Simulation reliability evaluation system and method for digital power distribution network

Abstract

The invention discloses a simulation reliability evaluation system and method for a digital power distribution network. The simulation reliability evaluation system comprises a first power generation prediction unit, a second power generation prediction unit, a power storage prediction unit, a region division unit, a reliability evaluation unit and a simulation data acquisition unit, the simulation data acquisition unit is used for acquiring load power of all load points in the power distribution network and a target power distribution network topological structure, transmitting the acquired target power distribution network topological structure to the power distribution network coding unit and the region division unit, and transmitting the acquired load power of all the load points in the power distribution network to the reliability evaluation unit. According to the method, the topological structure of the target power distribution network is converted into the tree structure through the breadth-first algorithm, intuitive understanding of the structure of the power distribution network is improved, subsequent analysis and optimization are facilitated, the tree structure is coded through the coding unit of the power distribution network, a fault query tree is formed, and an effective tool is provided for fault positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a simulation reliability evaluation system and method for a digital power distribution network. Background Art

[0002] High-penetration distributed power refers to a system in which a large number of distributed power sources (such as solar photovoltaic power generation, wind power generation, energy storage systems, etc.) are widely connected and integrated into the distribution network, forming a high proportion of distributed energy devices. High-penetration distributed power can promote the high proportion of renewable energy (such as solar and wind energy) connected to the power system, thereby reducing dependence on traditional fossil fuels and reducing greenhouse gas emissions.

[0003] Traditional distribution network reliability assessment systems may not convert the distribution network structure into an easy-to-understand form, which may lead to insufficient intuitive understanding of the system structure, thereby affecting subsequent analysis and optimization work, and traditional distribution network reliability assessment systems may lack effective fault query tools, making it difficult to quickly and accurately locate faults in the distribution network, affecting the reliability assessment of the system. When conducting reliability assessments on photovoltaic and wind power generation, traditional distribution network reliability assessment systems are prone to inaccurate estimates of power generation reliability, and traditional distribution network reliability assessment systems may only use a single assessment method and are unable to fully consider various possible fault conditions, resulting in insufficient comprehensiveness and accuracy of the assessment, and traditional distribution network reliability assessment systems may lack adaptability to new technologies such as new energy and energy storage, and are unable to fully consider the impact of high-penetration distributed power sources on distribution network reliability. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a simulation reliability assessment system and method for a digital distribution network, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a simulation reliability assessment system for a digital distribution network, comprising: a simulation data acquisition unit, a tree structure construction unit, a distribution network encoding unit, a fault query unit, a first power generation prediction unit, a second power generation prediction unit, a power storage prediction unit, a region division unit, and a reliability assessment unit;

[0009] The simulation data acquisition unit is used to acquire the load power of all load points in the distribution network and the target distribution network topology structure, and transmit the acquired target distribution network topology structure to the tree structure construction unit and the area division unit, and transmit the acquired load power of all load points in the distribution network to the reliability evaluation unit;

[0010] The tree structure construction unit receives the target distribution network topology structure transmitted by the simulation data acquisition unit, and traverses the target distribution network topology structure through a breadth-first algorithm, and when traversing to a node connected to a feeder partition, determines whether the node is a switch node.

[0011] As a preferred solution of the simulation reliability evaluation system of the digital distribution network described in the present invention, it also includes:

[0012] If it is a switch node, determine the type of the switch node;

[0013] If the type of the switch node is a circuit breaker or a fuse, the switch node is layered;

[0014] If the type of the switch node is an isolating switch and the number is greater than or equal to three, the switch node is layered;

[0015] If the switch node connected to the feeder partition is a disconnector, the switch node is graded;

[0016] If there are two or more switch nodes in the same layer, the switch nodes are branched, so as to convert the target distribution network topology structure into a tree structure of the target distribution network, and transmit the converted tree structure of the target distribution network to the distribution network encoding unit.

[0017] As a preferred solution of the simulation reliability evaluation system of the digital distribution network described in the present invention, it also includes:

[0018] The distribution network encoding unit receives the tree structure of the target distribution network transmitted by the tree structure building unit, and determines the first two bits of the node in the tree structure of the target distribution network according to the type of the switch node in the tree structure of the target distribution network, and determines the last N bits of the switch node according to the number of layers where the switch node is located, thereby obtaining a fault query tree of the distribution network, and transmitting the obtained fault query tree of the distribution network to the fault query unit;

[0019] The fault query unit receives the fault query tree transmitted by the distribution network encoding unit, and when a component fault occurs, queries the location of the faulty component in the target distribution network through the fault query tree.

[0020] As a preferred solution of the simulation reliability evaluation system of the digital distribution network described in the present invention, wherein: the first power generation prediction unit evaluates the output power of photovoltaic power generation through the reliability model of photovoltaic power generation, and evaluates the time-series output power of photovoltaic power generation through the time-series reliability model of photovoltaic power generation, and transmits the evaluated time-series output power of photovoltaic power generation to the power storage prediction unit and the reliability evaluation unit, and the reliability model of photovoltaic power generation is specifically:

[0021]

[0022] Among them, P T represents the output power of photovoltaic power generation, P0 represents the rated output power of photovoltaic power generation, I T represents the actual light intensity, I0 represents the light intensity under normal conditions, α T represents the temperature coefficient of the solar power system, T represents the temperature of the solar power system, and T0 represents the temperature of the solar power system under evaluation;

[0023] The timing reliability model of photovoltaic power generation is specifically:

[0024] P T′ =P T ×OSC t

[0025] Among them, P T ′ represents the sequential output power of photovoltaic power generation, P T Represents the output power of photovoltaic power generation, OSC t Represents the operating status of the photovoltaic power generation equipment at time t.

[0026] As a preferred solution of the simulation reliability evaluation system of the digital distribution network described in the present invention, the second power generation prediction unit evaluates the output power of wind power generation through the power generation prediction model of wind power generation, and evaluates the time-series output power of wind power generation through the time-series power generation prediction model of wind power generation. The evaluated time-series output power of wind power generation is transmitted to the power storage prediction unit and the reliability evaluation unit. The power generation prediction model of wind power generation is specifically:

[0027]

[0028] Among them, P Wind Represents the output power of wind power generation, P w0 represents the rated output power of wind power generation, v represents the actual light intensity, v0 represents the rated wind speed, vout represents the cut-off wind speed, v i Indicates the cut-in wind speed of the fan;

[0029] The specific time-series power generation prediction model of wind power generation is:

[0030] P wind0t =P windt ×OSC t

[0031] Among them, P Wind ′ represents the output power of wind power generation at time t, P Wind represents the corrected output power of wind power at time t, OSC t Indicates the operating status of wind power generation at time t.

[0032] As a preferred solution of the simulation reliability assessment system of the digital distribution network described in the present invention, wherein: the power storage prediction unit receives the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit and the time-series output power of wind power generation transmitted by the second power generation prediction unit, and evaluates the charge state at any time through the energy storage station model, the energy storage station model includes the energy storage station model in the charging state and the energy storage station model in the discharging state, and transmits the evaluated charge state of the energy storage station at any time to the reliability assessment unit, the energy storage station model in the charging state is specifically:

[0033]

[0034] Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i c represents the charging power of energy storage, η c represents the charging efficiency, c i represents the total capacity of the energy storage station, and P i c With P Wind ′ and P T 'Related;

[0035] The energy storage station model in the charging state is as follows:

[0036]

[0037] Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i d represents the discharge power of energy storage, η drepresents the discharge efficiency, c i Indicates the total capacity of the energy storage station.

[0038] As a preferred solution of the simulation reliability assessment system of the digital distribution network described in the present invention, wherein: the area division unit receives the target distribution network topology structure transmitted by the simulation data acquisition unit, and traverses the target distribution network topology structure through a breadth-first algorithm to partition the target distribution network, and determine the load points and distributed power sources in each area, and transmit the partitioned distribution network to the reliability assessment unit;

[0039] The reliability assessment unit receives the load power of all load points in the distribution network transmitted by the simulation data acquisition unit, the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit, the time-series output power of wind power generation transmitted by the second power generation prediction unit, the charge state of the energy storage station at any time transmitted by the power storage prediction unit, and the partitioned distribution network transmitted by the area division unit, and evaluates the reliability of the partitioned distribution network through a sequential Monte Carlo algorithm.

[0040] In a second aspect, the present invention provides a simulation reliability assessment method for a digital distribution network, comprising:

[0041] The simulation data acquisition unit collects the load power of all load points in the distribution network and the topological structure of the target distribution network, and transmits the collected load power of all load points in the distribution network and the topological structure of the target distribution network to the tree structure construction unit and the area division unit, and the load power information is also sent to the reliability assessment unit;

[0042] After receiving the topological structure, the tree structure construction unit uses the breadth-first algorithm to traverse it, and hierarchizes and grades the switch nodes according to specific rules, and finally transforms the distribution network topological structure into a tree structure. At the same time, the distribution network coding unit generates a fault query tree based on the tree structure.

[0043] The output power and the time sequence output power of the photovoltaic power generation and the wind power generation are respectively evaluated by the first and second power generation prediction units, and transmitted to the power storage prediction unit, and the power storage prediction unit evaluates the charge state of the energy storage station accordingly;

[0044] The reliability assessment unit summarizes all the above information and uses the sequential Monte Carlo algorithm to assess the reliability of the distribution network.

[0045] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0047] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a simulation reliability evaluation system and method for a digital distribution network. The invention provides the load power of all load points in the distribution network and the target distribution network topology structure through a simulation data acquisition unit, provides basic data for the system, converts the target distribution network topology structure into a tree structure through a breadth-first algorithm, improves the intuitive understanding of the distribution network structure, and is helpful for subsequent analysis and optimization. The tree structure is encoded by the distribution network encoding unit to form a fault query tree, which provides an effective tool for fault location and improves the fault location capability of the system. The output power of photovoltaic power generation is evaluated through a first power generation prediction model, which provides an accurate estimation of the reliability of photovoltaic power generation and provides key parameters for reliability evaluation. The output power of wind power generation is evaluated through a second power generation prediction model, which provides an accurate estimation of the reliability of wind power generation and provides key parameters for reliability evaluation. The invention evaluates the charge state of the energy storage station through an energy storage station model, provides an evaluation of the reliability of the energy storage system, partitions the distribution network through a breadth-first algorithm, helps to comprehensively consider the relationship between distributed power sources and load points, and improves the accuracy of the simulation. By adopting a sequential Monte Carlo algorithm to perform a comprehensive reliability evaluation on the system, various possible fault conditions are comprehensively considered, and the comprehensiveness and accuracy of the evaluation are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 A schematic diagram of the overall system structure of a simulation reliability assessment system and method for a digital distribution network provided by an embodiment of the present invention;

[0050] Figure 2 A method flow chart of traversing the topological structure of a target distribution network in a system and method for simulating reliability assessment of a digital distribution network provided by an embodiment of the present invention;

[0051] Figure 3 A method flow chart of a sequential Monte Carlo algorithm for a simulation reliability assessment system and method for a digital distribution network provided by an embodiment of the present invention;

[0052] Figure 4An internal module block diagram of a tree structure construction unit of a simulation reliability assessment system and method for a digital distribution network provided by an embodiment of the present invention;

[0053] Figure 5 An internal structural diagram of a computer device of a simulation reliability assessment system and method for a digital distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0055] Example 1

[0056] Reference Figure 1-Figure 5 , which is the first embodiment of the present invention, and which provides a simulation reliability assessment system and method for a digital distribution network, comprising:

[0057] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0058] Sequential Monte Carlo algorithm: This is a reliability assessment method that estimates the performance of the system by simulating random processes. In this context, it is used to evaluate the reliability of the distribution network. The specific steps include initializing the system state, calculating the working time (TTF) of each component, simulating the occurrence of faults and their repair time (TTR) over time, and determining whether the affected load points can enter the island operation mode. Finally, the simulation results are statistically analyzed, and the reliability index is calculated after the predetermined simulation period is reached.

[0059] Breadth-first algorithm: This is an algorithm used to traverse or search a tree or graph structure. In the context of the distribution network, it starts from a root node and first visits all nodes with a distance of 1 from the root node, then visits nodes with a distance of 2, and so on, until all nodes have been visited. This method is used here to convert the topology of the distribution network into a tree structure and help with regional division.

[0060] Fault query tree: This is formed by encoding the tree structure of the distribution network to quickly locate the fault location in the distribution network. When the system detects that a component has a fault, the specific location of the fault can be quickly determined based on the fault query tree, thereby improving the efficiency of fault handling.

[0061] There are some technical problems in the related technologies, such as the reliability assessment method of the distribution network is not accurate enough, and cannot fully consider the randomness and uncertainty of distributed power sources, as well as the complexity of the distribution network topology. In addition, the existing assessment methods often ignore the impact of energy storage systems on the reliability of the distribution network, resulting in deviations between the assessment results and the actual operation conditions.

[0062] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to establish a simulation reliability evaluation system for the digital distribution network.

[0063] Figure 1 The present invention shows a schematic diagram of the overall system structure of a simulation reliability assessment system and method for a digital distribution network, including: a simulation data acquisition unit 1, a tree structure construction unit 2, a distribution network encoding unit 3, a fault query unit 4, a first power generation prediction unit 5, a second power generation prediction unit 6, a power storage prediction unit 7, a region division unit 8 and a reliability assessment unit 9;

[0064] In the embodiment of the present application, the simulation data acquisition unit 1 is used to acquire the load power of all load points in the distribution network and the target distribution network topology structure, and transmit the acquired target distribution network topology structure to the tree structure construction unit 2 and the area division unit 8, and transmit the load power of all load points in the distribution network to the reliability evaluation unit 9;

[0065] In an embodiment of the present application, the tree structure construction unit 2 receives the target distribution network topology structure transmitted by the simulation data acquisition unit 1, and traverses the target distribution network topology structure through a breadth-first algorithm. When traversing to a node connected to a feeder partition, it determines whether the node is a switch node.

[0066] If it is a switch node, determine the type of the switch node;

[0067] If the type of the switch node is a circuit breaker or a fuse, the switch node is layered;

[0068] If the type of the switch node is an isolating switch and the number is greater than or equal to three, the switch node is layered;

[0069] If the switch node connected to the feeder partition is a disconnector, the switch node is graded;

[0070] If there are two or more switch nodes in the same layer, the switch nodes are branched, so as to convert the target distribution network topology structure into a tree structure of the target distribution network, and transmit the converted tree structure of the target distribution network to the distribution network encoding unit 3.

[0071] In an optional embodiment, the types of switch nodes may also include other types of switches, such as transfer switches or current limiters, etc. According to different switch types and functions, the tree structure building unit 2 may further refine its layering and branching strategies to ensure that the tree structure can accurately reflect the actual topological structure and operating characteristics of the distribution network.

[0072] It should be noted that the above steps can simplify the complex distribution network topology into a tree structure, which is convenient for subsequent regional division and fault query. Through this conversion, the reliability assessment of the distribution network can be carried out more efficiently while ensuring the accuracy of the assessment process.

[0073] In the embodiment of the present application, the distribution network encoding unit 3 receives the tree structure of the target distribution network transmitted by the tree structure construction unit 2, and determines the first two digits of the node in the tree structure of the target distribution network according to the type of the switch node in the tree structure of the target distribution network, and determines the last N digits of the switch node according to the number of layers where the switch node is located, thereby obtaining a fault query tree of the distribution network, and transmitting the obtained fault query tree of the distribution network to the fault query unit 4;

[0074] In the embodiment of the present application, the fault query unit 4 receives the fault query tree transmitted by the distribution network encoding unit 3, and when a component failure occurs, queries the location of the failed component in the target distribution network through the fault query tree.

[0075] Exemplarily, the distribution network encoding unit 3 receives the tree structure of the target distribution network transmitted by the tree structure building unit 2. The first two codes of the node are determined according to the type of the switch node in the tree structure of the target distribution network. Assume that there are three types of nodes: circuit breaker, fuse, disconnector, and their codes are DL, RD, and DK respectively.

[0076] In this example, further, the encoding of the N bits after the switch node is determined according to the layer number of the switch node. Assuming that the layers are numbered 0 starting from the root node, the position of each node in its layer can be represented by a number, such as 001 represents the first node in the layer.

[0077] In this example, further, the final encoding format may be xxYYY, where xx represents the node type and YYY represents the layer number of the node and its position in the layer.

[0078] In this example, further, the fault query unit 4 receives the fault query tree transmitted by the distribution network encoding unit 3, and when a component failure occurs, the location of the failed component in the target distribution network is queried through the fault query tree.

[0079] Assume that each node in the fault query tree has a unique code xxYYY, where double represents the node type and YYY represents the layer number and position of the node. When a fault occurs, the fault query unit 4 will determine the location of the faulty component according to the code xxYYY on the fault query tree. For example, if FU002 fails, the fault query unit 4 will determine that the fault occurs on the second fuse node of a certain layer according to the code FU002.

[0080] It should be noted that the above steps can quickly locate the fault location, thereby shortening the fault handling time and improving the operating efficiency of the distribution network. In addition, through the coding method of the fault query tree, accurate management and monitoring of each component in the distribution network can be achieved, providing strong support for the maintenance and upgrade of the distribution network.

[0081] In the embodiment of the present application, the first power generation prediction unit 5 evaluates the output power of photovoltaic power generation through the reliability model of photovoltaic power generation, and evaluates the time-series output power of photovoltaic power generation through the time-series reliability model of photovoltaic power generation, and transmits the evaluated time-series output power of photovoltaic power generation to the power storage prediction unit 7 and the reliability evaluation unit 9. The reliability model of photovoltaic power generation is specifically:

[0082]

[0083] Among them, P T represents the output power of photovoltaic power generation, P0 represents the rated output power of photovoltaic power generation, I T represents the actual light intensity, I0 represents the light intensity under normal conditions, α T represents the temperature coefficient of the solar power system, T represents the temperature of the solar power system, and T0 represents the temperature of the solar power system under evaluation;

[0084] The timing reliability model of photovoltaic power generation is specifically:

[0085] P T′ =P T ×OSC t

[0086] Among them, P T ′ represents the sequential output power of photovoltaic power generation, P T Represents the output power of photovoltaic power generation, OSC t Represents the operating status of the photovoltaic power generation equipment at time t.

[0087] It should be noted that the above steps can provide a more accurate and real-time prediction of photovoltaic power generation, thereby providing an important basis for the dispatch and optimization of the distribution network. By considering factors such as actual light intensity, temperature, and equipment operating status, the first power generation prediction unit 5 can more accurately evaluate the time-series output power of photovoltaic power generation, thereby providing data support for the reliability evaluation of the distribution network and the optimal configuration of the energy storage system.

[0088] In the embodiment of the present application, the second power generation prediction unit 6 evaluates the output power of wind power generation through the power generation prediction model of wind power generation, and evaluates the time-series output power of wind power generation through the time-series power generation prediction model of wind power generation. The evaluated time-series output power of wind power generation is transmitted to the power storage prediction unit 7 and the reliability evaluation unit 9. The power generation prediction model of wind power generation is specifically:

[0089]

[0090] Among them, P Wind Represents the output power of wind power generation, P w0 represents the rated output power of wind power generation, v represents the actual light intensity, v0 represents the rated wind speed, v out represents the cut-off wind speed, v i Indicates the cut-in wind speed of the fan;

[0091] The specific time-series power generation prediction model of wind power generation is:

[0092] P wind0t =P windt ×OSC t

[0093] Among them, P Wind ′ represents the output power of wind power generation at time t, P Wind represents the corrected output power of wind power at time t, OSC t Indicates the operating status of wind power generation at time t.

[0094] In the embodiment of the present application, the power storage prediction unit 7 receives the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit 5 and the time-series output power of wind power generation transmitted by the second power generation prediction unit 6, and evaluates the state of charge at any time through the energy storage station model, the energy storage station model includes an energy storage station model in a charging state and an energy storage station model in a discharging state, and transmits the evaluated state of charge of the energy storage station at any time to the reliability evaluation unit 9, the energy storage station model in the charging state is specifically:

[0095]

[0096] Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i c represents the charging power of energy storage, η c represents the charging efficiency, c i represents the total capacity of the energy storage station, and P i c With P Wind ′ and P T 'Related;

[0097] The energy storage station model in the charging state is as follows:

[0098]

[0099] Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i d represents the discharge power of energy storage, η d represents the discharge efficiency, c i Indicates the total capacity of the energy storage station.

[0100] In the embodiment of the present application, the power storage prediction unit 7 predicts the charge and discharge power of the energy storage system based on the photovoltaic power generation time sequence output power transmitted by the first power generation prediction unit 5, combined with the current state of the energy storage system and the characteristics of the energy storage device. The power storage prediction unit 7 calculates the optimal charge and discharge strategy of the energy storage system through an optimization algorithm, such as dynamic programming or particle swarm optimization algorithm, to ensure that the energy storage system can extend its service life and reduce operating costs while meeting the needs of the distribution network.

[0101] In the embodiment of the present application, the area division unit 8 traverses the target distribution network topology structure by using a breadth-first algorithm, including the following steps:

[0102] A1. Select the load point of the branch feeder where the distributed power source is located as the starting node

[0103] A2. Take the load point as the starting node, search for other nodes connected to it in a breadth-first manner, set the traversal distance threshold, and search for load points with a distance of n+1 in sequence until all load points connected to the load point are searched;

[0104] A3. During the traversal process, calculate the relationship between the rated output of the distributed generation and each load point, including distance and power;

[0105] A4. Combined with the island division principle, according to the obtained connection information and relationship, after the traversal is completed, select the load points that meet the island operation rules to obtain the island area;

[0106] In the embodiment of the present application, the sequential Monte Carlo algorithm includes the following steps:

[0107] B1. Initialization: traverse the topological structure of the partitioned distribution network, determine the access year, and set the initial time;

[0108] B2. Generate component working time: Calculate the TTF of each component in the partitioned distribution network through the TTF calculation formula;

[0109] B3, time stepping: compare the TTF of each component in the partitioned distribution network to obtain the minimum TTFmin of each component in the partitioned distribution network, and advance the time step to TTFmin;

[0110] B4. Fault repair time: Find the component corresponding to TTFmin and calculate the TTR of the component using the TTR calculation formula;

[0111] B5. Determination of load points affected by the fault: Find other load points affected by the faulty component and determine whether these load points are in the island area;

[0112] B6. Island operation processing: If the load point can continue to supply power to the load point through island operation, the output power P of photovoltaic power generation in the island area is determined. T and the sequential output power P of wind power generation Wind The relationship between the total load level L in the island area and the normal operation time TTF and failure time TTR of the load points in the island area are calculated;

[0113] B7. Non-island operation processing: If the load point cannot continue to be powered by island operation, its normal operation time TTF and fault time TTR are counted;

[0114] B8, simulation time judgment: judge whether the total simulation time is greater than or equal to the number of simulation years. If the condition is met, execute step B9, otherwise jump to step B2;

[0115] B9. Reliability index calculation: After the simulation time conditions are met, the reliability index is calculated;

[0116] The specific TTF calculation formula is:

[0117] TTF=-MTTF×lnσ

[0118] Among them, TTF represents the normal operation time of the component, and MTTF represents the average normal operation time of the component. λ represents the failure rate of the component, σ represents a random number [0-1] generated by a computer;

[0119] In the embodiment of the present application, the TTR calculation formula is specifically:

[0120] TTR=-MTTR×lnσ

[0121] Among them, TTR represents the component failure repair time, MTTR represents the component mean failure repair time, u represents the failure repair rate of the component, and σ represents a random number [0-1] generated by a computer;

[0122] In an optional embodiment, the Sequential Monte Carlo algorithm can use other optimization algorithms, such as genetic algorithms or simulated annealing algorithms, to improve computational efficiency and accuracy. These algorithms can effectively find the optimal solution or approximate optimal solution in a large-scale search space by simulating natural selection and genetic mechanisms, or by simulating physical annealing processes.

[0123] In the embodiment of the present application, the area division unit 8 receives the target distribution network topology structure transmitted by the simulation data acquisition unit 1, and traverses the target distribution network topology structure through a breadth-first algorithm to partition the target distribution network, and determine the load points and distributed power sources in each area, and transmit the partitioned distribution network to the reliability evaluation unit 9;

[0124] In an embodiment of the present application, the reliability assessment unit 9 receives the load power of all load points in the distribution network transmitted by the simulation data acquisition unit 1, the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit 5, the time-series output power of wind power generation transmitted by the second power generation prediction unit 6, the charge state of the energy storage station at any time transmitted by the power storage prediction unit 7, and the partitioned distribution network transmitted by the area division unit 8, and evaluates the reliability of the partitioned distribution network through the sequential Monte Carlo algorithm.

[0125] In the embodiment of the present application, the tree structure construction unit 2 includes a breadth-first traversal module 201, a switch node judgment and layering module 202, a tree structure conversion module 203 and a branch and layer processing module 204;

[0126] The breadth-first traversal module 201 receives the target distribution network topology structure transmitted from the simulation data acquisition unit 1, and starts from a certain node of the target distribution network topology structure, traverses the nodes layer by layer in a breadth-first manner, and determines whether the connected node is a switch node;

[0127] The switch node judgment and stratification module 202 judges the type of the traversed switch node and performs corresponding stratification operations. If it is a circuit breaker or a fuse, stratification operations are performed. If it is an isolating switch, the number is judged. If it is greater than or equal to three, stratification is performed. If it is an isolating switch connecting a feeder partition, grading is performed.

[0128] The tree structure conversion module 203 constructs a tree structure to represent the target distribution network according to the results of traversal and layering, and transmits the tree structure to the distribution network encoding unit 3;

[0129] The branch and level processing module 204 processes the situation where there are two or more switch nodes in the same level and performs a branch operation.

[0130] In summary, the present invention proposes a simulation reliability evaluation method for a digital distribution network. The present invention provides the load power of all load points in the distribution network and the target distribution network topology structure through a simulation data acquisition unit, provides basic data for the system, converts the target distribution network topology structure into a tree structure through a breadth-first algorithm, improves the intuitive understanding of the distribution network structure, and is helpful for subsequent analysis and optimization. The tree structure is encoded by the distribution network encoding unit to form a fault query tree, which provides an effective tool for fault location and improves the fault location capability of the system. The output power of photovoltaic power generation is evaluated by a first power generation prediction model, and an accurate estimation of the reliability of photovoltaic power generation is provided, which provides key parameters for reliability evaluation. The output power of wind power generation is evaluated by a second power generation prediction model, and an accurate estimation of the reliability of wind power generation is provided, which provides key parameters for reliability evaluation. The present invention evaluates the charge state of the energy storage station through an energy storage station model, and provides an evaluation of the reliability of the energy storage system. The distribution network is partitioned by a breadth-first algorithm, which helps to comprehensively consider the relationship between distributed power sources and load points, and improves the accuracy of the simulation. By adopting a sequential Monte Carlo algorithm to perform a comprehensive reliability evaluation on the system, various possible fault conditions are comprehensively considered, and the comprehensiveness and accuracy of the evaluation are improved.

[0131] This embodiment also provides a simulation reliability assessment method for a digital distribution network, including:

[0132] The simulation data acquisition unit collects the load power of all load points in the distribution network and the topological structure of the target distribution network, and transmits the collected load power of all load points in the distribution network and the topological structure of the target distribution network to the tree structure construction unit and the area division unit, and the load power information is also sent to the reliability assessment unit;

[0133] After receiving the topological structure, the tree structure construction unit uses the breadth-first algorithm to traverse it, and hierarchizes and grades the switch nodes according to specific rules, and finally transforms the distribution network topological structure into a tree structure. At the same time, the distribution network coding unit generates a fault query tree based on the tree structure.

[0134] The output power and the time sequence output power of the photovoltaic power generation and the wind power generation are respectively evaluated by the first and second power generation prediction units, and transmitted to the power storage prediction unit, and the power storage prediction unit evaluates the charge state of the energy storage station accordingly;

[0135] The reliability assessment unit summarizes all the above information and uses the sequential Monte Carlo algorithm to assess the reliability of the distribution network.

[0136] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0137] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a simulation reliability assessment method for a digital distribution network is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0138] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0139] The simulation data acquisition unit collects the load power of all load points in the distribution network and the topological structure of the target distribution network, and transmits the collected load power of all load points in the distribution network and the topological structure of the target distribution network to the tree structure construction unit and the area division unit, and the load power information is also sent to the reliability assessment unit;

[0140] After receiving the topological structure, the tree structure construction unit uses the breadth-first algorithm to traverse it, and hierarchizes and grades the switch nodes according to specific rules, and finally transforms the distribution network topological structure into a tree structure. At the same time, the distribution network coding unit generates a fault query tree based on the tree structure.

[0141] The output power and the time sequence output power of the photovoltaic power generation and the wind power generation are respectively evaluated by the first and second power generation prediction units, and transmitted to the power storage prediction unit, and the power storage prediction unit evaluates the charge state of the energy storage station accordingly;

[0142] The reliability assessment unit summarizes all the above information and uses the sequential Monte Carlo algorithm to assess the reliability of the distribution network.

[0143] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0144] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A simulation reliability assessment system for a digital distribution network, characterized in that: include: A simulation data acquisition unit (1), a tree structure construction unit (2), a distribution network encoding unit (3), a fault query unit (4), a first power generation prediction unit (5), a second power generation prediction unit (6), a power storage prediction unit (7), a region division unit (8) and a reliability evaluation unit (9); The simulation data acquisition unit (1) is used to acquire the load power of all load points in the distribution network and the target distribution network topology structure, and transmit the acquired target distribution network topology structure to the tree structure construction unit (2) and the area division unit (8), and transmit the acquired load power of all load points in the distribution network to the reliability evaluation unit (9); The tree structure construction unit (2) receives the target distribution network topology structure transmitted by the simulation data acquisition unit (1), and traverses the target distribution network topology structure using a breadth-first algorithm, and when traversing to a node connected to a feeder partition, determines whether the node is a switch node.

2. The simulation reliability evaluation system for a digital distribution network according to claim 1, characterized in that: Also includes: If it is a switch node, determine the type of the switch node; If the type of the switch node is a circuit breaker or a fuse, the switch node is layered; If the type of the switch node is an isolating switch and the number is greater than or equal to three, the switch node is layered; If the switch node connected to the feeder partition is a disconnector, the switch node is graded; If there are two or more switch nodes in the same layer, the switch nodes are branched, so as to convert the target distribution network topology structure into a tree structure of the target distribution network, and transmit the converted tree structure of the target distribution network to the distribution network encoding unit (3).

3. The simulation reliability evaluation system for a digital distribution network according to claim 2, characterized in that: Also includes: The distribution network encoding unit (3) receives the tree structure of the target distribution network transmitted by the tree structure building unit (2), and determines the first two bits of the node in the tree structure of the target distribution network according to the type of the switch node in the tree structure of the target distribution network, and determines the last N bits of the switch node according to the number of layers at which the switch node is located, thereby obtaining a fault query tree of the distribution network, and transmitting the obtained fault query tree of the distribution network to the fault query unit (4); The fault query unit (4) receives the fault query tree transmitted by the distribution network encoding unit (3), and when a component fault occurs, queries the location of the faulty component in the target distribution network through the fault query tree.

4. The simulation reliability assessment system for a digital distribution network according to claim 3, characterized in that: The first power generation prediction unit (5) evaluates the output power of photovoltaic power generation through the reliability model of photovoltaic power generation, and evaluates the time-series output power of photovoltaic power generation through the time-series reliability model of photovoltaic power generation, and transmits the evaluated time-series output power of photovoltaic power generation to the power storage prediction unit (7) and the reliability evaluation unit (9), wherein the reliability model of photovoltaic power generation is specifically: Among them, P T represents the output power of photovoltaic power generation, P0 represents the rated output power of photovoltaic power generation, I T represents the actual light intensity, I0 represents the light intensity under normal conditions, and α T represents the temperature coefficient of the solar power system, T represents the temperature of the solar power system, and T0 represents the temperature of the solar power system under evaluation; The timing reliability model of photovoltaic power generation is specifically: P T =P T ×OSC i Among them, P T ′ represents the sequential output power of photovoltaic power generation, P T Represents the output power of photovoltaic power generation, OSC t Represents the operating status of the photovoltaic power generation equipment at time t.

5. The simulation reliability evaluation system for a digital distribution network according to claim 4, characterized in that: The second power generation prediction unit (6) evaluates the output power of wind power generation through the power generation prediction model of wind power generation, and evaluates the time-series output power of wind power generation through the time-series power generation prediction model of wind power generation. The evaluated time-series output power of wind power generation is transmitted to the power storage prediction unit (7) and the reliability evaluation unit (9). The power generation prediction model of wind power generation is specifically: Among them, P Wind Represents the output power of wind power generation, P w0 represents the rated output power of wind power generation, v represents the actual light intensity, v0 represents the rated wind speed, v out represents the cut-off wind speed, v i Indicates the cut-in wind speed of the fan; The specific time-series power generation prediction model of wind power generation is: P wind0t =P windt ×OSC t Among them, P Wind ′ represents the output power of wind power generation at time t, P Wind represents the corrected output power of wind power at time t, OSC t Indicates the operating status of wind power generation at time t.

6. The simulation reliability evaluation system for a digital distribution network according to claim 5, characterized in that: The power storage prediction unit (7) receives the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit (5) and the time-series output power of wind power generation transmitted by the second power generation prediction unit (6), and evaluates the state of charge at any time through the energy storage station model, the energy storage station model includes an energy storage station model in a charging state and an energy storage station model in a discharging state, and transmits the evaluated state of charge of the energy storage station at any time to the reliability evaluation unit (9), the energy storage station model in a charging state is specifically: Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i c represents the charging power of energy storage, η c represents the charging efficiency, c i represents the total capacity of the energy storage station, and P i c With P Wind ′ and P T 'Related; The energy storage station model in the charging state is as follows: Where S(t) represents the state of charge of the energy storage at time t, α represents the self-discharge rate of the energy storage station, Δt represents the time interval, S(t-1) represents the state of charge of the energy storage at time t-1, and P i d represents the discharge power of energy storage, η d represents the discharge efficiency, c i Indicates the total capacity of the energy storage station.

7. The simulation reliability evaluation system for a digital distribution network according to claim 6, characterized in that: The area division unit (8) receives the target distribution network topology structure transmitted by the simulation data acquisition unit (1), and traverses the target distribution network topology structure by using a breadth-first algorithm to partition the target distribution network, determine the load points and distributed power sources in each area, and transmit the partitioned distribution network to the reliability evaluation unit (9); The reliability evaluation unit (9) receives the load power of all load points in the distribution network transmitted by the simulation data acquisition unit (1), the time-series output power of photovoltaic power generation transmitted by the first power generation prediction unit (5), the time-series output power of wind power generation transmitted by the second power generation prediction unit (6), the charge state of the energy storage station at any time transmitted by the power storage prediction unit (7), and the partitioned distribution network transmitted by the area division unit (8), and evaluates the reliability of the partitioned distribution network by using a sequential Monte Carlo algorithm.

8. A simulation reliability assessment method for a digital distribution network, characterized in that: include: The simulation data acquisition unit collects the load power of all load points in the distribution network and the topological structure of the target distribution network, and transmits the collected load power of all load points in the distribution network and the topological structure of the target distribution network to the tree structure construction unit and the area division unit, and the load power information is also sent to the reliability assessment unit; After receiving the topological structure, the tree structure construction unit uses the breadth-first algorithm to traverse it, and hierarchizes and grades the switch nodes according to specific rules, and finally transforms the distribution network topological structure into a tree structure. At the same time, the distribution network coding unit generates a fault query tree based on the tree structure. The output power and the time sequence output power of the photovoltaic power generation and the wind power generation are respectively evaluated by the first and second power generation prediction units, and transmitted to the power storage prediction unit, and the power storage prediction unit evaluates the charge state of the energy storage station accordingly; The reliability assessment unit summarizes all the above information and uses the sequential Monte Carlo algorithm to assess the reliability of the distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to claim 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

Images