Power grid operation state simulation method and device based on equivalent network node dynamic updating, terminal equipment and storage medium

Abstract

The invention discloses a power grid operation state simulation method and device based on equivalent network node dynamic updating, terminal equipment and a storage medium, and belongs to the field of power grid system simulation, and the method comprises the steps: collecting real-time equivalent parameters of a power grid simulation model in a target time period; calculating an evaluation index according to the total number of the sampling points in the target time period and the real-time equivalent parameters; if the evaluation index falls into a first interval, the equivalent network nodes in the model are split, if the evaluation index falls into a second interval, the equivalent network nodes of the model in a target time period serve as processed equivalent network nodes, and if the evaluation index falls into a third interval, the equivalent network nodes in the model are merged, the first interval is located on the left side of the second interval which is located on the left side of the third interval; and performing simulation according to the processed equivalent network nodes to obtain a power grid operation state. By implementing the method and the device, the problem of insufficient simulation precision caused by static equivalent network nodes in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power grid system simulation, and in particular to a power grid operation status simulation method, device, terminal equipment and storage medium based on dynamic updating of equivalent network nodes. Background Art

[0002] In the field of power system analysis and optimization, power grid simulation models are core tools for evaluating the operating status of power grids and formulating dispatching strategies. In the existing technology, the equivalent network nodes of power grid simulation models are usually set statically, that is, through a one-time network equivalent simplification, the nodes and branches in the complex power grid are fixedly merged or simplified. This static equivalent network node can still meet basic analysis needs in the early stage of power grid operation or in steady-state scenarios. However, with the large-scale access of new energy and the complexity of load characteristics, the operating status of the power grid has become highly dynamic and uncertain. Since static nodes cannot adjust their structures and parameters according to real-time changes in the power grid (such as fluctuations in the output of distributed power sources and changes in load peaks and valleys), the deviation between the model and the actual power grid operating status continues to accumulate, making it difficult to ensure simulation accuracy, which in turn affects the reliability of power grid planning, fault analysis, and dispatching decisions. Summary of the Invention

[0003] The present invention provides a method, apparatus, terminal equipment and storage medium for simulating the operation status of a power grid based on dynamic updating of equivalent network nodes. The method can solve the problem of insufficient simulation accuracy caused by static equivalent network nodes in the prior art.

[0004] An embodiment of the present invention provides a method for simulating power grid operation status based on dynamic updating of equivalent network nodes, comprising:

[0005] Collecting real-time equivalent parameters of a power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node;

[0006] Calculating an evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the degree of influence of the distribution of equivalent network nodes on the simulation accuracy;

[0007] If the evaluation index falls within a preset first interval, the equivalent network nodes in the power grid simulation model are split to obtain processed equivalent network nodes.

[0008] If the evaluation index falls within the preset second interval, the equivalent network node of the power grid simulation model within the target time period is used as the processed equivalent network node.

[0009] If the evaluation index falls within a preset third interval, merging the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval;

[0010] The power grid simulation model is updated according to the processed equivalent network nodes, and the power grid operation status is obtained by simulating the updated power grid simulation model.

[0011] Furthermore, the calculation of the evaluation index according to the total number of sampling points in the target time period and the real-time equivalent parameter includes:

[0012] Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter;

[0013] Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter;

[0014] The evaluation index is calculated based on the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period.

[0015] Furthermore, the calculating the evaluation index according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period includes:

[0016] Calculating a real-time evaluation index corresponding to the real-time equivalent parameter according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period;

[0017] Extracting the real-time equivalent parameter corresponding to the last sampling point in the target time period from the real-time equivalent parameter as the second target real-time equivalent parameter;

[0018] The evaluation index is calculated according to the real-time evaluation index, the real-time equivalent parameter and the second target real-time equivalent parameter.

[0019] Furthermore, the real-time equivalent parameters include total electric load power, total load reactive power demand, wind farm active output, photovoltaic power station active output, energy storage system active output and total reactive power output on the power supply side.

[0020] Furthermore, the real-time evaluation index includes a real-time evaluation index of the total power load power, a real-time evaluation index of the total load reactive power demand, a real-time evaluation index of the active output of the wind farm, a real-time evaluation index of the active output of the photovoltaic power station, a real-time evaluation index of the active output of the energy storage system, and a real-time evaluation index of the total reactive power output of the power supply side;

[0021] Furthermore, the calculating of the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter includes:

[0022] The real-time evaluation index is calculated using the following formula:

[0023]

[0024] Where, Represents the real-time evaluation index of total electric load power; P dfh,max Indicates the total power load power with the largest median value of the real-time equivalent parameter; P dfh,min Indicates the total electric load power with the minimum median value of the real-time equivalent parameter; Indicates the total electric load power collected at the xth sampling point; Indicates the normalized total power load power corresponding to the last sampling point in the target time period; n indicates the total number of sampling points in the target time period; Represents the real-time evaluation index of the active output of the wind farm; P fd,max Indicates the active output of the wind farm with the largest median value of the real-time equivalent parameter; P fd,min Indicates the active output of the wind farm with the smallest median value of the real-time equivalent parameter; Indicates the active output of the wind farm collected at the xth sampling point; Indicates the normalized active power output of the wind farm corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the active output of the photovoltaic power station; P gf,max Indicates the active output of the photovoltaic power station with the largest median value of the real-time equivalent parameter; P gf,min Indicates the active output of the photovoltaic power station with the smallest median value of the real-time equivalent parameters; Indicates the active output of the photovoltaic power station collected at the xth sampling point; Indicates the normalized active output of the PV power station corresponding to the last sampling point within the target time period; Represents the real-time evaluation index of the active output of the energy storage system; P cn,max Indicates the active output of the energy storage system with the largest median value of the real-time equivalent parameter; P cn,min Indicates the active output of the energy storage system with the minimum median value of the real-time equivalent parameters; Indicates the stored energy collected at the xth sampling point

[0025] System active output; Indicates the normalized active output of the energy storage system corresponding to the last sampling point within the target time period; Indicates the real-time evaluation index of the total reactive power output on the power supply side; Q dz,max Indicates the total reactive power output of the power supply side with the largest median value of the real-time equivalent parameter; Q dz,min Indicates the total reactive power output on the power supply side with the minimum median value of the real-time equivalent parameters; Indicates the total reactive power output on the power supply side collected at the xth sampling point; Indicates the normalized total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the total load reactive power demand; Q fh,max Indicates the total load reactive power demand with the largest median value of the real-time equivalent parameter; Q fh,min Indicates the total load reactive power demand with the minimum value of the real-time equivalent parameter; It represents the total load reactive power demand collected at the xth sampling point; Indicates the normalized total load reactive power demand corresponding to the last sampling point in the target time period.

[0026] Furthermore, the calculating of the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter includes:

[0027] The evaluation index is calculated by the following formula:

[0028]

[0029] Where, Indicates the total power load power corresponding to the last sampling point in the target time period; K indicates the total number of types included in the real-time equivalent parameter; Indicates the active power output of the wind farm corresponding to the last sampling point in the target time period; Indicates the active output of the PV power station corresponding to the last sampling point within the target time period; Indicates the active output of the energy storage system corresponding to the last sampling point in the target time period; Indicates the total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Indicates the total load reactive power demand corresponding to the last sampling point in the target time period; Represents the evaluation index.

[0030] An embodiment of the present invention further provides a power grid operation status simulation device based on dynamic update of equivalent network nodes, comprising: a data acquisition module, an evaluation index calculation module, an equivalent network node processing module, and a simulation module;

[0031] The data acquisition module is used to collect real-time equivalent parameters of the power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node;

[0032] The evaluation index calculation module is used to calculate the evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the influence of the distribution of equivalent network nodes on the simulation accuracy;

[0033] The equivalent network node processing module is configured to split the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset first interval; use the equivalent network nodes of the power grid simulation model within a target time period as processed equivalent network nodes if the evaluation index falls within a preset second interval; and merge the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset third interval; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval;

[0034] The simulation module is used to update the power grid simulation model according to the processed equivalent network nodes, and obtain the power grid operation status through simulation of the updated power grid simulation model.

[0035] Furthermore, the calculation of the evaluation index according to the total number of sampling points in the target time period and the real-time equivalent parameter includes:

[0036] Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter;

[0037] Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter;

[0038] The evaluation index is calculated based on the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period.

[0039] The present application also provides a terminal device, including:

[0040] one or more processors;

[0041] a memory, coupled to the processor, for storing one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid operation status simulation method based on dynamic update of equivalent network nodes as described in the above-mentioned embodiment of the invention.

[0043] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for simulating the operation status of a power grid based on dynamic updating of equivalent network nodes as described in the above-mentioned embodiment of the invention is implemented.

[0044] The following beneficial effects are achieved by implementing the present invention:

[0045] The present invention provides a power grid operation status simulation method, apparatus, terminal device and storage medium based on dynamic updating of equivalent network nodes. The method significantly improves the accuracy of the power grid simulation model by constructing a dynamic update mechanism. First, by real-time acquisition of equivalent parameters and calculation of evaluation indexes, the impact of the distribution of equivalent network nodes on simulation accuracy can be dynamically quantified. Second, the node merging and splitting strategy based on the evaluation index can adaptively adjust the model complexity according to the dynamic changes of the power grid operation status, and can still maintain high-precision simulation in complex operation scenarios. Finally, the updated power grid simulation model can more accurately simulate the power grid operation status, effectively reduce the power grid operation risk caused by model errors, and provide technical support for the efficient operation of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 This is a flowchart of a method for simulating power grid operation status based on dynamic updating of equivalent network nodes provided by an embodiment of the present application;

[0048] Figure 2 This is a structural diagram of a power grid operation status simulation device based on dynamic update of equivalent network nodes provided by a certain embodiment of the present application;

[0049] Figure 3 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0052] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0053] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0055] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0056] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0057] The following are the professional terms and known technical contents involved in the embodiments of the present invention:

[0058] (1) Equivalent network node: An equivalent network node simplifies the power sources, loads, lines and other components with specific associations in the power grid into a virtual node through aggregation, so as to equivalently represent the electrical characteristics of the original complex area.

[0059] See also Figure 1 To solve the problem of insufficient simulation accuracy caused by static equivalent network nodes in the prior art, an embodiment of the present invention provides a power grid operation status simulation method based on dynamic updating of equivalent network nodes, comprising:

[0060] S1. Collecting real-time equivalent parameters of a power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node;

[0061] In schematic form, since static equivalent network nodes cannot adapt to the dynamic changes in the grid operation status, the simulation results deviate greatly from the actual operation scenario. Therefore, this embodiment collects real-time equivalent parameters of the grid simulation model within the target time period to analyze whether the equivalent network nodes of the grid simulation model should be dynamically adjusted.

[0062] S2. Calculate an evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the influence of the distribution of equivalent network nodes on the simulation accuracy;

[0063] Schematically, in order to quantitatively analyze and accurately evaluate the impact of the distribution of equivalent network nodes on simulation accuracy, this embodiment calculates an evaluation index based on the total number of sampling points in the target time period and the collected real-time equivalent parameters, thereby providing a quantitative decision-making basis for the dynamic optimization of equivalent network nodes, ensuring that the power grid simulation model can accurately match the actual operating status and effectively avoiding the accumulation of simulation errors caused by unreasonable node distribution;

[0064] In a preferred embodiment, the calculation of the evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter includes:

[0065] Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter;

[0066] Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter;

[0067] Calculating the evaluation index according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period;

[0068] In a preferred embodiment, the real-time equivalent parameters include total electric load power, total load reactive power demand, wind farm active output, photovoltaic power station active output, energy storage system active output and power supply side total reactive power output;

[0069] Indicatively, the total power load power, total load reactive power demand, wind farm active output, photovoltaic power station active output, energy storage system active output, and total reactive power output of the power supply side in the real-time equivalent parameters need to be normalized respectively;

[0070] Specifically, assuming that there are n sampling points in the target time period, the normalization processing of the x-th sampling point is explained as an example. The normalization processing formula of the x-th sampling point is as follows:

[0071]

[0072] Where, represents the normalized total electric load power corresponding to the xth sampling point; P dfh,max Indicates the total power load power with the largest median value of the real-time equivalent parameter; P dfh,min Indicates the total electric load power with the minimum median value of the real-time equivalent parameter; Indicates the total electric load power collected at the xth sampling point; represents the normalized active output of the wind farm corresponding to the xth sampling point; P fd,max Indicates the active output of the wind farm with the largest median value of the real-time equivalent parameter; P fd,min Indicates the active output of the wind farm with the smallest median value of the real-time equivalent parameter; Indicates the active output of the wind farm collected at the xth sampling point; represents the normalized active output of the photovoltaic power station corresponding to the x-th sampling point; P gf,max Indicates the active output of the photovoltaic power station with the largest median value of the real-time equivalent parameter; P gf,minIndicates the active output of the photovoltaic power station with the smallest median value of the real-time equivalent parameters; Indicates the active output of the photovoltaic power station collected at the xth sampling point; represents the normalized active output of the energy storage system corresponding to the xth sampling point; P cn,max Indicates the active output of the energy storage system with the largest median value of the real-time equivalent parameter; P cn,min Indicates the active output of the energy storage system with the minimum median value of the real-time equivalent parameters; Indicates the active output of the energy storage system collected at the xth sampling point; represents the normalized total reactive power output of the power supply side corresponding to the xth sampling point; Q dz,max Indicates the total reactive power output of the power supply side with the largest median value of the real-time equivalent parameter; Q dz,min Indicates the total reactive power output on the power supply side with the minimum median value of the real-time equivalent parameters; Indicates the total reactive power output on the power supply side collected at the xth sampling point; represents the normalized total load reactive power demand corresponding to the xth sampling point; Q fh,max Indicates the total load reactive power demand with the largest median value of the real-time equivalent parameter; Q fh,min Indicates the total load reactive power demand with the minimum value of the real-time equivalent parameter; Indicates the total load reactive power demand collected at the xth sampling point.

[0073] In a preferred embodiment, the calculating the evaluation index according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period includes:

[0074] Calculating a real-time evaluation index corresponding to the real-time equivalent parameter according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period;

[0075] Extracting the real-time equivalent parameter corresponding to the last sampling point in the target time period from the real-time equivalent parameter as the second target real-time equivalent parameter;

[0076] Calculating the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter;

[0077] In a preferred embodiment, the real-time evaluation index includes a real-time evaluation index of the total power load power, a real-time evaluation index of the total load reactive power demand, a real-time evaluation index of the active output of the wind farm, a real-time evaluation index of the active output of the photovoltaic power station, a real-time evaluation index of the active output of the energy storage system, and a real-time evaluation index of the total reactive power output of the power supply side;

[0078] The calculating the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter includes:

[0079] The real-time evaluation index is calculated using the following formula:

[0080]

[0081] Where, Represents the real-time evaluation index of total electric load power; P dfh,max Indicates the total power load power with the largest median value of the real-time equivalent parameter; P dfh,min Indicates the total electric load power with the minimum median value of the real-time equivalent parameter; Indicates the total electric load power collected at the xth sampling point; Indicates the normalized total power load power corresponding to the last sampling point in the target time period; n indicates the total number of sampling points in the target time period; Represents the real-time evaluation index of the active output of the wind farm; P fd,max Indicates the active output of the wind farm with the largest median value of the real-time equivalent parameter; P fd,min Indicates the active output of the wind farm with the smallest median value of the real-time equivalent parameter; Indicates the active output of the wind farm collected at the xth sampling point; Indicates the normalized active power output of the wind farm corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the active output of the photovoltaic power station; P gf,max Indicates the active output of the photovoltaic power station with the largest median value of the real-time equivalent parameter; P gf,min Indicates the active output of the photovoltaic power station with the smallest median value of the real-time equivalent parameters; Indicates the active output of the photovoltaic power station collected at the xth sampling point; Indicates the normalized active output of the PV power station corresponding to the last sampling point within the target time period; Represents the real-time evaluation index of the active output of the energy storage system; P cn,max Indicates the active output of the energy storage system with the largest median value of the real-time equivalent parameter; P cn,min Indicates the active output of the energy storage system with the minimum median value of the real-time equivalent parameters; Indicates the active output of the energy storage system collected at the xth sampling point; Indicates the normalized active output of the energy storage system corresponding to the last sampling point within the target time period; Indicates the real-time evaluation index of the total reactive power output on the power supply side; Q dz,max Indicates the total reactive power output of the power supply side with the largest median value of the real-time equivalent parameter; Q dz,min Indicates the total reactive power output on the power supply side with the minimum median value of the real-time equivalent parameters; Indicates the total reactive power output on the power supply side collected at the xth sampling point; Indicates the normalized total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the total load reactive power demand; Q fh,max Indicates the total load reactive power demand with the largest median value of the real-time equivalent parameter; Q fh,min Indicates the total load reactive power demand with the minimum value of the real-time equivalent parameter; It represents the total load reactive power demand collected at the xth sampling point; Indicates the normalized total load reactive power demand corresponding to the last sampling point in the target time period;

[0082] Specifically, the real-time evaluation index of the total power load power is obtained according to the above formula: Real-time evaluation index of total load reactive power demand Real-time evaluation index of wind farm active output Real-time evaluation index of active output of photovoltaic power station Real-time evaluation index of active output of energy storage system and real-time evaluation index of total reactive power output on the power supply side

[0083] In a preferred embodiment, the calculating of the evaluation index according to the real-time evaluation index, the real-time equivalent parameter and the second target real-time equivalent parameter includes:

[0084] The evaluation index is calculated by the following formula:

[0085]

[0086] Where, Indicates the total power load power corresponding to the last sampling point in the target time period; K indicates the total number of types included in the real-time equivalent parameter; Indicates the active power output of the wind farm corresponding to the last sampling point in the target time period; Indicates the active output of the PV power station corresponding to the last sampling point within the target time period; Indicates the active output of the energy storage system corresponding to the last sampling point in the target time period; Indicates the total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Indicates the total load reactive power demand corresponding to the last sampling point in the target time period; represents the evaluation index;

[0087] Specifically, according to the real-time evaluation index of total power load power Real-time evaluation index of total load reactive power demand Real-time evaluation index of wind farm active output Real-time evaluation index of active output of photovoltaic power station Real-time evaluation index of active output of energy storage system and real-time evaluation index of total reactive power output on the power supply side Finally, the evaluation index is obtained, and then the comprehensive impact of the distribution of equivalent network nodes on the accuracy of the power grid simulation model is evaluated based on the evaluation index;

[0088] Specifically, in this embodiment, since the total number of types included in the real-time equivalent parameter is 6, K=6.

[0089] S3. If the evaluation index falls within a preset first interval, the equivalent network nodes in the power grid simulation model are split to obtain processed equivalent network nodes.

[0090] If the evaluation index falls within the preset second interval, the equivalent network node of the power grid simulation model within the target time period is used as the processed equivalent network node.

[0091] If the evaluation index falls within a preset third interval, merging the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval;

[0092] Specifically, it is assumed that the number of equivalent network nodes in the power grid simulation model within the target time period is In obtaining the evaluation index After that, it is necessary to determine the interval in which the evaluation index falls, so as to determine whether it is necessary to optimize (merge or split) the equivalent network nodes of the power grid simulation model within the target time period;

[0093] Specifically, the judgment formula for falling into the interval is as follows:

[0094]

[0095] Where m3 represents the percentage of equivalent network nodes that need to be optimized; Indicates the number of equivalent network nodes after processing; Indicates the number of equivalent network nodes in the power grid simulation model within the target time period; Indicates that the evaluation index falls into the preset first interval, Indicates that the evaluation index falls into the preset second interval, Indicates that the evaluation index falls into the preset third interval, m1 represents the lower threshold of the second interval, and m2 represents the upper threshold of the second interval;

[0096] Specifically, if the evaluation index falls into the preset first interval, it is considered that the number of equivalent network nodes of the power grid simulation model within the target time period is too small, resulting in insufficient simulation accuracy. At this time, it is necessary to appropriately increase the number of equivalent network nodes to improve the simulation accuracy; if the evaluation index falls into the preset second interval, it is considered that the number of equivalent network nodes of the power grid simulation model within the target time period is relatively reasonable. At this time, the simulation accuracy can be guaranteed, and there is no need to make additional adjustments to the number of equivalent network nodes; if the evaluation index falls into the preset third interval, it is considered that the number of equivalent network nodes of the power grid simulation model within the target time period is too large, resulting in excessive simulation calculation, which greatly extends the simulation time and reduces the simulation accuracy. At this time, it is necessary to appropriately reduce the number of equivalent network nodes to improve the simulation accuracy;

[0097] It should be noted that the values ​​of m1, m2, and m3 can be adjusted according to the actual simulation accuracy requirements. In this embodiment, m1 = 0.457, m2 = 0.564, and m3 = 15%;

[0098] It should be noted that the equivalent network nodes of the power grid simulation model within the target time period can be split or merged through cluster analysis, node optimization functions built into the power grid simulation software, etc. Since this application focuses on building the collaborative logic of the dynamic evaluation mechanism and node adjustment strategy through real-time equivalent parameters, and the existing technology already has mature node splitting / node merging processing technology, this embodiment will no longer elaborate on the specific processing process of splitting or merging.

[0099] S4. updating the power grid simulation model according to the processed equivalent network nodes, and obtaining a power grid operation state through simulation using the updated power grid simulation model;

[0100] Specifically, by merging or splitting equivalent network nodes, their number distribution is made highly consistent with the actual grid operation status. Subsequently, the optimized node parameters and connection relationships are integrated into the original grid simulation model to form a simulation architecture that is more in line with reality. Based on the updated grid simulation model, simulation calculations are performed, and the resulting grid operation status will be closer to the actual working conditions, and the simulation accuracy will be significantly improved, thereby providing more reliable data support for grid dispatching decisions, fault analysis and other tasks.

[0101] See Figure 2 , is a power grid operation status simulation device based on dynamic update of equivalent network nodes provided by one embodiment of the present invention, comprising: a data acquisition module, an evaluation index calculation module, an equivalent network node processing module and a simulation module;

[0102] The data acquisition module is used to collect real-time equivalent parameters of the power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node;

[0103] The evaluation index calculation module is used to calculate the evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the influence of the distribution of equivalent network nodes on the simulation accuracy;

[0104] The equivalent network node processing module is configured to split the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset first interval; use the equivalent network nodes of the power grid simulation model within a target time period as processed equivalent network nodes if the evaluation index falls within a preset second interval; and merge the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset third interval; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval;

[0105] The simulation module is used to update the power grid simulation model according to the processed equivalent network nodes, and obtain the power grid operation status through simulation of the updated power grid simulation model.

[0106] In a preferred embodiment, the calculation of the evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter includes:

[0107] Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter;

[0108] Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter;

[0109] The evaluation index is calculated based on the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period.

[0110] See also Figure 3 , an embodiment of the present application further provides a terminal device, including:

[0111] one or more processors;

[0112] a memory, coupled to the processor, for storing one or more programs;

[0113] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for simulating the operation status of a power grid based on dynamic updating of equivalent network nodes.

[0114] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned power grid operation status simulation method based on dynamic update of equivalent network nodes. The memory is used to store various types of data to support the operation of the terminal device. For example, these data may include instructions for any application or method used to operate on the terminal device, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0115] In an exemplary embodiment, the terminal device can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the power grid operation status simulation method based on dynamic update of equivalent network nodes as described in any of the above embodiments, and achieve technical effects consistent with the above methods.

[0116] In another exemplary embodiment, a computer-readable storage medium including a computer program is further provided. When executed by a processor, the computer program implements the steps of the method for simulating the operation status of a power grid based on dynamic updates of equivalent network nodes as described in any of the aforementioned embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program. The computer program may be executed by a processor of a terminal device to implement the method for simulating the operation status of a power grid based on dynamic updates of equivalent network nodes as described in any of the aforementioned embodiments, and achieve the same technical effects as the aforementioned methods.

[0117] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for simulating power grid operation status based on dynamic updating of equivalent network nodes, characterized in that: include: Collecting real-time equivalent parameters of a power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node; Calculating an evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the degree of influence of the distribution of equivalent network nodes on the simulation accuracy; If the evaluation index falls within a preset first interval, the equivalent network nodes in the power grid simulation model are split to obtain processed equivalent network nodes. If the evaluation index falls within the preset second interval, the equivalent network node of the power grid simulation model within the target time period is used as the processed equivalent network node. If the evaluation index falls within a preset third interval, merging the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval; The power grid simulation model is updated according to the processed equivalent network nodes, and the power grid operation status is obtained by simulating the updated power grid simulation model.

2. The method for simulating power grid operation status based on dynamic updating of equivalent network nodes according to claim 1, characterized in that: Calculating the evaluation index according to the total number of sampling points in the target time period and the real-time equivalent parameter includes: Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter; Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter; The evaluation index is calculated based on the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period.

3. The method for simulating power grid operation status based on dynamic updating of equivalent network nodes according to claim 2, characterized in that: The calculating the evaluation index according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period includes: Calculating a real-time evaluation index corresponding to the real-time equivalent parameter according to the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period; Extracting the real-time equivalent parameter corresponding to the last sampling point in the target time period from the real-time equivalent parameter as the second target real-time equivalent parameter; The evaluation index is calculated according to the real-time evaluation index, the real-time equivalent parameter and the second target real-time equivalent parameter.

4. The method for simulating power grid operation status based on dynamic updating of equivalent network nodes according to claim 3, characterized in that: The real-time equivalent parameters include total electric load power, total load reactive power demand, wind farm active output, photovoltaic power station active output, energy storage system active output and total reactive power output on the power supply side.

5. The method for simulating power grid operation status based on dynamic updating of equivalent network nodes according to claim 4, characterized in that: The real-time evaluation index includes the real-time evaluation index of the total power load power, the real-time evaluation index of the total load reactive power demand, the real-time evaluation index of the wind farm active output, the real-time evaluation index of the photovoltaic power station active output, the real-time evaluation index of the energy storage system active output and the real-time evaluation index of the total reactive power output on the power supply side; The calculating the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter includes: The real-time evaluation index is calculated using the following formula: Where, Represents the real-time evaluation index of total electric load power; P dfh,max Indicates the total electric load power with the maximum median value of the real-time equivalent parameter; P dfh,min Indicates the total electric load power with the minimum median value of the real-time equivalent parameter; Indicates the total electric load power collected at the xth sampling point; Indicates the normalized total power load power corresponding to the last sampling point in the target time period; n indicates the total number of sampling points in the target time period; Represents the real-time evaluation index of the active output of the wind farm; P fd,max Indicates the active output of the wind farm with the largest median value of the real-time equivalent parameter; P fd,min Indicates the active output of the wind farm with the smallest median value of the real-time equivalent parameter; Indicates the active output of the wind farm collected at the xth sampling point; Indicates the normalized active power output of the wind farm corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the active output of the photovoltaic power station; P gf,max Indicates the active output of the photovoltaic power station with the largest median value of the real-time equivalent parameter; P gf,min Indicates the active output of the photovoltaic power station with the smallest median value of the real-time equivalent parameters; Indicates the active output of the photovoltaic power station collected at the xth sampling point; Indicates the normalized active output of the PV power station corresponding to the last sampling point within the target time period; Represents the real-time evaluation index of the active output of the energy storage system; P cn,max Indicates the active output of the energy storage system with the largest median value of the real-time equivalent parameter; P cn,min Indicates the active output of the energy storage system with the minimum median value of the real-time equivalent parameters; Indicates the active output of the energy storage system collected at the xth sampling point; Indicates the normalized active output of the energy storage system corresponding to the last sampling point within the target time period; Indicates the real-time evaluation index of the total reactive power output on the power supply side; Q dz,max Indicates the total reactive power output of the power supply side with the largest median value of the real-time equivalent parameter; Q dz,min Indicates the total reactive power output on the power supply side with the minimum median value of the real-time equivalent parameters; Indicates the total reactive power output on the power supply side collected at the xth sampling point; Indicates the normalized total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Represents the real-time evaluation index of the total load reactive power demand; Q fh,max Indicates the total load reactive power demand with the largest median value of the real-time equivalent parameter; Q fh,min Indicates the total load reactive power demand with the minimum value of the real-time equivalent parameter; It represents the total load reactive power demand collected at the xth sampling point; Indicates the normalized total load reactive power demand corresponding to the last sampling point in the target time period.

6. The method for simulating power grid operation status based on dynamic updating of equivalent network nodes according to claim 5, characterized in that: The calculating the evaluation index according to the real-time evaluation index, the real-time equivalent parameter, and the second target real-time equivalent parameter includes: The evaluation index is calculated by the following formula: Where, Indicates the total power load power corresponding to the last sampling point in the target time period; K indicates the total number of types included in the real-time equivalent parameter; Indicates the active output of the wind farm corresponding to the last sampling point in the target time period; Indicates the active output of the PV power station corresponding to the last sampling point within the target time period; Indicates the active output of the energy storage system corresponding to the last sampling point in the target time period; Indicates the total reactive power output on the power supply side corresponding to the last sampling point in the target time period; Indicates the total load reactive power demand corresponding to the last sampling point in the target time period; Represents the evaluation index.

7. A power grid operation status simulation device based on dynamic update of equivalent network nodes, characterized in that: include: Data acquisition module, evaluation index calculation module, equivalent network node processing module and simulation module; The data acquisition module is used to collect real-time equivalent parameters of the power grid simulation model within a target time period; wherein the power grid simulation model includes a plurality of equivalent network nodes, each of which aggregates power sources and loads connected through the same electrical node; The evaluation index calculation module is used to calculate the evaluation index based on the total number of sampling points in the target time period and the real-time equivalent parameter; wherein the evaluation index is used to evaluate the influence of the distribution of equivalent network nodes on the simulation accuracy; The equivalent network node processing module is configured to split the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset first interval; use the equivalent network nodes of the power grid simulation model within a target time period as processed equivalent network nodes if the evaluation index falls within a preset second interval; and merge the equivalent network nodes in the power grid simulation model to obtain processed equivalent network nodes if the evaluation index falls within a preset third interval; wherein the first interval is located to the left of the second interval, and the second interval is located to the left of the third interval; The simulation module is used to update the power grid simulation model according to the processed equivalent network nodes, and obtain the power grid operation status through simulation of the updated power grid simulation model.

8. The power grid operation status simulation device based on dynamic update of equivalent network nodes according to claim 7, characterized in that: Calculating the evaluation index according to the total number of sampling points in the target time period and the real-time equivalent parameter includes: Normalizing the real-time equivalent parameter to obtain a normalized real-time equivalent parameter; Extracting the normalized real-time equivalent parameter corresponding to the last sampling point in the target time period from the normalized real-time equivalent parameter as the first target real-time equivalent parameter; The evaluation index is calculated based on the real-time equivalent parameter, the first target real-time equivalent parameter, and the total number of sampling points in the target time period.

9. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid operation status simulation method based on dynamic update of equivalent network nodes as described in any one of claims 1 to 6.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for simulating the operation status of a power grid based on dynamic updating of equivalent network nodes according to any one of claims 1 to 6 is implemented.

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