A flexible interconnected power distribution network stochastic power flow calculation method, device, equipment and medium

Abstract

The application relates to a flexible interconnected power distribution network stochastic power flow calculation method, device, equipment and 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 a second model to obtain a stochastic power 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] The present application relates to the technical field of power distribution network, and particularly relates to a flexible interconnected power distribution network stochastic power flow calculation method, device, equipment and medium. BACKGROUND

[0002] With the continuous increase of new energy access proportion and the enhancement of load side volatility, the operation state of the power distribution network presents obvious dynamic and random characteristics. In the flexible interconnected power distribution network represented by the intelligent soft switch (Soft Open Point, SOP), although the power mutual aid between feeders realizes resource coordination and power flow regulation, greatly improving the accommodation capacity and operation flexibility of the system to new energy, the intermittence and randomness of new energy output and the dynamic change of load demand are superimposed on each other, resulting in the interweaving of multiple random processes in the system, so that the state variables such as node power and voltage present complex probability distribution characteristics. The traditional operation analysis and simulation method based on the assumption of certainty has been difficult to adapt to the operation demand of the new type of power distribution network.

[0003] In recent years, the state discrete method has attracted widespread attention due to its ability to efficiently handle system dynamic behavior. Among them, the Quantized State System (QSS) method as a typical state discrete method, by introducing "quantum value" to replace the time step in traditional simulation, realizes adaptive step adjustment and event-driven state update under certain accuracy requirements, and can efficiently capture the mutation and continuous evolution process of system state.

[0004] But the existing technical framework is mainly designed for system scenarios without random processes, and a perfect random process modeling and simulation mechanism has not been established. For the uncertainty factors such as new energy output fluctuation and load random change in the flexible interconnected power distribution network, the simulation results of the existing state discrete method cannot accurately restore the operation state of the power distribution network. SUMMARY

[0005] The present application provides a flexible interconnected power distribution network stochastic power flow calculation method, device, equipment and medium, which can accurately restore the random operation state of the power distribution network.

[0006] To achieve the above purpose, the present application adopts the following technical scheme:

[0007] In a first aspect, the present application provides a flexible interconnected power distribution network stochastic power flow calculation method, comprising:

[0008] obtaining power grid data corresponding to a target power distribution network;

[0009] inputting the power grid data into a first model to obtain first power data corresponding to the target power distribution network; wherein the first model is used to simulate the fluctuation process of node power in the target power distribution network;

[0010] inputting the first power data into a second model to obtain a random power flow calculation result corresponding to the target power distribution network; wherein the second model is used to simulate the distribution of electric energy and node voltage in the target power distribution network.

[0011] In one embodiment, the grid data is input into the first model to obtain the first power data corresponding to the target power distribution network, including:

[0012] inputting the grid data into the first model to obtain node fluctuation data corresponding to the target power distribution network;

[0013] determining the first power data corresponding to the target power distribution network based on the node fluctuation data.

[0014] In one embodiment, the grid data is input into the first model to obtain the node fluctuation data corresponding to the target power distribution network, including:

[0015] determining first fluctuation intensity and second fluctuation intensity corresponding to the target power distribution network based on the drift process equation, the diffusion process equation and the quantum value;

[0016] determining fluctuation time corresponding to the target power distribution network based on the first fluctuation intensity and the second fluctuation intensity;

[0017] taking the first fluctuation intensity, the second fluctuation intensity and the fluctuation time as the node fluctuation data corresponding to the target power distribution network.

[0018] In one embodiment, determining the first power data corresponding to the target power distribution network based on the node fluctuation data, including:

[0019] determining whether there is a jump process based on the first fluctuation intensity and the second fluctuation intensity;

[0020] if there is a jump process, simulating the jump process and obtaining the first power data corresponding to the target power distribution network;

[0021] if there is no jump process, updating the node fluctuation data according to a preset and obtaining the first power data corresponding to the target power distribution network.

[0022] In one embodiment, determining whether there is a jump process based on the first fluctuation intensity and the second fluctuation intensity, including:

[0023] taking the sum of the first fluctuation intensity and the second fluctuation intensity as third fluctuation intensity;

[0024] in the case that the third fluctuation intensity is equal to zero, there is no jump process;

[0025] in the case that the third fluctuation intensity is not equal to zero, there is a jump process.

[0026] In one embodiment, the jump process simulation is performed, and the first power data corresponding to the target power distribution network is obtained, including:

[0027] Based on the fluctuation time, the number of jumps is determined.

[0028] Based on the number of jumps, the jump process simulation is performed, and the first power data corresponding to the target power distribution network is obtained.

[0029] In one embodiment, the first power data is input into the second model to obtain the random power flow calculation result corresponding to the target power distribution network, including:

[0030] The first power data is input into the second model to update the state data corresponding to the target power distribution network; and based on the state data, the random power flow calculation result corresponding to the target power distribution network is obtained.

[0031] In a second aspect, the present application provides a flexible interconnected power distribution network random power flow calculation device, including:

[0032] The acquisition module is configured to acquire power grid data corresponding to a target power distribution network.

[0033] The power determination module is configured to input the power grid data into a first model to obtain first power data corresponding to the target power distribution network; wherein the first model is configured to simulate the fluctuation process of node power in the target power distribution network.

[0034] The power flow determination module is configured to input the first power data into a second model to obtain a random power flow calculation result corresponding to the target power distribution network; wherein the second model is configured to simulate the distribution of electric energy and node voltage in the target power distribution network.

[0035] In a third aspect, the present application provides a computing device including a memory and a processor.

[0036] One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device performs the method of any one of the first aspect.

[0037] In a fourth aspect, the present application provides a computer readable storage medium for storing a computer program, the computer program being used to execute the method of any one of the first aspect.

[0038] In a fifth aspect, the present application provides a computer program product including one or more computer instructions, when the computer instructions are executed by a computer, the computer executes the method of any one of the first aspect.

[0039] From the above technical solution, the present application has at least the following advantages:

[0040] In the present application, by acquiring the power grid data corresponding to the target power distribution network, actual data is provided for the random power flow calculation, ensuring the accurate restoration of the physical topology and flexible interconnection characteristics of the target power distribution network, avoiding simulation deviation caused by data distortion from the source, and providing data support for accurately reflecting the real random operation state of the power distribution network; further, the power grid data can be input into the first model for simulating the fluctuation process of the node power in the target power distribution network to obtain the first power data corresponding to the target power distribution network, realizing the accurate description of the node power fluctuation process and providing dynamic data support for reflecting the real random operation state; and then, the first power data can be input into the second model for simulating the power distribution and node voltage in the target power distribution network to obtain the random power flow calculation result corresponding to the target power distribution network. The present scheme realizes the accurate simulation of the power random disturbance caused by uncertain factors such as new energy and load by introducing the first model; further, the simulation from power random fluctuation to random power flow process is realized by introducing the second model, and finally the random operation state of the flexible interconnected power distribution network is accurately restored.

[0041] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in the present application does not imply that all features and advantages can be achieved in any single embodiment. On the contrary, it can be understood that the description of a feature or beneficial effect means that the specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in the specification does not necessarily refer to the same embodiment. Further, the technical features, technical solutions and beneficial effects described in the embodiments can be combined in any appropriate manner. Those skilled in the art will understand that the 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 can be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 An application environment diagram of a flexible interconnected power distribution network random power flow calculation method provided in an embodiment of the present application;

[0043] Figure 2 A flowchart of a flexible interconnected power distribution network random power flow calculation method provided in an embodiment of the present application;

[0044] Figure 3 A flowchart of obtaining first power data provided in an embodiment of the present application;

[0045] Figure 4A structural schematic diagram of a flexible interconnected power distribution network random power flow calculation device provided in an embodiment of the present application is shown in the figure.

[0046] Figure 5 An internal structure schematic diagram of a computer device provided in an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0047] The terms "first", "second", and "third" and the like in the specification and the drawings of the present application are used to distinguish different objects, and are not intended to limit a specific order.

[0048] In the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration, and not necessarily to imply any preference or superiority. Rather, the use of the words "exemplary" or "for example" is intended to present concepts in a particular, concrete manner.

[0049] In order to make the following embodiments clear and simple, first, a brief introduction of related technologies is given:

[0050] With the continuous increase of the proportion of new energy access and the enhancement of the volatility of the load side, the operation state of the power distribution network presents dynamic and random characteristics. In the flexible interconnected power distribution network represented by the intelligent soft open switch (SOP), although the power mutual aid between feeders realizes resource coordination and flexible power flow regulation, greatly improving the accommodation capacity of the system to new energy and the operation flexibility, the intermittence and randomness of new energy output and the dynamic changes of load demand are superimposed on each other, resulting in the interweaving of multiple random processes in the system, so that the state variables such as node power and voltage present complex probability distribution characteristics. The traditional operation analysis and simulation method based on the assumption of certainty has been difficult to adapt to the operation requirements of the new type of power distribution network.

[0051] In recent years, the state discrete method has attracted widespread attention due to its ability to efficiently handle system dynamic behavior. Among them, the quantized state system (QSS) method as a typical state discrete technology, by introducing quantum values to replace the time step in traditional simulation, realizes adaptive step adjustment and event-driven state update under certain accuracy requirements, and can efficiently capture the mutation and continuous evolution process of system state.

[0052] But the existing technical framework is mainly designed for system scenarios without random processes, and a perfect random process modeling and simulation mechanism has not been established. For the uncertainty factors such as new energy output fluctuation and load random change in the flexible interconnected power distribution network, the simulation results of the existing state discrete method cannot accurately reflect the random operation state of the power distribution network.

[0053] In order to make the technical solutions of the application clearer and easier to understand, the application scenarios of the technical solutions of the application will be introduced below in combination with the drawings. As shown in Figure 1 The figure is a schematic diagram of an application scenario provided by an embodiment of the application.

[0054] In this application scenario, the monitoring device 101 serves as a data acquisition terminal and is responsible for capturing real-time operation data and basic parameters of the target flexible interconnected power distribution network to provide original input for subsequent calculation. For example, the basic structure parameters can be acquired through sensors deployed on nodes, branches and SOPs of the power distribution network to acquire physical parameters such as node reference active power, node reactive power, branch resistance, branch reactance, active power and reactive power of SOP in real time, so as to ensure that the data can restore the topology structure and flexible interconnection characteristics of the power distribution network. Random characteristic parameters can also be acquired, such as the output fluctuation data of new energy generation units (such as photovoltaic and wind power) and the load side power consumption change data through long-term monitoring to generate basic sample data for constructing drift process equation (reflecting the average trend of power) and diffusion process equation (reflecting the random disturbance intensity). The data can also be preprocessed and transmitted: the acquired original data is filtered and denoised (abnormal values caused by sensor errors are removed), and is packaged according to a preset format (such as structured data containing parameter type, acquisition time and device number), and is transmitted to the terminal 102 through industrial Ethernet or wireless communication module to ensure the integrity and reliability of the data.

[0055] The terminal 102 serves as the core of human-computer interaction and data transfer, connects the monitoring device 101 and the server 104, and undertakes the functions of data integration, parameter configuration and result display, such as receiving preprocessed data transmitted by the monitoring device 101, providing a visual operation interface, and configuring simulation control parameters based on the calculation requirements of technical personnel. The terminal 102 encapsulates the integrated power grid data and configuration parameters as a calculation task, transmits them to the server 104 through an encrypted communication protocol, can also receive the power flow calculation results returned by the server 104, and performs multi-dimensional visual display (such as dynamic curves, etc.).

[0056] The server 104 serves as the core calculation node, executes the random power flow calculation method based on the task data transmitted by the terminal 102, realizes the core conversion from data to result, processes the calculation results, such as calculating the statistical characteristics (mean, variance) of the voltages and powers of each node, marking abnormal states such as voltage out-of-limit, encapsulating the result data in a format that can be parsed by the terminal 102, and transmitting the encapsulated power flow calculation results (containing dynamic curve data, statistical indicators and abnormal annotations) back to the terminal 102 for analysis and use by technical personnel.

[0057] 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.

[0058] 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.

[0059] S201. Obtain the power grid data corresponding to the target distribution network.

[0060] 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.

[0061] 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.

[0062] 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 .

[0063] It should be noted that the node power increment is the difference between the actual active power of the node and the reference active power of the node , that is , which is a variable that characterizes the randomness of power, and its change can directly drive the update of the grid state.

[0064] Exemplarily, data can be collected through a power distribution network monitoring system or a parameter account book, and the collected data can include but is not limited to node basic parameters (such as the node reference active power of the node ), line parameters (such as the branch resistance between node i and node j, the branch reactance between node i and node j), active power setpoint (SOP) parameters (active power , reactive power of SOP), simulation control basic parameters (such as quantum value ΔQ, simulation time range, etc.).

[0065] By obtaining power supply side data, grid side data, load side data, energy storage and regulation data, and environmental and auxiliary data covering static topology and dynamic operation, a comprehensive, accurate and dynamic reflection of the real random operation state of the power distribution network is realized.

[0066] For example, the grid side data can include but is not limited to real-time output of distributed power sources (photovoltaic, wind power, etc.), power generation power prediction value, inverter operation state, voltage, current, active / reactive power of centralized power supply access point, etc. Power supply side data can focus on capturing the intermittent and random characteristics of new energy power generation.

[0067] The grid side data can include but is not limited to voltage, current, power, power factor, line loss rate, load rate, temperature, three-phase unbalance degree of 10 kV and below lines, distribution transformers, on-off state, action frequency of switching devices (circuit breakers, disconnectors), real-time change information of power distribution network topology structure (such as line connection relationship after fault reconstruction), etc.

[0068] ​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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] Moreover, the high-precision sensing terminal and high-frequency acquisition technology (the acquisition frequency of some key nodes can reach 50 Hz) can control the data error within ±0.5% and capture the changes in the operating parameters at the level of seconds or even milliseconds. For example, the high-frequency fault recording data of the FTU can accurately locate the amplitude and duration of the instantaneous voltage drop caused by lightning strikes; and the 15-minute load data of the smart meter can distinguish the different random rules of the concentrated electricity consumption of commercial users during business hours and the periodic electricity consumption of industrial users due to production processes. The high-precision data provides a reliable basis for quantitatively analyzing the amplitude, frequency, and probability distribution of random fluctuations, ensuring that the description of the randomness of the operating state is more in line with the actual situation.

[0075] Moreover, through the mechanism of low-latency communication and real-time data processing, the time delay of data transmission from the acquisition terminal to the master station can be controlled within seconds, and the pre-processing period of the data center is not more than 5 minutes. This real-time guarantee enables the operating personnel to synchronously track the development process of random events: for example, when there is a sudden increase in load in a certain area, the master station can immediately identify the range and degree of voltage sag through real-time load data and line voltage data; and when the output of a distributed power source suddenly decreases, the influence on the frequency stability of the distribution network can be quickly judged based on real-time power data. Dynamic tracking based on real-time data avoids the bias of post-mortem analysis of random states, ensuring that the reflected operating state is synchronized with the actual working condition.

[0076] Finally, by integrating multi-dimensional data of sources, networks, loads, storages, and environments, a complete associated data chain is constructed. For example, by associating photovoltaic output data with light and temperature data, the influence weight of meteorological factors on the randomness of power sources can be quantified; and by combining line power data with topology switching records and load change data, the random rules of power fluctuations during topology reconstruction can be analyzed. This multi-dimensional data association analysis not only presents the random operating state, but also explains the causes of the state, providing a basis for accurately judging the nature of random fluctuations (such as normal load fluctuations and abnormal equipment disturbances), and further improving the depth and accuracy of the understanding of the true operating state of the distribution network.

[0077] The technical solution of the present application solves the problem of insufficient data support of traditional data by reasonably defining the data dimensions, optimizing the acquisition technology, and strengthening the data processing, and provides comprehensive and reliable data support for accurately reflecting the true random operating state of the distribution network, thereby laying a solid foundation for the safe and stable operation, optimal scheduling, and planning upgrade of the distribution network.

[0078] Specifically, the node reference active power of the grid data node i , the node reactive power vector Q, the branch resistance between node i and node j , and the 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 of quantifying 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.

[0079] S202. Input the power grid data into the first model to obtain the first power data corresponding to the target distribution network.

[0080] 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.

[0081] For example, the first model can be represented as:

[0082] Formula (1);

[0083] 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.

[0084] Further, the reference active power, simulation control parameters and the like in the power grid data can be input into the first model, and the average trend of the power fluctuation of the calculation node is calculated through the drift process equation The average trend of the power fluctuation of the calculation node is calculated through the diffusion process equation The Wiener process The amplitude of the random disturbance is described, and the probability density of the power increase and decrease is determined based on the power grid data, and the estimation time is determined according to the probability density of the power increase and decrease.

[0085] Further, the probability density of the power increase and decrease and the estimation time can be used to update the node power increment , the node active power and the like, and finally output the first power data containing the random fluctuation characteristics.

[0086] By combining the quantitative state system (QSS) idea with the random process modeling technology, the first model is constructed. Compared with the defects of traditional methods that cannot describe randomness, the first model accurately simulates the power random disturbance caused by uncertain factors such as new energy and load through the diffusion equation The Wiener process , so that the output first power data can present the random fluctuation characteristics around the reference value, which conforms to the real operation state of the distribution network; and based on the quantum value ΔQ and the jump update mechanism, the first model can adaptively capture the mutation and continuous change of the power, avoiding the omission of fast-changing processes caused by fixed time steps, and the output node power increment dynamic data can accurately restore the instantaneous characteristics of power fluctuation; further, the first power data as a dynamic sequence of node active power replaces the fixed power input in traditional deterministic calculation, so that the subsequent power flow calculation can be based on time-varying random power, rather than static ideal value; further, the output power data of the model can accurately describe the random fluctuation process, providing a real data basis for subsequent power flow calculation.

[0087] The first model separates the average trend and random disturbance by splitting the power fluctuation. For example, for residential area nodes, the model can fit the average trend curve of "8 o'clock in the morning and 7 o'clock in the evening load peak" from the actual power data, and extract the millisecond-level random disturbance data caused by "refrigerator start-stop and light switching". The description of such trend combined with disturbance avoids the problem of misjudging regular changes as random fluctuations, so that the data can accurately correspond to the random core characteristics of the distribution network operation state, and provides clear data support for understanding the essence of random operation state.

[0088] The first model relies on the dual technical advantages of quantized state and stochastic process to correct bias: on the one hand, based on the idea of quantized state system, the physical law of distribution network (such as node power balance) is used as a constraint to check and correct systematic errors in the simulation process; on the other hand, through stochastic process modeling technology, the probability of historical disturbance data is learned to optimize the simulation accuracy of small probability and sudden disturbance; for example, for the randomness of wind power output, the model can fit the random distribution of wind speed, reduce the power simulation error from 15% to less than 5% compared with the traditional method, and the output of the first power data is more consistent with the actual power of the node, ensuring that the mapping of the random running state is closer to the real working condition.

[0089] The first model establishes a link between disturbance factors, power increment and running state while outputting the first power data: by tracking the dynamic change data of power increment, the corresponding disturbance source can be matched in reverse (for example, if the node power increment is negative and negatively correlated with the photovoltaic output increment at a certain time period, it is determined that the disturbance source is the sudden drop of photovoltaic output); for example, when the power of a branch node of the distribution network suddenly rises randomly, the correlation analysis between the increment change in the first power data and the disturbance factor can quickly locate whether it is a load disturbance caused by the centralized start of commercial user air conditioning or a power disturbance caused by the overproduction of distributed power. This data + mechanism restoration capability enables the power data not only to present the random running state, but also to explain the root cause of the state, improving the accuracy and depth of the real running state cognition.

[0090] The first power data, with the dynamic change of node power increment as the core, can directly support the quantitative calculation of fluctuation characteristics: based on the time series data of power increment, the fluctuation frequency and maximum increment value in unit time can be calculated; combined with the probability parameters output by the stochastic process modeling, the distribution function of power increment (such as normal distribution, 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 load disturbance; for photovoltaic grid-connected points, the first power data can quantify the low amplitude and high frequency characteristics of output disturbance. These quantified characteristic data provide a unified data scale for the random running state of different nodes and different time periods, enabling the description of the real random running state to shift from qualitative judgment to quantitative analysis, further strengthening the data guarantee.

[0091] In summary, the first model outputs the first power data by splitting the fluctuation composition, correcting the simulation bias, restoring the disturbance mechanism, and quantifying the fluctuation characteristics, accurately capturing the essential law of node power fluctuation in the distribution network, from four aspects of fine description, accurate mapping, root analysis and quantitative evaluation, providing comprehensive and reliable data guarantee for accurately reflecting the real random running state of the distribution network, and laying a solid foundation for the safe dispatch, fault handling and planning optimization of the distribution network.

[0092] S203, input the first power data into the second model to obtain a random power flow calculation result corresponding to the target distribution network.

[0093] The second model is used to simulate the power distribution and node voltage in the target distribution network, and can be a mathematical model constructed based on the Kirchhoff's law and the power balance principle, and the core is an active power balance formula and a reactive power balance formula. The random power flow calculation result can refer to the core operating state variable of the target distribution network, and can include but is not limited to a node voltage amplitude vector and a node voltage phase angle vector. The power distribution and the node voltage refer to the dynamic distribution and balance process of power, voltage, phase angle and other electrical quantities when power is transmitted from a power source node to a load node in the distribution network.

[0094] In an implementation manner, the first power data is input into the second model to update the state data corresponding to the target distribution network; and based on the state data, the random power flow calculation result corresponding to the target distribution network is obtained.

[0095] For example, the node active power P in the first power data is input into the second model in combination with the line parameters and the SOP power in the grid data, the correlation between the node power and the voltage and the phase angle is established through the power balance formula in the model, the voltage amplitude and the phase angle of each node are iteratively calculated, and finally the random power flow calculation result reflecting the power distribution state is output. If the node power jumps, the updated first power data needs to be input again and repeated calculation is needed to obtain the dynamic power flow result.

[0096] The second model is a node power flow model with flexible interconnection characteristics, which converts the random fluctuation of node power into the dynamic distribution of the whole network power flow, and finally realizes the complete restoration of the real random operating state of the distribution network. Specifically, the time-varying nature of the first power data breaks through the limitation of static power flow calculation, and can simulate the real evolution process from power random fluctuation to power flow dynamic adjustment. In addition, the second model explicitly takes into account the active power and the reactive power , can reflect the regulation effect of flexible devices on random power flow, and avoid the distortion of power flow distribution calculation caused by ignoring SOP; Finally, the random power flow calculation results (probability distribution and dynamic sequence of voltage and phase angle) can directly reflect the fluctuation range of the operation state of the distribution network under random disturbance, that is, the output results fit the actual operation characteristics, which can provide accurate basis for risk assessment (such as voltage out-of-limit probability). Through the closed loop of the three-step process of data-fluctuation simulation-power flow calculation, accurate data input is realized, and then the core random fluctuation is captured, and finally the whole network power flow response is restored, which breaks through the defects of traditional methods that cannot consider randomness and flexible interconnection characteristics. Through the random power flow calculation results output by the process, the real evolution process of new energy / load fluctuation, node power change, and whole network power flow adjustment can be presented, and its statistical characteristics (mean, variance) and dynamic characteristics (instantaneous fluctuation) are consistent with the actual operation state of the distribution network.

[0097] The dynamic change of power increment provided by the first power data injects a dynamic boundary condition into the second model: for example, when the first power data captures the active power of the photovoltaic grid-connected point from 500kW to 100kW within 10 seconds, the second model can output the dynamic change process of the voltage amplitude of the node and the adjacent node from 0.98pu to 0.92pu within 10 seconds through power flow iteration calculation, and also give the synchronous offset data of voltage phase angle. This dynamic mapping of power fluctuation-voltage response restores the voltage state change of the distribution network caused by random disturbance, so that the random power flow calculation result can directly correspond to the random voltage characteristics in real operation, and provides core state data for recognizing random operation state.

[0098] The random power flow calculation results output by the second model establish a strong correlation link between the first power data and the power flow results: through the time sequence comparison of the voltage amplitude vector and the node active power vector, the quantitative relationship between power increment change and voltage amplitude fluctuation can be clearly presented; with the help of the change of node voltage phase angle vector, the influence of power flow direction on voltage state can be located (such as the increase of phase angle difference corresponds to the increase of branch power transmission, and then leads to the change of line voltage drop); for example, when the voltage of a certain residential area node appears random fluctuation, combined with the power flow results and the first power data, it can be quickly judged that: if the fluctuation is positively correlated with the local load power increment, it is caused by user appliance start-stop; if it is negatively correlated with the remote new energy output increment, it is caused by insufficient power support due to power fluctuation. This data correlation + mechanism derivation capability makes the random power flow calculation results not only present the random operation state, but also explain the root cause of the state, which improves the accuracy of the cognition of the real operation state.

[0099] The first power data provides real-time power increment dynamic data, effectively making up for the static defects of traditional input: the second model accesses power increment data by time period, upgrades static power flow calculation to dynamic power flow calculation, and can capture millisecond-level power fluctuation-induced voltage micro changes; for example, for the high-frequency start-stop load of industrial users, the 100ms-level power increment recorded by the first power data can output corresponding 0.01pu-level voltage amplitude fluctuations after calculation by the second model, while traditional static calculation will miss such random disturbances. At the same time, based on the physical constraints of Kirchhoff's law, the second model can make secondary corrections to 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 calculation, and making the power flow results reflect the random operating state more closely to reality.

[0100] The second model relies on the full-node power coverage advantage of the first power data to achieve full-domain completion of voltage state: the first power data completes the power data of branch and end nodes through model inversion, which is used as the input of the second model, and the voltage amplitude and phase angle of all nodes of the distribution network (including end nodes without monitoring terminals) can be calculated; for example, based on the total power of the main line and the proportion of branch power, the first power data completes the active power of 3 end branches; the second model calculates the voltage amplitude vector of the end nodes of the 3 branches, and finds that the voltage of one of the end nodes is lower than 0.95pu due to random fluctuations of the load. This power completion and voltage completion logic fills the end voltage blind area and forms full-domain voltage state data covering the main line-branch-end, fully presenting the spatial and temporal distribution characteristics of the random operating state of the distribution network, providing panoramic data guarantee for comprehensive understanding of the real operating state.

[0101] In summary, the second model takes the first power data as the core input, converts power random fluctuations into voltage state changes through dynamic power flow calculation, and the output of random power flow calculation solves the problems of traditional power flow data static, deviation, isolation and blind area, providing comprehensive and reliable data guarantee for accurately reflecting the real random operating state of the distribution network from four aspects of core mapping, machine analysis, accurate reflection and panoramic presentation, and further providing key support for core operation and maintenance work such as voltage regulation, fault location and reactive power optimization of the distribution network.

[0102] The flexible interconnected power distribution network random power flow calculation method obtains the power grid data corresponding to the target power distribution network, provides actual data for random power flow calculation, ensures accurate restoration of the physical topology and flexible interconnection characteristics of the target power distribution network, avoids simulation deviation caused by data distortion from the source, and provides data support for accurately reflecting the real random operation state of the power distribution network. Further, the power grid data can be input into the first model for simulating the node power fluctuation process of the target power distribution network to obtain the first power data corresponding to the target power distribution network, accurately depicting the node power fluctuation process, and providing dynamic data support for reflecting the real random operation state. Furthermore, the first power data can be input into the second model for simulating the power distribution and node voltage of the target power distribution network to obtain the random power flow calculation result corresponding to the target power distribution network. The scheme introduces the first model to accurately simulate the power random disturbance caused by uncertain factors such as new energy and load. Moreover, the second model is introduced to simulate the process from power random fluctuation to random power flow dynamic adjustment, and finally to accurately restore the random operation state of the flexible interconnected power distribution network.

[0103] On the basis of the above-mentioned embodiments, the S202 is explained in detail in the embodiments of the present application. Specifically, the process of obtaining the first power data in the embodiments of the present application includes the following steps as shown in the following table: Figure 3

[0104] S301, input the power grid data into the first model to obtain the node fluctuation data corresponding to the target power distribution network.

[0105] One implementation manner is to determine the first fluctuation intensity and the second fluctuation intensity corresponding to the target power distribution network based on the drift process equation, the diffusion process equation and the quantum value, determine the fluctuation time corresponding to the target power distribution network based on the first fluctuation intensity and the second fluctuation intensity, and take the first fluctuation intensity, the second fluctuation intensity and the fluctuation time as the node fluctuation data corresponding to the target power distribution network.

[0106] ​Wherein, the node fluctuation data is a core parameter set depicting the random mutation characteristics of the node power increment; the first fluctuation intensity is a parameter describing the possibility size of the upward mutation of the node power increment (the fluctuation amount deviating from the benchmark value becomes larger), that is, the positive jump intensity of the node power increment, which is the probability density depicting the jumping of the node power increment in the positive direction (power increase), and can reflect the random disturbance intensity of power increase; the second fluctuation intensity is a parameter describing the possibility size of the downward mutation of the node power increment (the fluctuation amount deviating from the benchmark value becomes smaller), that is, the negative jump intensity of the node power increment, which is the probability density depicting the jumping of the node power increment in the negative direction (power decrease), and can reflect the random disturbance intensity of power decrease; the fluctuation time is the time at which the next random mutation of the node power increment is expected to occur from the current time; 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 time characteristics of the power state change caused by random disturbance.

[0107] Exemplarily, the node power increment , the drift process equation , the diffusion process equation and the quantum value can be extracted from the grid data first, and input into the first model, and the first model is calculated and processed, such as the first fluctuation intensity and the second fluctuation intensity can be calculated, and the calculation formula can be as follows:

[0108] Equation (2)

[0109] Equation (3)

[0110] Further, based on the first fluctuation intensity and the second fluctuation intensity , the fluctuation time can be calculated, and the calculation formula can be as follows:

[0111] Equation (4)

[0112] Wherein, e is a natural constant, which is the base number of natural logarithm.

[0113] Finally, the first fluctuation intensity , the second fluctuation intensity and the fluctuation time These three parameters are integrated into the node fluctuation data.

[0114] The node fluctuation data is converted into quantifiable probability density indicators by the first fluctuation intensity and the second fluctuation intensity parameters, and the random mutation characteristics are converted into quantifiable probability density indicators. For example, for a certain photovoltaic grid-connected point, the first model calculates that the first fluctuation intensity is 0.02 times / minute (power surge probability), and the second fluctuation intensity is 0.05 times / minute (power sudden drop probability), which reflects the difference in the possibility of upward and downward mutation of the node power increment. This quantitative expression breaks through the qualitative description of whether the mutation occurs, realizes the quantitative description of the random disturbance intensity, and enables the data to directly correspond to the possibility of mutation in real operation, providing accurate data for capturing the essential characteristics of the random operation state of the power distribution network.

[0115] The separation design of the first fluctuation intensity and the second fluctuation intensity realizes the differentiation of the mutation direction: for example, at around 18:00 on weekdays, the first fluctuation intensity of a certain commercial area node increases (corresponding to the power surge caused by the concentrated start of air conditioning and lighting loads), while the second fluctuation intensity remains stable; in the early morning, the second fluctuation intensity increases (corresponding to the power sudden drop caused by the shutdown of equipment). This direction differentiation enables operation personnel to analyze the random rules of power surge or power sudden drop, avoids the cognitive bias of mixing different directions of mutation, and improves the accuracy of analyzing the random operation state.

[0116] The fluctuation time parameter realizes the time sequence prediction of random changes by predicting 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 (i.e. the probability of the next power mutation within 15 minutes is the highest), combined with the first fluctuation intensity and the second fluctuation intensity of the node, a comprehensive judgment of 60% probability of power surge within 15 minutes can be formed. This time prediction capability enables operation personnel to perceive the possible occurrence period of random disturbance in advance, breaking the passive mode of post-recording, and providing forward-looking data support for dynamically tracking the random operation state of the power distribution network.

[0117] The node fluctuation data can directly support the correlation analysis of risk scenarios through the synergistic effect of the three parameters: for example, when the first fluctuation intensity of a certain line node continues to rise (high probability of power surge) and the fluctuation time is shortened (smaller mutation interval), combined with the line rated capacity, the risk of line overload can be predicted to increase; when the second fluctuation intensity of a distributed power grid-connected point suddenly increases (high probability of power sudden drop), combined with the voltage stability threshold, the risk of voltage drop can be predicted to increase. The correlation between fluctuation parameters and risk scenarios enables the data not only to reflect the phenomenon of random operation state, but also to reveal its potential impact, deepening the understanding of the real random operation state of the power distribution network, and providing more valuable data support for risk warning and decision-making.

[0118] 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 features 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.

[0119] S302. Based on node fluctuation data, determine the first power data corresponding to the target distribution network.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the quantum value). The process of (integer multiples of) power data; the first power data refers to 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.

[0125] 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:

[0126] Formula (5)

[0127] in, The time step can be any length of time during the advance. ,Will As the updated node power increment.

[0128] 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.

[0129] 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.

[0130] 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:

[0131] Formula (6)

[0132] Formula (7)

[0133] in, The probability of a positive jump is... It represents a negative jump probability.

[0134] 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]. .

[0135] 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.

[0136] The update process for a non-integer jump can be represented as:

[0137] Process 1: Determine whether a jump will occur within the remaining time. The determination method is as follows:

[0138] Formula (8)

[0139] Formula (9)

[0140] in, For the remaining time, For time, This represents the probability of a jump occurring within the remaining time.

[0141] 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... .

[0142] Step 2: Calculate the positive jump probability with the negative jump probability The process is shown in formula (6) and formula (7).

[0143] Process 3: According to the calculated positive jump probability and negative jump probability, randomly perform positive jump or negative jump:

[0144] Take a random number with a uniform distribution For example, according to the following rules, update the state: if , perform positive jump, and take the sum of and quantum value ΔQ as the updated node power increment of node i; if , perform negative jump, and take the difference between and quantum value ΔQ as the updated node power increment of node i, and advance the time to .

[0145] If the third fluctuation intensity is equal to 0, it is determined that there is no jump process, that is, the preset update is performed on the node fluctuation data, and the process of the preset update is as follows:

[0146] Formula (10)

[0147] Wherein, is the time step, take as the updated node power increment, and the length of the time advancement can be , is a sign function.

[0148] Finally, substitute the updated into = + , to obtain the first power data.

[0149] Through the determination mechanism of the third fluctuation intensity, the two processes can be accurately distinguished and differentiated: for example, in cloudy weather, the third fluctuation intensity of a certain photovoltaic grid-connected point is greater than zero (there is a jump process), the model starts the jump simulation, and generates power data containing multiple sudden rises and falls; in sunny and stable weather, the third fluctuation intensity is close to zero (no jump process), the model starts the smooth update, and generates slowly changing power data. This scene processing mechanism avoids the distortion problem of smoothing the jump process or suddenly changing the smooth process, so that the first power data can accurately restore the essential characteristics of different random states.

[0150] Based on the simulation process of the first fluctuation intensity, the second fluctuation intensity, and the fluctuation time, these rules can be accurately quantified: for example, for a certain industrial node, the model simulates the positive and negative mutations of power according to the probability ratio of 3:1 based on the first fluctuation intensity (0.03 times / min) and the second fluctuation intensity (0.01 times / min); combined with the fluctuation time (average 10 minutes) to control the mutation interval, the generated power jump process is consistent with the actual rules of the node device start-stop, and the consistency is improved by more than 40%. At the same time, the determination of the third fluctuation intensity being zero (no jump) can avoid meaningless random disturbance addition, ensuring that the power data in the steady state maintains a reasonable trend. This probability-driven + time-constrained simulation method improves the accuracy of the first power data in describing random disturbances, making it closer to the real operating conditions of the power distribution network.

[0151] The separate application of the first fluctuation intensity and the second fluctuation intensity can completely retain the bidirectional mutation feature: for example, for a certain residential node during the morning peak period, the first fluctuation intensity (positive mutation) is significantly higher than the second fluctuation intensity (negative mutation), and the generated first power data presents the characteristics of more rising and less falling, accurately reflecting the actual situation of concentrated user power consumption; during the late-night period, it is the opposite, and the data presents the characteristics of more falling and less rising. The retention of this directional feature allows operational personnel to accurately analyze the differential impact of mutations in different directions on line load and voltage stability, avoiding cognitive bias caused by the loss of directional information, and improving the accuracy of the analysis of random operating states.

[0152] Based on the dynamic updating mechanism of node fluctuation data, the scene changes can be adapted 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 simultaneously, thereby driving the feature adjustment of the first power data. For example, the third fluctuation intensity of a commercial node on weekends is generally lower than that on weekdays, and the fluctuation time is extended, resulting in a decrease in the jump process in the generated first power data, which is more consistent with the stable characteristics of weekend commercial power consumption. This dynamic link between fluctuation data updating and power data adaptation ensures that the first power data in different time periods can reflect the random operating characteristics of the corresponding scene, avoiding time sequence distortion caused by one-size-fits-all simulation, and providing time sequence consistency for continuous and accurate tracking of the real random operating state of the power distribution network.

[0153] In summary, through the process of determining the first power data based on node fluctuation data, and with the help of the four mechanisms of jump / steady differentiation, probability / time quantification, bidirectional feature retention, and dynamic scene adaptation, the problems of process confusion, insufficient accuracy, direction loss, and time sequence distortion in traditional power data are solved, providing comprehensive and reliable data support for accurately reflecting the real random operating state of the power distribution network from multiple dimensions, and laying a solid foundation for dynamic monitoring, risk warning, and optimal scheduling of the power distribution network.

[0154] In the embodiment of the present application, by introducing the first model, a way is provided for obtaining the core parameters of the random mutation characteristics of the node power increment, thereby providing a data basis for obtaining accurate power data, and finally obtaining accurate power flow data.

[0155] The above Figures 1 to 3 The flexible interconnected power distribution network random power flow calculation method provided by the embodiment of the present application is described in detail, and the device and equipment provided by the embodiment of the present application will be introduced below with reference to the drawings.

[0156] As Figure 4 shown, the figure is a schematic diagram of a flexible interconnected power distribution network random power flow calculation device provided by the embodiment of the present application, and the flexible interconnected power distribution network random power flow calculation device 600 comprises an acquisition module 601, a power determination module 602 and a power flow determination module 603, wherein:

[0157] The acquisition module 601 is configured to acquire power grid data corresponding to a target power distribution network.

[0158] The power determination module 602 is configured to input the power grid data into a first model to obtain first power data corresponding to the target power distribution network, wherein the first model is configured to simulate a fluctuation process of node power in the target power distribution network.

[0159] The power flow determination module 603 is configured to input the first power data into a second model to obtain a random power flow calculation result corresponding to the target power distribution network, wherein the second model is configured to simulate the distribution of electric energy and node voltage in the target power distribution network.

[0160] In one embodiment, the power determination module 602 is specifically configured to:

[0161] input the power grid data into the first model to obtain node fluctuation data corresponding to the target power distribution network;

[0162] determine the first power data corresponding to the target power distribution network based on the node fluctuation data.

[0163] In one embodiment, the power determination module 602 is specifically configured to:

[0164] determine first fluctuation intensity and second fluctuation intensity corresponding to the target power distribution network based on a drift process equation, a diffusion process equation and a quantum value;

[0165] determine fluctuation time corresponding to the target power distribution network based on the first fluctuation intensity and the second fluctuation intensity;

[0166] use the first fluctuation intensity, the second fluctuation intensity and the fluctuation time as the node fluctuation data corresponding to the target power distribution network.

[0167] In one embodiment, the power determination module 602 is specifically configured to:

[0168] determine whether there is a jump process based on the first fluctuation intensity and the second fluctuation intensity;

[0169] if there is a jump process, perform jump process simulation and obtain first power data corresponding to the target power distribution network;

[0170] if there is no jump process, update the node fluctuation data according to a preset update rule and obtain first power data corresponding to the target power distribution network.

[0171] In one embodiment, the power determination module 602 is specifically configured to:

[0172] sum the first fluctuation intensity and the second fluctuation intensity as a third fluctuation intensity;

[0173] if the third fluctuation intensity is equal to zero, there is no jump process;

[0174] if the third fluctuation intensity is not equal to zero, there is a jump process.

[0175] In one embodiment, the power determination module 602 is specifically configured to:

[0176] determine the number of jumps based on the fluctuation time;

[0177] perform jump process simulation based on the number of jumps to obtain first power data corresponding to the target power distribution network.

[0178] In one embodiment, the power flow determination module 603 is specifically configured to:

[0179] input the first power data into a second model to update state data corresponding to the target power distribution network;

[0180] obtain a random power flow calculation result corresponding to the target power distribution network based on the state data.

[0181] The flexible interconnected power distribution network random power flow calculation device 600 according to the embodiment of the present application can correspond to the method described in the embodiment of the present application, and the above-mentioned other operations and / or functions of each module / unit of the flexible interconnected power distribution network random power flow calculation device 600 are respectively realized Figure 2 、 Figure 3 The above-mentioned other operations and / or functions of each module / unit of the flexible interconnected power distribution network random power flow calculation device 600 are respectively realized

[0182] The embodiment of the present application also provides a computing device. The computing device can be a local computing device or an application server.

[0183] As shown in Figure 5As shown, the figure is a schematic diagram of a computing device provided by an embodiment of the application, which computing device 700 comprises 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 through the bus 701.

[0184] The bus 701 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0185] The processor 702 can be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP), etc.

[0186] The communication interface 703 is used for communication with the outside. For example, the communication interface 703 can be used to communicate with the terminal 102. The communication interface 703 is used to send the random power flow calculation result to the terminal 102, so that the terminal 102 presents the random power flow calculation result.

[0187] The memory 704 can include a volatile memory, such as a random access memory (RAM). The memory 704 can also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0188] The memory 704 stores executable code, and the processor 702 executes the executable code to perform the flexible interconnected power distribution network random power flow calculation method.

[0189] Specifically, in the case of implementing the embodiment shown, and Figure 4 In the case of implementing the embodiment shown, and Figure 4The modules or units of the flexible interconnected power distribution network stochastic power flow calculation device described in the embodiments are implemented by software, and the software or program codes required for performing the functions of the modules / units in the above embodiments can be partially or entirely stored in the memory 704. Figure 4 The software or program codes required for performing the functions of the modules / units in the above embodiments can be partially or entirely stored in the memory 704. The processor 702 executes the program codes corresponding to the modules stored in the memory 704 to perform the flexible interconnected power distribution network stochastic power flow calculation method.

[0190] The embodiments of the present application also provide a computer readable storage medium. The computer readable storage medium can be any available medium or data storage device that can be used to store data that can be accessed by a computing device, such as a data center. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk), etc. The computer readable storage medium includes instructions that instruct the computing device to perform the flexible interconnected power distribution network stochastic power flow calculation method described above.

[0191] The embodiments of the present application also provide a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0192] The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer or data center to another website, computer or data center through wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) mode.

[0193] The computer program product is executed by a computer, and the computer executes any of the flexible interconnected power distribution network stochastic power flow calculation methods described above. The computer program product can be a software installation package, and when any of the flexible interconnected power distribution network stochastic power flow calculation methods described above is needed, the computer program product can be downloaded and executed on the computer.

[0194] The descriptions of the processes or structures corresponding to the above respective figures are each focused on, and the parts not described in detail in a certain process or structure can be referred to the related descriptions of other processes or structures.

[0195] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed by the present application should be covered within the protection scope of the present application.

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; 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; 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; 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.

2. The method according to claim 1, 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.

3. A stochastic power flow calculation device for a flexible interconnected distribution network, characterized in that, The flexible interconnected distribution network stochastic power flow calculation device is used to perform the method as described in any one of claims 1 to 2, the device comprising: 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.

4. A computing device, characterized in that, The device includes a memory and a processor; wherein one or more computer programs are stored in the memory, 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 2.

5. 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 2.

Images