Distributed photovoltaic access power distribution network loss calculation method

Abstract

The invention discloses a distributed photovoltaic access power distribution network loss calculation method. The method comprises the following steps: a model construction step; an algorithm optimization step; a verification and comparison step; and an absorption capability evaluation step. According to the method, the influence of distributed photovoltaic access on the power distribution network loss is researched, the positive influence of distributed photovoltaic power generation is maximized by means of optimizing dispatching, reactive compensation, an energy storage system and the like, and meanwhile, the negative effect of the distributed photovoltaic power generation is reduced.

Description

Technical Field

[0001] The present invention relates to a power system distribution network, and in particular to a method for calculating network loss of a distributed photovoltaic access distribution network. Technical Background

[0002] In recent years, with the transformation of global energy structure and the increasing pressure of environmental protection, distributed photovoltaic power generation, as an important renewable energy technology, has received widespread attention and rapid development. Distributed photovoltaic systems are widely used in urban building roofs, industrial parks and rural areas due to their flexible installation methods and low environmental impact. They not only help reduce carbon emissions and alleviate energy shortages, but also drive regional economic development and technological innovation.

[0003] However, the large-scale access of distributed photovoltaic power generation has brought new challenges to the traditional distribution network. The distribution network was originally designed for centralized power generation and one-way power transmission, but the access of distributed photovoltaics has changed this model, making the flow of power more complicated. The intermittent and uncertain nature of photovoltaic power generation has led to increased grid voltage fluctuations, reverse power flow, power quality problems, and power losses. These problems not only affect the operating efficiency and stability of the power grid, but may also pose a threat to the security of the power system.

[0004] In order to solve these problems, academia and industry have conducted a lot of research, focusing on the impact of distributed photovoltaic access on distribution networks and their optimization strategies. These studies include: how to balance photovoltaic power generation and load demand through intelligent scheduling and control algorithms; how to use reactive power compensation technology to maintain voltage stability; how to smooth the volatility of photovoltaic power generation through energy storage systems; and how to use big data and artificial intelligence technologies to improve the operation optimization capabilities of distribution networks. Despite some significant progress, existing research still faces many challenges, such as the establishment of efficient prediction models, cost-effectiveness balance, and the complexity of system integration.

[0005] Therefore, continuing to conduct in-depth research on the impact of distributed photovoltaic access on distribution networks and developing more advanced optimization and control technologies are of great practical significance for improving the operating efficiency, stability and safety of distribution networks and promoting the widespread application of renewable energy.

[0006] With the large-scale access of distributed photovoltaic power generation systems, studying their impact on distribution network losses has become a hot topic in the current power system field. Distributed photovoltaic power generation can reduce the distance of power transmission by supplying power locally during peak load periods, thereby effectively reducing line losses and distribution network losses. This form of distributed energy can, to a certain extent, reduce the peak load of the distribution network and reduce the loss rate of the power grid.

[0007] However, the intermittent and unstable nature of photovoltaic power generation also poses challenges to the stable operation of the distribution network. Photovoltaic power generation is closely related to sunshine conditions, and its output power has obvious fluctuations, which may lead to voltage fluctuations and reverse power flow, increase the complexity of distribution network operation, and even increase network losses in some cases. Especially during the peak period of photovoltaic power generation, if the local load is insufficient, the remaining power needs to be sent back to the upper grid, which not only increases line losses, but also may cause grid safety and stability issues.

[0008] To meet these challenges, researchers have proposed a variety of optimization methods to maximize the positive impact of distributed photovoltaics while reducing its negative effects. Intelligent dispatching systems have been widely studied to coordinate the operation of distributed photovoltaic power generation and traditional power sources, and dynamic reactive power compensation technology has also been introduced to maintain voltage stability. In addition, the introduction of energy storage systems is considered to be an effective means to balance the volatility of photovoltaic power generation. It can store electricity when there is excess electricity, release electricity when there is insufficient electricity, and smooth the grid load. Distribution network operation optimization technology based on big data and artificial intelligence is also constantly developing, further improving the operation efficiency and stability of the distribution network through accurate prediction and scheduling. These studies will not only help reduce network losses, but also improve the overall operation performance of the distribution network and promote the widespread application of clean energy.

[0009] Although existing research on the impact of distributed photovoltaic access on distribution network losses has achieved certain results in optimizing scheduling, reactive power compensation and energy storage systems, it also has some significant shortcomings and limitations.

[0010] Although the optimization scheduling technology can effectively coordinate the operation of distributed photovoltaic and traditional power sources, its complex mathematical model and high computational cost limit its practical application in large-scale systems. In addition, these models have extremely high requirements for the prediction accuracy of photovoltaic power generation and load, and the current prediction methods still have large errors when facing weather changes and load fluctuations, which affects the actual effect of scheduling optimization.

[0011] Although dynamic reactive power compensation technology can improve voltage stability, its equipment cost is high and has strict requirements on the response speed and regulation accuracy of the equipment. It faces economic and technical challenges in actual deployment.

[0012] Energy storage systems are considered to be an effective means to balance the volatility of photovoltaic power generation, but existing energy storage technologies still need to be improved in terms of cost, lifespan and energy density. In addition, large-scale deployment of energy storage systems needs to resolve compatibility and safety issues with the power grid.

[0013] Although optimization technologies based on big data and artificial intelligence have great potential, they still need to overcome problems such as data quality, model generalization ability and real-time performance in practical applications. The practicality and reliability of these technologies have not been fully verified in complex power grid environments.

[0014] Therefore, existing research and technologies still need to be further improved and perfected in the practical application of the impact of distributed photovoltaic access on distribution network losses. Summary of the invention

[0015] The purpose of the present invention is to provide a method for calculating the network loss of distributed photovoltaic access to distribution network. By studying the impact of distributed photovoltaic access on the network loss of distribution network, the present invention aims to maximize the positive impact of distributed photovoltaic power generation while reducing its negative effects by optimizing scheduling, reactive power compensation and energy storage systems.

[0016] The technical solution to achieve the above purpose is: a method for calculating network loss of distributed photovoltaic access to distribution network, comprising the following steps:

[0017] Model construction steps: Before and after distributed photovoltaic access, the voltage and current changes of each node are analyzed, and the impact on the total loss of the distribution network is estimated. The voltage, current and network loss mathematical model of the distributed photovoltaic access node is established;

[0018] Optimization algorithm steps: An improved particle swarm algorithm is used to optimize the distributed photovoltaic grid connection points to minimize voltage deviation and power loss. The algorithm includes initialization, fitness evaluation, speed and position update, and the introduction of mutation operations and local search steps;

[0019] Verification and comparison steps: The simulation verification is carried out through the IEEE 33-node distribution network. The results show that the improved particle swarm algorithm is superior to the traditional genetic algorithm and the ordinary particle swarm algorithm in terms of network loss and voltage optimization.

[0020] Absorption capacity assessment steps: Four indicators, namely photovoltaic power generation ratio, grid carrying capacity, grid voltage fluctuation and thermal stability, are proposed to assess the grid's absorption capacity, and the impact of different access locations and access capacities on grid losses is verified.

[0021] In the above-mentioned method for calculating network loss of distributed photovoltaic access to distribution network, in the model building step, after the distributed photovoltaic access to node m, the expression of the voltage change of the node is:

[0022]

[0023] Where: U0 is the voltage at the beginning of the circuit, P i and Q i is the active power and reactive power of each node, R j is the line resistance, P DGis the active power output of photovoltaic, Q DG is the active power output of photovoltaic, X j is the line reactance, U j-1 is the voltage of the j-1th node, i and j refer to a node between nodes 0 and m respectively.

[0024] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to the distribution network, in the model building step, the distributed photovoltaic access has an impact on the current. The larger the capacity, the greater the reverse current.

[0025]

[0026] Where: I T is the current flowing through the transformer, I L ,I DG are the load current and the current injected by the photovoltaic power source, U L is the transformer low voltage side voltage, U H is the voltage on the high voltage side of the transformer, P L is the transmission power of the transformer, P DG is the photovoltaic injection power, η is the transmission efficiency, U DG is the photovoltaic power supply voltage.

[0027] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to the distribution network, in the model building step, after the distributed power source is connected, the line loss is:

[0028]

[0029] The total loss of the distribution network is:

[0030]

[0031] Or: The total loss of the distribution network can also be expressed as follows:

[0032]

[0033] In the formula, U is the rated voltage of the line, I is the current of the line, is the power factor, P k and Q k is the active power and reactive power of node k, P n and Q n is the active power and reactive power of node n, U n is the voltage of node n, N is the total number of nodes, Q L is the reactive power transmitted by the transformer, ΔP DG is the active power loss of the photovoltaic power source, ΔP nis the active power loss of node n, R is the line resistance, and n, k refers to a node between nodes 0-N.

[0034] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to a distribution network, in the optimization algorithm step, an improved particle swarm algorithm is used to optimize the distributed photovoltaic grid connection point to minimize the loss of voltage and power.

[0035] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to the distribution network, the objective function for minimizing voltage and power loss is:

[0036] Z=min{β·ΔP loss +(1-β)·ΔU k} (18)

[0037] Where: β is the weight, ΔP loss is the power loss of the distribution network, ΔU k is the voltage loss, and the constraints are:

[0038] V kmin <V k <V kmax

[0039]

[0040] Where: V kmin and V kmax are the minimum and maximum voltages at node k, G ij is the conductance of node i, j, θ ij is the power angle difference between nodes i and j, B ij is the susceptance between nodes i and j, V i is the voltage at node i.

[0041] In the above-mentioned distributed photovoltaic access distribution network loss calculation method, the improved particle swarm algorithm process is:

[0042] Step 1, initialize particle position X i and speed V i , set parameters such as inertia weight and learning factor;

[0043] Step 2, calculate the initial fitness value, update the individual best position and the global best position;

[0044] Step 3, the iteration starts, and whether the convergence condition is met;

[0045] Step 4: Dynamically adjust the inertia weight, update the particle velocity and position, and handle boundary conditions;

[0046] Step 5, calculate the fitness value, update the individual best position and the global best position;

[0047] Step 6: Introduce mutation operation and local search;

[0048] Step 7: The iteration ends and the optimal solution is output.

[0049] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to the distribution network, in step 4, the following formula is used to update the speed of each particle:

[0050] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t)) (19)

[0051] Where: v i (t+1) is the velocity of particle i at time t+1; ω is the inertia weight, which is used to balance the global search and local search capabilities; v i (t) is the velocity vector of particle i at time t; c1 and c2 are learning factors, which control the speed at which particles learn to their individual optimal positions and global optimal positions, respectively; r1 and r2 are random numbers between [0,1] to increase randomness; p i (t) is the individual optimal position vector of particle i at time t; x i (t) is the position vector of particle i at time t; g(t) is the global optimal position vector.

[0052] In the above-mentioned method for calculating the network loss of distributed photovoltaic access to the distribution network, in step 4, the formula for position update is:

[0053] x i (t+1)=x i (t)+v i (t+1) (20)

[0054] Where: x i (t+1) is the position vector of particle i at time t+1; x i (t) is the position vector of particle i at time t; v i (t+1) is the velocity vector of particle i at t+1.

[0055] In the above-mentioned distributed photovoltaic access distribution network loss calculation method, in the absorption capacity evaluation step, the absorption capacity of the power grid is evaluated by using four aspects: photovoltaic power generation ratio, power grid carrying capacity, power grid voltage fluctuation and thermal stability.

[0056] Photovoltaic power generation ratio:

[0057]

[0058] Where: P G represents photovoltaic power generation, P represents load power, P L Indicates the maximum transmission power of the line. This indicator reflects the ratio of photovoltaic output to power shortage.

[0059] Grid carrying capacity:

[0060]

[0061] Where: P L is the power load of the power grid; P DG is the possible photovoltaic output; P S For the connected photovoltaic output,

[0062] Grid voltage fluctuation:

[0063]

[0064] Where: U t-1 is the voltage at the previous moment; U t is the voltage at this moment,

[0065] Capacity ratio:

[0066]

[0067] Where: P DG (t) is the photovoltaic power at time t; P O (t) is the output of other power sources at time t; P L (t) is the load power at time t; P N (t) is the power of the line at time t; S is the line limit capacity.

[0068] The beneficial effects of the present invention are as follows: the present invention significantly improves the effects of the prior art in the following aspects:

[0069] 1. Reduce network loss

[0070] By optimizing the access location and capacity of distributed photovoltaics, the line loss of the distribution network is significantly reduced and the efficiency of power utilization is improved. In the IEEE33-node distribution network simulation, the improved particle swarm algorithm reduces the power loss from 6.33kW to 3.03kW.

[0071] 2. Voltage stability

[0072] Dynamic reactive power compensation and intelligent dispatching systems are used to optimize voltage regulation, reduce voltage deviation caused by photovoltaic power generation fluctuations, and improve the power quality of the power grid. In the simulation, the improved particle swarm algorithm increased the average voltage from 0.9534 to 0.9784, significantly improving voltage stability.

[0073] 3. Improve system carrying capacity

[0074] By evaluating and optimizing the grid's absorption capacity, the grid's carrying capacity for distributed photovoltaic power generation has been increased, ensuring the feasibility and safety of large-scale photovoltaic access and promoting the widespread application of clean energy.

[0075] 4. Intelligent dispatching and energy storage system

[0076] Combining big data and artificial intelligence technologies, an intelligent dispatching system has been developed that can dynamically adjust the charging and discharging behavior of the energy storage system according to load and photovoltaic power generation forecast data, smooth the grid load, and further improve the operating efficiency and stability of the grid.

[0077] 5. Economic benefits and safety

[0078] By optimizing dispatching and reactive power compensation technology, the economic cost of power grid operation is reduced and the economic benefits of the system are improved. At the same time, the application of improved dispatching algorithms and energy storage systems enhances the safety and reliability of power grid operation and reduces equipment loss and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of the influence of different access positions on the power distribution network loss in the present invention;

[0080] Figure 2 It is a schematic diagram of the influence of different access capacities on the power distribution network loss in the present invention;

[0081] Figure 3 is a schematic diagram of the IEEE 33 node network topology in the present invention;

[0082] Figure 4 It is a schematic diagram of the simulation results of the present invention. DETAILED DESCRIPTION

[0083] The present invention will be further described below in conjunction with the accompanying drawings.

[0084] The present invention discloses a method for calculating network loss of distributed photovoltaic access to a distribution network, which relates to a study on the influence of distributed photovoltaic access on the network loss of a distribution network, and aims to maximize the positive influence of distributed photovoltaic power generation while reducing its negative effects by optimizing scheduling, reactive power compensation and energy storage systems.

[0085] Specific solutions include:

[0086] 1. Model construction

[0087] The changes in voltage and current of each node before and after distributed photovoltaic access are expressed by formulas, and the impact on the total loss of the distribution network is evaluated. A mathematical model of voltage, current and network loss of distributed photovoltaic access nodes is established, and the impact of photovoltaic access on the distribution network is described in detail.

[0088] 2. Optimization algorithm

[0089] An improved particle swarm algorithm is used to optimize distributed photovoltaic grid connection points to minimize voltage deviation and power loss. The algorithm includes initialization, fitness evaluation, speed and position update, and introduction of mutation operation and local search.

[0090] 3. Verification and comparison

[0091] The simulation results of IEEE 33-node distribution network show that the improved particle swarm algorithm is superior to traditional genetic algorithm and ordinary particle swarm algorithm in network loss and voltage optimization.

[0092] 4. Assessment of consumption capacity

[0093] Four indicators, namely photovoltaic power generation ratio, grid carrying capacity, grid voltage fluctuation and thermal stability, are proposed to evaluate the grid's absorption capacity, and examples are used to verify the impact of different access locations and access capacities on network losses.

[0094] In model building, consider:

[0095] a. Impact of distributed photovoltaic access on voltage, current and network loss

[0096] Before the distributed photovoltaic system is connected, the voltage expression of each node is:

[0097]

[0098] Where: U0 is the voltage at the beginning of the circuit, ΔU n is the voltage drop at each level, P i and Q i is the active power and reactive power of each node, R j is the line resistance.

[0099] After the distributed photovoltaic is connected to node m, the voltage expression of the node is:

[0100]

[0101] Where: P DG is the active power output of photovoltaic, Q DG is the active power output of photovoltaic, X j is the line reactance, Uj-1 is the voltage of the j-1th node, i and j refer to a node between nodes 0 and m respectively.

[0102] Distributed photovoltaics will also have an impact on current after being connected. The larger the capacity, the greater the reverse current.

[0103]

[0104] Where: I T is the current flowing through the transformer, I L ,I DG are the load current and the current injected by the photovoltaic power source, U L is the transformer low voltage side voltage, U H is the voltage on the high voltage side of the transformer, P L is the transmission power of the transformer, P DG is the photovoltaic injection power, η is the transmission efficiency, U DG is the photovoltaic power supply voltage.

[0105] Before the distributed generation is connected, the total loss of the distribution network is:

[0106]

[0107] After the distributed power source is connected, the line loss is:

[0108]

[0109] The total loss of the distribution network is:

[0110]

[0111] This formula can be further written as:

[0112]

[0113] In the formula, U is the rated voltage of the line, I is the current of the line, is the power factor, P k and Q k is the active power and reactive power of node k, P n and Q n is the active power and reactive power of node n, U n is the voltage of node n, N is the total number of nodes, Q L is the reactive power transmitted by the transformer, ΔP DG is the active power loss of the photovoltaic power source, ΔP n is the active power loss of node n, R is the line resistance, and n, k refers to a node between nodes 0-N.

[0114] Taking the IEEE 33-node distribution network as an example, the impact of different access locations and access capacities on network loss is shown in the figure.

[0115] The grid's absorption capacity is evaluated using four aspects: photovoltaic power generation ratio, grid carrying capacity, grid voltage fluctuation and thermal stability.

[0116] Photovoltaic power generation ratio:

[0117]

[0118] Where: P G represents photovoltaic power generation, P represents load power, P L Indicates the maximum transmission power of the line. This indicator reflects the ratio of photovoltaic output to power shortage.

[0119] Grid carrying capacity:

[0120]

[0121] Where: P L is the power load of the power grid; P DG is the possible photovoltaic output; P S The connected photovoltaic power output.

[0122] Grid voltage fluctuation:

[0123]

[0124] Where: U t-1 is the voltage at the previous moment; U t is the voltage at this moment.

[0125] Capacity ratio:

[0126]

[0127] Where: P DG (t) is the photovoltaic power at time t; P O (t) is the output of other power sources at time t; P L (t) is the load power at time t; P N (t) is the power of the line at time t; S is the line limit capacity.

[0128] b. Example Analysis

[0129] IEEE33 node system topology Figure 3 As shown:

[0130] An improved particle swarm algorithm is used to optimize the distributed photovoltaic grid connection points to minimize the voltage and power losses.

[0131] The objective function is:

[0132] Z=min{β·ΔP loss +(1-β)·ΔU k} (36)

[0133] Where: β is the weight, ΔP loss is the power loss of the distribution network, ΔU k is the voltage loss.

[0134] The constraints are:

[0135]

[0136] Where: V kmin and V kmax are the minimum and maximum voltages at node k, G ij is the conductance of node i, j, θ ij is the power angle difference between nodes i and j, B ij is the susceptance between nodes i and j, V i is the voltage at node i.

[0137] The improved particle swarm algorithm process is:

[0138] (1) Initialize particle position X i and speed V i , set parameters such as inertia weight, learning factor, etc.

[0139] (2) Calculate the initial fitness value and update the individual best position and the global best position

[0140] (3) Iteration starts, judging whether the convergence condition is met

[0141] (4) Dynamically adjust inertia weights, update particle speeds and positions, and handle boundary conditions

[0142] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t)) (38)

[0143] Where: v i (t+1) is the velocity of particle i at time t+1; ω is the inertia weight, which is used to balance the global search and local search capabilities; v i (t) is the velocity vector of particle i at time t; c1 and c2 are learning factors, which control the speed at which particles learn to their individual optimal positions and global optimal positions, respectively; r1 and r2 are random numbers between [0,1] to increase randomness; p i(t) is the individual optimal position vector of particle i at time t; x i (t) is the position vector of particle i at time t; g(t) is the global optimal position vector.

[0144] Use the above formula to update the velocity of each particle. The formula for position update is:

[0145] x i (t+1)=x i (t)+v i (t+1) (39)

[0146] Where: x i (t+1) is the position vector of particle i at time t+1; x i (t) is the position vector of particle i at time t; v i (t+1) is the velocity vector of particle i at t+1.

[0147] (5) Calculate the fitness value and update the individual best position and the global best position

[0148] (6) Introducing mutation operations and local search

[0149] (7) The iteration ends and the optimal solution is output

[0150] Then it is verified in the IEEE33 node network, and the results are shown in Table 1:

[0151] Table 1. Comparison of various algorithm results

[0152]

[0153] From the above table, we can see that the particle swarm optimization algorithm has a significant optimization effect on network loss and voltage deviation. In addition to the particle swarm algorithm, there are also intelligent algorithms such as simulated annealing algorithm and gray wolf optimization algorithm that can be applied to the study of such problems and can achieve good results. Figure 4 Shown is a schematic diagram of the final simulation results.

[0154] The present invention involves the study of the impact of distributed photovoltaic access on distribution network losses, aiming to maximize the positive impact of distributed photovoltaic power generation while reducing its negative effects by optimizing scheduling, reactive power compensation and energy storage systems.

[0155] The present invention is described in detail above in conjunction with the embodiments of the accompanying drawings. A person skilled in the art can make various variations of the present invention according to the above description. Therefore, certain details in the embodiments should not constitute a limitation of the present invention, and the scope of protection of the present invention shall be defined by the scope of the attached claims.

Claims

1. A method for calculating network loss of distributed photovoltaic access to a distribution network, comprising the following steps: Model construction steps: Before and after the distributed photovoltaic access, the voltage and current changes of each node are analyzed, and the impact on the total loss of the distribution network is estimated, and the voltage, current and network loss mathematical model of the distributed photovoltaic access node is established; Optimization algorithm steps: The improved particle swarm algorithm is used to optimize the distributed photovoltaic grid-connected points to minimize voltage deviation and power loss. The algorithm includes initialization, fitness evaluation, speed and position update, and the introduction of mutation operations and local search steps; Verification and comparison steps: The IEEE33-node distribution network is simulated and verified. The results show that the improved particle swarm algorithm is superior to the traditional genetic algorithm and the ordinary particle swarm algorithm in network loss and voltage optimization; Absorption capacity evaluation step: Four indicators of photovoltaic power generation ratio, grid carrying capacity, grid voltage fluctuation and thermal stability are proposed to evaluate the grid absorption capacity, and the influence of different access locations and access capacities on network loss is verified.

2. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 1, characterized in that: In the model building step, after the distributed photovoltaic is connected to the node m, the expression of the voltage change of the node is: Where: U0 is the voltage at the beginning of the circuit, P i and Q i is the active power and reactive power of each node, R j is the line resistance, P DG is the active power output of photovoltaic, Q DG is the active power output of photovoltaic, X j is the line reactance, U j-1 is the voltage of the j-1th node, i and j refer to a node between nodes 0 and m respectively.

3. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 1, characterized in that: In the model building step, the distributed photovoltaic connection has an impact on the current. The larger the capacity, the greater the reverse current. I T =I L -I DG Where: I T is the current flowing through the transformer, I L ,I DG are the load current and the current injected by the photovoltaic power source, U L is the transformer low voltage side voltage, U H is the voltage on the high voltage side of the transformer, P L is the transmission power of the transformer, P DG is the photovoltaic injection power, η is the transmission efficiency, U DG is the photovoltaic power supply voltage.

4. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 1, characterized in that: In the model building step, after the distributed power source is connected, the line loss is: The total loss of the distribution network is: Or: The total loss of the distribution network can also be expressed as follows: In the formula, U is the rated voltage of the line, I is the current of the line, is the power factor, P k and Q k is the active power and reactive power of node k, P n and Q n is the active power and reactive power of node n, U n is the voltage of node n, N is the total number of nodes, Q L is the reactive power transmitted by the transformer, ΔP DG is the active power loss of the photovoltaic power source, ΔP n is the active power loss of node n, R is the line resistance, and n, k refers to a node between nodes 0-N.

5. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 1, characterized in that: In the optimization algorithm step, an improved particle swarm algorithm is used to optimize the distributed photovoltaic grid connection points to minimize the voltage and power losses.

6. A method for calculating network loss of distributed photovoltaic access to a distribution network according to claim 5, characterized in that: The objective function for minimizing voltage and power loss is: Z=min{β·ΔP loss +(1-β)·ΔU k } (6) Where: β is the weight, ΔP loss is the power loss of the distribution network, ΔU k is the voltage loss, The constraints are: V kmin <V k <V kmax Where: V kmin and V kmax are the minimum and maximum voltages at node k, G ij is the conductance of node i, j, θ ij is the power angle difference between nodes i and j, B ij is the susceptance between nodes i and j, V i is the voltage at node i.

7. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 5, characterized in that: The improved particle swarm algorithm process is: Step 1, initialize particle position X i and speed V i , set parameters such as inertia weight and learning factor; Step 2, calculate the initial fitness value, update the individual best position and the global best position; Step 3, the iteration starts, and whether the convergence condition is met; Step 4: Dynamically adjust the inertia weight, update the particle velocity and position, and handle boundary conditions; Step 5, calculate the fitness value, update the individual best position and the global best position; Step 6, introduce mutation operation and local search; Step 7: The iteration ends and the optimal solution is output.

8. A method for calculating network loss of distributed photovoltaic access to a distribution network according to claim 7, characterized in that: In step 4, the following formula is used to update the velocity of each particle: v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t)) (7) Where: v i (t+1) is the velocity of particle i at time t+1; ω is the inertia weight, which is used to balance the global search and local search capabilities; v i (t) is the velocity vector of particle i at time t; c1 and c2 are learning factors, which control the speed at which particles learn to their individual optimal positions and global optimal positions, respectively; r1 and r2 are random numbers between [0,1] to increase randomness; p i (t) is the individual optimal position vector of particle i at time t; x i (t) is the position vector of particle i at time t; g(t) is the global optimal position vector.

9. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 7, characterized in that: In step 4, the formula for position update is: x i (t+1)=x i (t)+v i (t+1) (8) Where: x i (t+1) is the position vector of particle i at time t+1; x i (t) is the position vector of particle i at time t; v i (t+1) is the velocity vector of particle i at t+1.

10. A method for calculating network loss of distributed photovoltaic access to distribution network according to claim 1, characterized in that: In the absorption capacity evaluation step, the absorption capacity of the power grid is evaluated by using four aspects: photovoltaic power generation ratio, power grid carrying capacity, power grid voltage fluctuation and thermal stability. Photovoltaic power generation ratio: Where: P G represents photovoltaic power generation, P represents load power, P L Indicates the maximum transmission power of the line. This indicator reflects the ratio of photovoltaic output to power shortage. Grid carrying capacity: Where: P L is the power load of the power grid; P DG is the possible photovoltaic output; P S For the connected photovoltaic output, Grid voltage fluctuation: Where: U t-1 is the voltage at the previous moment; U t is the voltage at this moment, Capacity ratio: Where: P DG (t) is the photovoltaic power at time t; P O (t) is the output of other power sources at time t; P L (t) is the load power at time t; P N (t) is the power of the line at time t; S is the line limit capacity.

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