Flexible interconnected power distribution network stochastic load flow calculation method, device, equipment and medium

Abstract

The invention relates to a stochastic load flow calculation method, device and equipment for a flexible interconnected power distribution network and a medium, and relates to the technical field of power distribution networks. The method comprises the following steps: acquiring power grid data corresponding to a target power distribution network; inputting the power grid data into a first model to obtain first power data corresponding to the target power distribution network; and inputting the first power data into the second model to obtain a stochastic load flow calculation result corresponding to the target power distribution network. The method can accurately restore the operation state of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method, apparatus, equipment and medium for calculating stochastic power flow in a flexible interconnected power distribution network. Background Technology

[0002] With the continuous increase in the proportion of renewable energy integration and the strengthening of load-side volatility, the operating status of distribution networks exhibits obvious dynamic and stochastic characteristics. In flexible interconnected distribution networks represented by smart soft-switching (Soft Open Point, SOP), although resource coordination and power flow regulation are achieved through power exchange between feeders, significantly improving the system's ability to absorb renewable energy and its operational flexibility, the intermittent and stochastic nature of renewable energy output and the dynamic changes in load demand overlap, leading to the interweaving of multiple stochastic processes within the system. This results in complex probability distribution characteristics for state variables such as node power and voltage. Traditional operational analysis and simulation methods based on deterministic assumptions are no longer adequate for the operational requirements of new distribution networks.

[0003] In recent years, state discretization methods have attracted widespread attention due to their ability to efficiently handle the dynamic behavior of systems. Among them, the Quantized State System (QSS) method, as a typical state discretization method, introduces "quantum values" to replace the time step in traditional simulations, realizing adaptive step size adjustment and event-driven state updates under specific accuracy requirements. It can efficiently capture the abrupt changes and continuous evolution of the system state.

[0004] However, the existing technical framework is mainly designed for system scenarios without stochastic processes, and a complete modeling and simulation mechanism for stochastic processes has not yet been established. For uncertainties such as fluctuations in renewable energy output and random load changes in flexible interconnected distribution networks, the simulation results of existing state discretization methods cannot accurately reproduce the operating state of the distribution network. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for calculating stochastic power flow in a flexible interconnected distribution network, which can accurately reproduce the stochastic operating state of the distribution network.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for calculating stochastic power flow in a flexible interconnected distribution network, comprising: Obtain the power grid data corresponding to the target distribution network; The power grid data is input into the first model to obtain the first power data corresponding to the target distribution network; wherein, the first model is used to simulate the power fluctuation process of the nodes in the target distribution network; The first power data is input into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network; the second model is used to simulate the power distribution and node voltage in the target distribution network.

[0007] In one embodiment, grid data is input into a first model to obtain first power data corresponding to the target distribution network, including: Input the power grid data into the first model to obtain the node fluctuation data corresponding to the target distribution network; Based on node fluctuation data, the first power data corresponding to the target distribution network is determined.

[0008] In one embodiment, grid data is input into a first model to obtain node fluctuation data corresponding to the target distribution network, including: Based on the drift process equation, the diffusion process equation, and quantum values, the first and second fluctuation intensities corresponding to the target distribution network are determined. Based on the first and second fluctuation intensities, the fluctuation time corresponding to the target distribution network is determined. The first fluctuation intensity, the second fluctuation intensity, and the fluctuation time are used as the node fluctuation data corresponding to the target distribution network.

[0009] In one embodiment, determining the first power data corresponding to the target distribution network based on node fluctuation data includes: Based on the first and second wave intensities, determine whether a jump process exists; If a jump process exists, the jump process is simulated, and the first power data corresponding to the target distribution network is obtained; If there is no jump process, the node fluctuation data is preset and updated to obtain the first power data corresponding to the target distribution network.

[0010] In one embodiment, determining whether a jump process exists based on a first wave intensity and a second wave intensity includes: The sum of the first and second wave intensities is taken as the third wave intensity; When the intensity of the third wave is zero, there is no jump process; When the intensity of the third wave is not equal to zero, a jump process exists.

[0011] In one embodiment, a jump process simulation is performed to obtain the first power data corresponding to the target distribution network, including: Determine the number of jumps based on the fluctuation time; Based on the number of jumps, the jump process is simulated to obtain the first power data corresponding to the target distribution network.

[0012] In one embodiment, the first power data is input into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network, including: The first power data is input into the second model to update the state data corresponding to the target distribution network; based on the state data, the stochastic power flow calculation results corresponding to the target distribution network are obtained.

[0013] In a second aspect, the present invention provides a stochastic power flow calculation device for a flexible interconnected distribution network, comprising: The acquisition module is used to acquire the power grid data corresponding to the target distribution network. The power determination module is used to input grid data into the first model to obtain the first power data corresponding to the target distribution network; wherein, the first model is used to simulate the power fluctuation process of nodes in the target distribution network; The power flow determination module is used to input the first power data into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network; wherein, the second model is used to simulate the power distribution and node voltage in the target distribution network.

[0014] Thirdly, the present invention provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0015] Fourthly, the present invention provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0016] Fifthly, the present invention provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.

[0017] As can be seen from the above technical solution, the present invention has at least the following beneficial effects: In this invention, by acquiring grid data corresponding to the target distribution network, realistic data is provided for stochastic power flow calculation, ensuring accurate restoration of the physical topology and flexible interconnection characteristics of the target distribution network. This avoids simulation deviations caused by data distortion from the source, providing data assurance for accurately reflecting the true stochastic operating state of the distribution network. Furthermore, the grid data can be input into a first model used to simulate the power fluctuation process of nodes in the target distribution network to obtain the first power data corresponding to the target distribution network, achieving accurate characterization of the node power fluctuation process and providing dynamic data support for reflecting the true stochastic operating state. Subsequently, the first power data can be input into a second model used to simulate the power distribution and node voltage in the target distribution network to obtain the stochastic power flow calculation results corresponding to the target distribution network. This scheme, by introducing the first model, achieves accurate simulation of the stochastic power disturbance caused by uncertainties such as new energy sources and loads; furthermore, by introducing the second model, it achieves the simulation from stochastic power fluctuations to stochastic power flow processes, ultimately realizing accurate restoration of the stochastic operating state of the flexible interconnected distribution network.

[0018] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this invention do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0019] Figure 1 This is an application environment diagram of a flexible interconnected distribution network stochastic power flow calculation method provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating a method for calculating stochastic power flow in a flexible interconnected distribution network, as provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a process for obtaining first power data provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a flexible interconnected distribution network stochastic power flow calculation device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the internal structure of a computer device provided in the embodiments of the application. Detailed Implementation

[0020] The terms "first," "second," and "third," etc., used in this specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0021] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first: With the continuous increase in the proportion of renewable energy integration and the strengthening of load-side volatility, the operating status of distribution networks exhibits dynamic and stochastic characteristics. In flexible interconnected distribution networks represented by smart soft-open points (SOPs), although resource coordination and flexible power flow control are achieved through power exchange between feeders, significantly improving the system's ability to absorb renewable energy and its operational flexibility, the intermittent and stochastic nature of renewable energy output, coupled with the dynamic changes in load demand, leads to the interweaving of multiple stochastic processes within the system. This results in complex probability distribution characteristics for state variables such as node power and voltage. Traditional operational analysis and simulation methods based on deterministic assumptions are no longer adequate for the operational requirements of new distribution networks.

[0023] In recent years, state discretization methods have attracted widespread attention due to their ability to efficiently handle the dynamic behavior of systems. Among them, the Quantized State System (QSS) method, as a typical state discretization technique, introduces quantum values ​​to replace the time step in traditional simulations, realizing adaptive step size adjustment and event-driven state updates under specific accuracy requirements. It can efficiently capture the abrupt changes and continuous evolution of system states.

[0024] However, the existing technical framework is mainly designed for system scenarios without stochastic processes, and a complete modeling and simulation mechanism for stochastic processes has not yet been established. For uncertainties such as fluctuations in renewable energy output and random load changes in flexible interconnected distribution networks, the simulation results of existing state discretization methods cannot accurately reflect the stochastic operating state of the distribution network.

[0025] To make the technical solution of the present invention clearer and easier to understand, the application scenarios of the technical solution of the present invention will be described below with reference to the accompanying drawings. For example... Figure 1 As shown in the figure, this figure is a schematic diagram of an application scenario provided by an embodiment of the present invention.

[0026] In this application scenario, monitoring device 101 acts as a data acquisition terminal, responsible for capturing real-time operating data and basic parameters of the target flexible interconnected distribution network, providing raw input for subsequent calculations. For example, it can collect basic structural parameters by using sensors deployed on distribution network nodes, branches, and SOPs to collect real-time physical parameters such as node baseline active power, node reactive power, branch resistance, branch reactance, and SOP active power and reactive power, ensuring that the data can reconstruct the topology and flexible interconnection characteristics of the distribution network. It can also collect random characteristic parameters, such as generating basic sample data for constructing drift process equations (reflecting the average power trend) and diffusion process equations (reflecting the intensity of random disturbances) by long-term monitoring of power output fluctuation data of new energy power generation units (such as photovoltaic and wind power) and load-side electricity consumption change data. It can also preprocess and transmit the data: filtering and denoising the collected raw data (removing outliers caused by sensor errors), and packaging it according to a preset format (such as structured data containing parameter type, acquisition time, and device number), and transmitting it to terminal 102 through industrial Ethernet or wireless communication module to ensure the integrity and reliability of the data.

[0027] Terminal 102 serves as the core for human-computer interaction and data transfer, connecting monitoring equipment 101 and server 104. It undertakes functions such as data integration, parameter configuration, and result display. For example, it receives preprocessed data transmitted from monitoring equipment 101, provides a visual operation interface for technicians to configure simulation control parameters based on computational needs, etc. Terminal 102 encapsulates the integrated power grid data and configuration parameters into computational tasks and transmits them to server 104 through an encrypted communication protocol. It can also receive power flow calculation results returned by server 104 and perform multi-dimensional visualization (such as dynamic curves).

[0028] Server 104, as the core computing node, executes the stochastic power flow calculation method based on the task data transmitted by terminal 102, realizing the core transformation from data to results. It processes the calculation results, such as calculating the statistical characteristics (mean and variance) of voltage and power of each node, marking abnormal states such as voltage exceeding limits, encapsulating the result data in a format that terminal 102 can parse, and transmitting the encapsulated power flow calculation results (including dynamic curve data, statistical indicators, and anomaly labels) back to terminal 102 for technical personnel to analyze and use.

[0029] Monitoring device 101 lays the data foundation through precise data collection, terminal 102 realizes human-computer interaction and task flow through integrated configuration, and server 104 completes core calculations through model operation. The three devices follow a coherent process of data collection, integrated configuration, model calculation, and result display, which transforms the stochastic power flow calculation method of discrete state integral from a theoretical model into a practical engineering application. Finally, terminal 102 presents the power flow calculation results that can accurately reflect the real stochastic operating state of the distribution network to the technicians, providing reliable support for risk assessment, operation optimization, and scheduling decisions.

[0030] To make the technical solution of this invention clearer and easier to understand, the following describes a method for calculating stochastic power flow in a flexible interconnected distribution network, based on the above application scenarios. Figure 2 As shown in the figure, this is a flowchart of a method for calculating stochastic power flow in a flexible interconnected distribution network provided by an embodiment of the present invention.

[0031] S201. Obtain the power grid data corresponding to the target distribution network.

[0032] The target distribution network can refer to a flexible interconnected distribution network (which may include flexible interconnected devices such as smart soft switches) that requires stochastic power flow calculation and analysis; the power grid data can refer to various types of data that characterize the distribution network structure, equipment parameters and basic operating conditions, including but not limited to drift process equations, diffusion process equations and quantum values.

[0033] Optionally, the drift process equation describes the deterministic trend of the random power increment and can reflect the average evolution rate of the node power without random perturbation; the diffusion process equation describes the intensity of the random fluctuation of the random power increment and can reflect the perturbation amplitude of the source load uncertainty on the node power; the quantum value can be the smallest unit for quantizing the update of state variables and can determine the precision of state discretization. Power grid data may include, but is not limited to, distribution network stochastic energy flow process parameters and simulation control parameters; distribution network stochastic energy flow process parameters may include node active power vectors. Node reactive power vector Drift process equation Diffusion process equation and distribution network state variables ,in, It is a node The node power increment, Represents the vector of node voltage magnitudes. Represents the node voltage phase angle vector; simulation control parameters may include the simulation start time. Simulation end time and the size of quantum values .

[0034] It should be noted that the node power increment It is a node actual active power of nodes Compared with the reference active power The difference, i.e. , is a variable that characterizes the randomness of power, and its changes can directly drive the update of the power grid state.

[0035] For example, data can be collected through a distribution network monitoring system or parameter ledger. The collected data may include, but is not limited to, basic node parameters (such as node...). Node reference active power ), line parameters (such as the branch resistance between node i and node j) Branch reactance between node i and node j ), Active power setpoint (SOP) parameter (SOP active power) reactive power Simulation control basic parameters (such as quantum value ΔQ, simulation time range, etc.).

[0036] By acquiring data covering both static topology and dynamic operation, including power source data, grid data, load data, energy storage and control data, as well as environmental and auxiliary data, a comprehensive, accurate, and dynamic reflection of the actual random operating state of the distribution network can be achieved.

[0037] For example, grid-side data can include, but is not limited to, real-time output of distributed power sources (photovoltaics, wind power, etc.), predicted power generation, inverter operating status, voltage, current, active / reactive power at centralized power access points, and other power source-side data, which can help capture the intermittent and random characteristics of new energy power generation.

[0038] Data from the power grid side may include, but is not limited to, voltage, current, power, power factor, line loss rate, load rate of distribution transformers, temperature, three-phase imbalance, opening and closing status and number of operations of switching equipment (circuit breakers, disconnectors), and real-time changes in the distribution network topology (such as the line connection relationship after fault reconstruction) for 10kV and below lines.

[0039] Load-side data may include, but is not limited to, real-time electricity load, load curves, voltage sags / boosts, and other power quality parameters collected according to user type (residential, commercial, industrial); available capacity, response speed, and adjustment range of demand response resources, reflecting the spatiotemporal randomness and adjustability of the load; energy storage and control data may include, but is not limited to, the state of charge (SOC), charging and discharging power, and number of charging and discharging cycles of energy storage systems; remote control commands, protection device action information, and fault recording data of distribution automation systems, recording the intervention process of control behavior on the operating status.

[0040] Environmental and auxiliary data may include, but are not limited to, meteorological data such as light intensity, wind speed, and temperature (which are related to the output of new energy sources and load fluctuations); equipment life cycle information (commissioning time, maintenance records), etc., to provide background support for the analysis of the randomness of operating status.

[0041] Smart inverters and power sensors can be deployed at the grid connection points of distributed power sources; fault indicators and feeder terminal units (FTUs) can be installed at key nodes of the line; transformer monitoring terminals (TTUs) can be configured on the low-voltage side of the distribution transformer; and smart meters and load monitors can be promoted on the user side to achieve comprehensive perception of data from all nodes of the source-grid-load.

[0042] Furthermore, 4G / 5G and wireless private networks can be used to achieve data transmission at the end terminals; a backbone communication network for the power distribution network can be built through fiber optic communication to ensure high-bandwidth, low-latency data interaction of core nodes (such as distribution automation master stations and substations) and solve the problem of data collection in remote areas.

[0043] A data middle platform can be built based on the power distribution master station system to clean (remove outliers and missing values), verify (verify the rationality of data by combining topological relationships and physical constraints), and standardize (unify data format, units and timestamps) the collected raw data to form a structured operating database.

[0044] By collecting data across all stages, full-chain data coverage is achieved, encompassing everything from power output fluctuations to line power flow and load consumption changes. For example, by collecting time-of-use electricity data from residential users, random load fluctuations caused by the start-up and shutdown of household appliances can be accurately captured; by monitoring the second-level output data of photovoltaic power stations, random power changes caused by cloud cover can be reconstructed. This blind-spot-free data coverage breaks the limitation of inferring the overall state from local data, providing a fundamental guarantee for fully reconstructing the random operating characteristics of the distribution network.

[0045] Furthermore, relying on high-precision sensing terminals and high-frequency acquisition technology (with acquisition frequencies reaching 50Hz at some key nodes), data errors can be controlled within ±0.5%, and changes in operating parameters at the second or even millisecond level can be captured. For example, through high-frequency fault recording data from the FTU, the amplitude and duration of instantaneous voltage drops caused by lightning strikes can be accurately located; through 15-minute load data from smart meters, the different random patterns of concentrated electricity consumption during business hours for commercial users and periodic electricity consumption during production processes for industrial users can be distinguished. High-precision data provides a reliable basis for quantitatively analyzing the amplitude, frequency, probability distribution, and other characteristics of random fluctuations, ensuring that the characterization of the randomness of operating states is closer to reality.

[0046] Furthermore, through low-latency communication and real-time data processing mechanisms, the latency of data transmission from the acquisition terminal to the main station can be controlled within seconds, and the preprocessing cycle of the data platform does not exceed 5 minutes. This real-time guarantee allows operators to synchronously track the development of random events: for example, when a load surge occurs in a certain area, the main station can immediately identify the range and degree of voltage dips through real-time load data and line voltage data; when the output of distributed power sources drops sharply, it can quickly determine the impact on the frequency stability of the distribution network based on real-time power data. Dynamic tracking supported by real-time data avoids the deviation of retrospective analysis of random states, ensuring that the reflected operating status remains synchronized with the actual operating conditions.

[0047] Finally, by integrating multi-dimensional data from power generation, grid, load, storage, and environment, a complete interconnected data chain was constructed. For example, linking photovoltaic output data with sunlight and temperature data can quantify the impact weight of meteorological factors on power supply randomness; combining line power data with topology switching records and load change data can analyze the random patterns of power fluctuations during topology reconfiguration. This multi-dimensional data correlation analysis not only presents the random operating state but also explains the reasons for the state, providing a basis for accurately judging the nature of random fluctuations (such as normal load fluctuations and abnormal equipment disturbances), further enhancing the depth and accuracy of our understanding of the actual operating state of the distribution network.

[0048] The technical solution of this invention, by reasonably defining data dimensions, optimizing acquisition technology, and strengthening data processing, constructs a power distribution network data acquisition system that solves the problem of insufficient traditional data support from three levels: coverage, accuracy and timeliness, and correlation analysis. It provides comprehensive and reliable data assurance for accurately reflecting the real random operating state of the power distribution network, thereby laying a solid foundation for the safe and stable operation, optimized scheduling, and planning upgrade of the power distribution network.

[0049] Specifically, the node reference active power of node i in the power grid data. The reactive power vector at nodes Q, and the branch resistance between nodes i and j. Reactance between node i and node j Active power of SOP and reactive power The basic structural parameters were used to ensure the accuracy of reproducing the physical topology and flexible interconnection characteristics of the target distribution network, avoiding simulation deviations from reality due to structural description biases. Furthermore, the drift process equations characterizing the power fluctuation trend of node i were obtained. Equations for the diffusion process that quantify the intensity of random disturbances Stochastic modeling parameters directly related to uncertainties such as fluctuations in new energy output and load changes provide data support for capturing the dual characteristics of average trends and random disturbances in power fluctuations; and, by obtaining the quantum value ΔQ of state discreteness and simulation start time... Simulation end time By balancing computational accuracy and efficiency, simulation control parameters were established, laying the foundation for discretized simulation of stochastic processes. Ultimately, simulation deviations caused by input data distortion were avoided at the source, providing data assurance for accurately reflecting the true stochastic operating state of the distribution network.

[0050] S202. Input the power grid data into the first model to obtain the first power data corresponding to the target distribution network.

[0051] The first model is used to simulate the fluctuation process of node power in the target distribution network. It is a mathematical model constructed by combining the concept of quantized state system with stochastic process modeling technology. The first power data can refer to the active power vector of the node calculated by the first model. Its core is the dynamic change data of the node power increment. The fluctuation process of node power refers to the random change process of the actual active power of the node deviating from the reference power due to factors such as the fluctuation of new energy output and load changes. It includes two parts: average trend and random disturbance.

[0052] For example, the first model can be represented as: Formula (1); in, The instantaneous change (differential form) of the node power increment at node i is used to obtain the first power data. For nodes The node power increment, i.e., node active power of nodes Compared with the reference active power The difference, Here is the equation for the drift process, where t is time. For time step, The equation for the diffusion process is as follows: This is the Wiener process.

[0053] Furthermore, the baseline active power and simulation control parameters from the power grid data can be input into the first model, and the drift process equations can be used to... The average trend of node power fluctuations is calculated using the diffusion process equations. With Wiener process It characterizes the magnitude of random disturbances, determines the probability density of power increases and decreases based on grid data, and determines the estimated time based on the probability density of power increases and decreases.

[0054] Furthermore, based on the probability density and estimated time of power increases and decreases, the node power increment can be calculated. Active power at nodes The data is updated, and the final output is the first power data containing random fluctuation characteristics.

[0055] By combining the concept of Quantized State Systems (QSS) with stochastic process modeling techniques, a first model was constructed. Compared to traditional methods that cannot characterize stochasticity, this first model utilizes diffusion equations... With Wiener process It accurately simulates the random power disturbances caused by uncertainties such as new energy sources and loads, enabling the output first power data to exhibit random fluctuation characteristics around the benchmark value, closely matching the actual operating state of the distribution network; and based on the quantum value ΔQ and the jump update mechanism, the first model can adaptively capture sudden and continuous changes in power, avoiding the omission of rapid changes caused by fixed time steps, and its output node power increment dynamic data ( It can accurately reproduce the instantaneous characteristics of power fluctuations; furthermore, the first power data, as a dynamic sequence of node active power, replaces the fixed power input in traditional deterministic calculations, enabling subsequent power flow calculations to be based on time-varying random power rather than static ideal values; thus, the output power data of this model can accurately characterize the random fluctuation process, providing a real data foundation for subsequent power flow calculations.

[0056] The first model breaks down power fluctuations, clearly separating the average trend from random disturbances in the output power data. For example, for residential area nodes, the model can fit the average trend curve of "peak load at 8 am and 7 pm" from the actual power data, while simultaneously extracting millisecond-level random disturbance data caused by "refrigerator start-stop and light switching". This characterization of trends combined with disturbances avoids the problem of misjudging regular changes as random fluctuations, enabling the data to accurately correspond to the core random characteristics of the distribution network's operating state, providing clear data support for understanding the essence of random operating states.

[0057] The first model leverages the dual technical advantages of quantified state and stochastic processes to correct deviations: On the one hand, based on the concept of quantified state systems, it uses the physical laws of the distribution network (such as node power balance) as constraints to verify and correct systematic errors in the simulation process; on the other hand, through stochastic process modeling technology, it performs probabilistic learning on historical disturbance data to optimize the simulation accuracy for low-probability, sudden disturbances. For example, for the random fluctuations in wind power output, the model can reduce the power simulation error from 15% in traditional methods to below 5% by fitting the random distribution of wind speed. The first power data output is more consistent with the actual power of the nodes, ensuring that the mapping of the random operating state is closer to the real operating conditions.

[0058] While outputting the first power data, the first model establishes a correlation link between disturbance factors, power increment, and operating status: by tracking the dynamic changes in power increment data, it can reversely match the corresponding disturbance source (e.g., if the node power increment is negative and negatively correlated with the photovoltaic output increment during a certain period, it is determined that the disturbance originates from a sudden drop in photovoltaic output); for example, when the power of a branch node in the distribution network experiences a random surge, by using the correlation analysis between the increment changes in the first power data and disturbance factors, it can quickly locate whether the load disturbance is caused by the concentrated start-up of air conditioners by commercial users or the power disturbance caused by the over-generation of distributed power sources. This data + mechanism reconstruction capability enables the power data to not only present random operating status but also explain the root cause of the status, improving the accuracy and depth of the understanding of the real operating status.

[0059] The first power data, centered on the dynamic changes in node power increments, can directly support the quantitative calculation of fluctuation characteristics: based on time-series data of power increments, the number of fluctuations (frequency) and the maximum increment value (amplitude) per unit time can be statistically determined; combined with the probability parameters output from stochastic process modeling, the distribution function of power increments (such as normal distribution or Poisson distribution) can be fitted; for example, for industrial user nodes, the first power data can quantify the high-amplitude and low-frequency characteristics of their load disturbances; for photovoltaic grid-connected points, the low-amplitude and high-frequency characteristics of their output disturbances can be quantified. These quantified characteristic data provide a unified data benchmark for the stochastic operating states of different nodes and different time periods, enabling the description of the real stochastic operating state to shift from qualitative judgment to quantitative analysis, further strengthening the role of data assurance.

[0060] In summary, the first model accurately captures the essential laws of power fluctuations at distribution network nodes by decomposing the components of fluctuations, correcting simulation biases, restoring the disturbance mechanism, and quantifying fluctuation characteristics. From four levels—detailed characterization, accurate mapping, root cause analysis, and quantitative evaluation—it provides comprehensive and reliable data support for accurately reflecting the real random operating state of the distribution network, laying a solid foundation for the safe scheduling, fault handling, and planning optimization of the distribution network.

[0061] S203. Input the first power data into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network.

[0062] The second model is used to simulate the power distribution and node voltage in the target distribution network. It can be a mathematical model based on Kirchhoff's laws and the power balance principle, with the active power balance formula and reactive power balance formula as its core. The stochastic power flow calculation results can refer to the core operating state variables of the target distribution network, which may include, but are not limited to, the node voltage magnitude vector and the node voltage phase angle vector. Power distribution and node voltage refer to the dynamic distribution and balance process of electrical quantities such as power, voltage, and phase angle when power is transmitted from the power source node to the load node in the distribution network.

[0063] One possible approach is to input the first power data into the second model to update the state data corresponding to the target distribution network; based on the state data, the stochastic power flow calculation results corresponding to the target distribution network are obtained.

[0064] For example, the active power P of the nodes in the first power data is input into the second model along with the line parameters and SOP power in the power grid data. The correlation between node power and voltage and phase angle is established through the power balance formula in the model. The voltage amplitude and phase angle of each node are calculated iteratively, and finally the stochastic power flow calculation results reflecting the power distribution state are output. If the node power jumps, the updated first power data needs to be re-inputted and the calculation repeated to obtain the dynamic power flow results.

[0065] The second model is a node power flow model with flexible interconnection characteristics. It transforms the random fluctuations of node power into a dynamic distribution of power flow across the entire network, ultimately achieving a complete restoration of the true random operating state of the distribution network. Specifically, the time-varying nature of the first power data enables the second model to overcome the limitations of static power flow calculations, simulating the real evolution process from random power fluctuations to dynamic power flow adjustments. Furthermore, the second model explicitly includes the active power of the State of Operation (SOP) in the power flow equations. and reactive power This method reflects the regulatory role of flexible equipment on stochastic power flow, avoiding distortion in power flow distribution calculations caused by ignoring standard operating procedures (SOPs). Finally, the obtained stochastic power flow calculation results (probability distributions and dynamic sequences of voltage and phase angles) directly reflect the fluctuation range of the distribution network's operating state under random disturbances, meaning the output results closely match actual operating characteristics and can provide accurate basis for risk assessment (such as voltage over-limit probability). Through a closed-loop process of data-fluctuation simulation-power flow calculation, precise data input is achieved, capturing core stochastic fluctuations and finally reconstructing the entire network's power flow response. This synergistic approach overcomes the shortcomings of traditional methods that cannot simultaneously consider randomness and flexible interconnection characteristics. The stochastic power flow calculation results output by this process fully present the real evolution process of new energy / load fluctuations, node power changes, and overall network power flow adjustments. Its statistical characteristics (mean, variance) and dynamic characteristics (instantaneous fluctuations) closely match the actual operating state of the distribution network.

[0066] The dynamic changes in power increments provided by the first power data inject dynamic boundary conditions into the second model. For example, when the first power data captures a sudden drop in active power from 500kW to 100kW within 10 seconds at a photovoltaic grid-connected point, the second model can output the dynamic change process of the voltage amplitude of the node and its adjacent nodes from 0.98pu to 0.92pu within 10 seconds through power flow iteration calculation. At the same time, it provides synchronous offset data of voltage phase angle. This dynamic mapping of power fluctuation and voltage response restores the voltage state changes caused by random disturbances in the distribution network, enabling the stochastic power flow calculation results to directly correspond to the random voltage characteristics in actual operation, and providing core state data for understanding the stochastic operating state.

[0067] The stochastic power flow calculation results output by the second model establish a strong correlation link between the first power data and the power flow results: by comparing the time series of voltage amplitude vectors and node active power vectors, the quantitative relationship between power increment changes and voltage amplitude fluctuations can be clearly presented; by using the changes in node voltage phase angle vectors, the impact of power flow direction on voltage state can be located (e.g., an increase in phase angle difference corresponds to an increase in branch power transmission, which in turn leads to changes in line voltage drop); for example, when the node voltage in a residential area experiences random fluctuations, combining the power flow results with the first power data can quickly determine: if the fluctuation is positively correlated with the local load power increment, it originates from the start-up and shutdown of user appliances; if it is negatively correlated with the increase in power output from distant renewable energy sources, it originates from insufficient power support caused by power fluctuations. This ability to correlate data and deduce mechanisms enables the stochastic power flow calculation results not only to present the stochastic operating state but also to explain the root cause of the state, improving the accuracy of the understanding of the actual operating state.

[0068] The real-time dynamic power increment data provided by the first power data effectively compensates for the static deficiencies of traditional inputs. The second model, by accessing power increment data in time periods, upgrades static power flow calculation to dynamic power flow calculation, capturing minute voltage changes caused by millisecond-level power fluctuations. For example, for industrial users with high-frequency start-stop loads, the 100ms-level power increment recorded by the first power data, after calculation by the second model, can output a corresponding voltage amplitude fluctuation of 0.01pu, while traditional static calculations would miss such random disturbances. At the same time, based on the physical constraints of Kirchhoff's laws, the second model can perform secondary corrections on the small errors in the first power data (such as adjusting the node power input value through power balance verification), further improving the accuracy of voltage amplitude and phase angle calculations, making the power flow results more closely reflect the actual random operating state.

[0069] The second model leverages the full-node power coverage advantage of the first power data to achieve comprehensive voltage status completion. The first power data, through model inversion, retrieves the power data of completed branches and terminal nodes. Using this as input to the second model, the voltage amplitude and phase angle of all nodes in the distribution network (including terminal nodes without monitoring terminals) can be calculated. For example, based on the total power of the main line and the power proportion of each branch, the first power data completes the active power of three terminal branches. The second model then calculates the voltage amplitude vectors of the terminal nodes of these three branches, discovering that one branch's terminal voltage has been consistently below 0.95 pu due to random load fluctuations. This logic of power and voltage completion fills in the voltage blind spots at the terminals, forming comprehensive voltage status data covering the main line, branches, and terminals. It fully presents the spatiotemporal distribution characteristics of the distribution network's random operating state, providing panoramic data support for a comprehensive understanding of the actual operating state.

[0070] In summary, the second model uses the first power data as its core input and transforms random power fluctuations into voltage state changes through dynamic power flow calculation. The output random power flow calculation results solve the problems of static, biased, isolated, and blind spots in traditional power flow data. From four levels—core mapping, mechanism analysis, accurate reflection, and panoramic presentation—it provides comprehensive and reliable data support for accurately reflecting the real random operating state of the distribution network, thereby providing key support for core operation and maintenance work such as voltage regulation, fault location, and reactive power optimization in the distribution network.

[0071] The aforementioned method for calculating stochastic power flow in flexible interconnected distribution networks obtains grid data corresponding to the target distribution network, providing realistic data for stochastic power flow calculation. This ensures accurate reproduction of the physical topology and flexible interconnection characteristics of the target distribution network, avoiding simulation deviations caused by data distortion from the outset. This provides data assurance for accurately reflecting the true stochastic operating state of the distribution network. Furthermore, grid data can be input into a first model simulating the power fluctuation process at nodes in the target distribution network to obtain the first power data corresponding to the target distribution network. This achieves accurate characterization of the node power fluctuation process, providing dynamic data support for reflecting the true stochastic operating state. Subsequently, the first power data can be input into a second model simulating the power distribution and node voltage in the target distribution network to obtain the stochastic power flow calculation results corresponding to the target distribution network. This scheme, by introducing the first model, achieves accurate simulation of stochastic power disturbances caused by uncertainties such as new energy sources and loads. Furthermore, by introducing the second model, it achieves simulation of the dynamic adjustment process from stochastic power fluctuations to stochastic power flow, ultimately realizing accurate reproduction of the stochastic operating state of the flexible interconnected distribution network.

[0072] Based on the above embodiments, the present invention provides a detailed explanation of S202. Specifically, the present invention involves the process of obtaining the first power data, as follows: Figure 3 As shown, the specific steps include: S301. Input the power grid data into the first model to obtain the node fluctuation data corresponding to the target distribution network.

[0073] One possible approach is to determine the first and second fluctuation intensities corresponding to the target distribution network based on the drift process equation, the diffusion process equation, and quantum values; to determine the fluctuation time corresponding to the target distribution network based on the first and second fluctuation intensities; and to use the first fluctuation intensities, the second fluctuation intensities, and the fluctuation time as the node fluctuation data corresponding to the target distribution network.

[0074] Among them, node fluctuation data is the core parameter set that characterizes the random mutation characteristics of node power increment; the first fluctuation intensity is a parameter describing the probability of an upward mutation in node power increment (the fluctuation amount deviating from the baseline value increases), that is, the positive jump intensity of node power increment, which is the probability density characterizing the jump of node power increment in the positive direction (power increase), and can reflect the intensity of random disturbances of power increase; the second fluctuation intensity is a parameter describing the probability of a downward mutation in node power increment (the fluctuation amount deviating from the baseline value decreases), that is, the negative jump intensity of node power increment, which is the probability density characterizing the jump of node power increment in the negative direction (power decrease), and can reflect the intensity of random disturbances of power decrease; the fluctuation time is the time from the current moment when the next random mutation of node power increment is expected to occur; the fluctuation time, that is, the expected jump time, is the time interval at which the next power jump is expected to occur, and can reflect the temporal characteristics of power state changes caused by random disturbances.

[0075] For example, node power increments can be extracted first from grid data. Drift process equations Diffusion process equation and quantum value It is then input into the first model, which performs calculations and processes them, such as calculating the first wave intensity. Second wave intensity The calculation formula can be as follows: Formula (2) Formula (3) Therefore, it is possible to base it on the first wave intensity Second wave intensity Calculate fluctuation time The calculation formula can be as follows: Formula (4) Where e is the natural constant and is the base of the natural logarithm.

[0076] Ultimately, the intensity of the first wave can be... Second wave intensity and fluctuation time These three parameters are integrated into node fluctuation data.

[0077] Node fluctuation data transforms random abrupt changes into quantifiable probability density indicators through first and second fluctuation intensity parameters. For example, for a specific photovoltaic grid-connected point, the first model calculates its first fluctuation intensity as 0.02 times / minute (probability of power surge) and its second fluctuation intensity as 0.05 times / minute (probability of power drop), reflecting the difference in the probability of upward and downward power surges at that node. This quantitative expression transcends the qualitative description of whether a sudden change occurs, achieving a quantitative characterization of the intensity of random disturbances. This allows the data to directly correspond to the probability of sudden changes in actual operation, providing accurate data for capturing the essential characteristics of the random operating state of the distribution network.

[0078] The separate design of the first and second fluctuation intensities allows for the differentiation of the direction of abrupt changes: for example, in a commercial area, around 6:00 PM on weekdays, the first fluctuation intensity increases (corresponding to a power surge caused by the concentrated start-up of air conditioning and lighting loads), while the second fluctuation intensity remains stable; conversely, in the early morning hours, the second fluctuation intensity increases (corresponding to a power drop caused by equipment shutdown). This directional differentiation enables operators to specifically analyze the random patterns of power surges or drops, avoiding the cognitive bias of conflating abrupt changes in different directions, and improving the accuracy of analyzing random operating states.

[0079] The fluctuation time parameter enables time-series prediction of random changes by forecasting the time interval of the next power increment mutation. For example, based on historical data, the first model calculates that the fluctuation time of a certain node is 15 minutes (meaning the probability of the next power mutation occurring within the next 15 minutes is the highest). Combining the first fluctuation intensity and the second fluctuation intensity of that node, a comprehensive judgment can be formed that there is a 60% probability of a power surge occurring within 15 minutes. This time prediction capability allows operators to perceive the possible periods of random disturbances in advance, breaking the passive mode of post-event recording and providing forward-looking data support for dynamically tracking the random operating status of the distribution network.

[0080] Node fluctuation data, through the synergistic effect of three parameters, can directly support the correlation analysis of risk scenarios: for example, when the intensity of the first fluctuation at a line node continuously increases (high probability of a power surge) and the fluctuation time shortens (smaller interval between abrupt changes), combined with the line's rated capacity, an increased risk of line overload can be predicted; when the intensity of the second fluctuation at a distributed power source grid connection point suddenly increases (high probability of a power drop), combined with the voltage stability threshold, an increased risk of voltage drop can be predicted. This correlation between fluctuation parameters and risk scenarios allows the data to not only reflect the phenomena of random operating states but also reveal their potential impacts, deepening the understanding of the true random operating state of the distribution network and providing more valuable data support for risk warning and decision-making.

[0081] In summary, the node fluctuation data output by the first model solves the problems of vague characterization, directional confusion, time lag, and missing correlation in the random power increment mutation characteristics of traditional data by quantifying mutation intensity, distinguishing mutation direction, predicting mutation time, and associating risk scenarios. From four levels of accurate capture, targeted analysis, time series perception, and cognitive depth, it provides comprehensive and reliable data guarantee for accurately reflecting the real random operating state of the distribution network, thereby providing support for the safe operation monitoring, risk warning, and dynamic scheduling of the distribution network.

[0082] S302. Based on node fluctuation data, determine the first power data corresponding to the target distribution network.

[0083] One possible approach is to determine whether a jump process exists based on a first fluctuation intensity and a second fluctuation intensity; if a jump process exists, a jump process simulation is performed, and the first power data corresponding to the target distribution network is obtained; if no jump process exists, the node fluctuation data is preset and updated, and the first power data corresponding to the target distribution network is obtained.

[0084] Optionally, the sum of the first and second wave intensities can be used as the third wave intensity; if the third wave intensity is equal to zero, there is no jump process; if the third wave intensity is not equal to zero, there is a jump process.

[0085] Among them, the third fluctuation intensity is an indicator for judging whether there is a random jump process in the node power, which can reflect the comprehensive driving effect of random disturbance on power fluctuation; the preset update can be a way to deterministically update the node power increment based on the direction and quantum value of the drift process equation, which only reflects the average trend of power change.

[0086] Furthermore, the number of jumps can be determined based on the fluctuation time; based on the number of jumps, the jump process can be simulated to obtain the first power data corresponding to the target distribution network.

[0087] The jump process refers to a sudden, step-like change in the increment of node power at a certain moment (the magnitude of the change is a quantum value). The process of (integer multiples of); the first power data refers to the nodes containing random fluctuation characteristics. actual active power of nodes , by node power increment Compared with the reference active power Calculation yields ( = + Jump process simulation is a numerical simulation operation that dynamically updates the random change process of node power increment based on jump intensity, jump probability and fluctuation time. It can include two types: whole jump process and non-whole jump process.

[0088] For example, one can first determine whether a jump process exists by calculating the intensity of the third wave, i.e. + If the intensity of the third wave is equal to 0, it is determined that there is no jump process. At this time, the preset update is performed. The preset update process can be represented as follows: Formula (5) in, The time step can be any length of time during the advance. ,Will As the updated node power increment.

[0089] Optional, As a sign function, it can be determined by judging the equation of the drift process. The sign of the positive or negative value determines the node power increment. The deterministic update direction, such as when the drift process equation When >0, sgn( The value ΔQ = 1 indicates that the node power increment shows a positive average trend (power is likely to increase over time). At this point, the quantum value ΔQ is used to... A positive update is about to be implemented. The sum of the quantum value ΔQ is used as the updated node power increment.

[0090] when When <0, sgn( The value ΔQ = -1 indicates that the node power increment shows a negative average trend (power likely decreases over time). In this case, the quantum value ΔQ is used to... Perform a negative update, soon The difference between the quantum value ΔQ and the updated node power increment is used.

[0091] If the intensity of the third wave is not equal to 0, then a jump process is confirmed, and the simulation phase of the jump process can begin. That is, the number of jumps can be determined based on the wave duration; for example, the current time t and the simulation end time t can be calculated. end The difference, if t+τ≤t end If a complete jump occurs, the probabilities of a positive jump and a negative jump can be calculated. The calculation method can be expressed as follows: Formula (6) Formula (7) in, The probability of a positive jump is... It represents a negative jump probability.

[0092] Furthermore, it can be based on the calculated positive jump probability. With negative jump probability It can randomly perform positive or negative jumps; for example, it can use a uniformly distributed random number. For example, in satisfy In this case, a positive jump will be performed, and The sum of the quantum value ΔQ as a node The updated node power increment can have a propagation time of up to [number]. ;exist satisfy In this case, a negative jump will be performed. The difference between the quantum value ΔQ and the node The updated node power increment can have a propagation time of up to [number]. .

[0093] If t+τ> t end If there is a non-integer jump, it can be determined based on the positive jump strength. Negative jump intensity With fluctuation time For nodes node power increment Perform a non-integer jump process update.

[0094] The update process for a non-integer jump can be represented as: Process 1: Determine whether a jump will occur within the remaining time. The determination method is as follows: Formula (8) Formula (9) in, For the remaining time, For time, This represents the probability of a jump occurring within the remaining time.

[0095] Using a uniformly distributed random number For example, we can determine whether a jump exists according to the following rules: if If the jump occurs, the subsequent calculations continue (calculating the positive and negative jump probabilities, etc.); otherwise, no jump occurs, the node power increment remains unchanged, and the advancement time is... .

[0096] Step 2: Calculate the positive jump probability With negative jump probability The process is shown in formulas (6) and (7).

[0097] Step 3: Based on the calculated positive and negative jump probabilities, randomly perform either a positive or negative jump: Using a uniformly distributed random number For example, the state is updated according to the following rules: If Then a positive jump will be performed. The sum of the quantum value ΔQ and the node power increment after node i is updated; if Then a negative jump will be performed. The difference between the quantum value ΔQ and the value is used as the node power increment after node i is updated, advancing the time to... .

[0098] If the intensity of the third fluctuation is equal to 0, it is determined that there is no jump process, that is, the node fluctuation data is preset to be updated. The preset update process is as follows: Formula (10) in, For the time step, As the updated node power increment, the length of the propagation time can be , It is a symbolic function.

[0099] Finally, the updated version Substitution = + The first power data was obtained.

[0100] By using a third fluctuation intensity determination mechanism, the two processes can be accurately distinguished and differentiated: for example, in cloudy weather, the third fluctuation intensity of a photovoltaic grid-connected point is greater than zero (there is a jump process), and the model initiates a jump simulation, generating power data containing multiple sudden increases and decreases; in clear and stable weather, the third fluctuation intensity is close to zero (no jump process), and the model initiates a smooth update, generating slowly changing power data. This scenario-specific processing mechanism avoids the distortion problem of smoothing jump processes or abruptly changing stable processes, enabling the first power data to accurately reproduce the essential characteristics of different random states.

[0101] Based on the simulation process using the first fluctuation intensity, the second fluctuation intensity, and the fluctuation time, these patterns can be accurately quantified. For example, for a specific industrial node, the model simulates positive and negative power jumps with a 3:1 probability ratio based on the first fluctuation intensity (0.03 times / minute) and the second fluctuation intensity (0.01 times / minute). Combined with the fluctuation time (average 10 minutes) to control the jump interval, the generated power jump process achieves a more than 40% match with the actual start-up and shutdown patterns of equipment at that node. Simultaneously, the determination that the third fluctuation intensity is zero (no jump) avoids the addition of meaningless random disturbances, ensuring that the power data maintains a reasonable trend under stable conditions. This probability-driven + time-constrained simulation method improves the accuracy of the first power data in characterizing random disturbances, making it closer to the actual operating conditions of the power distribution network.

[0102] Separating the application of the first and second fluctuation intensities fully preserves the bidirectional mutation characteristics: for example, during the morning peak hours at a residential node, the first fluctuation intensity (positive mutation) is significantly higher than the second fluctuation intensity (negative mutation), and the generated first power data shows a pattern of more increases and fewer decreases, accurately reflecting the actual situation of concentrated electricity consumption by users; conversely, during late-night hours, the data shows a pattern of more decreases and fewer increases. This preservation of directional characteristics allows operators to accurately analyze the differentiated impacts of mutations in different directions on line load and voltage stability, avoiding cognitive biases caused by the loss of directional information and improving the accuracy of analyzing random operating states.

[0103] Based on a dynamic update mechanism for node fluctuation data, it can adapt to changes in scenarios in real time: when external conditions (such as weather and user behavior) change, the node fluctuation data (especially the first and second fluctuation intensities) will be updated synchronously, thereby driving the characteristic adjustment of the first power data. For example, the third fluctuation intensity of commercial nodes is generally lower on weekends than on weekdays, and the fluctuation time is longer. The number of jumps in the generated first power data is reduced, which is more in line with the stable characteristics of commercial electricity consumption on weekends. This dynamic link of fluctuation data update and power data adaptation ensures that the first power data at different times can reflect the random operating characteristics of the corresponding scenarios, avoiding the time series distortion caused by a one-size-fits-all simulation, and providing a time series consistency guarantee for continuously and accurately tracking the real random operating state of the distribution network.

[0104] In summary, the process of determining the first power data through node fluctuation data, by employing four mechanisms—jump / stationary differentiation, probability-time quantization, bidirectional feature preservation, and dynamic scenario adaptation—solves the problems of confusion, insufficient accuracy, loss of direction, and time-series distortion in traditional power data processes. It provides comprehensive and reliable data assurance from multiple dimensions for accurately reflecting the real random operating state of the distribution network, laying a solid foundation for dynamic monitoring, risk warning, and optimized scheduling of the distribution network.

[0105] In this embodiment of the invention, by introducing a first model, a way is provided to obtain the core parameters of the random mutation characteristics of node power increment, thereby providing a data foundation for obtaining accurate power data, and finally obtaining accurate power flow data.

[0106] The above text combined Figures 1 to 3 The method for calculating stochastic power flow in flexible interconnected distribution networks provided in the embodiments of the present invention has been described in detail. The apparatus and equipment provided in the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0107] like Figure 4As shown in the figure, this is a schematic diagram of a stochastic power flow calculation device for a flexible interconnected distribution network provided in an embodiment of the present invention. The stochastic power flow calculation device 600 for a flexible interconnected distribution network includes: an acquisition module 601, a power determination module 602, and a power flow determination module 603, wherein: The acquisition module 601 is used to acquire the power grid data corresponding to the target distribution network; The power determination module 602 is used to input grid data into the first model to obtain the first power data corresponding to the target distribution network; wherein, the first model is used to simulate the power fluctuation process of nodes in the target distribution network; The power flow determination module 603 is used to input the first power data into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network; wherein, the second model is used to simulate the power distribution and node voltage in the target distribution network.

[0108] In one embodiment, the power determination module 602 is specifically used for: Input the power grid data into the first model to obtain the node fluctuation data corresponding to the target distribution network; Based on node fluctuation data, the first power data corresponding to the target distribution network is determined.

[0109] In one embodiment, the power determination module 602 is specifically used for: Based on the drift process equation, the diffusion process equation, and quantum values, the first and second fluctuation intensities corresponding to the target distribution network are determined. Based on the first and second fluctuation intensities, the fluctuation time corresponding to the target distribution network is determined. The first fluctuation intensity, the second fluctuation intensity, and the fluctuation time are used as the node fluctuation data corresponding to the target distribution network.

[0110] In one embodiment, the power determination module 602 is specifically used for: Based on the first and second wave intensities, determine whether a jump process exists; If a jump process exists, the jump process is simulated, and the first power data corresponding to the target distribution network is obtained; If there is no jump process, the node fluctuation data is preset and updated to obtain the first power data corresponding to the target distribution network.

[0111] In one embodiment, the power determination module 602 is specifically used for: The sum of the first and second wave intensities is taken as the third wave intensity; When the intensity of the third wave is zero, there is no jump process; When the intensity of the third wave is not equal to zero, a jump process exists.

[0112] In one embodiment, the power determination module 602 is specifically used for: Determine the number of jumps based on the fluctuation time; Based on the number of jumps, the jump process is simulated to obtain the first power data corresponding to the target distribution network.

[0113] In one embodiment, the power flow determination module 603 is specifically used for: The first power data is input into the second model to update the state data corresponding to the target distribution network. Based on the state data, the stochastic power flow calculation results corresponding to the target distribution network are obtained.

[0114] The flexible interconnected distribution network stochastic power flow calculation device 600 according to embodiments of the present invention can correspond to executing the method described in the embodiments of the present invention, and the other operations and / or functions of each module / unit of the flexible interconnected distribution network stochastic power flow calculation device 600 are respectively for implementing Figure 2 , Figure 3 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0115] This invention also provides a computing device. This computing device can be a local computing device or an application server.

[0116] like Figure 5 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of the present invention. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.

[0117] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0118] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0119] Communication interface 703 is used for external communication. For example, communication interface 703 can be used to communicate with terminal 102. Communication interface 703 is used to send stochastic power flow calculation results to terminal 102 so that terminal 102 can display the stochastic power flow calculation results.

[0120] Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0121] The memory 704 stores executable code, and the processor 702 executes the executable code to perform the aforementioned method for calculating the stochastic power flow of a flexible interconnected distribution network.

[0122] Specifically, in achieving Figure 4 In the case of the illustrated embodiment, and Figure 4 When the modules or units of the flexible interconnected distribution network stochastic power flow calculation device described in the embodiments are implemented by software, the execution... Figure 4 The software or program code required for the functions of each module / unit can be partially or entirely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704 and executes the aforementioned method for calculating the stochastic power flow of the flexible interconnected distribution network.

[0123] This invention also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned method for calculating the stochastic power flow in a flexible interconnected distribution network.

[0124] This invention also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this invention are generated.

[0125] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0126] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods of the flexible interconnected distribution network stochastic power flow calculation method. The computer program product can be a software installation package; when any of the aforementioned methods of the flexible interconnected distribution network stochastic power flow calculation method is required, the computer program product can be downloaded and executed on the computer.

[0127] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating stochastic power flow in a flexible interconnected distribution network, characterized in that, The method includes: Obtain the power grid data corresponding to the target distribution network; The power grid data is input into the first model to obtain the node fluctuation data corresponding to the target distribution network; wherein, the first model is used to simulate the node power fluctuation process in the target distribution network, and is a mathematical model constructed by combining the idea of ​​quantized state system and stochastic process modeling technology; Based on the node fluctuation data, the first power data corresponding to the target distribution network is determined; The first power data is input into the second model to update the state data corresponding to the target distribution network; wherein, the second model is used to simulate the power distribution and node voltage in the target distribution network, and is a mathematical model constructed based on Kirchhoff's laws and the power balance principle; Based on the state data, the stochastic power flow calculation results corresponding to the target distribution network are obtained.

2. The method according to claim 1, characterized in that, The power grid data includes drift process equations, diffusion process equations, and quantum values. The step of inputting the power grid data into the first model to obtain the node fluctuation data corresponding to the target distribution network includes: Based on the drift process equation, diffusion process equation, and quantum value, the first fluctuation intensity and the second fluctuation intensity corresponding to the target distribution network are determined. Based on the first fluctuation intensity and the second fluctuation intensity, the fluctuation time corresponding to the target distribution network is determined; The first fluctuation intensity, the second fluctuation intensity, and the fluctuation time are used as the node fluctuation data corresponding to the target distribution network.

3. The method according to claim 2, characterized in that, The step of determining the first power data corresponding to the target distribution network based on the node fluctuation data includes: Based on the first and second wave intensities, determine whether a jump process exists; If a jump process exists, the jump process is simulated, and the first power data corresponding to the target distribution network is obtained; If there is no jump process, the node fluctuation data is preset and updated to obtain the first power data corresponding to the target distribution network.

4. The method according to claim 3, characterized in that, The step of determining whether a jump process exists based on the first wave intensity and the second wave intensity includes: The sum of the first and second wave intensities is taken as the third wave intensity; When the intensity of the third wave is equal to zero, there is no jump process; When the intensity of the third wave is not equal to zero, a jump process exists.

5. The method according to claim 3, characterized in that, The step of simulating the jump process and obtaining the first power data corresponding to the target distribution network includes: Based on the fluctuation time, determine the number of jumps; Based on the number of jumps, a jump process simulation is performed to obtain the first power data corresponding to the target distribution network.

6. A stochastic power flow calculation device for a flexible interconnected distribution network, characterized in that, The device includes: The acquisition module is used to acquire the power grid data corresponding to the target distribution network. A power determination module is used to input the power grid data into a first model to obtain first power data corresponding to the target distribution network; wherein, the first model is used to simulate the power fluctuation process of nodes in the target distribution network; The power flow determination module is used to input the first power data into the second model to obtain the stochastic power flow calculation results corresponding to the target distribution network; wherein, the second model is used to simulate the power distribution and node voltage in the target distribution network; A power determination module is used to input the power grid data into a first model to obtain node fluctuation data corresponding to the target distribution network; wherein, the first model is used to simulate the node power fluctuation process in the target distribution network, and is a mathematical model constructed by combining the concept of quantized state systems and stochastic process modeling techniques; based on the node fluctuation data, the first power data corresponding to the target distribution network is determined; The power flow determination module is used to input the first power data into the second model and update the state data corresponding to the target distribution network; wherein, the second model is used to simulate the power distribution and node voltage in the target distribution network, and is a mathematical model constructed based on Kirchhoff's laws and the power balance principle; based on the state data, the stochastic power flow calculation results corresponding to the target distribution network are obtained.

7. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 5.

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