Distributed intelligent power grid digital simulation method, device, equipment, medium and product

Abstract

The invention relates to a distributed smart power grid digital simulation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: dividing a distributed smart power grid into a plurality of primary regional networks; each primary area network comprises a distributed power supply, and a junction point between two adjacent primary area networks is a digital twin terminal; for each primary area network, determining equivalent parameters of the primary area network according to the load operation condition and the distributed power supply operation condition in the primary area network; performing clustering analysis on the plurality of initial regional networks by taking the equivalent parameters as characteristic values to obtain an aggregated regional network; repeating the clustering process until a preset cut-off condition is reached; and determining an equivalent model of the distributed smart power grid according to the equivalent parameters of the distributed smart power grid meeting the preset cut-off condition. Large-scale node simulation calculation can be decomposed into simulation calculation of a plurality of small networks, and rapid simulation of the local area network can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, and in particular to a distributed smart grid digital simulation method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] The new power system mainly uses new energy as the main body, and large-scale distributed power is connected to the distribution network. Especially, the whole county roof distributed photovoltaic is connected. The distribution network will have a large number of power access points. Fast simulation and analysis of the voltage and current distribution of the distribution network in normal operation can help to plan the network structure, transformer capacity and access point selection, reasonably formulate the dispatching operation strategy, reduce power backflow, improve the local consumption rate, and simulate and analyze the operation in the event of a fault. It is helpful to develop a reasonable transfer power strategy and protection control rule.

[0003] At present, the digital simulation technology for the distribution network focuses on the calculation and analysis of the whole power grid, and the output is the operation result of the whole power grid. It is not possible to perform targeted and fast simulation and analysis on a local area. SUMMARY

[0004] Therefore, it is necessary to provide a distributed smart grid digital simulation method, device, computer equipment, computer readable storage medium and computer program product capable of fast simulation of a local area network to solve the above technical problems.

[0005] In a first aspect, the present application provides a distributed smart grid digital simulation method applied to a distributed smart grid, wherein the distributed smart grid includes a plurality of distributed power sources, a plurality of digital twin terminals and a plurality of loads, and the method comprises:

[0006] The distributed smart grid is divided into a plurality of primary area networks; each primary area network includes one distributed power source, and the junction between two adjacent primary area networks is a digital twin terminal;

[0007] For each primary area network, the equivalent parameters of the primary area network are determined according to the load operating condition and the distributed power source operating condition in the primary area network;

[0008] The plurality of initial area networks are subjected to cluster analysis with the equivalent parameters as characteristic values to obtain an aggregated area network;

[0009] repeating the steps of determining equivalent parameters of the aggregated regional network according to load operating conditions and distributed power operating conditions within the aggregated regional network, clustering the aggregated regional network and the primary regional network not aggregated with the equivalent parameters as characteristic values, and updating the aggregated regional network until a preset stopping condition is reached, in a case where the aggregated regional network does not meet the preset stopping condition;

[0010] determining an equivalent model of the distributed smart grid according to the equivalent parameters of the distributed smart grid under the preset stopping condition.

[0011] In one of the embodiments, the equivalent parameters include static equivalent parameters and dynamic equivalent parameters.

[0012] The determining of the equivalent parameters of the primary regional network according to the load operating conditions and the distributed power operating conditions within the primary regional network includes:

[0013] determining the static equivalent parameters according to the regional outlet voltage of the primary regional network, the active power and the reactive power of each load, and the active power and the reactive power of the distributed power;

[0014] controlling the primary regional network to access the demand power of the load to change multiple times, and obtaining corresponding disturbance power and regional outlet voltage of the primary regional network;

[0015] determining the dynamic equivalent parameters according to multiple disturbance power and corresponding regional outlet voltage.

[0016] In one of the embodiments, the determining of the static equivalent parameters according to the regional outlet voltage of the primary regional network, the active power and the reactive power of each load, and the active power and the reactive power of the distributed power includes:

[0017] for each load in the primary regional network, inputting the active power and the reactive power of the load into a pre-constructed load equivalent model to obtain first equivalent model parameters;

[0018] inputting the active power and the reactive power of the distributed power in the primary regional network into a pre-constructed distributed power equivalent model to obtain second equivalent model parameters;

[0019] inputting the first equivalent model parameters corresponding to each load and the second equivalent model parameters corresponding to the distributed power into a pre-constructed static parameter calculation model to obtain the static equivalent parameters.

[0020] In one of the embodiments, the determining of the dynamic equivalent parameters according to multiple disturbance power and corresponding regional outlet voltage includes:

[0021] The disturbance power and the change amount of the corresponding area outlet voltage are input into a pre-constructed dynamic parameter calculation model to obtain the dynamic equivalent parameter.

[0022] In one of the embodiments, the equivalent parameter includes a static equivalent parameter and a dynamic equivalent parameter; the aggregated area network includes a static aggregated area network and a dynamic aggregated area network; and the clustering analysis of the multiple initial area networks with the equivalent parameter as a characteristic value to obtain the aggregated area network includes:

[0023] The first clustering analysis of the multiple initial area networks with the static equivalent parameter as a characteristic value to obtain the static aggregated area network;

[0024] The second clustering analysis of the multiple initial area networks with the dynamic equivalent parameter as a characteristic value to obtain the dynamic aggregated area network.

[0025] In one of the embodiments, the method further includes:

[0026] In the case where the total number of the static aggregated area network and the initial area network is 1, the first clustering analysis is stopped;

[0027] The first electrical distance between the dynamic aggregated area network and the un-aggregated initial area network and the second electrical distance between two dynamic aggregated area networks are obtained;

[0028] In the case where the first electrical distance and the second electrical distance are both greater than or equal to a preset clustering threshold, the second clustering analysis is stopped.

[0029] In a second aspect, the application further provides a distributed smart grid digital simulation device, which includes:

[0030] A network division module is configured to divide the distributed smart grid into multiple initial area networks; each of the initial area networks includes one distributed power supply, and the boundary between two adjacent initial area networks is the digital twin terminal;

[0031] An equivalent model establishment module is configured to determine the equivalent parameter of each initial area network according to the load operating condition and the distributed power supply operating condition in the initial area network.

[0032] The clustering module is configured to perform clustering analysis on the plurality of initial regional networks with the equivalent parameters as characteristic values to obtain aggregated regional networks; and in a case where the aggregated regional networks do not satisfy a preset stop condition, repeatedly performing the steps of determining the equivalent parameters of the aggregated regional networks according to the load operating conditions and the distributed power supply operating conditions within the aggregated regional networks, performing clustering analysis on the aggregated regional networks and the initial regional networks that have not been aggregated with the equivalent parameters as characteristic values, and updating the aggregated regional networks until the preset stop condition is reached.

[0033] The equivalent model establishing module is further configured to determine the equivalent model of the distributed smart grid according to the equivalent parameters of the distributed smart grid under the condition that the distributed smart grid satisfies the preset stop condition.

[0034] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the distributed smart grid digital simulation method provided in any of the above embodiments when executing the computer program.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the distributed smart grid digital simulation method provided in any of the above embodiments.

[0036] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the distributed smart grid digital simulation method provided in any of the above embodiments.

[0037] The distributed smart grid digital simulation method, device, computer equipment, computer readable storage medium and computer program product divide the distributed smart grid into a plurality of primary regional networks with a distributed power supply as a regional center and a digital twin terminal as a regional boundary, determine equivalent parameters of each primary regional network according to the load operating condition and the distributed power supply operating condition in the primary regional network, construct an equivalent model of each primary regional network, and then perform clustering analysis on the plurality of initial regional networks with the equivalent parameters as characteristic values to obtain an aggregated regional network. In this way, when a local regional network of the distributed smart grid is simulated and analyzed, such as power flow calculation, the primary regional networks except the local regional network can be clustered into an aggregated regional network, and only the external characteristics of the aggregated regional network are needed to participate in the simulation calculation of the local regional network, so that fast local power flow calculation can be realized. In the case that the aggregated regional network does not meet the preset cutoff condition, the steps of determining the equivalent parameters of the aggregated regional network according to the load operating condition and the distributed power supply operating condition in the aggregated regional network, performing clustering analysis on the aggregated regional network and the primary regional networks that are not aggregated with the equivalent parameters as characteristic values, and updating the aggregated regional network are repeatedly performed until the preset cutoff condition is reached, and the equivalent model of the entire distributed smart grid can be determined according to the equivalent parameters of the distributed smart grid that meet the preset cutoff condition. It can be understood that the entire distributed smart grid can be divided into a plurality of first-level regional networks, each first-level regional network can be divided into a plurality of second-level regional networks, and the division can continue until the nth-level network. The large-scale node simulation calculation can be divided into a plurality of small network simulation calculations, the equivalent parameters of the nth-level regional network can be calculated first, then the equivalent parameters and the equivalent model of the entire distributed smart grid can be obtained by clustering layer by layer upwards in a parallel manner, and the simulation speed is accelerated. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0039] Figure 1 A flowchart of a distributed smart grid digital simulation method in an embodiment;

[0040] Figure 2 A topology diagram of a distributed smart grid in an embodiment;

[0041] Figure 3 A topology diagram in which the distributed smart grid in an embodiment is divided into a plurality of initial regional networks;

[0042] Figure 4 A clustering diagram for performing a first clustering analysis in an embodiment;

[0043] Figure 5 A clustering diagram for performing a second clustering analysis in an embodiment;

[0044] Figure 6 A flow diagram of a distributed smart grid digital simulation method in another embodiment;

[0045] Figure 7 A block diagram of a distributed smart grid digital simulation device in an embodiment;

[0046] Figure 8 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0047] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0048] The present application provides a distributed smart grid digital simulation method, which can be applied in a distributed smart grid. The distributed smart grid includes a plurality of distributed power sources, a plurality of digital twin terminals and a plurality of loads. The distributed power sources can include photovoltaic power generation, wind power generation, hydroelectric power generation, etc. The digital twin terminals can be integrated in the inverters of the distributed power sources or integrated in the middle, and can also be installed on the load side. By constructing a virtual model of the overall distributed power source and distributed grid, and performing real-time data interaction with the physical world, simulation, monitoring and optimization of the grid can be realized.

[0049] In an exemplary embodiment, as shown in FIG. 1, the distributed smart grid digital simulation method includes the following steps S100-S500. Figure 1

[0050] S100, divide the distributed smart grid into a plurality of primary area networks, each primary area including a distributed power source, and the junction between two adjacent primary area networks being a digital twin terminal.

[0051] The distributed power source can be taken as the center to search outwardly, and when a digital twin terminal is searched, the search is stopped, and the searched load and the distributed power source form a primary area network. Figure 2 An exemplary topological structure of a distributed smart grid. The distributed smart grid includes 69 nodes and 6 distributed power sources (i.e. Figure 2 ​DG) and each node represents a load. Six digital twin terminals are integrated at node 3, node 11, node 18, node 28, node 42 and node 59 respectively. Centered on the distributed power supply, the node where the digital twin terminal is located is the boundary, and the distributed intelligent power grid can be divided into six primary regional networks, as shown in Figure 3 For ease of calculation and analysis, the range of the primary regional network cannot be too small, and each primary regional network includes at least 8 nodes.

[0052] S200, for each primary regional network, determining the equivalent parameters of the primary regional network according to the load operating condition and the distributed power supply operating condition in the primary regional network.

[0053] The equivalent model of the primary regional network is to equivalent the primary regional network to a simple model of a generator + impedance, as long as the external characteristics (such as current, voltage, power, frequency, etc.) of the model are consistent with the external characteristics of the boundary of the primary regional network, which can be used to support power flow calculation and steady-state analysis, etc. The equivalent parameters are characteristic values used to describe the equivalent model of the primary regional network.

[0054] The load operating condition refers to the operating condition of each load in the primary regional network, including the demand power and demand voltage of each load. The distributed power supply operating condition refers to the operating condition of the distributed power supply in the primary regional network, including the output power, output voltage and working frequency of the distributed power supply.

[0055] According to the operating condition of each load and the operating condition of the distributed power supply, the corresponding equivalent parameters can be calculated, and the equivalent model of the primary regional network can be established.

[0056] S300, using the equivalent parameters as characteristic values, performing clustering analysis on the multiple initial regional networks to obtain an aggregated regional network.

[0057] The goal of clustering analysis is to aggregate the primary regional networks with similar electrical distances. The clustering objective function can be expressed as formula (1).

[0058]

[0059] wherein,

[0060]

[0061]

[0062]

[0063] wherein, indicates the characteristic value set of the kth regional network, which is the equivalent parameter here; represents all regional networks except the kth regional network; represents the electrical distance between two regional networks; M represents the number of regional networks participating in clustering; represents the distance set of other regional networks to the kth regional network as a clustering center; mean() is an average function; is an intermediate variable; is a clustering coefficient.

[0064] S400, in the case where the aggregated regional network does not satisfy the preset stopping condition, repeating the steps of determining the equivalent parameters of the aggregated regional network according to the load operating conditions and the distributed power source operating conditions in the aggregated regional network, taking the equivalent parameters as characteristic values, performing clustering analysis on the aggregated regional network and the primary regional networks not aggregated, and updating the aggregated regional network until the preset stopping condition is reached.

[0065] Exemplarily, the preset stopping condition can be that a plurality of regional networks have been aggregated into one aggregated regional network. In actual application, if it is necessary to simulate and analyze the local regional network of the distributed smart grid, then the local regional network can be clustered into one aggregated regional network. If it is necessary to simulate and analyze the entire distributed smart grid, then a plurality of primary regional networks can be clustered into one aggregated regional network, and the equivalent parameters of the new aggregated regional network are obtained.

[0066] S500, determining the equivalent model of the distributed smart grid according to the equivalent parameters of the distributed smart grid under the condition that the preset stopping condition is satisfied.

[0067] In the embodiments of the present application, the distributed smart grid is divided into a plurality of primary regional networks with the distributed power supply as the regional center and the digital twin terminal as the regional boundary. For each primary regional network, equivalent parameters of the primary regional network are determined according to the load operating condition and the distributed power supply operating condition in the primary regional network, to construct an equivalent model of each primary regional network. Then, the multiple initial regional networks can be clustered and analyzed by taking the equivalent parameters as characteristic values, to obtain an aggregated regional network. In this way, when simulating and analyzing the local regional network of the distributed smart grid, such as power flow calculation, the primary regional networks other than the local regional network can be clustered into an aggregated regional network, and only the external characteristics of the aggregated regional network are needed to participate in the simulation calculation of the local regional network, so that fast local power flow calculation can be realized. In the case where the aggregated regional network does not meet the preset cutoff condition, the steps of determining the equivalent parameters of the aggregated regional network according to the load operating condition and the distributed power supply operating condition in the aggregated regional network, clustering and analyzing the aggregated regional network and the primary regional networks that have not been aggregated by taking the equivalent parameters as characteristic values, and updating the aggregated regional network are repeatedly performed until the preset cutoff condition is met. The equivalent model of the entire distributed smart grid can be determined according to the equivalent parameters of the distributed smart grid that meet the preset cutoff condition. It can be understood that the entire distributed smart grid can be divided into a plurality of first-level regional networks, each first-level regional network can be divided into a plurality of second-level regional networks, and the division can continue until the nth-level network. The large-scale node simulation calculation can be decomposed into a plurality of small network simulation calculations. The equivalent parameters of the nth-level regional network can be calculated first, and then the equivalent parameters and the equivalent model of the entire distributed smart grid can be obtained by clustering layer by layer upwards in a parallel manner, to speed up the simulation.

[0068] In one exemplary embodiment, the equivalent parameters include static equivalent parameters and dynamic equivalent parameters. The static equivalent parameters are used to describe a static equivalent model, and the dynamic equivalent parameters are used to describe a dynamic equivalent model. The static equivalent model can be used for power flow calculation, and the dynamic equivalent model can be used for disturbance transient analysis.

[0069] According to the load operating condition and the distributed power supply operating condition in the primary regional network, the equivalent parameters of the primary regional network are determined, including the following steps S210-S230.

[0070] S210, according to the regional outlet voltage of the primary regional network, the active power and the reactive power of each load, and the active power and the reactive power of the distributed power supply, the static equivalent parameters are determined.

[0071] S210 can include the following steps S211-S213.

[0072] S211, for each load in the primary area network, input the active power and reactive power of the load into a pre-built load equivalent model to obtain the first equivalent model parameters.

[0073] The load equivalent model can be represented by the ZIP model:

[0074]

[0075]

[0076] in, This indicates the voltage at the output of the regional network. Indicates the active power of the load; Indicates load The constant impedance component of the active power component; Indicates load The constant current component of the active part; This indicates the reactive power of the load; Indicates load The constant impedance component of the reactive part; Indicates load The constant current component of the reactive power component. The first equivalent model may include... , , and .

[0077] S212, input the active and reactive power of the distributed power sources in the primary regional network into the pre-built equivalent model of the distributed power sources to obtain the parameters of the second equivalent model.

[0078] The equivalent model of distributed power sources can be represented using the ZIP pattern:

[0079]

[0080]

[0081] in, Indicates the active power of the load; Indicates load The constant impedance component of the active power component; Indicates load The constant current component of the active part; This indicates the reactive power of the load; Indicates load The constant impedance component of the reactive part; Indicates load The constant current component of the reactive power component. The second equivalent model may include... , , and .

[0082] S213, input the first equivalent model parameters corresponding to each load and the second equivalent model parameters corresponding to the distributed power into the pre-constructed static parameter calculation model to obtain static equivalent parameters.

[0083] The static parameter calculation model can be expressed as:

[0084]

[0085]

[0086]

[0087]

[0088] Wherein, A, B, C, D represent static equivalent parameters.

[0089] After the static equivalent parameters are calculated, the equivalent model of the primary regional network can be constructed:

[0090]

[0091]

[0092] Wherein, P represents the total active power of the primary regional network, and Q represents the total reactive power of the primary regional network.

[0093] S220, control the demand power of the primary regional network to access the load to change multiple times, and obtain the disturbance power and the regional outlet voltage corresponding to the primary regional network.

[0094] Exemplarily, the output power of the distributed power in the primary regional network can be controlled to increase to reduce the demand power of the load, and the disturbance power and the regional outlet voltage change value at the connection between the primary regional network and the power grid are observed to form a disturbance data pair , and the above steps are repeated several times. The output power of the distributed power in the primary regional network can be controlled to decrease to increase the demand power of the load, and the disturbance power and the regional outlet voltage change value at the connection between the primary regional network and the power grid are observed to form a disturbance data pair , and the above steps are repeated several times.

[0095] S230, determine the dynamic equivalent parameters according to the multiple disturbance powers and the corresponding regional outlet voltages.

[0096] The disturbance power and the corresponding change amount of the area outlet voltage can be input into a pre-constructed dynamic parameter calculation model to obtain the dynamic equivalent parameters.

[0097] The dynamic parameter calculation model can be expressed as:

[0098] wherein, represents a transient equivalent coefficient, and r represents a transient equivalent index.

[0099] The dynamic parameter calculation model can be identified by using a least square method according to the observed plurality of disturbance data pairs to obtain the dynamic equivalent parameters and r.

[0100] In an exemplary embodiment, the equivalent parameters are taken as characteristic values to perform clustering analysis on the plurality of initial regional networks to obtain the aggregated regional networks, including the following steps S310-S320.

[0101] S310, the static equivalent parameters are taken as characteristic values to perform first clustering analysis on the plurality of initial regional networks to obtain the static aggregated regional networks.

[0102] The static equivalent parameters can be taken as characteristic values to perform first clustering analysis on the plurality of initial regional networks to obtain the static aggregated regional networks. Exemplarily, as shown in FIG. 3, the initial regional networks A1 and A2 are aggregated into the static aggregated regional network B1, and the initial regional networks A3 and A4 are aggregated into the static aggregated regional network B2. Figure 4

[0103] S320, the dynamic equivalent parameters are taken as characteristic values to perform second clustering analysis on the plurality of initial regional networks to obtain the dynamic aggregated regional networks.

[0104] The dynamic equivalent parameters can be taken as characteristic values to perform second clustering analysis on the plurality of initial regional networks to obtain the dynamic aggregated regional networks. Exemplarily, as shown in FIG. 4, the initial regional networks A1 and A2 are aggregated into the dynamic aggregated regional network D1, and the initial regional networks A3 and A4 are aggregated into the dynamic aggregated regional network D2. Figure 5

[0105] The first clustering analysis and the second clustering analysis can be performed synchronously until a preset stop condition is reached.

[0106] In an exemplary embodiment, the distributed smart grid digital simulation method provided by the present application further includes the following steps S600-S800.

[0107] S600, in the case where the total number of the static aggregated regional networks and the initial regional networks is 1, the first clustering analysis is stopped.

[0108] ​​Because the static equivalent model, or also known as the steady-state power flow equivalent model, is a linear model, it can continuously perform cyclic clustering until the overall aggregated network of the distributed smart grid is obtained. For example, as... Figure 4 As shown, after obtaining the static aggregated regional networks B1 and B2, clustering can be performed to aggregate B1, A5, and A6 into a static aggregated regional network B3. Then, clustering can be performed to aggregate B3 and B2 into a regional network C.

[0109] S700, obtain the first electrical distance between the dynamically aggregated regional network and the unaggregated primary regional network, and the second electrical distance between the two dynamically aggregated regional networks.

[0110] S800, if both the first electrical distance and the second electrical distance are greater than or equal to the preset clustering threshold, stop the second clustering analysis.

[0111] The dynamic equivalent model, also known as the transient equivalent model, is an exponential model. When clustering reaches a certain stage, the electrical distance between dynamic equivalent models increases, making clustering impossible. In this embodiment, a preset clustering threshold is set to limit the cutoff condition for the second clustering analysis. The second clustering analysis stops when the distance sets between all region networks exceed the preset clustering threshold. For example, as shown... Figure 5 As shown, after obtaining the dynamic aggregation regional networks D1 and D2, the second clustering analysis is stopped because the electrical distance between the remaining regional networks is greater than the preset clustering threshold, and only partial aggregation is achieved.

[0112] In a more specific embodiment, such as Figure 6 As shown, the distributed smart grid digital simulation method of this application includes the following steps:

[0113] S1, the distributed smart grid to be simulated is divided into multiple initial regional networks;

[0114] S2, solve for the dynamic and static equivalent models of each initial region network;

[0115] S3, use the static isovalues ​​as feature values ​​for iterative clustering until a single network is formed, and obtain the corresponding static isovalues.

[0116] S4, synchronously perform cyclic clustering using dynamic isoparameters as feature values, cluster into several networks, and obtain the dynamic isoparameters of each network;

[0117] S5 performs static simulation calculations based on static equivalent parameters and transient simulation calculations based on dynamic equivalent parameters.

[0118] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0119] Based on the same inventive concept, this application also provides a distributed smart grid digital simulation device for implementing the distributed smart grid digital simulation method described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more embodiments of the distributed smart grid digital simulation device provided below can be found in the limitations of the distributed smart grid digital simulation method described above, and will not be repeated here.

[0120] In one exemplary embodiment, such as Figure 7 As shown, a distributed smart grid digital simulation device is provided, including: a network partitioning module 702, an equivalent model establishment module 704, and a clustering module 706, wherein:

[0121] The network partitioning module 702 is used to divide the distributed smart grid into multiple primary area networks; each primary area network includes a distributed power source, and the boundary point between two adjacent primary area networks is a digital twin terminal;

[0122] The equivalent model establishment module 704 is used to determine the equivalent parameters of each primary area network based on the load operating conditions and distributed power supply operating conditions within the primary area network.

[0123] The clustering module 706 is used to perform clustering analysis on multiple initial regional networks using the equivalent parameters as feature values ​​to obtain the aggregated regional network. If the aggregated regional network does not meet the preset cutoff condition, the steps of determining the equivalent parameters of the aggregated regional network based on the load operating conditions and distributed power supply operating conditions within the aggregated regional network, performing clustering analysis on the aggregated regional network and the unaggregated primary regional networks using the equivalent parameters as feature values, and updating the aggregated regional network are repeated until the preset cutoff condition is met.

[0124] The equivalent model establishment module 704 is also used to determine the equivalent model of the distributed smart grid based on the equivalent parameters of the distributed smart grid under the preset cutoff conditions.

[0125] In an exemplary embodiment, the equivalent model building module is further configured to determine static equivalent parameters based on the regional outlet voltage of the primary area network, the active and reactive power of each load, and the active and reactive power of the distributed power source.

[0126] The required power of the primary area network access load changes multiple times, and the corresponding disturbance power and area output voltage of the primary area network are obtained.

[0127] Dynamic equivalent parameters are determined based on multiple disturbance powers and corresponding regional output voltages.

[0128] In an exemplary embodiment, the equivalent model building module is further configured to input the active and reactive power of each load in the primary area network into a pre-built load equivalent model to obtain first equivalent model parameters.

[0129] The active and reactive power of the distributed power sources in the primary regional network are input into the pre-built equivalent model of the distributed power sources to obtain the parameters of the second equivalent model.

[0130] Input the first equivalent model parameters corresponding to each load and the second equivalent model parameters corresponding to the distributed power source into the pre-built static parameter calculation model to obtain static equivalent parameters.

[0131] In an exemplary embodiment, the equivalent model building module is also used to input the disturbance power and the corresponding change in the regional outlet voltage into a pre-built dynamic parameter calculation model to obtain dynamic equivalent parameters.

[0132] In an exemplary embodiment, the clustering module is further configured to perform clustering analysis on multiple initial region networks using isometry parameters as feature values ​​to obtain aggregated region networks, including:

[0133] Using static isoparameters as feature values, a first clustering analysis is performed on multiple initial regional networks to obtain static aggregated regional networks;

[0134] Using dynamic isoparameters as feature values, a second clustering analysis is performed on multiple initial regional networks to obtain a dynamically aggregated regional network.

[0135] In an exemplary embodiment, the clustering module is further configured to stop the first clustering analysis when the total number of static aggregated regional networks and the initial regional network is 1.

[0136] Obtain the first electrical distance between the dynamically aggregated regional network and the unaggregated primary regional network, and the second electrical distance between the two dynamically aggregated regional networks;

[0137] If both the first and second electrical distances are greater than or equal to the preset clustering threshold, the second clustering analysis is stopped.

[0138] Each module in the aforementioned distributed smart grid digital simulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0139] In one exemplary embodiment, a computer device is provided, which may be a digital twin terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a distributed smart grid digital simulation method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0140] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the distributed smart grid digital simulation method provided in any of the above embodiments.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the distributed smart grid digital simulation method provided in any of the above embodiments.

[0143] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the distributed smart grid digital simulation method provided in any of the above embodiments.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A distributed smart grid digital simulation method, characterized in that, Applied to a distributed smart grid, the distributed smart grid comprising multiple distributed power sources, multiple digital twin terminals, and multiple loads, the method includes: The distributed smart grid is divided into multiple primary regional networks; each primary regional network includes one of the distributed power sources, and the boundary point between two adjacent primary regional networks is the digital twin terminal; For each primary area network, the equivalent parameters of the primary area network are determined based on the load operating conditions and distributed power supply operating conditions within the primary area network. Using the equivalent parameters as feature values, cluster analysis is performed on the multiple initial regional networks to obtain the aggregated regional network; If the aggregated regional network does not meet the preset cutoff condition, the steps of determining the equivalent parameters of the aggregated regional network based on the load operating conditions and distributed power supply operating conditions within the aggregated regional network, using the equivalent parameters as feature values ​​to perform cluster analysis on the aggregated regional network and the unaggregated primary regional network, and updating the aggregated regional network are repeated until the preset cutoff condition is met. The equivalent model of the distributed smart grid is determined based on the equivalent parameters that meet the preset cutoff conditions.

2. The method according to claim 1, characterized in that, The equivalent parameters include static equivalent parameters and dynamic equivalent parameters; The step of determining the equivalent parameters of the primary area network based on the load operating conditions and distributed power supply operating conditions within the primary area network includes: The static equivalent parameters are determined based on the regional outlet voltage of the primary area network, the active and reactive power of each load, and the active and reactive power of the distributed power source. The required power of the primary area network access load changes multiple times, and the corresponding disturbance power and area outlet voltage of the primary area network are obtained. The dynamic equivalent parameters are determined based on the multiple disturbance powers and the corresponding regional outlet voltages.

3. The method according to claim 2, characterized in that, The step of determining the static equivalent parameters based on the regional outlet voltage of the primary area network, the active and reactive power of each load, and the active and reactive power of the distributed power source includes: For each load in the primary area network, the active power and reactive power of the load are input into a pre-constructed load equivalent model to obtain the first equivalent model parameters. The active and reactive power of the distributed power sources in the primary regional network are input into a pre-constructed equivalent model of the distributed power sources to obtain the parameters of the second equivalent model. The first equivalent model parameters corresponding to each load and the second equivalent model parameters corresponding to the distributed power source are input into the pre-constructed static parameter calculation model to obtain the static equivalent parameters.

4. The method according to claim 2, characterized in that, The step of determining the dynamic equivalent parameters based on multiple disturbance powers and corresponding regional outlet voltages includes: The disturbance power and the corresponding change in the regional outlet voltage are input into a pre-built dynamic parameter calculation model to obtain the dynamic equivalent parameters.

5. The method according to claim 1, characterized in that, The isovalued parameters include static isovalued parameters and dynamic isovalued parameters; the aggregated region network includes static aggregated region networks and dynamic aggregated region networks; the step of using the isovalued parameters as feature values ​​to perform cluster analysis on the multiple initial region networks to obtain the aggregated region network includes: Using the static iso-parameters as feature values, a first clustering analysis is performed on the multiple initial region networks to obtain a static aggregated region network; Using the dynamic iso-parameters as feature values, a second clustering analysis is performed on the multiple initial regional networks to obtain a dynamic aggregated regional network.

6. The method according to claim 5, characterized in that, The method further includes: The first clustering analysis is stopped when the total number of the static aggregated region network and the initial region network is 1. Obtain the first electrical distance between the dynamically aggregated regional network and the unaggregated primary regional network, and the second electrical distance between the two dynamically aggregated regional networks; If both the first electrical distance and the second electrical distance are greater than or equal to a preset clustering threshold, the second clustering analysis is stopped.

7. A distributed smart grid digital simulation device, characterized in that, The device includes: A network partitioning module is used to divide a distributed smart grid into multiple primary area networks; each primary area network includes a distributed power source, and the boundary point between two adjacent primary area networks is a digital twin terminal; The equivalent model establishment module is used to determine the equivalent parameters of each primary area network based on the load operating conditions and distributed power supply operating conditions within the primary area network. The clustering module is used to perform clustering analysis on the multiple initial regional networks using the equivalent parameters as feature values ​​to obtain an aggregated regional network. If the aggregated regional network does not meet the preset cutoff condition, the steps of determining the equivalent parameters of the aggregated regional network based on the load operating conditions and distributed power supply operating conditions within the aggregated regional network, performing clustering analysis on the aggregated regional network and the unaggregated primary regional networks using the equivalent parameters as feature values, and updating the aggregated regional network are repeated until the preset cutoff condition is met. The equivalent model establishment module is also used to determine the equivalent model of the distributed smart grid based on the equivalent parameters that meet the preset cutoff conditions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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