fault periods; Ω n is the set of all nodes in the distribution network; U i,t is the per-unit voltage value of node i in period t; U0 is the voltage reference value.

[0070] Specifically, the load loss amount I 23 Use the missing area S of the load curve L To express:

[0071]

[0072] Where: L T (t) represents the system fault-free state curve, that is, the target load curve; L R (t) represents the system disaster state curve; T0 is the time when the distribution network is affected by the disaster, T0 = t4-t1, t4 is the time when the system recovers to normal operation, and t1 is the time when the disaster occurs.

[0073] S3. Use the analytic hierarchy process to calculate the subjective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. s,i Perform calculations.

[0074] Specifically, the step S3 uses the analytic hierarchy process to calculate the subjective weight ω s,i Specifically include: using the 0.1-0.9 scaling method to construct the judgment matrix and calculate the weight ω s,i , use the square root method to take the square root of the product of each row of elements in the judgment matrix, solve its geometric mean, obtain the weight after normalization, and calculate the maximum eigenvalue λ of the judgment matrix max , consistency test is performed through consistency ratio CR, where CR = CI / RI, where CI = λ max -n / n-1, n is the order of the judgment matrix, RI is the average random consistency index, and when CR<0.1, the judgment matrix has satisfactory consistency.

[0075] S4. Use the anti-entropy weight method to calculate the objective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. o,i Perform calculations.

[0076] Specifically, in step S4, the objective weight ω is calculated using the anti-entropy weight method. o,i Expressed as:

[0077]

[0078] Where: h i is the anti-entropy of an evaluation index, x ij (i=1,2,…,k;j=1,2,…m) are the index values ​​of m evaluation objects and n evaluation indicators, and the evaluation matrix is ​​X=(x ij ) n×m , x ijIt can be obtained by multiple experts scoring the weight of each evaluation indicator.

[0079] S5. Comprehensive subjective and objective weights to obtain the optimal weight ω i .

[0080] Specifically, the important coefficient α of the subjective weight and the objective weight in step S5 is i and β i The calculation formula is:

[0081]

[0082] Then the optimal weight ω i Expressed as:

[0083] .

[0084] S6. Calculate the comprehensive elasticity index value I based on the analysis of positive and negative correlation indicators F ; The positive and negative correlations between the resilience evaluation indicators and the system in the fault prevention stage and the fault persistence stage are analyzed respectively, the original values ​​of the negatively correlated indicators are complemented, and then added to the positively correlated indicators to obtain the final distribution network comprehensive resilience index value I F .

[0085] Specifically, the final distribution network comprehensive elasticity index value in step S6 is calculated as follows:

[0086]

[0087] Where: A and B are the number of positive and negative correlation indicators respectively; ω a and ω b are the optimal weights of positive and negative correlation indicators respectively; I a and I b are the original index values ​​of positive and negative correlation indicators, respectively.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A multi-dimensional elasticity assessment method for distribution networks considering the uncertainty of flexible resources, characterized by: The assessment steps include: S1. Construction of resilience evaluation index in fault defense stage; using grid infrastructure density I 11 、Distributed power supply ratio I 12 、Ratio of energy storage device I 13 , load loss rate I 14 As a resilience evaluation indicator in the fault prevention phase; S2, construction of resilience evaluation indicators during the fault persistence phase; Using load recovery rate I 21 , voltage offset I 22 、Load loss I 23 As a resilience assessment indicator during the fault persistence phase; S3. Use the analytic hierarchy process to calculate the subjective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. s,i Perform calculations; S4. Use the anti-entropy weight method to calculate the objective weights ω of the resilience evaluation indicators in the fault prevention stage and the fault persistence stage respectively. o,i Perform calculations; S5. Comprehensive subjective and objective weights to obtain the optimal weight ω i ; S6. Calculate the comprehensive elasticity index value I based on the analysis of positive and negative correlation indicators F ; The positive and negative correlations between the resilience evaluation indicators and the system in the fault prevention stage and the fault persistence stage are analyzed respectively, the original values ​​of the negatively correlated indicators are complemented, and then added to the positively correlated indicators to obtain the final distribution network comprehensive resilience index value I F .

2. A method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: In step S1, the grid infrastructure density I 11 It is expressed as the number of elastic power sources per unit area: Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; S DN The geographical area covered by the distribution network; The distributed power supply ratio I 12 It is expressed as the ratio of the distributed power capacity connected to the distribution network to the total power capacity: Where: is the configuration capacity of the kth type of elastic support power source connected to the distribution network; S0 is the capacity of the thermal power unit connected to the distribution network; The energy storage device ratio I 13 Expressed as: Where: Ω K is the set of power types in the distribution network; N k is the total number of devices with the kth type of elastic support power supply; N E is the number of energy storage power stations in the distribution network; N T is the number of thermal power units in the distribution network; The load loss rate I 14 It is expressed by the average speed at which the distribution network switches from normal operation to load reduction operation: Where: t1 is the time when the disaster occurs; t2 is the time when the system first takes load shedding measures; L R (t) represents the system disaster status curve.

3. The method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: In step S2, the load recovery rate I 21 It is expressed as the average speed at which the distribution network recovers from a fault state to a normal operating state after recovery measures are taken: Where: t3 is the time when recovery measures are started; t4 is the time when the system returns to normal operation; L R (t) represents the system disaster state curve; The voltage offset I 22 It is expressed as the sum of the differences between the per-unit voltage value of each node and the reference value during the fault period: Where: Ω t is the set of distribution network fault periods; Ω n is the set of all nodes in the distribution network; U i,t is the per-unit voltage value of node i in period t; U0 is the voltage reference value; The load loss amount I 23 Use the missing area S of the load curve L To express: Where: L T (t) represents the system fault-free state curve, that is, the target load curve; L R (t) represents the system disaster state curve; T0 is the time when the distribution network is affected by the disaster, T0 = t4-t1, t4 is the time when the system recovers to normal operation, and t1 is the time when the disaster occurs.

4. The method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: In step S3, the subjective weight ω is calculated using the hierarchical analysis method. s,i Specifically include: using the 0.1-0.9 scaling method to construct the judgment matrix and calculate the weight ω s,i , use the square root method to take the square root of the product of each row of elements in the judgment matrix, solve its geometric mean, obtain the weight after normalization, and calculate the maximum eigenvalue λ of the judgment matrix max , consistency test is performed through consistency ratio CR, where CR = CI / RI, where CI = λ max -n / n-1, n is the order of the judgment matrix, RI is the average random consistency index, and when CR<0.1, the judgment matrix has satisfactory consistency.

5. The method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: In step S4, the objective weight ω is calculated using the anti-entropy weight method. o,i Expressed as: Where: h i is the anti-entropy of an evaluation index, x ij (i=1,2,…,k;j=1,2,…m) are the index values ​​of m evaluation objects and n evaluation indicators, and the evaluation matrix is ​​X=(x ij ) n×m , x ij It can be obtained by multiple experts scoring the weight of each evaluation indicator.

6. The method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: The important coefficient α of the subjective weight and the objective weight in step S5 i and β i The calculation formula is: Then the optimal weight ω i Expressed as:

7. The method for evaluating the multi-dimensional elasticity of a distribution network considering the uncertainty of flexible resources according to claim 1, characterized in that: The final distribution network comprehensive elasticity index value in step S6 is calculated as follows: Where: A and B are the number of positive and negative correlation indicators respectively; ω a and ω b are the optimal weights of positive and negative correlation indicators respectively; I a and I b are the original index values ​​of positive and negative correlation indicators, respectively.

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