Active power distribution network linear load flow calculation method and system based on active and reactive coupling characteristic analysis

Abstract

The invention provides an active power distribution network linear load flow calculation method and system based on active and reactive coupling characteristic analysis, and relates to the technical field of active power distribution network reconstruction. According to the method, firstly, an active power distribution network model containing multiple types of flexible resources is established, and a quantitative analysis method of active and reactive combined regulation capability is provided based on operation constraint and regulation characteristics of the flexible resources, so that the influence of multi-source cooperation on voltage, current and power flow distribution is comprehensively evaluated. According to the operation characteristics of the static load characteristics of the node voltage of the power distribution network, an improved linearization power flow calculation model is constructed, and the coupling relation between the active power and the reactive power in the power distribution network is rapidly solved by performing linear approximation and coupling decoupling processing on a power flow equation. According to the method, the precision of traditional nonlinear load flow calculation is reserved, the calculation complexity is remarkably reduced, and the controllability and the real-time performance of the active power distribution network in multiple scenes are improved.

Description

A method and system for calculating linear power flow in active distribution networks based on active and reactive power coupling characteristics analysis Technical Field

[0001] This invention relates to the field of active distribution network reconfiguration technology, and in particular to an active distribution network linear power flow calculation method and system based on active and reactive power coupling characteristic analysis. Background Technology

[0002] The massive number of distributed photovoltaic power generation units, decentralized energy storage systems, large-scale electric vehicle charging pile clusters, and an increasing number of flexible and adjustable loads are widely connected to the distribution network. While injecting huge green energy into the entire energy system and significantly increasing the proportion of clean energy consumption, this also makes the operating environment and control logic of the traditional distribution network face unprecedented complexity and challenges.

[0003] The access of massive distributed and fluctuating resources has placed more stringent demands on the voltage stability and support capabilities of active distribution networks. Active distribution networks are experiencing bidirectional power flow and even reverse power transmission to the upper-level grid, with risks of local node voltage exceeding limits. The computational complexity and real-time requirements for system operation status analysis, optimization scheduling, and control decisions are increasing exponentially, posing a severe challenge to the efficiency of existing power flow calculation methods and algorithms. The dynamic power coupling and mutual influence relationships between different devices (such as photovoltaics, energy storage, charging piles, and adjustable loads) in the spatiotemporal dimensions are unprecedentedly complex, urgently requiring more accurate and efficient coupling analysis models and methods.

[0004] Therefore, the active distribution network technology system urgently needs to achieve revolutionary breakthroughs and innovations in the aforementioned key areas—namely, active voltage support capability, ultra-large-scale real-time computing efficiency, and analysis of complex multi-source power coupling mechanisms—in order to effectively support the safe, stable, and efficient operation of the energy internet under the future high proportion of distributed energy access.

[0005] With the large-scale integration of flexible resources such as distributed power sources, energy storage devices, and adjustable loads, the operation of active distribution networks exhibits dynamism, complexity, and uncertainty. Traditional power flow calculation methods based on Newton-Raphson have problems such as low computational efficiency, difficulty in convergence, and insufficient accuracy in active distribution networks. In particular, when considering the coupling relationship between active and reactive power, the computational complexity increases significantly. Summary of the Invention

[0006] Objective: To propose a linear power flow calculation method and system for active distribution networks based on the analysis of active and reactive power coupling characteristics. This method establishes multiple flexible resource models to construct a quantitative analysis method for the joint regulation capability of active and reactive power. Furthermore, by introducing voltage static load characteristics, an improved linearized power flow calculation method is formed. Further, through active-reactive power decoupling and second-order cone relaxation optimization, the method significantly improves computational efficiency and convergence while maintaining accuracy, providing reliable theoretical support and computational tools for real-time operation analysis and optimized scheduling of active distribution networks.

[0007] To achieve the above objectives, this invention proposes a method for calculating the linear power flow of an active distribution network based on the analysis of active and reactive power coupling characteristics, comprising the following steps:

[0008] An active distribution network model incorporating multiple types of flexible resources is established to reflect the power flow distribution characteristics of the distribution network under different operating conditions. Based on the operating constraints and regulation characteristics of multiple types of flexible resources, a quantitative analysis method for the joint regulation capability of active and reactive power is constructed. The active distribution network model is solved by deconvexification using second-order cone programming to evaluate the impact of multi-source collaboration on node voltage, current, and power flow distribution. An improved linearized power flow calculation method is constructed for the voltage static load characteristics and operating characteristics of distribution network nodes. The power flow equations are linearly approximated and decoupled from active-reactive power coupling to achieve rapid solution of the coupling relationship between active and reactive power in the distribution network.

[0009] As a preferred option, the various flexible resources include distributed power sources, on-load tap-changing transformers, group-switched capacitors, energy storage units, and adjustable loads.

[0010] As a preferred option, the active distribution network model uses the minimum overall operating cost within the scheduling cycle as the objective function. :

[0011] In the formula, t represents the current time, and the scheduling time window is 24 hours. For distribution network loss costs; To avoid the cost of abandoning renewable distributed power sources; To reduce costs by reducing workload.

[0012] As a preferred option, the operational constraints of various flexible resources include: distribution network power flow constraints, distribution network reconfiguration constraints, on-load tap-changing transformer operation constraints, group switching capacitor constraints, photovoltaic and wind turbine operation constraints, energy storage system operation constraints, and demand-side response constraints.

[0013] As a preferred embodiment, the active distribution network model is a mixed-integer nonlinear programming model, which is transformed into a mixed-integer second-order cone model by performing second-order cone relaxation on the mixed-integer nonlinear programming model before solving it.

[0014] As a preferred embodiment, the improved linearized power flow calculation method specifically includes: using a ZIP load model to represent the static characteristics of the load voltage, and linearizing its nonlinear part through Taylor expansion.

[0015] As a preferred approach, by performing a Taylor series expansion on the sine and cosine functions of the phase angle difference and ignoring second-order and higher-order terms, the voltage amplitude and phase angle can be decoupled.

[0016] Furthermore, this invention proposes an active distribution network linear power flow calculation system. This system can execute the aforementioned active distribution network linear power flow calculation method based on active-reactive coupling characteristic analysis. The system includes: a flexibility resource modeling module, which establishes an active distribution network model for various flexibility resources in the distribution network, including distributed generation, on-load tap-changing transformers, grouped switching capacitors, energy storage units, and adjustable loads; a linearized power flow equation construction module, which establishes the relationship between node voltage and power injection based on Kirchhoff's current law. First-order Taylor expansion is used to linearize the node voltage magnitude and phase angle, resulting in improved linear power flow equations containing active-reactive coupling terms; and a voltage static characteristic modeling module, which incorporates the voltage dependence characteristics of loads in the distribution network during the linearization of the power flow equations.

[0017] Furthermore, the present invention also proposes an electronic device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the above-mentioned method for calculating the linear power flow of an active distribution network based on the analysis of active and reactive power coupling characteristics.

[0018] Furthermore, the present invention also proposes a computer-readable storage medium storing at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the above-described active distribution network linear power flow calculation method based on active and reactive power coupling characteristic analysis.

[0019] Compared with the prior art, the present invention has the following beneficial effects: First, it improves the accuracy and applicability of power flow calculation.

[0020] This invention achieves a high-precision linear approximation of the operating characteristics of an active distribution network by introducing an improved linearized power flow model that considers the static load characteristics of voltage and combining it with a description of the active and reactive power joint regulation capabilities of multiple types of flexible resources. This method maintains high computational accuracy and result stability even in low- and medium-voltage distribution networks with large voltage fluctuations, overcoming the problem of insufficient accuracy of traditional DC power flow or primary linearized models under conditions of high distributed generation penetration.

[0021] II. Enhance the universality and scalability of the model.

[0022] This invention establishes a unified flexible resource modeling framework that supports the expression of active and reactive power coupling characteristics for various resources, including distributed power sources, energy storage devices, and adjustable loads. This framework is applicable to active distribution networks with different topologies and access ratios, and can be extended to multi-energy coupling scenarios such as microgrids and regional integrated energy systems, demonstrating good versatility and expansion potential.

[0023] Third, it significantly improves computational efficiency and convergence performance.

[0024] This invention employs an active-reactive power decoupling method during modeling and introduces a second-order cone relaxation optimization (SOCP) solution strategy, transforming the originally nonlinear and non-convex power flow equations into a convex optimization problem that can be solved efficiently. This method significantly improves the solution speed and convergence stability while ensuring the physical feasibility and computational accuracy of the solution, enabling rapid power flow analysis and real-time scheduling calculations for active distribution networks.

[0025] IV. Analysis of the ability to coordinate and regulate flexible resources.

[0026] This invention constructs a quantitative analysis model of active and reactive power joint regulation capability, which can accurately describe the coordinated regulation characteristics of various flexible resources on system voltage and power balance, providing precise linearization support for distributed resource optimization output calculation and regional voltage coordination control, and achieving the operational goal of balancing economy and safety.

[0027] V. Enhance the intelligent operation and decision support capabilities of the active distribution network.

[0028] The improved linear power flow calculation framework established in this invention can be directly applied to functional modules such as state estimation, flexibility assessment, power flow optimization, and voltage control in active distribution networks. It provides a unified, concise, and efficient theoretical basis and calculation tools for real-time operation analysis and optimized scheduling of active distribution networks, significantly improving the system's intelligent analysis and decision-making capabilities. Attached Figure Description

[0029] Figure 1 shows the distribution of multiple flexible resource nodes in the active distribution network in the embodiment. Detailed Implementation

[0030] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0031] As shown in Figure 1, the method of the present invention mainly includes the following functional modules: (1) Flexible resource modeling module: This module establishes a unified active-reactive joint regulation capability model for various types of adjustable resources in the distribution network (including distributed power sources, energy storage systems, controllable loads, and active and reactive power compensation devices, etc.). Through linearization approximation technology, the nonlinear regulation constraints are transformed into piecewise linear or cone constraint expressions to achieve consistency in multi-source coupling modeling.

[0032] (2) Voltage static characteristic modeling module: Considering the voltage dependence characteristics of the load in the distribution network, the load adopts a polynomial model to simulate the characteristics of the system load change caused by voltage fluctuation, and this characteristic is introduced in the process of power flow equation linearization to improve the accuracy of the traditional linear model in the voltage offset scenario.

[0033] (3) Linearized power flow equation construction module: Under the node injection model framework, the relationship between node voltage and power injection is established based on Kirchhoff's Current Law (KCL). The node voltage magnitude and phase angle are linearized by first-order Taylor expansion to obtain an improved linear power flow equation containing active-reactive coupling terms. The calculation process includes the following steps: S1. Input distribution network topology and node parameters; S2. Extract flexibility resources and load model parameters; S3. Establish a linearized equation set of node power and voltage; S4. Perform active-reactive decoupling and matrix sparsification; S5. Output power flow results and perform error correction and sensitivity analysis.

[0034] The following details the implementation method. A linear power flow calculation method for an active distribution network based on active and reactive power coupling characteristic analysis is presented, with the following steps: An active distribution network model is established, incorporating various flexible resources such as distributed generation (DG), on-load tap-changing transformers (OLTC), grouped switching capacitors, energy storage units, and adjustable loads. This model reflects the power flow distribution characteristics of the distribution network under different operating conditions, providing a mathematical basis for coupling characteristic analysis and scheduling optimization.

[0035] Based on the operational constraints and regulation characteristics of the aforementioned flexible resources, a quantitative analysis method for the joint regulation capability of active and reactive power is constructed. This method comprehensively characterizes the impact of the synergistic effects of distributed power sources, energy storage, and demand-side response on node voltage, current, and power flow distribution, enabling a quantitative assessment of the system's regulation potential.

[0036] For the voltage static load characteristics of distribution network nodes and the operating characteristics of PV nodes, the ZIP load model is used to characterize the voltage static characteristics of the load, and the Taylor expansion is used to approximate the linearization of its nonlinear part, thereby reducing the computational complexity while ensuring the computational accuracy.

[0037] Considering that the admittance matrix in the power flow equation contains a product of voltage and trigonometric functions, voltage and phase angle are tightly coupled. This invention, based on the small difference in phase angle between node voltages, performs a Taylor series expansion on the sine and cosine functions of the phase angle difference, ignoring second-order and higher-order terms, thus achieving effective decoupling of voltage amplitude and phase angle.

[0038] Based on power flow calculation methods, this paper applies them to the optimization scheduling problem of active distribution networks. The scheduling model takes minimizing operating costs as the objective function, including network loss costs, costs of abandoning renewable distributed generation, and load reduction costs, and optimizes the scheduling within a 24-hour time window.

[0039] The model constraints include: distribution network power flow constraints, network reconfiguration constraints, OLTC operation constraints, capacitor switching constraints, photovoltaic and wind turbine output constraints, energy storage operation constraints, and demand-side response constraints. The scheduling model is a mixed-integer nonlinear programming (MINLP) model, which is transformed into a mixed-integer second-order cone programming (MISOCP) model through second-order cone relaxation (SOCR) to improve solvability.

[0040] The active distribution network model for multiple flexible resources includes: taking the minimum comprehensive operating cost within the scheduling cycle as the objective function, including the minimum energy loss within the scheduling cycle, the cost of abandoning Renewable Distributed Generation (RDG), and the load shedding cost, which are calculated as follows: (1) Where: η t Let t be the electricity price for period t; R represents the energy loss during time period t; ij I is the equivalent resistance of line segment ij; ij,t It is the magnitude of the current flowing through the line during time period t; Ω AL This is a collection of distribution network branches.

[0041] (2) Where: and These represent the cost per unit of electricity curtailed from solar and wind power. and These represent the predicted active power output of PV and WT installed at node i during time period t. and These represent the active power outputs of PV and WT installed at node i during time period t.

[0042] (3) Where: Ω load For the set of nodes whose load can be reduced; η load Compensation for the reduction in electricity consumption per unit of load; This refers to the active power of the load that is reduced after demand response.

[0043] For a node j in the active distribution network, the power flow pattern is as follows: (4) For a branch ij in the distribution network, its voltage relationship is as follows: (5) Decoupling active and reactive power yields the following formula: (6) Where: A ij A represents the set of starting nodes of a branch with node j as the ending node in a distribution network; jk U represents the set of end nodes of a branch in a distribution network with node j as the first end node; i,t P represents the voltage magnitude at node i; ij,t and Q ij,t P represents the active power and reactive power flowing from node i to node j, respectively; jk,t and Q jk,t P represents the active power and reactive power flowing from node j to node k, respectively; j,t and Q j,t These represent the net active power and net reactive power injection values ​​at node j, respectively. and These represent the active power and reactive power injected by the wind turbine at node j, respectively. Represents the active and reactive power injected by the photovoltaic system at node j; This represents the active power of energy storage discharge at node j; This represents the active power of energy storage charging at node j; and This represents the active and reactive power of the load at node j; and This represents the reduction in active and reactive power of the load at node j in response to demand. The input power of the capacitor in the distribution network is represented; rij and jxij represent the resistance and reactance on line ij; Uj,t represents the voltage amplitude at node j.

[0044] The network reconfiguration constraints must ensure that the distribution network is radial after reconfiguration, and that ring networks and islanded operation are not allowed. The specific constraint formula is as follows: (7) Where: E ij,t and δ ij,t Let δ be a Boolean variable.ij,t Used to describe the on / off state of a branch; its value is 1 when the branch is closed and 0 when the branch is open; E ij,t Let be the power flow direction variable of ij at time t. When its value is equal to 1, it means that node j is the parent node of node i, and the power flow direction is from node j to node i. Otherwise, its value is 0. M(i) represents the set of nodes connected to node i. The first term in the formula restricts the power flow of branch ij to only one direction at the same time, but ensures the bidirectional nature of the power flow at other times. The second term in the formula stipulates that there is only one parent node in the distribution network, the substation node. The third term in the formula avoids the case where other nodes are parent nodes. The power flow constraint equation at this time is transformed from equation (4): (8) The OLTC constraint adds a virtual node a in branch ij to divide the branch containing the transformer into segment ia and segment aj, where segment ia contains impedance r. ij +jx ij The active and reactive power relationship of the branch can be represented by equation (9). The aj segment is a branch containing only an ideal transformer, and its constraint relationship is expressed by the following equation: (9) Where: Δk t The tap value is the tap position of the transformer, and k is the transformer turns ratio.

[0045] The capacitor constraint is shown in the following formula: (10) Where: Let be the number of capacitor banks put into operation at time t. QCB represents the maximum number of capacitor banks that can be put into operation; QCB represents the capacity of a single capacitor.

[0046] The constraints for the operation of new energy sources are shown in the following formula: (11) In the formula, φ WT WT is the power factor angle.

[0047] The energy storage operation constraints mentioned above take into account the unstable output of wind and solar power. In order to effectively reduce the curtailment of wind and solar power, the constraints are as follows: (12) Where: Let be the charge value of ESS at node i at time t; and ηcharge and ηdischarge are the charging and discharging power of the ESS at node i at time t; Δt is the scheduling time interval; the formula in the second row of equation (12) constrains the initial charge of the ESS in the next cycle to be equal to the charge value of the ESS at the end of the previous cycle.

[0048] (13) Where: and These are the upper limits of ESS charging and discharging power, respectively. and Let be a Boolean variable, and its constraint condition indicates that ESS cannot charge and discharge simultaneously at any time t.

[0049] The demand-side response constraint, during peak load periods, incentivizes users to reduce load by providing power outage compensation, as shown in equation (14): (14) Where: k i,t Let k be the load reduction factor of node i at time t; max This represents the upper limit of the load reduction factor.

[0050] The static characteristics of the load voltage characterized by the ZIP load model are shown in the following equation: (15) of which

[0051] In the formula: U0 is the system rated voltage, P0 and Q0 are the system rated active power and reactive power, respectively; C Z C I C P and , , These are the proportional coefficients corresponding to active constant impedance loads, constant current loads, and constant power loads, and reactive constant impedance loads, constant current loads, and constant power loads, respectively.

[0052] The quantitative analysis method for constructing the joint regulation capability of active and reactive power achieves deconvexity and linearization of the model by improving the second-order cone programming. The specific steps include: firstly, linearizing the mixed integer nonlinear programming model by second-order cone relaxation; secondly, using variable substitution to transform the voltage and current square terms in equations (4), (6), (9), and (15) into linear terms, as shown in the following equations: (16) After the above transformation, the original power flow equation will become the following: (17) (18) A system of linear equations and a quadratic idempotent equation are obtained. Further relaxation of the quadratic idempotent equation yields equation (19). (19) After performing an equivalent transformation, we get the standard second-order cone form: (20) The second-order cone form described above is only applicable to branches with closed voltage. The model described above considers network reconstruction, includes Boolean variables, and has non-convex sources. Therefore, new branch variables UIJi,t and UIJj,t are introduced to replace the original node variables. At the same time, new constraints are added to separate the variable product terms and linearize the mixed-integer nonlinear expression. The relevant constraints of the new variables are as follows: (21) Combining the above transformations, the original model is transformed into a new form, as shown in the following equation: (22) In this way, all constraints in the model are linear constraints, and the original mixed integer non-convex nonlinear problem is transformed into a solvable second-order cone programming problem.

[0053] The power flow calculation method considering the static characteristics of load voltage and the operating characteristics of PV nodes is first expressed in polar coordinates using admittance form, as shown in the following equation: (23) To simplify the analysis, time t is omitted in the above formula. In the formula, G... ij and B ij Combining equation (15) and converting U0 to the nominal voltage, we can obtain equation (24): (24) Based on the characteristics of a typical distribution network, it can be seen that the voltage amplitude of each node in the system approaches the per-unit value. Therefore, the relationship between voltage amplitude and voltage drop can be expressed as follows: (25) Taking the reciprocal of the right side of the above equation and performing a Taylor expansion at ΔU, we obtain the following equation: (26) Under normal operating conditions of the distribution network, the voltage drop between each node is relatively small. Therefore, the linearized result can be obtained by retaining only the first-order term in equation (26): (27) From this, we can obtain the linearized expression for the left side of equation (23): (28) The diagonal and off-diagonal elements of the nodal admittance matrix in a distribution network have the following properties: (29) Considering the above properties, by transforming the right side of equation (23), we can obtain the following equation: (30) Observing that there are a large number of coupling terms between trigonometric functions and voltage, the Taylor series expansion of the sine and cosine functions of the phase angle difference, ignoring second-order and higher-order terms, effectively decouples the voltage amplitude and phase angle to obtain sinθ. ij =θ ij cosθ ij =1, substituting into equation (30) can achieve decoupling of voltage amplitude and phase angle: (31) Where: δ i δ j These are the voltage phase angles of nodes i and j, respectively.

[0054] Similarly, the reactive power injected at the nodes can be processed in the same way to obtain the following formula: (32) Combining equations (31) and (32), we get the following equation: (33) Where: and These are the real and imaginary parts of the elements in the i-th row and j-th column of the admittance matrix after subtracting the nodal self-admittance, respectively.

[0055] Combining equations (28) and (33), the nodal power equations can be rearranged as follows: Equation (34) is the power flow calculation equation that completes the linearization.

[0056] The logical ideas behind the methods disclosed in the above embodiments can be implemented, in whole or in part, through software, hardware, firmware, or any other combination. When implemented in software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0057] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0060] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis, characterized in that, Includes the following steps: An active distribution network model incorporating multiple types of flexible resources is established to reflect the power flow distribution characteristics of the distribution network under different operating conditions. Based on the operating constraints and regulation characteristics of multiple types of flexible resources, a quantitative analysis method for the joint regulation capability of active and reactive power is constructed. The active distribution network model is solved by deconvexification using second-order cone programming to evaluate the impact of multi-source collaboration on node voltage, current, and power flow distribution. An improved linearized power flow calculation method is constructed for the voltage static load characteristics and operating characteristics of distribution network nodes. The power flow equations are linearly approximated and decoupled from active-reactive power coupling to achieve rapid solution of the coupling relationship between active and reactive power in the distribution network.

2. The method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis as described in claim 1, characterized in that, The various types of flexible resources include distributed power sources, on-load tap-changing transformers, group-switched capacitors, energy storage units, and adjustable loads.

3. The method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis as described in claim 1, characterized in that, In the active distribution network model, the objective function is to minimize the overall operating cost within the scheduling cycle. ： In the formula, t represents the current time, and the scheduling time window is 24 hours. For distribution network loss costs; To avoid the cost of abandoning renewable distributed power sources; To reduce costs by reducing workload.

4. The method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis according to claim 2, characterized in that, Operational constraints for various flexible resources include: power flow constraints of distribution networks, reconfiguration constraints of distribution networks, operation constraints of on-load tap-changing transformers, constraints of grouped switching of capacitors, operation constraints of photovoltaic and wind turbines, operation constraints of energy storage systems, and demand-side response constraints.

5. The method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis according to claim 1, characterized in that, The active distribution network model is a mixed integer nonlinear programming model. After performing second-order cone relaxation on the mixed integer nonlinear programming model, it is transformed into a mixed integer second-order cone model for solution.

6. The method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis according to claim 1, characterized in that, The improved linearized power flow calculation method specifically includes: using the ZIP load model to represent the static characteristics of the load voltage, and linearizing its nonlinear part through Taylor expansion.

7. A method for calculating linear power flow in an active distribution network based on active and reactive power coupling characteristic analysis, as described in claim 1 or 6, characterized in that... By performing a Taylor series expansion on the sine and cosine functions of the phase angle difference and ignoring second-order and higher-order terms, the voltage amplitude and phase angle can be decoupled.

8. A linear power flow calculation system for an active distribution network, characterized in that, The system is used to execute the active distribution network linear power flow calculation method based on active-reactive coupling characteristic analysis as described in any one of claims 1 to 7. The system includes: a flexibility resource modeling module, which establishes an active distribution network model for various flexibility resources in the distribution network, including distributed generation, on-load tap-changing transformers, grouped switching capacitors, energy storage units, and adjustable loads; a linearized power flow equation construction module, which establishes the relationship between node voltage and power injection based on Kirchhoff's current law. The node voltage magnitude and phase angle are linearized using a first-order Taylor expansion to obtain an improved linear power flow equation containing active-reactive coupling terms; and a voltage static characteristic modeling module, which incorporates the voltage dependence characteristics of loads in the distribution network during the linearization of the power flow equation.

9. An electronic device, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the active distribution network linear power flow calculation method based on active and reactive power coupling characteristic analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the active distribution network linear power flow calculation method based on active and reactive power coupling characteristic analysis as described in any one of claims 1 to 7.

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