A power grid reliability evaluation method and system for distribution network, and a medium

Abstract

The present application relates to the technical field of power system reliability evaluation, and more particularly to a power grid reliability evaluation distribution network accurate equivalent method and system, medium, first, obtaining distribution network original data; according to the original data obtained, the probability distribution of the load demand level of each level is calculated, and the failure load level and its probability distribution of the distribution system to be accurately equivalent under each system state are calculated according to the original data obtained; finally, the equivalent model of the distribution network is obtained according to the calculated probability distribution, and the load expectation of the distribution network is obtained in real time by using the model. The method proposed in the present application fully takes into account the influence of uncertain factors and load level changes in the operation of the distribution network on the safe operation of the power transmission system, and does not increase the calculation burden of the power transmission network operation reliability evaluation.

Description

Technical Field

[0001] This invention relates to the field of power system reliability assessment technology, and in particular to a method, system, and medium for accurate equivalent assessment of distribution networks in power grid reliability assessment. Background Technology

[0002] In a power grid, the transmission system, as the backbone network, plays a crucial role in transmitting electrical energy from the generation side to the distribution side. Faults in this system can cause widespread power outages, severely impacting social and economic life. Therefore, research on transmission system reliability assessment has received significant attention and provides vital decision-making support for power system planning in the modern power industry. Transmission systems inherently operate in an uncertain environment. The large-scale integration of wind and solar renewable energy sources on the power generation side and the substitution of loads with electricity on the load side in modern power systems further exacerbates the volatility and randomness of power distribution in the grid. Therefore, conducting accurate and rapid real-time reliability assessments of the transmission system and formulating reasonable operational support decisions based on these assessments is a crucial way to ensure the safe and economical operation of the power grid.

[0003] The primary application scenario for reliability assessment of conventional power transmission systems is power grid planning and design, which mainly consists of three steps: system fault selection, post-fault system state analysis, and reliability index calculation. Post-fault system state analysis, through power flow calculations or optimal power flow calculations, yields the distribution of voltage and power in the power grid after a fault, as well as the load shedding amount under safety constraints, which is then used to complete the reliability index calculation and assessment. Therefore, post-fault system state analysis is the most crucial part of power transmission system reliability assessment. In the reliability assessment during the power grid planning phase, to balance calculation accuracy and speed, the downstream distribution network is typically treated as a constant load in power flow calculations. Furthermore, to ensure that the calculation results reflect the system's reliability under the most severe operating conditions, this load value is often taken as the rated capacity value of the distribution network.

[0004] Conventional power transmission system reliability assessment techniques have the following problems in real-time operational reliability assessment and decision support for power transmission systems:

[0005] (1) It is impossible to account for changes in the distribution network load level caused by uncertainties in operation;

[0006] (2) The reliability assessment of conventional power transmission systems reflects the average reliability of the power system over a long period of time. It does not take into account the impact of operating conditions, severe weather, etc. on equipment outages and reliability. Conventional methods cannot be directly applied to operational reliability assessment.

[0007] (3) In the conventional power transmission system reliability assessment, the distribution network is equivalent to a completely reliable constant load. However, according to statistics, more than 80% of the faults in the power system occur in the distribution network. Therefore, conventional methods cannot accurately reflect the impact of the distribution network operation reliability on the transmission network. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention treats the distribution system as an equivalent load of the transmission system, proposing the concepts of load demand grading and failure load level. These are used to reflect the impact of distribution network load energy uncertainty and distribution network operational reliability on the safe operation of the transmission network, respectively. Furthermore, it utilizes the concept and method of discrete convolution operations to construct a computational model for accurate load expectation of the distribution network. The method proposed in this invention fully considers the impact of uncertainties in distribution network operation and load level changes on the safe operation of the transmission system, without adding additional computational burden to the transmission network operational reliability assessment.

[0009] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions:

[0010] A precise equivalent method for distribution networks for reliability assessment of power transmission systems is characterized by,

[0011] Obtain raw data from the power distribution network;

[0012] Calculate the probability distribution of load demand levels at each level based on the acquired raw data.

[0013] Calculate the failure load level and probability distribution of the power distribution system to be accurately equivalent under each system state based on the acquired raw data;

[0014] The equivalent model of the distribution network is obtained based on the calculated probability distribution, and the expected load of the distribution network is obtained in real time using this model.

[0015] In the above method, the raw data includes system topology, failure rate of each component, repair time, and load duration curves of each load point.

[0016] In the above method, for each distribution system to be precisely equivalent, a multi-level load demand distribution is used to replace the original load curve based on the load duration curve, and the probability distribution of each level of load demand is calculated according to the following formula:

[0017]

[0018] In the formula, T i The duration of load level i is represented by T, and the total duration of the load curve is represented by T. This represents the probability of the i-th load level, and a discrete probability model of the load demand level is formed based on the above formula.

[0019] In the above method, for each distribution system to be accurately equivalent, the sequential Monte Carlo method is used to sample the system and form the state transition process of the distribution network system. The failure load level of the distribution system to be accurately equivalent under each state is calculated using the following formula:

[0020]

[0021] In the formula L represents the failure load level of the distribution network under system state s. i Represents the load of node i, b ij This represents the element in the i-th row and j-th column of the reachability matrix formed by the system topology. Its value is 1 when there is a path between node i and node j, and 0 otherwise. Node j is the root node of the distribution network, i.e., the equivalent power source node. n represents the total number of load nodes in the distribution network.

[0022] In the above method, the state duration T corresponding to the failed load level of the distribution network under system state s is... s The probability distribution of the failure load level can be obtained using the following formula:

[0023]

[0024] In the formula, L F-i Let represent the i-th failed load level in the distribution network, T represent the total sampling duration, and n represent the number of all failed load levels in the system. This indicates the load level corresponding to failure. F-i The probability of occurrence.

[0025] Using the above method, the accurate equivalent model of the distribution network is obtained using the following formula:

[0026] E(L' D )=E(L' R ·L' F )=E(L' R )·E(L' F )

[0027] In the formula, E(L' D ), E(L' R ) and E(L' F ) represent the expected precise equivalent load, the expected load demand level, and the expected failure load level of the distribution network, respectively.

[0028] In the above method,

[0029]

[0030]

[0031] p Fand p R Let L' represent the probability distributions of the failure load level and the load demand level, respectively, where i and j are the number of variables related to the failure load level and the load demand level, respectively. R-j and L' F-i These represent the normalized load demand level and the distribution network failure load level, respectively.

[0032] In the above method,

[0033] L' F-i =L F-i / L S

[0034] L' R-j =L R-j / L S

[0035] L S L represents the design rated capacity of the distribution network. F-i L represents the i-th failed load level in the distribution network. R-j This represents the j-th load demand level in the distribution network.

[0036] A system, including

[0037] The first module is configured to acquire raw data from the power distribution network.

[0038] The second module is configured to calculate the probability distribution of load demand levels at each stage based on the acquired raw data.

[0039] The third module is configured to calculate the failure load level and probability distribution of the power distribution system to be accurately equivalent under each system state based on the acquired raw data.

[0040] The fourth module is configured to obtain an equivalent model of the distribution network based on the calculated probability distribution, and use this model to obtain the expected load of the distribution network in real time.

[0041] A computer medium storing a computer program capable of executing the method steps of any one of claims 1 to 8.

[0042] Therefore, the present invention has the following advantages:

[0043] 1. It can effectively improve the rationality and accuracy of power grid reliability assessment results;

[0044] 2. It can fully account for the impact of uncertainties in the operation of the distribution network and changes in load levels on the safe operation of the transmission system;

[0045] 3. It will not increase the computational burden during reliability assessment, nor will it affect the computational speed of reliability assessment. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a precise equivalent method for distribution networks used in power transmission system reliability assessment.

[0047] Figure 2 This is the primary wiring diagram for the IEEE-RBTS power transmission system.

[0048] Figure 3 This is a topology diagram of the power distribution system at BUS5 of the IEEE-RBTS power transmission system.

[0049] Figure 4 This is the load curve for level 8 of BUS5.

[0050] Figure 5 The probability density curve of the failed load in the BUS5 distribution network.

[0051] Figure 6 The variation of the accurate load expectation of the distribution network with the increase of component failure rate. Detailed Implementation

[0052] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0053] Example:

[0054] Figure 1 This is a schematic diagram of a technical solution for a precise equivalent method for distribution networks used in power transmission system reliability assessment. Figure 1 As shown, the main technical steps of this patent application include:

[0055] (1) Input the raw data required for calculation, including system topology, failure rate of each component, repair time, and load duration curve of each load point.

[0056] (2) For each distribution system to be precisely equivalent, based on the load duration curve, a multi-level load demand distribution is used to replace the original load curve, and the probability distribution of each level of load demand is calculated according to the following formula:

[0057]

[0058] In the formula, T i The duration of load level i is represented by T, and the total duration of the load curve is represented by T. Let represent the probability of the i-th load level. Based on the above formula, a discrete probability model of the load demand level is formed.

[0059] (3) For each distribution system to be precisely equivalent, the sequential Monte Carlo method is used to sample the system and form the state transition process of the distribution network system. The failure load level of the distribution system to be precisely equivalent under each state is calculated using the following formula:

[0060]

[0061] In the formula L represents the failure load level of the distribution network under system state s. i Represents the load of node i, b ij This represents the element in the i-th row and j-th column of the reachability matrix formed by the system topology. Its value is 1 when there is a path between node i and node j, and 0 otherwise. Node j is the root node of the distribution network, i.e., the equivalent power source node. n represents the total number of load nodes in the distribution network.

[0062] (4) Statistical steps (3) for each The corresponding state duration T s The probability distribution of the failure load level is obtained using the following formula:

[0063]

[0064] In the formula, L R-i Let represent the i-th failed load level in the distribution network, T represent the total sampling duration, and n represent the number of all failed load levels in the system. This indicates the load level corresponding to failure. R-i The probability of occurrence.

[0065] (5) The accurate equivalent model of the distribution network is obtained using the following formula:

[0066] E(L' D )=E(L' R ·L' F )=E(L' R )·E(L' F )

[0067] In the formula, E(L' D ), E(L' R ) and E(L' F ) represent the expected precise equivalent load, the expected load demand level, and the expected failure load level of the distribution network, respectively, where:

[0068]

[0069]

[0070] L' F-i =L F-i / L S

[0071] L' R-j =L R-j / L S

[0072] p F and p R Let L' represent the probability distributions of the failure load level and the load demand level, respectively, where i and j are the number of variables related to the failure load level and the load demand level, respectively. R-j and L' F-i L represents the normalized load demand level and the distribution network failure load level, respectively. S This indicates the design rated capacity (MW) of the distribution network.

[0073] Taking the IEEE-RBTS test system as an example, this paper provides a detailed explanation of the method for accurate equivalent evaluation of distribution networks according to this invention, and compares and analyzes the results with those of conventional evaluation methods. Figure 2 This is the primary wiring diagram for the IEEE-RBTS power transmission system.

[0074] First, obtain the basic information of the system. The voltage level of this power transmission system is 230kV. The system has 2 generator sets, 9 transmission lines, and 6 busbars. The rated capacity of the generators is 240MW, and the rated capacity of the load is 185MW. The primary wiring diagram of the system is as follows: Figure 2 As shown, buses 2, 3, 4, 5 and 6 are connected to loads, meaning that each of these buses has its own downstream power distribution system.

[0075] To facilitate the analysis of the impact of equivalent load changes in the distribution system on the transmission system, this invention only performs precise equivalence analysis on the distribution system at BUS5 and studies the impact of the equivalent results on the safe operation of the transmission system under various operating conditions. The topology of the distribution system at BUS5 is as follows: Figure 3 As shown.

[0076] The peak load of this power distribution system is 20 MW, with 4 feeders and 26 load nodes. According to the method of step (3) of this invention, the load duration curve is divided into 8 levels, forming the following... Figure 4 The 8-level load curves of BUS5 are shown. The duration of each load level is statistically analyzed, and the discrete probability distribution of load demand levels is shown in Table 1.

[0077] Table 1 Discrete Probability Distribution of Load Demand Levels in BUS5 Power Distribution System

[0078]

[0079] Then, according to the method of technical step (4) of the present invention, the discrete probability distribution of the failure load classification level of the distribution network is calculated. The state of the components is randomly sampled using the sequential Monte Carlo method. The simulation period for this example is 1000 years. First, according to The state transition process of all components in the power distribution system during the simulation time is obtained, combined with Figure 3 The topology shown represents the system state transition process, and the process from the root node in each state is analyzed. Figure 3 The load after system failure is viewed from the 138kV busbar. During the simulation period, numerous recurring system states will occur. These identical system states will be merged, and their occurrence probabilities will be statistically analyzed to obtain the following results: Figure 5 The failure load probability density curve at BUS5 is shown, and it is represented in the form shown in Table 2.

[0080] Table 2 Discrete Probability Distribution of Failure Load Classification Levels in BUS5 Power Distribution System

[0081] Probability (%) 88.1 1.50 1.29 1.28 1.06 0.99 0.76 0.29

[0082] Calculations show that the power distribution system at BUS5 has 23 possible system states within a 1000-year sampling period. For ease of presentation, only the top 8 states with the highest probability of occurrence are listed, as shown in Table 2. This result indicates that, given the equipment reliability parameters and considering only the impact of power distribution system operational reliability, the busbar where BUS5 is located has an 88.1% probability of requiring 20 MW of power. This system state corresponds to the normal operating state of the power distribution network.

[0083] Combining the data in Tables 1 and 2, and using the technical step (5) of this invention, the expected precise operating load of the bus where BUS5 is located can be obtained. This value is calculated to be E(L'). R )·E(L' F =0.6651 * 0.9893 = 0.6580, which translates to a nominal value of 13.16MW, approximately 34.2% lower than the 20MW used in conventional reliability assessments.

[0084] In particular, when distribution network equipment ages or operates in harsh environments, such as during typhoons or hailstorms, the reliability of internal components deteriorates, leading to a significant increase in failure rate and repair time. Therefore, this invention takes BUS5 as an example to study the impact of distribution network component reliability on the accurate load expectation of the busbar under different conditions. The results are as follows: Figure 6 As shown.

[0085] from Figure 6It can be seen that when the failure rate of components in the distribution network increases due to the deterioration of the operating environment or the aging of equipment, the expected accurate load of the bus where BUS5 is located will decrease rapidly. When the failure rate increases to 8 times the original value, the expected accurate load of the distribution network will decrease to 7.29MW, which is 63.55% less than the 20MW used in the reliability assessment of conventional transmission systems.

[0086] As can be seen from the calculation results of this invention, the operating curve of the distribution network load and the equipment reliability index both lead to changes in the accurate load expectation of the distribution network, thus introducing errors into the transmission network operation reliability assessment. The method of this invention effectively considers the impact of the distribution network load continuity curve and its own operation reliability on the transmission network operation reliability assessment, making the assessment results more reasonable and accurate.

[0087] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A precise equivalent method for distribution networks in power grid reliability assessment, characterized in that, Obtain raw data from the power distribution network; Calculate the probability distribution of load demand levels at each level based on the acquired raw data. Calculate the failure load level and probability distribution of the power distribution system to be accurately equivalent under each system state based on the acquired raw data; Based on the calculated probability distribution, an equivalent model of the distribution network is obtained, and this model is used to obtain the expected load of the distribution network in real time. For each distribution system to be precisely equivalent, based on the load duration curve, a multi-level load demand distribution is used to replace the original load curve, and the probability distribution of each level of load demand is calculated according to the following formula: In the formula, The duration of load level i is represented by T, and the total duration of the load curve is represented by T. Let i represent the probability of the i-th load level, and form a discrete probability model of the load demand level based on the above formula; For each distribution system to be precisely equivalent, the sequential Monte Carlo method is used to sample the system and form the state transition process of the distribution network system. The failure load level of the distribution system to be precisely equivalent under each state is calculated using the following formula: In the formula This indicates the level of failed loads in the distribution network under system state s. This represents the load of node i. The element in the i-th row and j-th column of the reachability matrix formed by the system topology is represented. Its value is 1 when there is a path between node i and node j, and 0 otherwise. Node j is the root node of the distribution network, i.e., the equivalent power source node. n represents the total number of load nodes in the distribution network. The duration of the state corresponding to the failed load level in the distribution network under system state s. The probability distribution of the failure load level can be obtained using the following formula: In the formula, This represents the i-th failed load level in the distribution network. This represents the total sampling duration, and n represents the number of all failure load levels in the system. Indicates the level corresponding to the failure load. The probability of occurrence; The accurate equivalent model of the distribution network is obtained using the following formula: In the formula, , and These represent the expected precise equivalent load, the expected load demand level, and the expected failure load level of the distribution network, respectively.

2. The method according to claim 1, characterized in that, The raw data includes system topology, failure rate of each component, repair time, and load duration curves at each load point.

3. The method according to claim 1, characterized in that, and Let i and j represent the probability distributions of the failure load level and the load demand level, respectively, where i and j are the number of variables related to the failure load level and the load demand level, respectively. and These represent the normalized load demand level and the distribution network failure load level, respectively.

4. The method according to claim 3, characterized in that, Indicates the design rated capacity of the distribution network. This represents the i-th failed load level in the distribution network. This represents the j-th load demand level in the distribution network.

5. A system, characterized in that, The system is used in the method as described in any one of claims 1-4, the system comprising The first module is configured to acquire raw data from the power distribution network. The second module is configured to calculate the probability distribution of load demand levels at each stage based on the acquired raw data. The third module is configured to calculate the failure load level and probability distribution of the power distribution system to be accurately equivalent under each system state based on the acquired raw data. The fourth module is configured to obtain an equivalent model of the distribution network based on the calculated probability distribution, and use this model to obtain the expected load of the distribution network in real time.

6. A computer medium, characterized in that, The device contains a computer program capable of executing the method steps of any one of claims 1 to 4.

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