Method and device for evaluating stability of power grid

Abstract

The invention provides a method and device for evaluating the stability of a power grid. The method comprises the following steps: acquiring load information and operating parameters of an electric vehicle connected to a power grid, integrating to form real-time data, and calculating an electrical load index, an online user number index, a working capacity index and a total charging power index of each node and line; and performing weighted summation on the indexes to obtain parameter indexes of the power grid state, and comparing the parameter indexes with the extreme value of the previous day to obtain the operation state of each node and line. And sampling and accumulating the operation states to obtain an operation state probability, and calculating stability indexes of the power grid in different operation states by adopting a Monte Carlo algorithm. According to the method, the stability of the power grid is evaluated through the quantitative indexes, and higher accuracy and credibility are achieved. Meanwhile, the method can also provide important reference basis for power grid planning and operation, the stability and reliability of the power grid can be improved, and safe operation of a power system is guaranteed.

Description

Technical Field

[0001] This application relates to the field of power grid assessment, and particularly to a method and device for assessing the stability of a power grid. Background Art

[0002] The stability index of the distribution network for electric vehicle charging is a key criterion for evaluating its continuous power supply capacity, and provides an important basis for the management level, planning scheme, and investment strategy of the distribution network. A robust power system should have sufficient power generation capacity and transmission capacity to ensure that the peak load demand of users can be met under any conditions, which reflects the steady-state performance of the power grid. However, there is still a lack of a technical solution for comprehensively evaluating the stability of the power grid. Summary of the Invention

[0003] The purpose of this application is to overcome the defects in the above-mentioned prior art and provide a method and device for assessing the stability of a power grid.

[0004] This application provides a method for assessing the stability of a power grid, including:

[0005] Obtaining the load information and operating parameters of electric vehicles connected to the power grid;

[0006] Integrating the load information and operating parameters to form real-time data of the power grid operation;

[0007] Calculating the power consumption load index, the number of online users index, the working capacity index of the power grid transformer, and the total charging power index for each node and line of the power grid according to the real-time data;

[0008] Weighted summing the power consumption load index, the number of online users index, the working capacity index, and the total charging power index to obtain a parameter index of the power grid state;

[0009] Comparing the extreme values of the parameter index of the previous day with the current parameter index to obtain the operating state of each node and line of the power grid;

[0010] Sampling and accumulating the operating state to obtain the operating state probability of the node and the line;

[0011] Calculating the stability index of the power grid under different operating states by using the Monte Carlo algorithm according to the operating state probability.

[0012] Optionally, it further includes:

[0013] Determining the accuracy of the stability index according to the variance of the stability index.

[0014] Optionally, comparing the extreme values of the parameter index of the previous day with the current parameter index, the expression is as follows:

[0015]

[0016] Among them, w1, w2, w3, and w4 are assigned values according to different degrees of influence, and K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c are the power consumption load index, the number of online users index, the working capacity index, and the total charging power index respectively.

[0017] Optionally, the operating state includes:

[0018] When it is good;

[0019] When it is average;

[0020] When it is poor;

[0021] Among them, the K represents a parameter index.

[0022] Optionally, obtaining the load information of electric vehicles connected to the power grid includes:

[0023] Transmitting the load information and the operating parameters of the electric vehicle connected to the power grid to the power grid through the Beidou short message function.

[0024] This application also provides a device for evaluating the stability of the power grid, including:

[0025] An acquisition module, configured to acquire the load information and operating parameters of electric vehicles connected to the power grid, and integrate the load information and operating parameters to form real-time data of the power grid operation;

[0026] A calculation module, configured to calculate the power consumption load index, the number of online users index, the working capacity index of the power grid transformer, and the total charging power index of each node and line of the power grid according to the real-time data;

[0027] An index module, configured to perform a weighted sum of the power consumption load index, the number of online users index, the working capacity index, and the total charging power index to obtain a parameter index of the power grid state;

[0028] A state module, which compares the extreme values of the parameter index of the previous day with the current parameter index to obtain the operating state of each node and line of the power grid;

[0029] A probability module, configured to sample and accumulate the operating state to obtain the operating state probability of the node and the line;

[0030] A result module, configured to calculate stability indexes of a power grid in different operating states according to the operating state probabilities by using a Monte Carlo algorithm.

[0031] Optionally, it further includes:

[0032] An accuracy module, configured to determine the accuracy of the stability indexes according to the variances of the stability indexes.

[0033] Optionally, compare the extreme values of the parameter indexes of the previous day with the current parameter indexes. The expression is as follows:

[0034]

[0035] wherein, w1, w2, w3, w4 are assigned values according to different degrees of influence, and K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c are respectively an electricity consumption load index, an online user number index, a working capacity index, and a total charging power index.

[0036] Optionally, the operating state includes:

[0037] When it is good;

[0038] When it is average;

[0039] When it is poor;

[0040] wherein, the K represents a parameter index.

[0041] Optionally, obtaining the load information of electric vehicles accessing the power grid includes:

[0042] Transmitting the load information and the operating parameters of the electric vehicles accessing the power grid to the power grid through the Beidou short message function.

[0043] The beneficial effects of this application are:

[0044] The present application provides a method for evaluating the stability of a power grid, including: obtaining the load information and operating parameters of electric vehicles connected to the power grid; integrating the load information and operating parameters to form real-time data of the power grid operation; calculating the power consumption load index, the number of online users index, the working capacity index of the power grid transformer, and the total charging power index for each node and line of the power grid according to the real-time data; performing a weighted sum of the power consumption load index, the number of online users index, the working capacity index, and the total charging power index to obtain a parameter index of the power grid state; comparing the extreme values of the parameter index on the previous day with the current parameter index to obtain the operating state of each node and line of the power grid; sampling and accumulating the operating state to obtain the operating state probability of the node and the line; and calculating the stability index of the power grid under different operating states by using the Monte Carlo algorithm. The present application combines the calculation model of the stability index and the Monte Carlo calculation method to macroscopically, specifically, and accurately control the stability data of the power grid, and achieves the prediction effect of the power grid stability through the quantization index. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic diagram of the process for evaluating the stability of the power grid in the present application.

[0046] Figure 2 is a schematic diagram of the device for evaluating the stability of the power grid in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further describes the present application with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present application and be able to implement it.

[0048] The following content is all examples of the specific implementation process provided to detail the technical solution to be protected by the present application. However, the present application can also be implemented in other ways different from the descriptions herein. Those skilled in the art can implement the present application by using different technical means under the guidance of the concept of the present application. Therefore, the present application is not limited by the following specific embodiments.

[0049] As Figure 1 shown, a method for evaluating the stability of the power grid provided by the present application includes the following steps:

[0050] S101. Obtain the load information and operating parameters of electric vehicles connected to the power grid.

[0051] The load information and related operating parameters of electric vehicles connected to the power grid can be transmitted to the power grid through the Beidou short message function. Specifically, in this process, sensors installed on new energy vehicles collect data such as the load information and operating parameters of the vehicles connected to the power grid, and then send the data to the power grid platform through the Beidou short message function.

[0052] S102. Integrate the load information and operating parameters to form real-time data of power grid operation.

[0053] The power grid platform will sense, integrate and analyze the received data to obtain real-time data of power grid operation.

[0054] The real-time data at least includes power grid load (peak, valley) of electric vehicles accessing the power grid, real-time online user data of power grid nodes, transformer operation data, total charging power data and other information.

[0055] S103. Calculate the power consumption load index, online user number index, working capacity index of power grid transformers and total charging power index of each node and line of the power grid according to the real-time data.

[0056] First, number each node and each section of line of the power grid.

[0057] Secondly, based on the numbering, establish an algorithm for the influence weights of the line, transformer operation data, real-time online user number of nodes and total electric vehicle charging power, and calculate the power consumption load index, online user number index, working capacity index of power grid transformers and total charging power index of each node and line of the power grid.

[0058] Specifically, assume that the i-th line of the numbering is located at the j-th node, then the influencing factors of this distribution line can be quantified into the following indicators:

[0059] Power consumption load index, the expression is:

[0060]

[0061] where l i is the length of line i, S j u(t) is the power consumption load of node j at time t, is the sum of the power consumption loads of all nodes in the area to be evaluated. z0 is the impedance parameter of the power supply line.

[0062] Working capacity index of the transformer, the expression is:

[0063]

[0064] S (t) tr(j) is the working capacity of all transformers of node j at time t, S max tr(j) is the maximum working capacity of all transformers of node j.

[0065] Online user number index, the expression is:

[0066]

[0067] Among them, N (t) i is the number of online users of line i at time t, and N i is the total number of users of line i.

[0068] The total charging power index is expressed as:

[0069]

[0070] Among them, P (j)c (t) is the total charging power of electric vehicles at node j at time t, is the average power (per hour) of node j on the previous day, W is the total grid load of the area to be evaluated, and W ch is the total planned online load of registered electric vehicles at each node.

[0071] S104. Weightedly sum the electricity load index, the number of online users index, the working capacity index, and the total charging power index to obtain a parameter index of the grid state.

[0072] Specifically, weightedly sum the above data to obtain a parameter index of the grid operation state, and the expression is as follows:

[0073] K = w1·K l(i) + w2·K tr(j) + w3·K u(i) + w4·K c

[0074] Among them, w1, w2, w3, and w4 are weights, which need to be assigned according to different degrees of influence, and the K is the parameter index.

[0075] S105. Compare the extreme values of the parameter index of the previous day with the current parameter index to obtain the operation state of each node and line of the grid.

[0076] Specifically, obtain the parameter index of the grid operation state of the previous day, and the expression is as follows:

[0077]

[0078] Among them, w1, w2, w3, and w4 are weights, which need to be assigned according to different degrees of influence. K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c is the corresponding influence factor value at the load peak time of the previous day, that is, the extreme value of the parameter index.

[0079] Compare the corresponding influencing factor values at the peak load moment of the previous day with the current parameter indicators to obtain the operating states of nodes and lines, specifically including:

[0080] When it is in good condition;

[0081] When it is average;

[0082] When it is poor;

[0083] Among them, the K represents the parameter indicator.

[0084] S106. Sample and accumulate the operating states to obtain the operating state probabilities of the nodes and the lines.

[0085] Record the state data of node i at time t, and obtain probability data through sampling and accumulation. The probability of node j being in good condition is P j1 , the average state is P j2 , and the probability of the poor state is P j3 .

[0086] Preferably, the probability data is obtained by sampling and accumulation in this application, including obtaining the probabilities of various states of the normal operation of the power grid according to the law of large numbers.

[0087] Specifically, the influencing factors of each node and line at time t are represented by matrices and vectors:

[0088]

[0089]

[0090]

[0091] Then the states of each node and the corresponding probability data at time t are:

[0092]

[0093] S107. According to the operating state probabilities, use the Monte Carlo algorithm to calculate the stability indicators of the power grid under different operating states.

[0094] Macroscopically evaluate the stability of the overall operating conditions of the power grid through the Monte Carlo algorithm based on the obtained corresponding probability data of different nodes at different times in their respective states.

[0095] In this application, a mathematical model of a macroscopic index of the operating reliability of the power grid is established based on the probability data:

[0096]

[0097] Then, the average stability index of the system is:

[0098]

[0099] Wherein, the λ1, λ2, and λ3 are preset weight values.

[0100] To obtain the prediction accuracy of the stability prediction index under the above scheme, we need to calculate the sample variance:

[0101]

[0102]

[0103]

[0104] The accuracy is:

[0105]

[0106]

[0107]

[0108] The β1, β2, and β3 are the precisions corresponding to the probabilities of the good state, general state, and poor state.

[0109] This application also provides a device for evaluating the stability of the power grid to implement the steps of the above method.

[0110] As Figure 2 shown, the device for evaluating the stability of the power grid includes:

[0111] An acquisition module 201, configured to acquire the load information and operating parameters of the electric vehicle accessing the power grid, and integrate the load information and operating parameters to form real-time data of the power grid operation.

[0112] The load information and related operating parameters of the electric vehicle accessing the power grid can be transmitted to the power grid through the Beidou short message function. Specifically, in this process, sensors installed on new energy vehicles collect data such as the load information and operating parameters of the vehicle accessing the power grid, and then send the data to the power grid platform through the Beidou short message function.

[0113] A calculation module 202, configured to calculate the power consumption load index, the number of online users index, the working capacity index of the power grid transformer, and the total charging power index of each node and line of the power grid according to the real-time data.

[0114] The power grid platform will perform information perception integration and analysis on the received data to obtain real-time data of the power grid operation.

[0115] The real-time data at least includes information such as grid load (peak and trough) of electric vehicles accessing the grid, real-time online user data of grid nodes, transformer operation data, total charging power data, etc.

[0116] First, number each node and each section of the line in the grid.

[0117] Secondly, based on the said numbering, establish an algorithm for the influence weights of the line, transformer operation data, real-time online user numbers of nodes, and total charging power of electric vehicles, and calculate the power consumption load index, online user number index, working capacity index of grid transformers, and total charging power index for each node and line in the grid.

[0118] Specifically, assume that the i-th line of the said numbering is located at the j-th node, then the influencing factors of this distribution line can be quantified into the following indicators:

[0119] Power consumption load index, the expression is:

[0120]

[0121] Where l i is the length of line i, S j u(t) is the power consumption load of the j-th node at time t, is the sum of the power consumption loads of all nodes in the area to be evaluated. z0 is the impedance parameter of the power supply line.

[0122] Working capacity index of the transformer, the expression is:

[0123]

[0124] S (t) tr(j) is the working capacity of all transformers at the j-th node at time t, S max tr(j) is the maximum working capacity of all transformers at the j-th node.

[0125] Online user number index, the expression is:

[0126]

[0127] Where, N (t) i is the number of online users of line i at time t, N i is the total number of users of line i.

[0128] Total charging power index, the expression is:

[0129]

[0130] Where, P(j)c (t) is the total charging power of the electric vehicle at node j at time t, is the average power (per hour) of node j on the previous day, W is the total grid load of the area to be evaluated, W ch is the total planned grid-connected load of the registered electric vehicles at each node.

[0131] The index module 203 is used to perform a weighted sum of the electricity load index, the number of online users index, the working capacity index, and the total charging power index to obtain a parameter index of the grid state.

[0132] Specifically, a parameter index of the grid operation state is obtained by performing a weighted sum of the above data, and the expression is as follows:

[0133] K = w1·K l(i) +w2·K tr(j) +w3·K u(i) +w4·K c

[0134] where w1, w2, w3, and w4 are weights that need to be assigned according to different degrees of influence, and K is the parameter index.

[0135] The state module 204 compares the extreme values of the parameter index on the previous day with the current parameter index to obtain the operation state of each node and line of the grid.

[0136] Specifically, the parameter index of the grid operation state on the previous day is obtained, and the expression is as follows:

[0137]

[0138] where w1, w2, w3, and w4 are weights that need to be assigned according to different degrees of influence. K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c is the corresponding influence factor value at the peak load time on the previous day, that is, the extreme value of the parameter index.

[0139] Comparing the corresponding influence factor value at the peak load time on the previous day with the current parameter index to obtain the operation state of the node and line, specifically including:

[0140] When it is good;

[0141] When it is average;

[0142] When is relatively poor;

[0143] Among them, the K represents a parameter index.

[0144] The probability module 205 is used to sample and accumulate the operating states to obtain the operating state probabilities of the nodes and the lines.

[0145] Record the state data of node i at time t, obtain probability data through sampling and accumulation, and the probability of the good state of node j is P j1 , the general state is P j2 , the probability of the relatively poor state is P j3 .

[0146] Preferably, the present application obtains probability data through sampling and accumulation, including obtaining the probabilities of various states of the normal operation of the power grid according to the law of large numbers.

[0147] Specifically, the influencing factors of each node and line at time t are represented by matrices and vectors:

[0148]

[0149]

[0150]

[0151] Then, the states of each node and the corresponding probability data at time t are:

[0152]

[0153] The result module 206 is used to calculate the stability indexes of the power grid under different operating states by using the Monte Carlo algorithm according to the operating state probabilities.

[0154] Macroscopically evaluate the stability of the overall working conditions of the power grid through the Monte Carlo algorithm based on the obtained corresponding probability data of different nodes at different times in their respective states.

[0155] In the present application, a mathematical model of a macroscopic index of the operation reliability of the power grid is established based on the probability data:

[0156]

[0157] Then, the average stability index of the system is:

[0158]

[0159] Among them, the λ1, λ2, and λ3 are preset weight values.

[0160] In order to obtain the prediction accuracy of the stability prediction index under the above scheme, we need to calculate the sample variance:

[0161]

[0162]

[0163]

[0164] The precision is:

[0165]

[0166]

[0167]

[0168] Said β1, β2, and β3 are the precisions corresponding to the probabilities of the good state, the general state, and the poor state.

Claims

1. A method for evaluating the stability of a power grid, characterized in that, Including: Obtaining the load information and operating parameters of electric vehicles connected to the power grid; Integrating the load information and operating parameters to form real-time data of the power grid operation; Calculating the power consumption load index, number of online users index, working capacity index of the power grid transformer, and total charging power index of each node and line of the power grid according to the real-time data; Weighted summing the power consumption load index, number of online users index, working capacity index, and total charging power index to obtain a parameter index of the power grid state; Comparing the extreme values of the parameter index of the previous day with the current parameter index to obtain the operating state of each node and line of the power grid; Sampling and accumulating the operating state to obtain the operating state probability of the node and the line; Calculating the stability index of the power grid under different operating states by using the Monte Carlo algorithm according to the operating state probability.

2. The method for evaluating the stability of a power grid according to claim 1, wherein Also including: Determining the accuracy of the stability index according to the variance of the stability index.

3. The method for evaluating the stability of a power grid according to claim 1, characterized in that Comparing the extreme values of the parameter index of the previous day with the current parameter index, and the expression is as follows: Among them, w1, w2, w3, w4 are assigned values according to different degrees of influence, and K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c are the electricity load index, the number of online users index, the working capacity index, and the total charging power index respectively.

4. The method for evaluating the stability of the power grid according to claim 3, wherein The operating state includes: When is good; When is general; When is poor; Wherein, the K represents the parameter index.

5. The method for evaluating the stability of a power grid according to claim 1, wherein Obtaining the load information of electric vehicles connected to the power grid includes: Transmitting the load information and operating parameters of the electric vehicle connected to the power grid to the power grid through the Beidou short message function.

6. A device for evaluating the stability of a power grid, characterized in that, Including: An acquisition module, configured to obtain the load information and operating parameters of electric vehicles connected to the power grid, and integrate the load information and operating parameters to form real-time data of the power grid operation; A calculation module, configured to calculate the power consumption load index, number of online users index, working capacity index of the power grid transformer, and total charging power index of each node and line of the power grid according to the real-time data; An index module, configured to weighted sum the power consumption load index, number of online users index, working capacity index, and total charging power index to obtain a parameter index of the power grid state; A state module, comparing the extreme values of the parameter index of the previous day with the current parameter index to obtain the operating state of each node and line of the power grid; A probability module, configured to sample and accumulate the operating state to obtain the operating state probability of the node and the line; A result module, configured to calculate the stability index of the power grid under different operating states by using the Monte Carlo algorithm according to the operating state probability.

7. The device for evaluating power grid stability according to claim 6, characterized in that, Also including: An accuracy module, configured to determine the accuracy of the stability index according to the variance of the stability index.

8. The method for evaluating the stability of a power grid according to claim 6, wherein Comparing the extreme values of the parameter index of the previous day with the current parameter index, and the expression is as follows: Among them, w1, w2, w3, w4 are assigned values according to different degrees of influence, K (f) l(i) , K (f) tr(j) , K (f) u(i) , K (f) c are the electricity load index, the number of online users index, the working capacity index, and the total charging power index respectively.

9. The device for evaluating power grid stability according to claim 8, characterized in that, The operating state includes: When it is good; When is general; When is poor; Wherein, the K represents the parameter index.

10. The device for evaluating power grid stability according to claim 6, characterized in that, Obtaining the load information of electric vehicles connected to the power grid includes: Transmitting the load information and operating parameters of the electric vehicle connected to the power grid to the power grid through the Beidou short message function.

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