PSO-SSA-based distributed power supply access power distribution network bearing capacity evaluation method

Abstract

The invention relates to the technical field of power distribution network technologies, in particular to a PSO-SSA-based distributed power supply access power distribution network bearing capacity evaluation method, which comprises the following steps of S1, comprehensively evaluating power quality by combining power quality indexes of voltage deviation, voltage fluctuation and voltage harmonic waves, and constructing a photovoltaic grid-connected power quality evaluation model; s2, considering the photovoltaic grid-connected capacity and the network loss, and constructing a photovoltaic grid-connected capacity evaluation model; s3, based on a photovoltaic grid-connected electric energy quality evaluation model and a capacity evaluation model; s4, based on a PSO-SSA algorithm; s5, solution set optimization is carried out based on a fuzzy set theory; according to the method, the problems of node voltage out-of-limit and reverse overload of a distribution transformer caused by distributed photovoltaic grid connection at present are accurately reflected, the method has high applicability to evaluation of the distributed photovoltaic bearing capacity and safe operation of a power distribution network, and under the same constraint condition limit value, the obtained line distributed photovoltaic bearing capacity is higher, and the system network loss is lower.

Description

Technical Field

[0001] This invention relates to the technical field of distribution network technology, and in particular to a method for assessing the carrying capacity of distributed power sources connected to distribution networks based on PSO-SSA. Background Technology

[0002] The large-scale integration of distributed power sources into the distribution network has significantly altered the structure and operation of the traditional distribution network due to its intermittent and random characteristics. This has brought many negative impacts to the distribution network, such as frequent occurrences of node voltage exceeding limits, reverse overload of distribution transformers, voltage and current distortion, and increased network losses.

[0003] The high proportion of distributed generation access significantly affects the operation of the distribution network. Therefore, it is necessary to conduct an impact analysis of distributed generation access to the distribution network based on various distribution network indicators, and then carry out an assessment of the carrying capacity of distributed generation access to the distribution network.

[0004] Distribution network carrying capacity is an evaluation indicator for distribution networks. It is usually measured by the maximum installed capacity of new energy sources to measure the carrying capacity of the distribution network under the access of new energy sources. However, this indicator does not reflect the impact of factors such as power electronic equipment and photovoltaic grid connection. It is often not comprehensive enough for photovoltaic installation planning. The existing carrying capacity evaluation indicators are lacking in consideration of the safe operation of photovoltaic equipment connected to the grid and power quality.

[0005] In conclusion, assessing the carrying capacity of the distribution network by combining power quality and safe operation, constructing an assessment index for the carrying capacity of distributed power sources connected to the distribution network, and then carrying out the assessment of the carrying capacity of distributed power sources connected to the distribution network are of practical and innovative significance. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for assessing the carrying capacity of distributed power sources connected to the distribution network based on PSO-SSA. This method accurately reflects the issues of node voltage exceeding limits and reverse overload of distribution transformers caused by current distributed photovoltaic grid connection. It is highly applicable for evaluating the carrying capacity of distributed photovoltaic power sources and the safe operation of the distribution network. Under the same constraint conditions, the method yields a higher carrying capacity of distributed photovoltaic power sources on the line and lower system network losses.

[0007] The present invention provides a method for assessing the carrying capacity of distributed generation in distribution networks based on PSO-SSA, comprising the following steps: S1. By combining power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics, a comprehensive assessment of power quality is conducted to construct a power quality assessment model for photovoltaic grid connection. S2. Considering the grid-connected capacity of photovoltaics and network losses, construct a photovoltaic grid-connected capacity assessment model; S3. Based on the photovoltaic grid-connected power quality assessment model and capacity assessment model, construct a comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network; S4. Based on the PSO-SSA algorithm, calculate the maximum carrying capacity solution set for distributed generation to access the distribution network; S5. Based on fuzzy set theory, the solution set is optimized to obtain the maximum carrying capacity of distributed power generation connected to the distribution network. This invention evaluates the carrying capacity from the aspects of power quality and safe operation, and establishes a carrying capacity evaluation model for distributed power generation connected to the distribution network. It can accurately reflect the node voltage over-limit and reverse overload problems of distribution transformers caused by the current distributed photovoltaic grid connection. It has strong applicability for evaluating the carrying capacity of distributed photovoltaic and the safe operation of the distribution network. Compared with other algorithms, the PSO-SSA algorithm improves the convergence speed and optimization ability of the evaluation model calculation. Under the same constraint conditions, the obtained distributed photovoltaic carrying capacity of the line is higher and the system network loss is lower.

[0008] Preferably, in step S1, the power quality indicators for voltage deviation, voltage fluctuation, and voltage harmonics are as follows: (1) Voltage deviation Voltage deviation refers to the relative deviation between the actual operating voltage at the photovoltaic grid-connected point and the nominal voltage of the system, such as... Figure 2 The model shown has a voltage deviation at node k. The expression is as follows: (1) In the formula: Let k be the voltage at node k. This is the system's rated voltage; Voltage at node k as follows: (2) In the formula: This refers to the power node voltage. For nodes and Voltage deviation between , For nodes and The line resistance and reactance between them , For the active and reactive loads of node k, , The active and reactive power output of the photovoltaic system at node k; (2) Voltage fluctuation The calculation method for voltage fluctuations when distributed generation is connected to the distribution network is as follows: (3) In the formula: The photovoltaic effect at node k causes voltage fluctuations at node k. The present invention sets the instantaneous power change of photovoltaic power as a proportion of the output power. , Let k be the active power output of the photovoltaic system at node k. This is the system's rated voltage; (3) Voltage harmonics The total harmonic distortion (THD) rate is used to measure voltage harmonics caused by grid-connected distributed photovoltaic systems. (3) In the formula: The h-th harmonic voltage injected into the photovoltaic unit Total harmonic voltage content, The fundamental voltage at the photovoltaic grid connection point. Let be the total harmonic distortion rate of the voltage at the photovoltaic grid connection point at node k; A comprehensive assessment of power quality at photovoltaic grid-connected points is conducted by combining calculation formulas for power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics. The resulting photovoltaic grid-connected power quality assessment model is as follows: (4) (5) In the formula: This is the sum of the power quality assessment values ​​for all distributed photovoltaic grid-connected systems. The power quality assessment value for photovoltaic node i. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. Let be the vector of actual measured power quality indicators for photovoltaic node i. The figures show the actual measured results of voltage deviation, voltage fluctuation, and voltage harmonics for photovoltaic node i. Let i be the power quality index weight vector of the photovoltaic node i. These represent the weights of voltage deviation, voltage fluctuation, and voltage harmonics for photovoltaic node i.

[0009] Preferably, step S2 considers both the grid-connected photovoltaic (PV) capacity and network losses. In constructing the PV grid-connected capacity assessment model, distributed PV grid connection alters the distribution network losses. Therefore, when constructing the PV grid-connected capacity assessment model, it is necessary to consider not only the grid-connected capacity of distributed PV but also the network losses caused by distributed PV. The equivalent grid-connected capacity of distributed PV is the difference between the two. Therefore, the PV grid-connected capacity assessment model is as follows: (6) In the formula: This represents the total grid-connected photovoltaic capacity. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. The active power output of the photovoltaic at node i. This indicates the network loss of the distribution network; Network loss The calculation formula is as follows: (7) In the formula: For the system node set, For the line Current, , branch road The active and reactive power transmitted. For nodes Voltage, For the line resistance.

[0010] Preferably, in step S3, based on the photovoltaic grid-connected power quality assessment model and the capacity assessment model, the comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network is constructed as follows: (8).

[0011] Preferably, in S3, the distributed power generation access to the distribution network carrying capacity assessment model also includes power flow constraints, voltage deviation constraints, voltage fluctuation constraints, voltage harmonic constraints, branch current constraints, reverse load rate constraints, short-circuit current constraints, and distributed photovoltaic capacity constraints. (1) Current constraints The power and voltage balance of the system is the foundation of the distribution network operation. Therefore, the power flow constraint is treated as a system equality constraint and expressed as: (9) In the formula: , The active and reactive power output of the photovoltaic node. , For nodes Active and reactive power, For nodes Voltage, For nodes With nodes The electrical conductance between them For nodes With nodes The susceptance between them For nodes With nodes The voltage phase angle difference between them; (2) Voltage deviation constraint According to the national standard GB / T12325-2008 Power Quality - Supply Voltage Deviation, the voltage deviation limits are different for different voltage levels, and the constraints can be described as follows: (10) In the formula: This indicates the voltage deviation rate at a specific voltage level. For nodes voltage, This is the system's rated voltage; (3) Voltage fluctuation constraint According to the national standard GB / T 12326-2008 Power Quality Voltage Fluctuation and Flicker, the voltage fluctuation limits are different for different voltage levels, and the constraints can be described as follows: (11) In the formula: For the node System voltage fluctuation caused by photovoltaics, This is the maximum voltage fluctuation value specified by the national standard. (4) Voltage harmonic constraint According to the national standard GB / T14549-93 Power Quality - Harmonics in Public Power Grids, the limits for total harmonic distortion (THD) vary depending on the voltage level. These constraints can be described as follows: (12) In the formula: Let be the total harmonic distortion of the voltage of the photovoltaic at node i. The maximum total harmonic distortion (THD) of the voltage specified in the national standard; (5) Branch current constraints can be expressed as follows: (13) In the formula: For the line Current, For the line The maximum current allowed to flow through it; (6) Reverse load factor constraint, which can be expressed as follows: Reverse load rate It is an important indicator for measuring the impact of distributed photovoltaic grid connection on the operational safety of distribution transformers, and is defined as follows: (14) In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: (15) In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: (15) For nodes The industry standard "Guidelines for Assessing the Capacity of Distributed Power Generation Connection to the Power Grid" stipulates that the reverse load rate of distribution transformers should not exceed 80%, i.e. Set at 80%; (7) Short-circuit current constraint can be expressed as follows: (16) This refers to the short-circuit current of the system bus. The permissible short-circuit current limit; (8) Distributed photovoltaic capacity constraints can be expressed as follows: In the formula, , These represent the minimum and maximum active power of the distributed photovoltaic (PV) grid connection capacity, respectively. Let be the active power output of the photovoltaic at node i.

[0012] Preferably, in step S4, the maximum carrying capacity solution set for distributed generation access to the distribution network is calculated based on the PSO-SSA algorithm. The specific process is as follows: Step 1: Initialize population parameters, set parameters such as the maximum number of algorithm iterations and inertia factor, and determine the ratio of explorer particles to follower particles within the population. and the corresponding number, where the proportionality coefficient As shown in the following formula: In the formula, This represents the number of explorer particles with good fitness values ​​in the population. This represents the number of follower particles with poor fitness values ​​in the population. Step 2: Calculate the fitness values ​​of the particles and sort them; Step 3: Based on the dominance relationship, select the individual optimal solution from the non-dominated solutions, and store all non-dominated solutions in the external file pbest. Then, select the global optimal solution gbest from the external file. Step 4: Set the warning value to always be less than the safety value. The explorer particle updates its position according to the following formula and performs a large-scale rapid search in the problem solution space. In the formula: This represents the current iteration number. The maximum number of iterations, sparrow exist At the moment Location information within the dimension; Step 5: Follower particles update their velocity and position, and generate influencing factors based on changes in the explorer particles' capabilities. This is then introduced into the particle swarm optimization algorithm to dynamically adjust the inertia factor, changing the influence of the particle's past position and velocity on the present. Specifically, follower particles will update their velocity and position using the following formula: In the formula, This represents the current iteration number. Indicates the velocity of the particle. Indicates the current position of the particle. The inertia factor is used to adjust the algorithm's local and global search capabilities. and As a learning factor, and A random number between 0 and 1; Step 6: Calculate the fitness values ​​of the particles after velocity and position updates; Step 7: Update the individual extreme value pbest, the global extreme value gbest, and the external archive; Step 8: Determine if the termination condition (maximum number of iterations) is met. If yes, output the Pareto optimal solution set; otherwise, skip to the next step.

[0013] Preferably, step S5 performs solution set optimization based on fuzzy set theory to obtain the maximum carrying capacity of distributed power generation connected to the distribution network, specifically as follows: The Pareto solution set of the particle swarm optimization and sparrow multi-objective hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making process for assessing the carrying capacity of distributed generation (DG) access to the distribution network involves selecting a definite optimal solution from the Pareto solution set. Based on fuzzy set theory, a compromise approach is adopted to comprehensively select the optimal solution for DG access to the distribution network carrying capacity. The membership function is defined as follows: In the formula, and These are the solutions in the Pareto set. The minimum and maximum values ​​of an optimization objective function. and They represent the first The solution of the first... The current value and membership value of each objective function.

[0014] Preferably, for each non-dominated solution in the Pareto set obtained by the particle swarm and sparrow multi-objective hybrid intelligent algorithm, its dominance function is defined as: In the formula, To optimize the number of non-dominated solutions in the Pareto solution, To determine the number of objectives in the layout optimization model, the dominance value of each non-dominated solution in the Pareto solution set can be calculated. The larger the value, the better the overall performance of the solution. Then, the solution with the largest dominance value is selected from the Pareto solution set as the optimal solution, and the maximum carrying capacity of distributed power generation connected to the distribution network is obtained.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention evaluates the carrying capacity from the aspects of power quality and safe operation, and establishes a carrying capacity evaluation model for distributed power sources connected to the distribution network. It can accurately reflect the node voltage over-limit and reverse overload problems of distribution transformers caused by the current distributed photovoltaic grid connection. It has strong applicability for evaluating the carrying capacity of distributed photovoltaic and the safe operation of the distribution network. Compared with other algorithms, the PSO-SSA algorithm improves the convergence speed and optimization ability of the evaluation model calculation. Under the same constraint conditions, the obtained distributed photovoltaic carrying capacity of the line is higher and the system network loss is lower. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the module structure of the evaluation method of the present invention; Figure 2 This is a schematic diagram of the voltage deviation structure; Figure 3 This is a schematic diagram of the load-bearing capacity optimization calculation process. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] Example 1 like Figures 1 to 3As shown, the distributed generation capacity assessment method for distribution network access based on PSO-SSA of the present invention includes the following steps: S1. By combining power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics, a comprehensive assessment of power quality is conducted to construct a power quality assessment model for photovoltaic grid connection. S2. Considering the grid-connected capacity of photovoltaics and network losses, construct a photovoltaic grid-connected capacity assessment model; S3. Based on the photovoltaic grid-connected power quality assessment model and capacity assessment model, construct a comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network; S4. Based on the PSO-SSA algorithm, calculate the maximum carrying capacity solution set for distributed generation to access the distribution network; S5. Based on fuzzy set theory, the solution set is optimized to obtain the maximum carrying capacity of distributed power generation connected to the distribution network. In S1, the power quality indicators for voltage deviation, voltage fluctuation, and voltage harmonics are as follows: (1) Voltage deviation Voltage deviation refers to the relative deviation between the actual operating voltage at the photovoltaic grid-connected point and the nominal voltage of the system, such as... Figure 2 The model shown has a voltage deviation at node k. The expression is as follows: (1) In the formula: Let k be the voltage at node k. This is the system's rated voltage; Voltage at node k as follows: (2) In the formula: This refers to the power node voltage. For nodes and Voltage deviation between , For nodes and The line resistance and reactance between them , For the active and reactive loads of node k, , The active and reactive power output of the photovoltaic system at node k; (2) Voltage fluctuation The calculation method for voltage fluctuations when distributed generation is connected to the distribution network is as follows: (3) In the formula: The photovoltaic effect at node k causes voltage fluctuations at node k. The present invention sets the instantaneous power change of photovoltaic power as a proportion of the output power. , Let k be the active power output of the photovoltaic system at node k. This is the system's rated voltage; (3) Voltage harmonics The total harmonic distortion (THD) rate is used to measure voltage harmonics caused by grid-connected distributed photovoltaic systems. (3) In the formula: The h-th harmonic voltage injected into the photovoltaic unit Total harmonic voltage content, The fundamental voltage at the photovoltaic grid connection point. Let be the total harmonic distortion rate of the voltage at the photovoltaic grid connection point at node k; A comprehensive assessment of power quality at photovoltaic grid-connected points is conducted by combining calculation formulas for power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics. The resulting photovoltaic grid-connected power quality assessment model is as follows: (4) (5) In the formula: This is the sum of the power quality assessment values ​​for all distributed photovoltaic grid-connected systems. The power quality assessment value for photovoltaic node i. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. Let be the vector of actual measured power quality indicators for photovoltaic node i. The figures show the actual measured results of voltage deviation, voltage fluctuation, and voltage harmonics for photovoltaic node i. Let i be the power quality index weight vector of the photovoltaic node i. These are the weights of voltage deviation, voltage fluctuation, and voltage harmonic indices for photovoltaic node i, respectively. In this embodiment, the present invention assesses the carrying capacity from aspects such as power quality and safe operation, and establishes a carrying capacity assessment model for distributed power sources connected to the distribution network. This model can accurately reflect the node voltage over-limit and reverse overload problems of distribution transformers caused by the current distributed photovoltaic grid connection, and has strong applicability for assessing the carrying capacity of distributed photovoltaic and the safe operation of the distribution network.

[0019] Example 2 Based on Example 1, the present invention provides a method for assessing the carrying capacity of distributed power generation access to the distribution network based on PSO-SSA. In step S2, considering both photovoltaic grid-connected capacity and network losses, the photovoltaic grid-connected capacity assessment model is constructed. Distributed photovoltaic grid connection alters the distribution network losses; therefore, the model must consider not only the grid-connected capacity of distributed photovoltaics but also the network losses caused by distributed photovoltaics. The equivalent grid-connected capacity of distributed photovoltaics is the difference between the two. Therefore, the photovoltaic grid-connected capacity assessment model is as follows: (6) In the formula: This represents the total grid-connected photovoltaic capacity. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. The active power output of the photovoltaic at node i. This indicates the network loss of the distribution network; Network loss The calculation formula is as follows: (7) In the formula: For the system node set, For the line Current, , branch road The active and reactive power transmitted. For nodes Voltage, For the line resistance.

[0020] Preferably, in step S3, based on the photovoltaic grid-connected power quality assessment model and the capacity assessment model, the comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network is constructed as follows: (8).

[0021] Preferably, in S3, the distributed power generation access to the distribution network carrying capacity assessment model also includes power flow constraints, voltage deviation constraints, voltage fluctuation constraints, voltage harmonic constraints, branch current constraints, reverse load rate constraints, short-circuit current constraints, and distributed photovoltaic capacity constraints. (1) Current constraints The power and voltage balance of the system is the foundation of the distribution network operation. Therefore, the power flow constraint is treated as a system equality constraint and expressed as: (9) In the formula: , The active and reactive power output of the photovoltaic node. , For nodes Active and reactive power, For nodes Voltage, For nodes With nodes The electrical conductance between them For nodes With nodes The susceptance between them For nodes With nodes The voltage phase angle difference between them; (2) Voltage deviation constraint According to the national standard GB / T12325-2008 Power Quality - Supply Voltage Deviation, the voltage deviation limits are different for different voltage levels, and the constraints can be described as follows: (10) In the formula: This indicates the voltage deviation rate at a specific voltage level. For nodes voltage, This is the system's rated voltage; (3) Voltage fluctuation constraint According to the national standard GB / T 12326-2008 Power Quality Voltage Fluctuation and Flicker, the voltage fluctuation limits are different for different voltage levels, and the constraints can be described as follows: (11) In the formula: For the node System voltage fluctuation caused by photovoltaics, This is the maximum voltage fluctuation value specified by the national standard. (4) Voltage harmonic constraint According to the national standard GB / T14549-93 Power Quality - Harmonics in Public Power Grids, the limits for total harmonic distortion (THD) vary depending on the voltage level. These constraints can be described as follows: (12) In the formula: Let be the total harmonic distortion of the voltage of the photovoltaic at node i. The maximum total harmonic distortion (THD) of the voltage specified in the national standard; (5) Branch current constraints can be expressed as follows: (13) In the formula: For the line Current, For the line The maximum current allowed to flow through it; (6) Reverse load factor constraint, which can be expressed as follows: Reverse load rate It is an important indicator for measuring the impact of distributed photovoltaic grid connection on the operational safety of distribution transformers, and is defined as follows: (14) In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: (15) In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: (15) For nodes The industry standard "Guidelines for Assessing the Capacity of Distributed Power Generation Connection to the Power Grid" stipulates that the reverse load rate of distribution transformers should not exceed 80%, i.e. Set at 80%; (7) Short-circuit current constraint can be expressed as follows: (16) This refers to the short-circuit current of the system bus. The permissible short-circuit current limit; (8) Distributed photovoltaic capacity constraints can be expressed as follows: In the formula, , These represent the minimum and maximum active power of the distributed photovoltaic (PV) grid connection capacity, respectively. The active power output of the photovoltaic system at node i; In step S4, the maximum carrying capacity solution set for distributed generation access to the distribution network is calculated based on the PSO-SSA algorithm. The specific process is as follows: Step 1: Initialize population parameters, set parameters such as the maximum number of algorithm iterations and inertia factor, and determine the ratio of explorer particles to follower particles within the population. and the corresponding number, where the proportionality coefficient As shown in the following formula: In the formula, This represents the number of explorer particles with good fitness values ​​in the population. This represents the number of follower particles with poor fitness values ​​in the population. Step 2: Calculate the fitness values ​​of the particles and sort them; Step 3: Based on the dominance relationship, select the individual optimal solution from the non-dominated solutions, and store all non-dominated solutions in the external file pbest. Then, select the global optimal solution gbest from the external file. Step 4: Set the warning value to always be less than the safety value. The explorer particle updates its position according to the following formula and performs a large-scale rapid search in the problem solution space. In the formula: This represents the current iteration number. The maximum number of iterations, sparrow exist At the moment Location information within the dimension; Step 5: Follower particles update their velocity and position, and generate influencing factors based on changes in the explorer particles' capabilities. This is then introduced into the particle swarm optimization algorithm to dynamically adjust the inertia factor, changing the influence of the particle's past position and velocity on the present. Specifically, follower particles will update their velocity and position using the following formula: In the formula, This represents the current iteration number. Indicates the velocity of the particle. Indicates the current position of the particle. The inertia factor is used to adjust the algorithm's local and global search capabilities. and As a learning factor, and A random number between 0 and 1; Step 6: Calculate the fitness values ​​of the particles after velocity and position updates; Step 7: Update the individual extreme value pbest, the global extreme value gbest, and the external archive; Step 8: Determine if the termination condition (maximum number of iterations) is met. If yes, output the Pareto optimal solution set; otherwise, skip to the next step. S5 performs solution set optimization based on fuzzy set theory to obtain the maximum carrying capacity of distributed power generation connected to the distribution network, specifically: The Pareto solution set of the particle swarm optimization and sparrow multi-objective hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making process for assessing the carrying capacity of distributed generation (DG) access to the distribution network involves selecting a definite optimal solution from the Pareto solution set. Based on fuzzy set theory, a compromise approach is adopted to comprehensively select the optimal solution for DG access to the distribution network carrying capacity. The membership function is defined as follows: In the formula, and These are the solutions in the Pareto set. The minimum and maximum values ​​of an optimization objective function. and They represent the first The solution of the first... The current value and membership value of each objective function.

[0022] Preferably, for each non-dominated solution in the Pareto set obtained by the particle swarm and sparrow multi-objective hybrid intelligent algorithm, its dominance function is defined as: In the formula, To optimize the number of non-dominated solutions in the Pareto solution, To determine the number of objectives in the layout optimization model, the dominance value of each non-dominated solution in the Pareto solution set can be calculated. The larger the value, the better the overall performance of the solution. Then, the solution with the largest dominance value is selected from the Pareto solution set as the optimal solution, and the maximum carrying capacity of distributed power generation connected to the distribution network is obtained. In this embodiment, compared with other algorithms, the PSO-SSA algorithm improves the convergence speed and optimization ability of the evaluation model calculation. Under the same constraint conditions, the obtained line distributed photovoltaic carrying capacity is higher and the system network loss is lower.

[0023] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the carrying capacity of distributed generation in distribution networks based on PSO-SSA, characterized in that, Includes the following steps: S1. By combining power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics, a comprehensive assessment of power quality is conducted to construct a power quality assessment model for photovoltaic grid connection. S2. Considering the grid-connected capacity of photovoltaics and network losses, construct a photovoltaic grid-connected capacity assessment model; S3. Based on the photovoltaic grid-connected power quality assessment model and capacity assessment model, construct a comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network; S4. Based on the PSO-SSA algorithm, calculate the maximum carrying capacity solution set for distributed generation to access the distribution network; S5. Based on fuzzy set theory, the solution set is optimized to obtain the maximum carrying capacity of distributed power generation connected to the distribution network.

2. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, In S1, the power quality indicators for voltage deviation, voltage fluctuation, and voltage harmonics are as follows: (1) Voltage deviation Voltage deviation refers to the relative deviation between the actual operating voltage at the photovoltaic grid connection point and the nominal voltage of the system. The voltage deviation at node k is... The expression is as follows: （1） In the formula: Let k be the voltage at node k. This is the system's rated voltage; Voltage at node k as follows: （2） In the formula: This refers to the power node voltage. For nodes and Voltage deviation between , For nodes and The line resistance and reactance between them , For the active and reactive loads of node k, , The active and reactive power output of the photovoltaic system at node k; (2) Voltage fluctuation The calculation method for voltage fluctuations when distributed generation is connected to the distribution network is as follows: （3） In the formula: The photovoltaic effect at node k causes voltage fluctuations at node k. The present invention sets the instantaneous power change of photovoltaic power as a proportion of the output power. , Let k be the active power output of the photovoltaic system at node k. This is the system's rated voltage; (3) Voltage harmonics The total harmonic distortion (THD) rate is used to measure voltage harmonics caused by grid-connected distributed photovoltaic systems. （3） In the formula: The h-th harmonic voltage injected into the photovoltaic unit Total harmonic voltage content, The fundamental voltage at the photovoltaic grid connection point. Let be the total harmonic distortion rate of the voltage at the photovoltaic grid connection point at node k; A comprehensive assessment of power quality at photovoltaic grid-connected points is conducted by combining calculation formulas for power quality indicators such as voltage deviation, voltage fluctuation, and voltage harmonics. The resulting photovoltaic grid-connected power quality assessment model is as follows: （4） （5） In the formula: This is the sum of the power quality assessment values ​​for all distributed photovoltaic grid-connected systems. The power quality assessment value for photovoltaic node i. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. Let be the vector of actual measured power quality indicators for photovoltaic node i. The figures show the actual measured results of voltage deviation, voltage fluctuation, and voltage harmonics for photovoltaic node i. Let i be the power quality index weight vector of the photovoltaic node i. These represent the weights of voltage deviation, voltage fluctuation, and voltage harmonics for photovoltaic node i.

3. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, S2 considers both grid-connected photovoltaic (PV) capacity and network losses. In constructing the PV grid-connected capacity assessment model, distributed PV grid connection alters the distribution network losses. Therefore, when constructing the PV grid-connected capacity assessment model, it is necessary to consider not only the grid-connected capacity of distributed PV but also the network losses caused by distributed PV. The equivalent grid-connected capacity of distributed PV is the difference between the two. Therefore, the PV grid-connected capacity assessment model is as follows: （6） In the formula: This represents the total grid-connected photovoltaic capacity. This refers to the number of distributed photovoltaic (PV) systems connected to the grid in the distribution network. The active power output of the photovoltaic at node i. This indicates the network loss of the distribution network; Network loss The calculation formula is as follows: （7） In the formula: For the system node set, For the line Current, , branch road The active and reactive power transmitted. For nodes Voltage, For the line resistance.

4. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, In S3, based on the photovoltaic grid-connected power quality assessment model and capacity assessment model, a comprehensive assessment model for the carrying capacity of distributed power sources connected to the distribution network is constructed as follows: （8）。 5. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, In S3, the distributed power generation access to the distribution network carrying capacity assessment model is constructed, which also includes power flow constraints, voltage deviation constraints, voltage fluctuation constraints, voltage harmonic constraints, branch current constraints, reverse load rate constraints, short-circuit current constraints, and distributed photovoltaic capacity constraints. (1) Current constraints The power and voltage balance of the system is the foundation of the distribution network operation. Therefore, the power flow constraint is treated as a system equality constraint and expressed as: （9） In the formula: , The active and reactive power output of the photovoltaic node. , For nodes Active and reactive power, For nodes Voltage, For nodes With nodes The electrical conductance between them For nodes With nodes The susceptance between them For nodes With nodes The voltage phase angle difference between them; (2) Voltage deviation constraint According to the national standard GB / T12325-2008 Power Quality - Supply Voltage Deviation, the voltage deviation limits are different for different voltage levels, and the constraints can be described as follows: （10） In the formula: This indicates the voltage deviation rate at a specific voltage level. For nodes voltage, This is the system's rated voltage; (3) Voltage fluctuation constraint According to the national standard GB / T 12326-2008 Power Quality Voltage Fluctuation and Flicker, the voltage fluctuation limits are different for different voltage levels, and the constraints can be described as follows: （11） In the formula: For the node System voltage fluctuation caused by photovoltaics, This is the maximum voltage fluctuation value specified by the national standard. (4) Voltage harmonic constraint According to the national standard GB / T14549-93 Power Quality - Harmonics in Public Power Grids, the limits for total harmonic distortion (THD) vary depending on the voltage level. These constraints can be described as follows: （12） In the formula: Let be the total harmonic distortion of the voltage of the photovoltaic at node i. The maximum total harmonic distortion (THD) of the voltage specified in the national standard; (5) Branch current constraints can be expressed as follows: （13） In the formula: For the line Current, For the line The maximum current allowed to flow through it; (6) Reverse load factor constraint, which can be expressed as follows: Reverse load rate It is an important indicator for measuring the impact of distributed photovoltaic grid connection on the operational safety of distribution transformers, and is defined as follows: （14） In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: （15） In the formula: The equivalent electrical load is the electrical load minus the output of power sources other than distributed generation. This represents the maximum limit for actual operation of the transformer; The reverse load rate constraints of each node must meet the following conditions: （15） For nodes The industry standard "Guidelines for Assessing the Capacity of Distributed Power Generation Connection to the Power Grid" stipulates that the reverse load rate of distribution transformers should not exceed 80%, i.e. Set at 80%; (7) Short-circuit current constraint can be expressed as follows: （16） This refers to the short-circuit current of the system bus. The permissible short-circuit current limit; (8) Distributed photovoltaic capacity constraints can be expressed as follows: In the formula, , These represent the minimum and maximum active power of the distributed photovoltaic (PV) grid connection capacity, respectively. Let be the active power output of the photovoltaic at node i.

6. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, In step S4, the maximum carrying capacity solution set for distributed generation access to the distribution network is calculated based on the PSO-SSA algorithm. The specific process is as follows: Step 1: Initialize population parameters, set parameters such as the maximum number of algorithm iterations and inertia factor, and determine the ratio of explorer particles to follower particles within the population. and the corresponding number, where the proportionality coefficient As shown in the following formula: In the formula, This represents the number of explorer particles with good fitness values ​​in the population. This represents the number of follower particles with poor fitness values ​​in the population. Step 2: Calculate the fitness values ​​of the particles and sort them; Step 3: Based on the dominance relationship, select the individual optimal solution from the non-dominated solutions, and store all non-dominated solutions in the external file pbest. Then, select the global optimal solution gbest from the external file. Step 4: Set the warning value to always be less than the safety value. The explorer particle updates its position according to the following formula and performs a large-scale rapid search in the problem solution space. In the formula: This represents the current iteration number. The maximum number of iterations, sparrow exist At the time of Location information within the dimension; Step 5: Follower particles update their velocity and position, and generate influencing factors based on changes in the explorer particles' capabilities. This is then introduced into the particle swarm optimization algorithm to dynamically adjust the inertia factor, changing the influence of the particle's past position and velocity on the present. Specifically, follower particles will update their velocity and position using the following formula: In the formula, This represents the current iteration number. Indicates the velocity of the particle. Indicates the current position of the particle. The inertia factor is used to adjust the algorithm's local and global search capabilities. and As a learning factor, and A random number between 0 and 1; Step 6: Calculate the fitness values ​​of the particles after velocity and position updates; Step 7: Update the individual extreme value pbest, the global extreme value gbest, and the external archive; Step 8: Determine if the termination condition (maximum number of iterations) is met. If yes, output the Pareto optimal solution set; otherwise, skip to the next step.

7. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 1, characterized in that, S5 performs solution set optimization based on fuzzy set theory to obtain the maximum carrying capacity of distributed power generation connected to the distribution network, specifically: The Pareto solution set of the particle swarm optimization and sparrow multi-objective hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making process for assessing the carrying capacity of distributed generation (DG) access to the distribution network involves selecting a definite optimal solution from the Pareto solution set. Based on fuzzy set theory, a compromise approach is adopted to comprehensively select the optimal solution for DG access to the distribution network carrying capacity. The membership function is defined as follows: In the formula, and These are the solutions in the Pareto set. The minimum and maximum values ​​of an optimization objective function. and They represent the first The solution of the first... The current value and membership value of each objective function.

8. The method for assessing the carrying capacity of distributed power generation in distribution networks based on PSO-SSA as described in claim 7, characterized in that, For each non-dominated solution in the Pareto set obtained by the particle swarm and sparrow multi-objective hybrid intelligent algorithms, its dominance function is defined as: In the formula, To optimize the number of non-dominated solutions in the Pareto solution, To determine the number of objectives in the layout optimization model, the dominance value of each non-dominated solution in the Pareto solution set can be calculated. The larger the value, the better the overall performance of the solution. Then, the solution with the largest dominance value is selected from the Pareto solution set as the optimal solution, and the maximum carrying capacity of distributed power generation connected to the distribution network is obtained.

Images