Method for reflecting incidence relation between wind speed and carrying capacity of power distribution network

Abstract

The invention provides a method for reflecting an incidence relation between wind speed and carrying capacity of a power distribution network. The method comprises the following steps: constructing an equipment model of heat limiting equipment in the power distribution network; obtaining real-time wind speed information, and calculating power limit information of each equipment model according to the real-time wind speed information; a power distribution network bearing capacity analysis model is constructed, the model comprises an objective function and a constraint set, the objective function is used for maximizing the sum of active outputs of all renewable energy power generation units in the power distribution network, and the constraint set is used for constraining safe operation of the power distribution network; after the power limit information is coupled with the constraint set, obtaining an optimal solution of a target function; the power distribution network bearing capacity and the control strategy corresponding to the optimal solution are the optimal power distribution network bearing capacity and the control strategy corresponding to the real-time wind speed. The bearing capacity evaluation result provided by the invention is a system-level optimal solution based on whole network topology and multi-class equipment constraints, so that the globality and accuracy of the evaluation result are ensured, and potential safety hazards caused by bottleneck transfer are avoided.

Description

Technical Field

[0001] This invention relates to the field of power distribution networks, and in particular to a method for reflecting the relationship between wind speed and the carrying capacity of power distribution networks. Background Technology

[0002] In existing technologies, the relationship between wind speed and distribution network carrying capacity is generally reflected through Dynamic Line Rating (DLR) systems. These systems input real-time environmental data, primarily wind speed, into the system and then, using the line's maximum permissible operating temperature (e.g., 80°C) as a constraint, calculate the maximum permissible current the line can carry under that condition. This is the dynamic line current carrying capacity at that moment. Under traditional static settings, the transmission capacity of a line is a fixed, conservative value. However, in the DLR system, the dispatcher sees a dynamically changing transmission upper limit. For example, during periods of high wind speed, the current carrying capacity limit calculated by the DLR system will be much higher than the static setting. The dispatcher can then use this higher limit to safely allow the line to transmit more power, such as accommodating more wind power output, or alleviating congestion on other lines, thereby improving the overall operating efficiency and flexibility of the power grid.

[0003] However, existing DLR technologies are strictly limited to evaluating a single, pre-defined overhead line. Their core algorithms aim to solve the thermophysical state of an individual component, lacking the ability to perceive the topological interconnectivity of the power grid system in which that component resides. In interconnected power grids, increasing the transmission capacity limit of a particular line inevitably leads to a redistribution of power flow across the entire network. This may cause the system's "weakest link" (i.e., the transmission bottleneck) to shift from the currently monitored line to other unmonitored series or parallel devices (such as adjacent lines or downstream distribution transformers). Existing technologies cannot predict or quantify this bottleneck shift, therefore their evaluation results may not reflect the true, system-level transmission margin, and may even lead to potential operational risks due to the unknown nature of the new bottleneck.

[0004] Furthermore, existing DLR (Dynamic Load Management) technology has a single evaluation dimension, outputting only the current carrying capacity limit of equipment based on thermal balance calculations. This approach does not inherently couple the verification of power quality, particularly node voltage distribution. According to power system steady-state analysis theory, a significant increase in line power flow inevitably leads to an increase in voltage drop. If dispatching is based solely on the thermal limits provided by DLR, it is highly likely that while meeting equipment thermal safety requirements, voltage deviations at the grid's end nodes will exceed legally or contractually stipulated ranges (e.g., ±7%), thereby compromising power quality. Existing technologies require dispatchers to perform independent, offline power flow calculations to additionally verify voltage issues, resulting in a fragmented and incomplete evaluation.

[0005] In reality, the existing DLR system is essentially positioned as a passive information monitoring and display tool. It presents operators with a dynamically changing boundary value, but the system itself does not provide any decision support on how to optimally utilize the margin provided by that boundary value. For example, in the presence of multiple adjustable power sources, how to allocate the incremental output of each power source to utilize the newly added line capacity most efficiently and economically depends entirely on the offline analysis and subjective decision-making of the dispatchers, making it difficult to guarantee its optimality. Summary of the Invention

[0006] To address the problems existing in the background art, this invention proposes a method that reflects the correlation between wind speed and the carrying capacity of the power distribution network.

[0007] A method for reflecting the relationship between wind speed and the carrying capacity of a power distribution network includes the following steps:

[0008] S100, Construct equipment models for thermally limited devices in the power distribution network;

[0009] S200: Obtain and calculate the power limit information for each device model based on real-time wind speed information;

[0010] S300. Construct a distribution network carrying capacity analysis model, including an objective function and a constraint set. The objective function is used to maximize the sum of active power output of all renewable energy generation units in the distribution network, and the constraint set is used to constrain the safe operation of the distribution network.

[0011] S400. After coupling the power limit information with the constraint set, obtain the optimal solution of the objective function;

[0012] S500, The distribution network carrying capacity and control strategy corresponding to the optimal solution are the optimal distribution network carrying capacity and control strategy corresponding to the real-time wind speed.

[0013] Based on the above, the equipment model includes at least an overhead line model and a transformer model.

[0014] Based on the above, the power limit of the overhead line model is:

[0015]

[0016] In the formula:

[0017] For the dynamic apparent power limit of the line; V L-L,rated P is the rated line voltage of the line; radiation P is the radiative heat dissipation power; solar R(T) represents the absorbed solar radiation power. c ) is at conductor temperature T c The resistance per unit length below; vwind ρ is the real-time wind speed; θ is the angle between the wind direction and the conductor's direction; D is the conductor's outer diameter; a ir is the air density; μ air aerodynamic viscosity; k air T is the thermal conductivity of air; c T represents the maximum permissible operating temperature of the conductor. a Let B be the ambient air temperature; B and n are dimensionless empirical coefficients, respectively.

[0018] Based on the above, the power limit of the transformer model is:

[0019]

[0020] In the formula, For dynamic apparent power limit; S base Based on capacity; v wind For real-time wind speed; Δθ o,R Rated top oil temperature rise; Δθ h,R The rated winding hot spot temperature rise; k cool This is the cooling efficiency coefficient.

[0021] Based on the above, the objective function is:

[0022]

[0023] In the formula, F1(P) G ) represents the objective function value; N DG A set of distributed power nodes; P G,i The active power output of node i.

[0024] Based on the above, the constraint set includes physical law constraints:

[0025]

[0026] In the formula, P G,i Active power injection for node i; P L,i Q represents the active power consumption of node i. G,i Reactive power injection for node i; Q L,i V represents the reactive power consumption of node i. i V represents the voltage magnitude at node i. j Let θ be the voltage magnitude at node j; ij G represents the voltage phase angle difference between node i and node j. ij B is the real part of the element in the i-th row and j-th column of the nodal admittance matrix; ij Let be the imaginary part of the element in the i-th row and j-th column of the node admittance matrix.

[0027] Based on the above, the constraint set includes safety criterion constraints, which include:

[0028] ①Dynamic thermal limit constraints of equipment:

[0029]

[0030] In the formula, S ij The actual apparent power of branch k; For the dynamic apparent power limit of branch k; v wind For real-time wind speed; N branch It is the set of all branches in the power grid;

[0031] ② Node voltage constraints:

[0032]

[0033] In the formula, V min V represents the minimum allowable voltage amplitude at the node. i V represents the actual voltage amplitude at node i. max This represents the maximum allowable voltage amplitude at the node.

[0034] ③ Generator output constraints:

[0035]

[0036] In the formula, P G,i,min P represents the minimum active power output of the power source at node i; G,i P represents the actual active power output of the power source at node i; G,i,max Q represents the maximum active power output of the power source at point i; G,i,min Q represents the minimum reactive power output of the power supply at node i; G,i Q represents the actual reactive power output of the power supply at node i; G,i,max Let i be the maximum reactive power output of the power supply at node i.

[0037] This invention has outstanding substantive features and significant progress compared to the prior art, specifically:

[0038] ① The technical solution adopted in this invention firstly expands the breadth of evaluation by constructing a full-network topology model covering key equipment such as lines and transformers. Furthermore, in the Optimal Power Flow (OPF) evaluation module, we treat the dynamic limits of all these devices as a unified set of constraints. During optimization, the OPF algorithm performs global synchronous calculations of the power flow of all branches and the voltage of all nodes in the network. Therefore, this method can identify the "real bottleneck" that poses the strongest constraint on the system under the current operating conditions in real time and accurately, regardless of which component the bottleneck is located on. The carrying capacity evaluation results provided by this invention are system-level optimal solutions based on the full-network topology and constraints of multiple types of equipment, rather than the limits of local components. This ensures the globality and accuracy of the evaluation results, avoids safety hazards caused by bottleneck shifts, and provides power grid managers with a reliable insight into the true potential of the entire network.

[0039] ② In constructing the OPF model, this invention considers node voltage constraints as a core constraint of equal importance to dynamic thermal limit constraints. This means that the method seeks not a simple "maximum allowable thermal power," but rather the optimal point within a multi-dimensional safe operating domain. Because this method ensures that any output capacity result and its corresponding operating strategy simultaneously meet the thermodynamic safety of the equipment and the voltage stability requirements of the system, the evaluation results are highly complete and feasible. It expands the definition of capacity from a single "thermal capacity" to a "usable capacity" that integrates multiple safety regulations, significantly improving the safety and reliability of decision-making.

[0040] ③ The Optimal Power Flow (OPF) model adopted in this invention is based on proactive optimization. In maximizing the objective function, it not only determines the feasibility of a state but also actively seeks the state among all feasible states that optimizes the objective function. The output of this invention, in addition to an optimal carrying capacity value, includes a set of corresponding, directly executable system-level optimal control strategies (e.g., the specific output values ​​of each distributed power source). It clearly answers the question, "What should be done to achieve maximum capacity?" This represents a fundamental upgrade of the decision support model from "passive monitoring" to "proactive optimization," providing an advanced application foundation for the automated and intelligent dispatching of the power grid and significantly improving the operational economy and management efficiency of the power grid. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the process of this invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, a method reflecting the correlation between wind speed and the carrying capacity of a distribution network includes the following steps: S100, constructing equipment models of thermally limited devices in the distribution network; S200, acquiring and calculating the power limit information of each equipment model based on real-time wind speed information; S300, constructing a distribution network carrying capacity analysis model, including an objective function and a constraint set, wherein the objective function is used to maximize the sum of active power output of all renewable energy generation units in the distribution network, and the constraint set is used to constrain the safe operation of the distribution network; S400, after coupling the power limit information with the constraint set, obtaining the optimal solution of the objective function; S500, the distribution network carrying capacity and control strategy corresponding to the optimal solution are the optimal distribution network carrying capacity and control strategy corresponding to the real-time wind speed.

[0044] Specifically:

[0045] 1. Model the dynamic load-bearing capacity of critical equipment (thermal limiting components).

[0046] The core objective is to calculate the dynamic apparent power limit of each thermally limited component (such as lines, transformers, etc.) at real-time wind speed, so as to serve as a unified input for upper-level network analysis:

[0047] 1.1 Overhead Line Model

[0048] Physical principle: The current carrying capacity of a circuit is limited by the temperature of the conductor, and its core is the heat balance equation: P Joule +P solar =P convection +P radiation .

[0049] In the formula:

[0050] P Joule Joule heating is the heat generated when an electric current passes through a resistor (heating term).

[0051] P solar Absorbed solar radiation heat (heat generation item);

[0052] P convection The heat dissipated through air convection is mainly affected by wind speed (heat dissipation item);

[0053] P radiationHeat dissipated through infrared radiation (heat dissipation item).

[0054] Wind speed greatly enhances the convective heat dissipation of the P-term. convection To improve heat dissipation efficiency.

[0055] Internal calculation: Based on real-time wind speed, the model first calculates the maximum current that the line can carry, provided that the conductor temperature does not exceed the safety limit (e.g., 80°C).

[0056] Unified Output (Power Limit): The calculated maximum current is converted to standard power units and used as the final output of the model.

[0057]

[0058] In the formula:

[0059] The line's dynamic apparent power limit (MVA);

[0060] V L-L,rated : Rated line voltage (V);

[0061] P radiation Radiative heat dissipation power (W / m) is mainly related to the conductor and ambient temperature;

[0062] P solar The absorbed solar radiation power (W / m) is mainly related to the solar radiation intensity.

[0063] R(T c ): Conductor at temperature T c Resistance per unit length (Ω / m);

[0064] v wind Real-time wind speed (m / s);

[0065] θ: The angle between the wind direction and the direction of the guide wire (rad);

[0066] D: Outer diameter of the conductor (m);

[0067] ρ air Air density (kg / m³) 3 );

[0068] μ air Aerodynamic viscosity (Pa·s);

[0069] k air Thermal conductivity of air (W / (m·K));

[0070] T c The maximum permissible operating temperature (°C) of the conductor;

[0071] T a Ambient air temperature (°C);

[0072] B,n: Dimensionless empirical coefficients determined by the range of Reynolds numbers according to IEEE standards.

[0073] 1.2 Distribution Transformer Model

[0074] Physical principle: The load-bearing capacity of a transformer is limited by the temperature of its internal winding hot spots. Airflow passing over the surface of the cooling tank enhances heat dissipation, thereby reducing the internal temperature.

[0075] Internal calculation: Based on real-time wind speed, the model calculates the maximum load rate that the transformer can withstand relative to its rated capacity, provided that the winding hot spot temperature does not exceed the safety limit (e.g., 105℃).

[0076] Unified Output (Power Limit): Converts the calculated maximum load rate into standard power units.

[0077]

[0078] In the formula:

[0079] Dynamic apparent power limit (MVA);

[0080] S base Basic capacity (MVA);

[0081] v wind Real-time wind speed (m / s);

[0082] Δθ o,R Rated top oil temperature rise (°C);

[0083] Δθ h,R Rated winding hot spot temperature rise (°C);

[0084] k cool Cooling efficiency coefficient.

[0085] 2. Distribution network system-level carrying capacity assessment: Optimal power flow (OPF) model

[0086] After modeling the physical characteristics of network components (overhead lines, distribution transformers), system-level analysis tools are needed to integrate the component-level dynamic margins into network-level carrying capacity measures. Optimal Power Flow (OPF) is a normative mathematical framework for solving such problems. Its essence is to optimize a preset performance index within a set of constraints that satisfy the physical laws of the entire network and the operational safety boundaries.

[0087] 2.1 Objective Function: A multidimensional definition of carrying capacity

[0088] The objective function is a mathematical abstraction of a specific engineering or economic goal, and its form defines the specific physical meaning of "carrying capacity" in this assessment.

[0089] Maximizing Renewable Energy Hosting Capacity:

[0090]

[0091] In the formula:

[0092] F1(P G ): The objective function value, in this example, refers to the total active power (MW);

[0093] N DG A collection of distributed power supply nodes;

[0094] P G,i : Active power output (MW) of node i.

[0095] This objective function aims to quantify the dynamic hosting capacity (DHC) of a distribution network for distributed generation (DG). It maximizes the sum of the active power output of all renewable energy generation units in the network. Its optimal solution characterizes the maximum renewable energy power that the distribution network can absorb under current system conditions and environmental constraints, without violating any operational constraints. It is a key indicator for evaluating grid flexibility and asset utilization efficiency.

[0096] 2.2 Constraint Set: A mathematical description of the physically feasible region.

[0097] The constraint set defines the feasible region within which an optimization problem must find a solution. It consists of physical laws (equality constraints) and safety criteria (inequality constraints).

[0098] 2.2.1 Equality Constraints: Power Flow Equations

[0099] Physical basis: This set of constraints represents the physical laws that must be satisfied for the steady-state operation of an AC power grid. Essentially, it is a manifestation of Kirchhoff's laws in node power, ensuring energy conservation.

[0100] Mathematical formula:

[0101]

[0102] In the formula:

[0103] P G,i :Power injection (MW) at node i;

[0104] P L,i : Active power consumption (MW) of node i;

[0105] Q G,i : Power reactive injection (Mvar) at node i;

[0106] Q L,i : Reactive power consumption of node i (Mvar);

[0107] V i : Voltage amplitude (kV) at node i;

[0108] V j : Voltage amplitude (kV) at node j;

[0109] θ ij The voltage phase angle difference (rad) between node i and node j;

[0110] G ij : The real part (S) of the element in the i-th row and j-th column of the nodal admittance matrix;

[0111] B ij : The imaginary part (S) of the element in the i-th row and j-th column of the nodal admittance matrix.

[0112] This set of nonlinear algebraic equations establishes a power balance relationship for all nodes N in the network, injecting power (P) into the nodes. G,i Q G,i ), load power consumption (P) L,i Q L,i The system of equations is closely coupled with network state variables (node ​​voltage magnitude (V) and phase angle (θ)). These form the core of the OPF problem, and any feasible solution must be an effective solution to this system of equations.

[0113] 2.2.2 Inequality Constraints: Security and Operational Boundaries

[0114] This constraint set defines the safety and quality standards for system operation and is a necessary condition for ensuring the stable and reliable operation of the power grid. The core innovation of this embodiment lies in the dynamic construction of this constraint set.

[0115] Dynamic Thermal Limit Constraints:

[0116]

[0117] In the formula:

[0118] S ij The actual apparent power (MVA) of branch k;

[0119] The dynamic apparent power limit (MVA) of branch k;

[0120] v wind Real-time wind speed (m / s);

[0121] N branch The set of all branches in a power grid.

[0122] It replaces the rated capacity of the equipment, which is a static constant in traditional OPF, with an exogenous variable (wind speed v). wind Related dynamic functions The specific value of this function is calculated and updated in real time by the aforementioned component-level thermodynamic model (DLR, DTR). This makes the feasible domain boundary of the OPF time-varying, capable of "expanding" or "contracting" with changes in environmental conditions, thereby enabling precise exploration of the grid's carrying capacity potential.

[0123] Nodal Voltage Constraints:

[0124]

[0125] In the formula:

[0126] V min : The minimum allowable voltage amplitude (kV) at the node;

[0127] V i : The actual voltage amplitude (kV) at node i;

[0128] V max : The maximum allowable voltage amplitude (kV) at the node.

[0129] This constraint is crucial for ensuring power quality. It stipulates that the voltage amplitude at all nodes in the network must be maintained within legal or contractual standards (for example, in a country's low-voltage distribution regulations, the permissible deviation of the standard voltage is usually within a specific range, such as 101V ± 6V). This constraint prevents the optimization process from producing suboptimal solutions that are thermodynamically feasible but unacceptable in terms of voltage quality.

[0130] Generator Capability Constraints:

[0131]

[0132] In the formula:

[0133] P G,i,min : The minimum active power output (MW) of the power source at node i;

[0134] P G,i : Actual active power output (MW) of the power source at node i;

[0135] P G,i,max The maximum active power output (MW) of the power source at point i;

[0136] Q G,i,min : The minimum reactive power output (Mvar) of node i;

[0137] Q G,i : Actual reactive power output (Mvar) of node i.

[0138] Q G,i,max : The maximum reactive power output (Mvar) of node i.

[0139] This constraint defines the physically feasible output range of the generator set. For renewable energy sources, its maximum active power output P... G,i,max It is itself a dynamic variable determined by environmental factors (such as wind speed, sunlight, etc.) and needs to be linked with the OPF model in real time.

[0140] 2.3 Problem Solving and Solution Interpretation

[0141] Integrating the aforementioned objective function and constraint set constitutes a large-scale, non-convex, nonlinear optimization problem. This problem can be solved using existing numerical methods, such as interior-point methods or sequential quadratic programming. The optimal solution not only includes the extrema of the objective function (e.g., maximum capacity), but also provides a complete control strategy to achieve this optimal state, namely, the set of control variables (optimal output of each generator unit) and the set of state variables (voltage distribution and power flow of the entire grid). This provides a comprehensive decision-making basis for achieving lean and adaptive operation of the power grid.

[0142] This embodiment uses the dynamic ratings of one or more power grid components calculated based on real-time wind speed data as dynamically changing constraints input into the Optimal Power Flow (OPF) model. By solving this OPF problem that takes into account dynamic boundaries, the overall carrying capacity of the entire distribution network system is obtained. The essence of this embodiment is not simply calculating the dynamic limits of individual devices, but rather creatively coupling the dynamic device model with the system-level network optimization algorithm (OPF), achieving a leap from "component-level" calculation to "system-level" evaluation.

[0143] This embodiment also considers the comprehensive modeling of the dynamic characteristics of multiple types of equipment. This is a crucial process to ensure the globality and accuracy of the assessment. When constructing the constraint set of the aforementioned OPF model, at least two different types of thermally limited equipment affected by wind speed are included. Specifically, a dynamic thermal balance model for overhead lines and a dynamic thermal model for distribution transformers are established simultaneously, and the dynamic limits calculated from both are used together as constraints for global optimization. This addresses the problem of existing technologies that only assess a single line and cannot identify new system bottlenecks appearing on other types of equipment such as transformers due to bottleneck shifts. The scope of protection should cover the comprehensive dynamic modeling and unified assessment of multiple types of equipment.

[0144] By employing a global optimization method under multidimensional security constraints, the evaluation results of this invention are ensured to be complete and usable. In the mathematical formula of the OPF model, its inequality constraint set not only includes dynamic thermal limit constraints determined by wind speed, but also forcibly includes grid-wide node voltage stability constraints to ensure power quality. The optimization process in this embodiment is conducted within a "multidimensional security domain" jointly defined by thermodynamic security and voltage stability. This guarantees that any resulting load-bearing capacity is physically safe and feasible, avoiding the one-sidedness of existing technologies that only consider thermal limits while neglecting voltage quality.

[0145] In practical applications, the input to the entire evaluation method is expanded from real-time meteorological data to include future meteorological forecasts (such as wind speed forecasts for the next 1 hour and 24 hours). By combining predictive inputs with the systematic evaluation model of this invention, the method can output a predictive curve of the distribution network's carrying capacity for future periods. This provides a key technical means for the power grid to shift from passive response to "preventive and forward-looking" dispatching, and has significant practical application value.

[0146] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network, characterized in that, Including the following steps: S100, Construct equipment models for thermally limited devices in the power distribution network; S200: Obtain and calculate the power limit information for each device model based on real-time wind speed information; S300. Construct a distribution network carrying capacity analysis model, including an objective function and a constraint set. The objective function is used to maximize the sum of active power output of all renewable energy generation units in the distribution network, and the constraint set is used to constrain the safe operation of the distribution network. S400. After coupling the power limit information with the constraint set, obtain the optimal solution of the objective function; S500, The distribution network carrying capacity and control strategy corresponding to the optimal solution are the optimal distribution network carrying capacity and control strategy corresponding to the real-time wind speed.

2. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 1, characterized in that: The equipment model includes at least an overhead line model and a transformer model.

3. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 2, characterized in that: The power limit of the overhead line model is: In the formula: For the dynamic apparent power limit of the line; V L-L,rated P is the rated line voltage of the line; radiation P is the radiative heat dissipation power; solar R(T) represents the absorbed solar radiation power. c ) is at conductor temperature T c The resistance per unit length below; v wind ρ is the real-time wind speed; θ is the angle between the wind direction and the conductor's direction; D is the conductor's outer diameter; air air density; μ air aerodynamic viscosity; k air T is the thermal conductivity of air; c T represents the maximum permissible operating temperature of the conductor. a Let B be the ambient air temperature; B and n are dimensionless empirical coefficients, respectively.

4. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 2, characterized in that: The power limit of the transformer model is: In the formula, For dynamic apparent power limit; S base Based on capacity; v wind For real-time wind speed; Δθ o,R Rated top oil temperature rise; Δθ h,R For the rated winding hot spot temperature rise; k cool This is the cooling efficiency coefficient.

5. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 1, characterized in that: The objective function is: In the formula, F1(P) G ) represents the objective function value; N DG A set of distributed power nodes; P G,i The active power output of node i.

6. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 1, characterized in that, The constraint set includes physical law constraints: In the formula, P G,i Active power injection for node i; P L,i Q represents the active power consumption of node i. G,i Reactive power injection for node i; Q L,i V represents the reactive power consumption of node i. i V represents the voltage magnitude at node i. j Let θ be the voltage magnitude at node j; ij G represents the voltage phase angle difference between node i and node j. ij B is the real part of the element in the i-th row and j-th column of the nodal admittance matrix; ij Let be the imaginary part of the element in the i-th row and j-th column of the node admittance matrix.

7. The method for reflecting the correlation between wind speed and the carrying capacity of a power distribution network according to claim 1, characterized in that, The constraint set includes safety criterion constraints, which include: ①Dynamic thermal limit constraints of equipment: In the formula, S ij The actual apparent power of branch k; For the dynamic apparent power limit of branch k; v wind For real-time wind speed; N branch It is the set of all branches in the power grid; ② Node voltage constraints: In min ≤V i ≤V max , In the formula, V min V represents the minimum allowable voltage amplitude at the node. i V represents the actual voltage amplitude at node i. max This represents the maximum allowable voltage amplitude at the node. ③ Generator output constraints: P G,i,min ≤P G,i ≤P G,i,max , Q G,i,min ≤Q G,i ≤Q G,i,max , In the formula, P G,i,min P represents the minimum active power output of the power source at node i; G,i P represents the actual active power output of the power source at node i; G,i,max Q represents the maximum active power output of the power source at point i. G,i,min Q represents the minimum reactive power output of the power supply at node i; G,i Q represents the actual reactive power output of the power supply at node i; G,i,max Let i be the maximum reactive power output of the power supply at node i.

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