A load restoration method for distribution network with distributed photovoltaic considering bi-directional power flow polarity

Abstract

The application discloses a kind of distributed photovoltaic network containing bidirectional current flow polarity's load reduction calculation method, the method is by establishing hierarchical partition topological architecture and node admittance matrix, the power transmission coupling strength between photovoltaic cluster and grid equipment is quantified;Again, the mechanism discriminant function of current flow polarity is deduced, the accurate identification of forward power supply and reverse power supply working condition is realized;Then, according to the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient of the weight coefficient

Description

Technical Field

[0001] This invention relates to the field of power system risk assessment technology, specifically to a method for calculating the reduced load of a distributed photovoltaic distribution network that considers bidirectional power flow polarity. Background Technology

[0002] With the large-scale deployment and high-proportion integration of distributed photovoltaic (PV) power into the distribution network, the traditional unidirectional radial power supply network has been disrupted, and the distribution network now exhibits typical bidirectional power flow characteristics. This leads to "net load" distortion in load measurements by grid equipment. Existing technologies primarily focus on load restoration methods for PV grid-connected scenarios, resulting in two main technological directions: Empirical linear superposition method: The core idea is to perform a simple linear superposition of the measured net load of the grid equipment and the photovoltaic output. That is, by collecting the real-time output data of the photovoltaic access point, it is directly added or subtracted from the measured load of the corresponding grid equipment to approximate the actual load of the grid equipment. Basic topology association method: Based on linear superposition, it initially considers the simple association relationship of the distribution network topology. By dividing the photovoltaic power supply area, the photovoltaic output is allocated to the corresponding grid equipment according to the area, and then combined with the measured load to complete the reconstruction.

[0003] The above two methods do not combine bidirectional power flow polarity and distribution network physical mechanism to build a complete model, and have the following core problems: (1) The load restoration accuracy is low and cannot meet the high-precision management and control requirements. The main reasons include: the topology coupling model based on distribution network physical mechanism is not built, the power loss of equipment is not considered, and the topology association is rough; (2) The operating condition adaptability is poor and cannot adapt to bidirectional power flow switching scenarios. The main reasons include: using the positive and negative of the measured power as the operating condition criterion is prone to misjudgment, the adaptive load fusion mechanism is not designed, and the technical design is out of touch with the actual operation scenario.

[0004] It is understood that the above statements only provide background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0005] The purpose of this invention is to provide a load reduction calculation method for distributed photovoltaic (PV) distribution networks that considers bidirectional power flow polarity, thereby solving the core technical problems of low load reduction accuracy and poor adaptability under high-proportion PV grid connection. At the same time, it optimizes the reduction results by combining multi-dimensional constraints for safe grid operation, thus meeting the actual needs of high-precision control and safe and stable operation of the distribution network.

[0006] To achieve the above objectives, this invention provides a method for calculating the reducing load of a distributed photovoltaic distribution network considering bidirectional power flow polarity, the specific steps of which include: Step S1: Establish a hierarchical and partitioned topology architecture and construct the corresponding node admittance matrix. Construct the topological coupling coefficient matrix between the photovoltaic cluster and the grid equipment nodes. Step S2: Calculate the topological coupling power; Step S3: Construct the power flow polarity mechanism discriminant function. Step S3: Dynamically adjust the weighting coefficients of the measured load and the topology coupling power to construct a source-load nonlinear dynamic fusion model; Step S4: Optimize the objective function of the restored load with the goal of minimizing the load restoration error; Step S5: Construct an improved adaptive particle swarm optimization algorithm model, combine it with the source-load nonlinear dynamic fusion model, and output the restored load value.

[0007] Preferably, in step S1, the mathematical expression of the hierarchical partitioning topology is: In the formula, It is a hierarchical set of partitioned topological levels. This is the power supply zoning level, which is the highest level of the hierarchical zoning system. It is at the substation level and belongs to the power supply zone. It is at the main transformer level and belongs to the substation. It is at the busbar level and belongs to the main transformer; It is at the line level and belongs to the busbar; The node admittance matrix The mathematical expression is: In the formula, This refers to the total number of nodes in the power grid, covering power supply zones, substations, main transformers, busbars, lines, and photovoltaic cluster access nodes. For nodes The self-admittance is equal to all the admittances of the nodes. The sum of the admittances of connected branches; hour, For nodes With nodes The mutual admittance is equal to the node With nodes The negative value of the branch admittance.

[0008] Preferably, in step S1, the topological coupling coefficient matrix The mathematical expression is: In the formula, To determine the total number of photovoltaic clusters, low-voltage uncollected photovoltaic and regulated photovoltaic systems of the same level and region are respectively aggregated into photovoltaic clusters. For the first The photovoltaic cluster and the first The topological coupling coefficient of each power grid equipment node, with a value range of [0,1]; For the first The photovoltaic cluster access node and the first Electrical distance between individual power grid equipment nodes; For the first The photovoltaic cluster access node and the first Mutual admittance of individual power grid equipment nodes; Based on the hierarchical partitioning topology and the node admittance matrix and the topological coupling coefficient matrix A photovoltaic-equipment power topology coupling model is established, and the mathematical expression of the photovoltaic-equipment power topology coupling model is as follows: In the formula, For the power output vector of the photovoltaic cluster, For the first Real-time output of a photovoltaic cluster; This refers to the node-coupled power vector of the power grid equipment, i.e., the topology-coupled power. For the first The photovoltaic cluster coupling power received by each grid equipment node.

[0009] Preferably, in step S2, the power flow polarity mechanism discrimination function The mathematical expression is: In the formula, Real-time measurement of active power of power grid equipment, in kW; The line power factor reflects the ratio of active power to apparent power, and its value ranges from [0,1]. The equivalent impedance of the lines connected to the power grid equipment, in Ω. Its amplitude; The resistance of the lines connected to the power grid equipment; Reactance of the lines connected to the power grid equipment; Based on the discriminant function of power flow polarity mechanism Establish working condition judgment rules: when When the power transmission direction is from the grid side to the load side, it is determined to be a positive power supply condition; when When this occurs, it indicates that the power transmission direction is from the photovoltaic side to the grid side, and it is determined to be a reverse power transmission condition; when When the power is zero, it indicates a transitional operating condition, and should be treated as a forward power supply condition.

[0010] Preferably, in step S3, the mathematical expression of the source-load nonlinear dynamic fusion model is: In the formula, The actual load of the power grid equipment is expressed in kW, which is the target value that this invention needs to restore. The dynamic weighting coefficient of the measurement load driven by the power flow polarity; The topology coupling power dynamic weighting coefficient is driven by the power flow polarity. This is the topology loss correction term for hierarchical partitioning, in kW; where, , They are nodes ,node The voltage amplitude; For nodes With nodes The voltage phase angle difference; For nodal admittance Argument; The dynamic weighting coefficient and satisfy The calculation formula is as follows: when or hour: when hour: Preferably, in step S4, the mathematical expression of the objective function for optimizing the restoration load is as follows: In the formula, The square of the L2 norm represents the error in reconstructing the actual load from the historical true load. This is the historical actual load baseline value; , These are multi-objective weighting coefficients; For node voltage deviation, This represents the actual voltage at the node. The node's rated voltage; For equipment thermal stability deviation, For the real-time operating temperature of the equipment, The ambient temperature.

[0011] Preferably, in step S4, the objective function for optimizing the load recovery also needs to incorporate multi-dimensional physical constraints such as reverse load rate, equipment thermal stability, voltage deviation, and rated capacity. The mathematical expression for these multi-dimensional physical constraints is as follows: In the formula, For the reverse load rate of the equipment; This refers to the rated capacity of the power grid equipment, expressed in kVA. This is the upper limit for the reverse load rate, typically set at 0.8 to 0.9. The real-time operating temperature of the equipment is expressed in °C. This refers to the maximum allowable operating temperature of the equipment. This is the rated temperature rise limit for the equipment, in °C, typically taken as 40~50 °C. The thermal resistivity of the equipment is expressed in °C / (A). 2 ); The current carrying capacity of the equipment is expressed in amperes (A). This is the power-current conversion factor; This represents the actual voltage at the node, in kV. This is the lower limit of the node voltage, typically taken as 0.95. ; This represents the upper limit of the node voltage, typically taken as 1.05. .

[0012] Preferably, step S5 includes the following specific steps: Step S5.1: Using dynamic weighting coefficients , The decision variable is the particle position. ; Step S5.2: Initialize parameters and set the particle swarm size. Maximum number of iterations Learning factors , Inertia weight range Randomly initialize particle positions With speed ; Step S5.3: Calculate the current power flow polarity Determine the adaptive inertia weights The calculation method; Step S5.4: Substitute the position of each particle into the source-load nonlinear dynamic fusion model to calculate the actual load. Then, substitute the values ​​into the penalty fitness function to obtain the fitness value of each particle; Step S5.5: Compare the current fitness value of each particle with its own historical best fitness value, and update the individual's optimal position. Compare the individual optimal fitness values ​​of all particles and update the global optimal position. ; Step S5.6: Based on the particle velocity and position formula, combined with adaptive inertial weights Update the velocity and position of each particle while ensuring that the weighting coefficients meet the normalization constraints. ; Step S5.7: Determine if the current iteration count has been reached. Check if the global optimal fitness value is less than a set threshold; if so, the iteration terminates; otherwise, return to step S5.3 to continue the iteration. Step S5.8: After the iteration terminates, output the global optimal position. Corresponding dynamic weight coefficients , Substituting these values ​​into the source-load nonlinear dynamic fusion model, the actual load of the power grid equipment is obtained. .

[0013] Preferably, the adaptive inertia weight The mathematical expression is: when hour: when hour: In the formula, This represents the current iteration number. , These are the maximum and minimum values ​​of the inertia weight, respectively. The mathematical expression for the particle velocity and position formula is as follows: In the formula, As a dimension of decision variables, correspond , correspond ; , The first The particle in the first Wei Shang Second-rate, The speed of each iteration; , The first The particle in the first Wei Shang Second-rate, The position of the next iteration; For the first The optimal position of each individual particle; This represents the global optimal position of the particle swarm. , It is a random number between [0,1].

[0014] Preferably, the mathematical expression of the penalized fitness function is: In the formula, For the first The fitness function value of each particle; The constraint penalty coefficient is set according to the constraint priority, and is taken as follows: , , , ; As the upper limit of voltage deviation, take ; When the actual load corresponding to the particle satisfies all constraints, the penalty term is 0; when there is a constraint exceeding the limit, the penalty term increases as the limit exceeding the limit increases, guiding the particle to converge toward the feasible region.

[0015] In summary, compared with the prior art, the present invention provides at least the following beneficial effects: 1. High accuracy of restoration: By constructing a hierarchical and partitioned topology coupling model, the power transmission coupling relationship between photovoltaic clusters and grid equipment is accurately quantified, avoiding the mixed calculation of photovoltaic output across regions and levels; the introduction of a topology loss correction term corrects the power loss of lines and main transformers, significantly improving the accuracy of restoration.

[0016] 2. Strong adaptability to operating conditions: Based on the power flow polarity mechanism discrimination function derived from measured power, power factor and equivalent impedance, it realizes accurate identification of forward power supply and reverse power flow conditions, overcoming the defect of easy misjudgment when judging only the positive and negative of measured power; the fusion weight is dynamically adjusted according to the operating conditions, and an inertial weight strategy of the adaptive particle swarm optimization algorithm driven by the operating conditions is designed, so that the model can adaptively adapt to different operating conditions and power flow switching scenarios.

[0017] 3. Efficient and safe solution: An improved adaptive particle swarm optimization algorithm is adopted, which incorporates power flow polarity guidance into the inertia weight, thereby improving optimization efficiency and global search capability and avoiding getting trapped in local optima. At the same time, safety constraints such as reverse load rate, thermal stability, and voltage deviation are transformed into penalty terms and incorporated into the fitness function to ensure that the solved restored load meets the requirements of power grid safe operation boundary while minimizing error. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the steps of the method for calculating the reduced load of a distributed photovoltaic distribution network that considers bidirectional power flow polarity in this invention. Figure 2 This is a schematic diagram illustrating the steps of solving the problem based on the improved particle swarm optimization algorithm model in this invention. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 The present invention will be further illustrated by describing a preferred embodiment in detail.

[0020] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only the elements expressly listed, but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0021] like Figure 1 As shown, the present invention provides a method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity, which specifically includes the following steps: Step S1: Hierarchical Partition Topology Coupling Modeling: Based on the actual distribution network topology, establish a five-level hierarchical partition topology architecture of "partition – substation – main transformer (main transformer) – busbar – line", and construct the corresponding node admittance matrix. Based on the aforementioned architecture and matrix, a topological coupling coefficient matrix between the photovoltaic cluster and grid equipment nodes is constructed. Furthermore, a photovoltaic-equipment power topology coupling model was established, and the topology coupling power was calculated.

[0022] Step S2: Power Flow Polarity Mechanism Modeling: Based on the measured active power of power grid equipment, line power factor, and line equivalent impedance, a power flow polarity mechanism discrimination function is constructed. The function value determines whether the current operating condition is a forward power supply condition or a reverse power supply condition.

[0023] Step S3: Source-load coupling modeling based on power flow polarity: Based on the operating conditions determined in step S2, dynamically adjust the weight coefficients of the measured load and the weight coefficients of the topology coupling power calculated in step S1, and introduce a topology loss correction term to construct a source-load nonlinear dynamic fusion model for calculating the actual load of the power grid equipment.

[0024] Step S4: Consider the load restoration optimization strategy for safe operation of the power grid: restore the actual load of the power grid equipment, take minimizing the load restoration error as the core objective, and combine multi-dimensional physical constraints such as reverse load rate, equipment thermal stability, voltage deviation and rated capacity to optimize the objective function of the restored load.

[0025] Step S5: Solve based on the improved adaptive particle swarm optimization algorithm model: Construct an improved adaptive particle swarm optimization algorithm model, and based on this algorithm model, combine it with the source-load nonlinear dynamic fusion model to perform optimization and solve the problem, and output the restored load value.

[0026] The mathematical expression for the hierarchical partitioning topology in step S1 is as follows: (1) In the formula, It is a hierarchical set of partitioned topological levels. This is the power supply zoning level, which is the highest level of the hierarchical zoning system. It is at the substation level and belongs to the power supply zone. It is at the main transformer level and belongs to the substation. It is at the busbar level and belongs to the main transformer; This is the line level, subordinate to the busbar. A strict hierarchical relationship exists between each level, ensuring precise integration of the photovoltaic cluster with the grid equipment.

[0027] Furthermore, the node admittance matrix based on the hierarchical partitioned topology architecture Its mathematical expression is: (2) In the formula, This refers to the total number of nodes in the hierarchical and zoned system, i.e., the total number of nodes in the power grid equipment, covering power supply zones, substations, main transformers, busbars, lines, and photovoltaic cluster access nodes. For nodes The self-admittance is equal to all the admittances of the nodes. The sum of the admittances of connected branches; ( ) is a node With nodes The mutual admittance is equal to the node With nodes The negative value of the branch admittance.

[0028] Furthermore, the nodal admittance matrix To satisfy the physical constraints of steady-state power flow in the distribution network, its construction process strictly follows Kirchhoff's laws to ensure the physical rationality of the topology coupling model. The specific construction steps are as follows: Extract the electrical parameters of equipment at each level of the distribution network, including line resistance R, reactance X, susceptance B, main transformer ratio, short-circuit impedance, etc.; determine the node numbering and connection relationships according to the hierarchical and partitioned topology architecture, and establish a node association table; based on the node association table and electrical parameters, calculate the self-admittance and mutual admittance of each node, and construct a complete node admittance matrix. .

[0029] Furthermore, to quantify the impact of photovoltaic cluster output on grid equipment load, a topology coupling coefficient matrix is ​​constructed. This matrix characterizes the power transmission coupling strength from the photovoltaic cluster output to each grid equipment node, and its value is derived from the node admittance matrix. Together with the topological subordination, its mathematical expression is: (3) (4) (5) In the formula, To determine the total number of photovoltaic clusters, low-voltage uncollected photovoltaic and regulated photovoltaic systems of the same level and region are respectively aggregated into photovoltaic clusters. For the first The photovoltaic cluster and the first The topological coupling coefficient of each power grid node is calculated based on electrical distance and topological dependency, and its value ranges from [0,1]. The larger the value, the greater the impact of the photovoltaic cluster's output on the load of the grid equipment nodes; For the first The photovoltaic cluster access node and the first The electrical distance between each power grid equipment node is determined by the node admittance matrix. It is derived that; For the first The photovoltaic cluster access node and the first Mutual admittance of each power grid equipment node.

[0030] Based on topological coupling coefficient matrix A correlation between the output of the photovoltaic cluster and the coupled power of the grid equipment nodes is established, i.e., a photovoltaic-equipment power topology coupling model. The topology coupling power is calculated, and its mathematical expression is as follows: (6) (7) (8) In the formula, For the power output vector of the photovoltaic cluster, For the first Real-time output of a photovoltaic cluster; This refers to the node-coupled power vector of the power grid equipment, i.e., the topology-coupled power. For the first The photovoltaic cluster coupling power received by each grid equipment node.

[0031] Among them, the power flow polarity mechanism discrimination function in step S2 The mathematical expression is: (9) (10) (11) In the formula, Real-time measurement of active power of power grid equipment, in kW; The line power factor reflects the ratio of active power to apparent power, and its value ranges from [0,1]. The equivalent impedance of the lines connected to the power grid equipment, in Ω. Its amplitude; The resistance of the lines connected to the power grid equipment; The reactance of the lines connected to the power grid equipment.

[0032] The Sign(.) function has the following rules: when the input value is greater than 0, it outputs 1; when the input value is less than 0, it outputs -1; and when the input value is equal to 0, it outputs 0.

[0033] Furthermore, based on the power flow polarity mechanism discriminant function Establish working condition judgment rules: when When the power transmission direction is from the grid side to the load side, it is determined to be a positive power supply condition; when When this occurs, it indicates that the power transmission direction is from the photovoltaic side to the grid side, and it is determined to be a reverse power transmission condition; when When the power is zero, it indicates a transitional operating condition, and should be treated as a forward power supply condition.

[0034] In step S3, the source-load nonlinear dynamic fusion model dynamically adjusts the weighting coefficients of the measured load and the topology coupling power calculated based on S1 according to the power flow polarity (operating condition), so that the fusion result can adapt to the load characteristics under different operating conditions. Simultaneously, a topology loss correction term is introduced to improve the restoration accuracy. The dynamic adjustment of the weighting coefficients is based on power flow polarity, topology coupling strength, and grid operating status to ensure the adaptive capability of the source-load nonlinear dynamic fusion model. Its mathematical expression is: (12) In the formula, The actual load of the power grid equipment is expressed in kW, which is the target value that this invention needs to restore. The dynamic weighting coefficient of the measured load is driven by the power flow polarity, and its value is determined by the power flow polarity. Determined by both measurement accuracy and other factors; The topology coupling power dynamic weighting coefficient is driven by power flow polarity, and its value is determined by the power flow polarity. Coupling coefficient with topology Joint decision; This is a hierarchical and zoned topology loss correction term, in kW, used to correct the impact of power losses in distribution network lines, main transformers, and other equipment on load restoration.

[0035] Among them, dynamic weight coefficient and The calculation formula is as follows: when or (In forward power supply condition): (13) (14) when (In reverse feed mode) (15) (16) Topology losses mainly include line losses and transformer losses, based on the nodal admittance matrix. Based on the relationship with power transmission, the topology loss correction term is derived. The calculation formula is as follows: (17) In the formula, , They are nodes ,node The voltage amplitude; For nodes With nodes The voltage phase angle difference; For nodal admittance Argument angle.

[0036] The optimization objective in step S4 is to optimize the actual load of the power grid equipment. Compared with historical real load To minimize the restoration error while also minimizing voltage deviation and equipment thermal stability deviation, the mathematical expression for the optimization function of the restoration load is as follows: (18) (19) (20) In the formula, The square of the L2 norm represents the error in reconstructing the actual load from the historical true load. The historical load benchmark value is taken from the measured load of grid equipment during the period of zero output of distributed photovoltaic and the historical period before photovoltaic grid connection; , These are multi-objective weighting coefficients, set according to the power grid operation priority, and are typically taken as follows: , ; For node voltage deviation, This represents the actual voltage at the node. The node's rated voltage; For equipment thermal stability deviation, For the real-time operating temperature of the equipment, The ambient temperature.

[0037] Among them, the reverse load rate constraint in step S4 is a core indicator for the safe operation of power grid equipment under reverse power transmission conditions. It must be ensured that it does not exceed the rated limit. Its mathematical expression is: (twenty one) In the formula, For the reverse load rate of the equipment; This refers to the rated capacity of the power grid equipment, expressed in kVA. This is the upper limit of the reverse load rate, usually taken as 0.8~0.9.

[0038] In step S4, the equipment thermal stability constraint is based on Joule's law and the thermal equilibrium mechanism, and it is necessary to ensure that the equipment operating temperature and temperature rise do not exceed the rated limits. Its mathematical expression is: (twenty two) (twenty three) (twenty four) (25) In the formula, The real-time operating temperature of the equipment is expressed in °C. This is the maximum allowable operating temperature of the equipment, set according to the type of equipment (e.g., the maximum allowable temperature for copper conductors is 70°C). This is the rated temperature rise limit for the equipment, in °C, typically taken as 40~50 °C. The thermal resistivity of the equipment is expressed in °C / (A). 2 ); The current carrying capacity of the equipment is expressed in amperes (A). This is the power-current conversion factor.

[0039] Specifically, the voltage deviation constraint in step S4 requires that the voltage at the distribution network nodes be maintained within the allowable range of the rated voltage to avoid voltage exceeding the limit and affecting equipment operation. Its mathematical expression is: (26) In the formula, This represents the actual voltage at the node, in kV. This is the lower limit of the node voltage, typically taken as 0.95. ; This represents the upper limit of the node voltage, typically taken as 1.05. .

[0040] Specifically, the rated capacity constraint in step S4 means that the actual load on the power grid equipment must not exceed its rated capacity to avoid overload damage. Its mathematical expression is: (27) Specifically, the improved adaptive particle swarm optimization algorithm model in step S5 is as follows: using "particles" as decision variable carriers, each particle corresponds to a set of dynamic weight coefficients. , By iteratively updating the particle velocity and position, the optimal solution of the load objective function is searched. Adaptive inertial weight driven by power flow polarity, topology coupling guiding factor and multi-constraint penalty mechanism are introduced to solve the defects of traditional PSO that are prone to getting trapped in local optima and have poor adaptability to operating conditions, so as to ensure that the solution results satisfy both the minimum restoration error and the physical constraints of the power grid.

[0041] Furthermore, the improved adaptive particle swarm optimization algorithm model includes defining key algorithm parameters, the mathematical expressions of which are as follows: (28) (29) (30) In the formula, Let be the particle position vector. The particle swarm size is typically 30 to 50. , The first The dynamic weighting coefficient of the measurement load and the dynamic weighting coefficient of the topological coupling power for each particle are both in the range of [0,1]. The particle velocity vector , The first Individual particles , The velocity component in the direction, with the upper limit of velocity set to... This avoids excessively large iteration steps that could lead to unstable convergence.

[0042] Furthermore, the improved adaptive particle swarm optimization algorithm model also includes initialization conditions, specifically: based on the power flow polarity. Coupling coefficient with topology Randomly initialize particle positions Ensure that the initial weights satisfy the normalization constraint; randomly initialize the particle velocities. The speed value is Randomly generated within a range; set iteration parameters: maximum number of iterations. Individual learning factor Global learning factor Initial inertia weights Minimum inertia weight .

[0043] Furthermore, improving the adaptive particle swarm optimization algorithm model also includes designing adaptive inertia weights, specifically designing adaptive inertia weights driven by power flow polarity. The particle search step size is dynamically adjusted to adapt to both forward power supply and reverse feedback modes: the forward mode focuses on local search accuracy, while the reverse mode focuses on global search capability to avoid local optima. The formula derivation is as follows: when (In the forward power supply condition) the inertia weight decreases linearly with the number of iterations, improving local convergence accuracy: (31) when In the reverse feed mode, a fixed, relatively small inertial weight is used to enhance the global search capability and avoid getting trapped in local optima. (32) In the formula, This represents the current iteration number. The maximum number of iterations, , These are the maximum and minimum values ​​of the inertia weight, respectively.

[0044] Furthermore, the improved adaptive particle swarm optimization algorithm model also includes particle velocity and position update formulas. Specifically, based on adaptive inertia weights and learning factors, the update formulas for particle velocity and position are derived to ensure that particles iterate in the direction of the optimal objective function. The mathematical expressions are as follows: (33) (34) In the formula, For decision variable dimensions ( correspond , correspond ); , The first The particle in the first Wei Shang Second-rate, The speed of each iteration; , The first The particle in the first Wei Shang Second-rate, The position of the next iteration; For the first The individual optimal position of each particle (the position where the objective function value is minimum during its own iteration process). This is the global optimal position of the particle swarm (the position where the objective function value is minimum during all particle iterations). , Use random numbers between [0,1] to increase iterative randomness and avoid local optima; , The learning factor controls the impact of individual optimality and global optimality on particle updates.

[0045] Furthermore, the improved adaptive particle swarm optimization algorithm model also includes the design of a multi-constraint penalty mechanism. Specifically, to ensure that the actual load corresponding to the particle position (weight coefficient) during the iteration process meets the four hard constraints of reverse load rate, equipment thermal stability, voltage deviation, and rated capacity, the constraints are transformed into penalty terms and incorporated into the objective function to construct a penalty-type fitness function, as shown in the following formula: (35) In the formula, For the first The fitness function value of each particle; The constraint penalty coefficient is set according to the constraint priority, and is taken as follows: , , , ; As the upper limit of voltage deviation, take .

[0046] When the actual load corresponding to the particle satisfies all constraints, the penalty term is 0; when there is a constraint violation, the penalty term increases as the violation amount increases, guiding the particle to converge toward the feasible region.

[0047] like Figure 2 As shown, the specific steps for solving the problem in step S5, based on the improved adaptive particle swarm optimization algorithm model and combined with the source-load nonlinear dynamic fusion model, include: Step S5.1: Determine the decision variables for the particles, using dynamic weighting coefficients. , The decision variable is the particle position. .

[0048] Step S5.2: Initialize parameters and set the particle swarm size. Maximum number of iterations Learning factors , Inertia weight range Randomly initialize particle positions With speed .

[0049] Step S5.3: Determine the current current polarity and calculate the current current polarity. Determine the adaptive inertia weights The calculation method.

[0050] Step S5.4: Fitness function calculation. Substitute the position (weight coefficient) of each particle into the source-load nonlinear dynamic fusion model to calculate the actual load. Then, substitute the values ​​into the penalty fitness function to obtain the fitness value for each particle.

[0051] Step S5.5: Individual and Global Optimal Updates. Compare the current fitness value of each particle with its own historical best fitness value, and update the individual optimal position. Compare the individual optimal fitness values ​​of all particles and update the global optimal position. .

[0052] Step S5.6: Particle velocity and position update, based on the particle velocity and position formulas, combined with adaptive inertia weights. Update the velocity and position of each particle while ensuring that the weighting coefficients meet the normalization constraints. .

[0053] Step S5.7: Constraint verification and iteration termination judgment, determine whether the current iteration count has been reached. Or whether the global optimal fitness value is less than a set threshold (usually taken as...). If the condition is met, the iteration terminates; otherwise, return to step S5.3 to continue the iteration.

[0054] Step S5.8: Output the optimal solution. After the iteration terminates, output the global optimal position. Corresponding dynamic weight coefficients , Substituting the source-load nonlinear dynamic fusion model, the actual load of the power grid equipment is obtained. This is the restored load value, which completes the load restoration.

[0055] The practical application effects of the present invention will be further illustrated below through specific embodiments.

[0056] Example This embodiment selects a 10kV urban distribution network section in a city as the test object. This distribution network area is connected to two sets of distributed photovoltaic clusters and exhibits typical bidirectional power flow conditions with forward power supply and reverse reverse transmission, which conforms to the operating characteristics of a high-proportion photovoltaic grid-connected distribution network. In this embodiment, the load calculation of core grid equipment such as main transformers and lines in this distribution network area is performed by comparing the empirical linear superposition method (the existing mainstream method) with the method of this invention. The measured load during the zero-output period of distributed photovoltaic is used as the historical true load benchmark value. The superiority of the method of this invention is verified by comparing and analyzing the reconstruction error.

[0057] The hierarchical topology of the power distribution zone is as follows: a five-level topology of power distribution zone-substation-main transformer-bus-line is constructed according to the present invention. The test area consists of 1 power distribution zone (G_zone), 1 110 / 10kV substation (G_sub), 2 10kV main transformers (G_trans: T1, T2, rated capacity 5000kVA), 4 10kV busbars (G_bus: B1-B4), and 6 10kV lines (G_line: L1-L6). The photovoltaic cluster parameters of this distribution network power supply zone are as follows: it is aggregated into two photovoltaic clusters (PV1 and PV2). PV1 is connected to the L2 line node with a rated output of 800kW and a real-time output of 650kW. PV2 is connected to the L5 line node with a rated output of 1000kW and a real-time output of 780kW. The electrical parameters of this distribution network power supply zone are as follows: the average equivalent impedance Z_line (L1-L6) is 0.8+j0.6Ω, the average power factor cosφ is 0.92, the short-circuit impedance of the main transformer is 7.5%, and the rated node voltage UN=10.5kV. The operating conditions of this distribution network power supply zone are set as follows: two test conditions are set: forward power supply condition (low photovoltaic output, power supply from the grid side to the load side, θ=1) and reverse reverse power supply condition (high photovoltaic output, reverse power supply from the photovoltaic side to the grid side, θ=-1). The measured load data of this distribution network power supply zone are shown in Table 1.

[0058] Table 1 Furthermore, the restored load was calculated using the method of this invention and the linear superposition method, respectively. The accuracy of the two methods was quantified using two core indicators: mean absolute error (MAE) and root mean square error (RMSE). The results are shown in Tables 2 and 3.

[0059] Table 2 Table 3 The results show that, compared with the mainstream empirical linear superposition method, the method of the present invention reduces MAE and RMSE to 8.3kW and 10.5kW respectively, and the average relative error is reduced from about 8% to less than 0.5%. The smaller the index value, the higher the restoration accuracy. This shows that the method of the present invention effectively improves the restoration accuracy and solves the problems of low restoration accuracy and poor adaptability of distribution network load under high proportion of photovoltaic grid connection.

[0060] In summary, this invention provides a method for calculating the restored load of a distributed photovoltaic (PV) distribution network that considers bidirectional power flow polarity. By constructing a hierarchical and partitioned topology coupling model, introducing a topology loss correction term, constructing a power flow polarity mechanism discrimination function, and employing an improved adaptive particle swarm optimization algorithm, while transforming safety constraints such as reverse load rate, thermal stability, and voltage deviation into penalty terms and incorporating them into the fitness function, the restoration accuracy is effectively improved. This solves the problems of low load restoration accuracy and poor adaptability under high-proportion PV grid connection, meeting the actual needs of high-precision control and safe and stable operation of the distribution network.

[0061] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for calculating the reducing load of a distributed photovoltaic distribution network considering bidirectional power flow polarity, characterized in that, include: Step S1: Establish a hierarchical and partitioned topology architecture and construct the corresponding node admittance matrix. Construct the topological coupling coefficient matrix between the photovoltaic cluster and the grid equipment nodes. Step S2: Calculate the topological coupling power; Step S3: Construct the power flow polarity mechanism discriminant function. It can determine whether the current operating condition is a forward power supply condition or a reverse power supply condition. Step S3: Dynamically adjust the weighting coefficients of the measured load and the topology coupling power to construct a source-load nonlinear dynamic fusion model; Step S4: Optimize the objective function of the restored load with the goal of minimizing the load restoration error; Step S5: Construct an improved adaptive particle swarm optimization algorithm model, combine it with the source-load nonlinear dynamic fusion model, and output the restored load value.

2. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 1, characterized in that, In step S1, the mathematical expression for the hierarchical partitioning topology is: In the formula, It is a hierarchical set of partitioned topological levels. This is the power supply zoning level, which is the highest level of the hierarchical zoning system. It is at the substation level and belongs to the power supply zone. It is at the main transformer level and belongs to the substation. It is at the busbar level and belongs to the main transformer; It is at the line level and belongs to the busbar; The node admittance matrix The mathematical expression is: In the formula, This refers to the total number of nodes in the power grid, covering power supply zones, substations, main transformers, busbars, lines, and photovoltaic cluster access nodes. For nodes The self-admittance is equal to all the admittances of the nodes. The sum of the admittances of connected branches; hour, For nodes With nodes The mutual admittance is equal to the node With nodes The negative value of the branch admittance.

3. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 2, characterized in that, In step S1, the topological coupling coefficient matrix The mathematical expression is: In the formula, To determine the total number of photovoltaic clusters, low-voltage uncollected photovoltaic and regulated photovoltaic systems of the same level and region are respectively aggregated into photovoltaic clusters. For the first The photovoltaic cluster and the first The topological coupling coefficient of each power grid equipment node, with a value range of [0,1]; For the first The photovoltaic cluster access node and the first Electrical distance between individual power grid equipment nodes; For the first The photovoltaic cluster access node and the first Mutual admittance of individual power grid equipment nodes; Based on the hierarchical partitioning topology and the node admittance matrix and the topological coupling coefficient matrix A photovoltaic-equipment power topology coupling model is established, and the mathematical expression of the photovoltaic-equipment power topology coupling model is as follows: In the formula, For the power output vector of the photovoltaic cluster, For the first Real-time output of a photovoltaic cluster; This refers to the node-coupled power vector of the power grid equipment, i.e., the topology-coupled power. For the first The photovoltaic cluster coupling power received by each grid equipment node.

4. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 3, characterized in that, In step S2, the power flow polarity mechanism discrimination function The mathematical expression is: In the formula, Real-time measurement of active power of power grid equipment, in kW; The line power factor reflects the ratio of active power to apparent power, and its value ranges from [0,1]. The equivalent impedance of the lines connected to the power grid equipment, in Ω. Its amplitude; The resistance of the lines connected to the power grid equipment; Reactance of the lines connected to the power grid equipment; Based on the discriminant function of power flow polarity mechanism Establish working condition judgment rules: when When the power transmission direction is from the grid side to the load side, it is determined to be a positive power supply condition; when When this occurs, it indicates that the power transmission direction is from the photovoltaic side to the grid side, and it is determined to be a reverse power transmission condition; when When the power is zero, it indicates a transitional operating condition, and should be treated as a forward power supply condition.

5. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 4, characterized in that, In step S3, the mathematical expression for the source-load nonlinear dynamic fusion model is: In the formula, The actual load of the power grid equipment is expressed in kW, which is the target value that this invention needs to restore. The dynamic weighting coefficient of the measurement load driven by the power flow polarity; The topology coupling power dynamic weighting coefficient is driven by the power flow polarity. This is the topology loss correction term for hierarchical partitioning, in kW; where, , They are nodes ,node The voltage amplitude; For nodes With nodes The voltage phase angle difference; For nodal admittance Argument; The dynamic weighting coefficient and satisfy The calculation formula is as follows: when or hour: when hour: .

6. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 5, characterized in that, In step S4, the mathematical expression of the objective function for optimizing the restoration load is as follows: In the formula, The square of the L2 norm represents the error in reconstructing the actual load from the historical true load. This is the historical actual load baseline value; , These are multi-objective weighting coefficients; For node voltage deviation, This represents the actual voltage at the node. The node's rated voltage; For equipment thermal stability deviation, For the real-time operating temperature of the equipment, The ambient temperature.

7. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 6, characterized in that, In step S4, the objective function for optimizing the load restoration also needs to incorporate multi-dimensional physical constraints such as reverse load rate, equipment thermal stability, voltage deviation, and rated capacity. The mathematical expression for these multi-dimensional physical constraints is as follows: In the formula, For the reverse load rate of the equipment; This refers to the rated capacity of the power grid equipment, expressed in kVA. This is the upper limit for the reverse load rate, typically set at 0.8 to 0.

9. The real-time operating temperature of the equipment is expressed in °C. This refers to the maximum allowable operating temperature of the equipment. This is the rated temperature rise limit for the equipment, in °C, typically taken as 40~50 °C. The thermal resistivity of the equipment is expressed in °C / (A). 2 ); The current carrying capacity of the equipment is expressed in amperes (A). This is the power-current conversion factor; This represents the actual voltage at the node, in kV. This is the lower limit of the node voltage, typically taken as 0.

95. ; This represents the upper limit of the node voltage, typically taken as 1.

05. .

8. The method for calculating the reduced load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 7, characterized in that, The specific steps of step S5 include: Step S5.1: Using dynamic weighting coefficients , The decision variable is the particle position. ; Step S5.2: Initialize parameters and set the particle swarm size. Maximum number of iterations Learning factors , Inertia weight range Randomly initialize particle positions With speed ; Step S5.3: Calculate the current power flow polarity Determine the adaptive inertia weights The calculation method; Step S5.4: Substitute the position of each particle into the source-load nonlinear dynamic fusion model to calculate the actual load. Then, substitute the values ​​into the penalty fitness function to obtain the fitness value of each particle; Step S5.5: Compare the current fitness value of each particle with its own historical best fitness value, and update the individual's optimal position. Compare the individual optimal fitness values ​​of all particles and update the global optimal position. ; Step S5.6: Based on the particle velocity and position formula, combined with adaptive inertial weights Update the velocity and position of each particle while ensuring that the weighting coefficients meet the normalization constraints. ; Step S5.7: Determine if the current iteration count has been reached. Check if the global optimal fitness value is less than a set threshold; if so, the iteration terminates; otherwise, return to step S5.3 to continue the iteration. Step S5.8: After the iteration terminates, output the global optimal position. Corresponding dynamic weight coefficients , Substituting these values ​​into the source-load nonlinear dynamic fusion model, the actual load of the power grid equipment is obtained. .

9. The method for calculating the reducing load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 8, characterized in that, The adaptive inertia weight The mathematical expression is: when hour: when hour: In the formula, This represents the current iteration number. , These are the maximum and minimum values ​​of the inertia weight, respectively. The mathematical expression for the particle velocity and position formula is as follows: In the formula, As a dimension of decision variables, correspond , correspond ; , The first The particle in the first Wei Shang Second-rate, The speed of each iteration; , The first The particle in the first Wei Shang Second-rate, The position of the next iteration; For the first The optimal position of each individual particle; This represents the global optimal position of the particle swarm. , It is a random number between [0,1].

10. The method for calculating the reducing load of a distributed photovoltaic distribution network considering bidirectional power flow polarity as described in claim 8, characterized in that, The mathematical expression for the penalized fitness function is: In the formula, For the first The fitness function value of each particle; The constraint penalty coefficient is set according to the constraint priority, and is taken as follows: , , , ; As the upper limit of voltage deviation, take ; When the actual load corresponding to the particle satisfies all constraints, the penalty term is 0; when there is a constraint exceeding the limit, the penalty term increases as the limit exceeding the limit increases, guiding the particle to converge toward the feasible region.

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