Siting and sizing optimization method and apparatus for distributed photovoltaic grid connection, device and storage medium

Abstract

Disclosed in the present invention are a siting and sizing optimization method and apparatus for distributed photovoltaic grid connection, a device, and a storage medium. The method comprises: on the basis of an access mode of distributed photovoltaic grid connection and a grid connection node type, with the objectives of maximum capacity, minimum network loss and the lowest voltage sensitivity of distributed photovoltaic integration into a distribution network, constructing corresponding first, second and third objective functions, as well as corresponding constraint conditions, respectively; and on the basis of the constraint conditions, solving the first objective function, the second objective function, and the third objective function to obtain a siting and sizing optimization scheme for distributed photovoltaic grid connection when the maximum capacity, the minimum network loss and the lowest voltage sensitivity of distributed photovoltaic integration into the distribution network are achieved, and optimizing the siting and sizing of distributed photovoltaic grid connection on the basis of the siting and sizing optimization scheme. The present invention can optimize the siting and sizing of distributed photovoltaic grid connection, thereby improving the power supply reliability and power quality of distributed photovoltaic grid connection.

Description

Methods, apparatus, equipment, and storage media for site selection and capacity optimization of distributed photovoltaic grid-connected systems. Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to a method, apparatus, equipment, and storage medium for site selection and capacity optimization of distributed photovoltaic grid connection. Background Technology

[0002] Against the backdrop of energy conservation, emission reduction, and environmental protection, distributed photovoltaic (PV) power has developed rapidly and begun to be widely integrated into distribution networks. However, the integration of distributed PV alters the distribution network structure, making power flow more complex. PV integration causes changes in short-circuit currents, leading to malfunctions in relay protection devices and reduced power supply reliability. Furthermore, PV power, connected to the grid via power electronic components, can also impact the power quality of traditional power systems, such as voltage and frequency. To ensure safer and more reliable operation of the distribution network, the site selection and capacity determination for distributed PV grid connection can be scientifically optimized to ensure stable grid connection. However, current technologies lack scientific optimization methods for the site selection and capacity determination of distributed PV grid connection. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and storage medium for optimizing the site selection and capacity of distributed photovoltaic grid connection, in order to solve the technical problem that there is no scientific optimization method for the site selection and capacity of distributed photovoltaic grid connection in the prior art.

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a method for optimizing the site selection and capacity of distributed photovoltaic grid connection, comprising:

[0005] Obtain the grid connection method and grid connection node type for distributed photovoltaic systems;

[0006] Based on the access method and the grid connection node type, a first objective function is constructed with the goal of maximizing the capacity of distributed photovoltaic power grid access, a second objective function is constructed with the goal of minimizing the network loss of distributed photovoltaic power grid access, and a third objective function is constructed with the goal of minimizing the voltage sensitivity of distributed photovoltaic power grid access. Constraints are also constructed for the first, second, and third objective functions based on the access method and the grid connection node type.

[0007] Based on the constraints, the first objective function, the second objective function, and the third objective function are solved respectively to obtain the optimal site selection and capacity setting scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, the site selection and capacity setting of distributed photovoltaic grid connection are optimized according to the optimal site selection and capacity setting scheme.

[0008] As a preferred embodiment, the first objective function, the second objective function, and the third objective function are solved according to the constraints to obtain an optimized site selection and capacity setting scheme for distributed photovoltaic grid connection that maximizes the capacity of distributed photovoltaic grid connection, minimizes network losses, and minimizes voltage sensitivity. This includes:

[0009] Based on the constraints and the preset NSGA-II algorithm, the first objective function, the second objective function, and the third objective function are solved respectively to obtain a Pareto front solution set containing feasible solutions for multiple objective functions;

[0010] The feasible solutions of the objective function in the Pareto front solution set are calculated based on fuzzy decision-making. A multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function. Based on the multi-objective comprehensive optimal solution, the site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized.

[0011] As a preferred embodiment, the first objective function, the second objective function, and the third objective function are solved according to the constraints and the preset NSGA-II algorithm, respectively, to obtain a Pareto front solution set containing feasible solutions to multiple objective functions, including:

[0012] Acquire distribution network data, and construct an initial population based on the distribution network data, constraints, a first objective function, a second objective function, and a third objective function; wherein, the distribution network data includes: power grid topology data, power grid load data, power grid line parameters, photovoltaic power generation data, and power grid economic parameters;

[0013] Based on the initial population, the photovoltaic access location is changed to generate a corresponding first offspring population, and the photovoltaic capacity is changed to generate a corresponding second offspring population.

[0014] The first and second offspring populations are merged to generate a corresponding merged population, and the fitness of each individual in the merged population is calculated.

[0015] The merged population is sorted rapidly according to the fitness, and the best front is selected from the merged population according to the sorting results. A new population is generated according to the selected best front until the iteration reaches the preset number of iterations or the iteration objective reaches convergence, thus obtaining a Pareto front solution set containing multiple feasible solutions of the objective function.

[0016] As a preferred embodiment, the step of calculating feasible solutions of the objective function in the Pareto front solution set based on fuzzy decision, and selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function, includes:

[0017] Obtain the preset evaluation indicators, evaluation indicator levels, and evaluation indicator weights;

[0018] Based on the evaluation indicators, evaluation indicator levels, and evaluation indicator weights, a single-factor fuzzy evaluation is performed on the feasible solutions of the objective function in the Pareto front solution set. The membership degree of each evaluation indicator level is calculated based on the feasible solutions of the objective function, and the corresponding fuzzy relation matrix is ​​obtained based on the membership degree.

[0019] Based on the fuzzy relation matrix and the preset weight vector, the corresponding fuzzy comprehensive evaluation result vector is obtained. Then, based on the fuzzy comprehensive evaluation result vector, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function.

[0020] As a preferred embodiment, the first objective function is: f1 = max f(P) pv ) = P pv ; P pv ≤P pv.s ;

[0021] Among them, P pv.s For distributed photovoltaic users' installed capacity;

[0022] The second objective function is:

[0023] Where N is the number of power sources in the distribution network; P Gi P represents the active power generated by the i-th power source; M represents the number of loads in the distribution network; Li This represents the active power consumed by the j-th load.

[0024] The third objective function is:

[0025] Where, ΔQ i ΔV represents the reactive power change at distributed photovoltaic access node i. j Let n be the voltage change at node j; n is the distribution network node excluding the balancing node.

[0026] As a preferred embodiment, the constraints include: power flow constraints, node voltage over-limit constraints, wideband oscillation constraints, relay protection constraints, thermal stability constraints, short-circuit current constraints, and reverse load rate constraints.

[0027] The power flow constraint is:

[0028] Among them, P is and Q is These represent the active power injection and reactive power injection at node i, respectively; U i Let G be the voltage magnitude of node i; j∈i are all nodes directly connected to node i; ij and Bij These are the real and imaginary parts of the nodal admittance matrix, respectively; θ ij Let be the phase difference between the two nodes of branch ij;

[0029] The node voltage over-limit constraint is as follows:

[0030] Where ΔU is the line voltage drop, P Li Let μ be a random variable representing the load at node i; i This represents the expected load over a given period of time. The variance of the load normal distribution over a period of time;

[0031] The broadband oscillation constraint is: Δf = ff N =m(P D -P ref -0.5≤Δf≤0.5;

[0032] Among them, f N The system's rated frequency; P D P represents the output active power of the photovoltaic system. ref The reference active power for photovoltaic grid-connected control;

[0033] The relay protection constraint is:

[0034] Among them, E s Z is the system phase potential; f Z represents the line impedance between the fault point and the grid connection point. S Z is the equivalent impedance of the system; AB P is the impedance of line AB; N and U N These are the rated power of the photovoltaic system and the rated voltage at the grid connection point, respectively; K' rel K” represents the reliability coefficient of the current stage I protection. rel The reliability coefficient of the current stage II protection;

[0035] The thermal stability constraint is:

[0036] Where S is the cross-sectional area of ​​the conductor; C is the thermal stability coefficient of the conductor; T is the short-circuit time; I ij For line short-circuit current; I L For conductor thermal stability current limit; S N U is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; N K is the rated voltage of the transformer. θ ρ is the ambient temperature correction factor; p is the transformer load rate.

[0037] The short-circuit current constraint is:

[0038] Among them, I m For the interrupting current of the switch;

[0039] The reverse load rate constraint is:

[0040] Among them, P D Powering distributed photovoltaic systems; P L S is the sum of all loads downstream of the photovoltaic grid connection point; e This refers to the rated current carrying capacity of the upstream line or the rated capacity of the upstream transformer at the photovoltaic access point.

[0041] Based on the above embodiments, another embodiment of the present invention provides a distributed photovoltaic grid-connected site selection and capacity optimization device, including: an access method and node type acquisition module, an objective function construction module, and a site selection and capacity optimization module;

[0042] The access method and node type acquisition module is used to acquire the access method and grid connection node type of distributed photovoltaic grid connection;

[0043] The objective function construction module is used to construct a first objective function with the maximum capacity of distributed photovoltaic power grid access as the objective, a second objective function with the minimum network loss of distributed photovoltaic power grid access as the objective, and a third objective function with the minimum voltage sensitivity of distributed photovoltaic power grid access as the objective, based on the access method and the grid connection node type. The module also constructs the constraints corresponding to the first objective function, the second objective function and the third objective function based on the access method and the grid connection node type.

[0044] The site selection and capacity optimization module is used to solve the first objective function, the second objective function, and the third objective function according to the constraints, respectively, to obtain the site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, the site selection and capacity of distributed photovoltaic grid connection are optimized according to the site selection and capacity optimization scheme.

[0045] As a preferred embodiment, the first objective function, the second objective function, and the third objective function are solved according to the constraints to obtain an optimized site selection and capacity setting scheme for distributed photovoltaic grid connection that maximizes the capacity of distributed photovoltaic grid connection, minimizes network losses, and minimizes voltage sensitivity. This includes:

[0046] Based on the constraints and the preset NSGA-II algorithm, the first objective function, the second objective function, and the third objective function are solved respectively to obtain a Pareto front solution set containing feasible solutions for multiple objective functions;

[0047] The feasible solutions of the objective function in the Pareto front solution set are calculated based on fuzzy decision-making. A multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function. Based on the multi-objective comprehensive optimal solution, the site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized.

[0048] Based on the above embodiments, another embodiment of the present invention provides an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the distributed photovoltaic grid-connected addressing and capacity optimization method described in the above embodiments of the invention.

[0049] Based on the above embodiments, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the distributed photovoltaic grid-connected site selection and capacity optimization method described in the above embodiments of the invention.

[0050] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0051] This invention provides a method for optimizing the site selection and capacity allocation of distributed photovoltaic (PV) grid connection. The method involves obtaining the grid connection access method and the type of grid-connected nodes. Based on these methods, a first objective function is constructed with the goal of maximizing the capacity of the distributed PV grid connection, a second objective function is constructed with the goal of minimizing network losses, and a third objective function is constructed with the goal of minimizing voltage sensitivity. Constraints are then established for these objective functions based on the grid connection method and the type of grid-connected nodes. The first, second, and third objective functions are solved according to these constraints to obtain the optimal site selection and capacity allocation scheme for distributed PV grid connection that maximizes grid capacity, minimizes network losses, and minimizes voltage sensitivity. Finally, the site selection and capacity allocation of the distributed PV grid connection are optimized based on this optimal scheme. This invention combines the access method and node type of distributed photovoltaic grid connection to construct objective functions and constraints for maximizing grid connection capacity, minimizing network loss, and minimizing voltage sensitivity. The optimal solution is obtained by solving the constructed objective functions, resulting in a site selection and capacity optimization scheme for distributed photovoltaic grid connection. This scheme has better globality, better meets the needs of distributed photovoltaic grid connection, and can improve the power supply reliability and power quality of distributed photovoltaic grid connection. Attached Figure Description

[0052] Figure 1 is a flowchart illustrating a method for optimizing the location and capacity of a distributed photovoltaic grid-connected system according to an embodiment of the present invention.

[0053] Figure 2 is the equivalent circuit diagram of the power supply line of the distribution network;

[0054] Figure 3 is a schematic diagram of a simple distribution network connected to distributed power sources;

[0055] Figure 4 is a schematic diagram of a simple distribution network model containing distributed volts;

[0056] Figure 5 is a schematic diagram of the structure of a distributed photovoltaic grid-connected site selection and capacity optimization device provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0059] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0060] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0061] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0062] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple groups" refers to two or more (including two groups), and "multiple pieces" refers to two or more (including two pieces).

[0063] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0064] Example 1

[0065] Please refer to Figure 1, which is a flowchart illustrating a distributed photovoltaic grid-connected site selection and capacity optimization method according to an embodiment of the present invention, including the following specific steps:

[0066] S1. Obtain the grid connection method and grid connection node type for distributed photovoltaic systems;

[0067] Specifically, the site selection and capacity optimization method for distributed photovoltaic grid connection of the present invention includes the following steps:

[0068] S1. Determine the method of distributed photovoltaic access, which is to connect to any 220V single-phase busbar under the molded case switch of the low-voltage distribution network using the photovoltaic single grid connection point access method;

[0069] Determine the type of distributed photovoltaic (PV) grid-connected node; confirm the type of PV grid connection based on the distributed PV grid connection method and its operation;

[0070] The main types of photovoltaic (PV) grid-connected nodes are PV, PV(Q), and PQ nodes. For distributed PV systems connected to medium- and low-voltage distribution networks, the inverter control strategy for PV / PV(Q) nodes is droop V / F control to ensure constant voltage and frequency at the grid connection point. This type of PV model can be considered a PV node. The active power reference value is the rated output of the PV system, and the voltage reference value is the voltage level at the connection point. If the voltage amplitude of the PV node is not equal to the set voltage amplitude, the reactive power of the PV node needs to be adjusted. This is done by injecting current into the PV node to generate a certain amount of reactive power, so that the voltage amplitude of the PV node reaches the reference value.

[0071] The increased reactive power and the corresponding injected current are as follows:

[0072] In the formula X represents the difference between the voltage amplitude and the rated value; X is the sum of the branch reactances from the PV node to the main transformer node, thus the injected current at the PV node is calculated as: This represents the total injected current at the PV node obtained in the k-th iteration.

[0073] In addition, since the adjustable reactive power of PV nodes is limited, it is necessary to determine whether the reactive power of the node exceeds the limit. When the reactive power of a PV node exceeds the capacity of the reactive power compensation device, the PV node will be converted into a PQ node. The reactive power conversion limit at the PV(Q) node is:

[0074] In the formula, Let be the load reactive power of the k-th power flow at the PV node; The reactive power capacity of the reactive power compensation device;

[0075] For photovoltaic (PV) power plants connected to medium-voltage distribution networks, the inverter control strategy for PQ nodes is droop PQ control to ensure the reliability of PV power supply to feeder loads. This type of PV model can be equivalent to a constant power load model, treating it as a PQ node with the current direction being the direction of injection into the distribution network. The apparent power is: S = -P - jQ. The injection current from the PV power plant to the connection node is:

[0076] In the formula: The voltage of the PQ-type photovoltaic access node obtained through power flow iteration; S PQ This refers to the apparent power of distributed photovoltaic systems.

[0077] S2. Based on the access method and the grid connection node type, construct a first objective function with the goal of maximizing the capacity of distributed photovoltaic power grid access, construct a second objective function with the goal of minimizing the network loss of distributed photovoltaic power grid access, and construct a third objective function with the goal of minimizing the voltage sensitivity of distributed photovoltaic power grid access. Then, construct the constraints corresponding to the first objective function, the second objective function, and the third objective function based on the access method and the grid connection node type.

[0078] Preferably, the first objective function is: f1 = max f(P) pv ) = P pv ; P pv ≤P pv.s ;

[0079] Among them, P pv.s For distributed photovoltaic users' installed capacity;

[0080] The second objective function is:

[0081] Where N is the number of power sources in the distribution network; P Gi P represents the active power generated by the i-th power source; M represents the number of loads in the distribution network; Li This represents the active power consumed by the j-th load.

[0082] The third objective function is:

[0083] Where, ΔQ i ΔV represents the reactive power change at distributed photovoltaic access node i. j Let n be the voltage change at node j; n is the distribution network node excluding the balancing node.

[0084] Preferably, the constraints include: power flow constraints, node voltage over-limit constraints, broadband oscillation constraints, relay protection constraints, thermal stability constraints, short-circuit current constraints, and reverse load factor constraints.

[0085] The power flow constraint is:

[0086] Among them, P is and Q is These represent the active power injection and reactive power injection at node i, respectively; U i Let G be the voltage magnitude of node i; j∈i are all nodes directly connected to node i; ij and B ij These are the real and imaginary parts of the nodal admittance matrix, respectively; θ ij Let be the phase difference between the two nodes of branch ij;

[0087] The node voltage over-limit constraint is as follows:

[0088] Where ΔU is the line voltage drop, P Li Let μ be a random variable representing the load at node i; i This represents the expected load over a given period of time. The variance of the load normal distribution over a period of time;

[0089] The broadband oscillation constraint is: Δf = ff N =m(P D -P ref -0.5≤Δf≤0.5;

[0090] Among them, f N The system's rated frequency; P D P represents the output active power of the photovoltaic system. ref The reference active power for photovoltaic grid-connected control;

[0091] The relay protection constraint is:

[0092] Among them, E s Z is the system phase potential; f Z represents the line impedance between the fault point and the grid connection point. S Z is the equivalent impedance of the system; AB P is the impedance of line AB; N and U N These are the rated power of the photovoltaic system and the rated voltage at the grid connection point, respectively; K' rel K” represents the reliability coefficient of the current stage I protection. rel The reliability coefficient of the current stage II protection;

[0093] The thermal stability constraint is:

[0094] Where S is the cross-sectional area of ​​the conductor; C is the thermal stability coefficient of the conductor; T is the short-circuit time; I ij For line short-circuit current; I L For conductor thermal stability current limit; S N U is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; N K is the rated voltage of the transformer. θ ρ is the ambient temperature correction factor; p is the transformer load rate.

[0095] The short-circuit current constraint is:

[0096] Among them, I m For the interrupting current of the switch;

[0097] The reverse load rate constraint is:

[0098] Among them, P D Powering distributed photovoltaic systems; P L S is the sum of all loads downstream of the photovoltaic grid connection point; e This refers to the rated current carrying capacity of the upstream line or the rated capacity of the upstream transformer at the photovoltaic access point.

[0099] S2. Based on the access method and the grid-connected node type, construct the corresponding objective function and its constraints; wherein the objective function includes grid-connected capacity, network loss and voltage sensitivity, and the constraints include power flow constraints, node voltage over-limit constraints, broadband oscillation constraints, relay protection constraints, thermal stability constraints, short-circuit current constraints and reverse load rate constraints.

[0100] The objective functions are respectively set as maximizing the capacity of distributed photovoltaic (PV) grid integration, minimizing network loss, and minimizing voltage sensitivity. The specific objective functions are shown below:

[0101] Considering distributed photovoltaic (PV) grid connection with unity power factor, and ignoring the impact of reactive power generated by PV, the first objective function is: f1 = max f(P) pv ) = P pv P pv ≤P pv.s ;

[0102] In the formula: P pv.s For distributed photovoltaic users, the installed capacity is applied for;

[0103] Distributed photovoltaic (PV) grid integration will cause changes in the power flow of the original distribution network, leading to changes in the losses of distribution transformers and lines. The second objective function is:

[0104] In the formula: N is the number of power sources in the distribution network; P GiP represents the active power generated by the i-th power source; M represents the number of loads in the distribution network; Li The active power consumed by the j-th load;

[0105] The reactive power injected by distributed photovoltaic systems will cause voltage changes at various nodes in the distribution network. Based on voltage sensitivity, the third objective function is used to measure the voltage changes at each node of the system:

[0106] Where: ΔQ i ΔV represents the reactive power change at distributed photovoltaic access node i. j Let n be the voltage change at node j; n is the distribution network node excluding the slack node.

[0107] The constraints are as follows:

[0108] Current constraints:

[0109] Power flow balance refers to the relationship between nodes and lines maintaining power balance when distributed photovoltaic (PV) power is injected into the distribution network, ensuring stable system operation. Therefore, distributed PV integration must satisfy the following power flow equation constraint:

[0110] In the formula: P is Q is These represent the active and reactive power injection amounts at node i, respectively; U i Let G be the voltage magnitude of node i; j∈i represents all nodes directly connected to node i; ij B ij These are the real and imaginary parts of the nodal admittance matrix, respectively; θ ij Let be the phase difference between the two nodes of branch ij.

[0111] Node voltage over-limit constraints:

[0112] The grid connection and disconnection of distributed photovoltaic systems can easily lead to high and low voltage phenomena at distribution network nodes. Please refer to Figure 2, which is the equivalent circuit diagram of the power supply line of the distribution network. In Figure 2, R and X are the line resistance and reactance; P+jQ is the power injected into the node through the line.

[0113] In Figure 2, when a photovoltaic (PV) power source is connected to the grid near a load, it compensates for the surrounding load based on its connected capacity, reducing the transmission capacity of the distribution network lines. This decreases the line voltage drop ΔU and raises the node voltage around the grid connection point. When the PV connected capacity is too large or the line is lightly loaded, the node voltage is excessively raised, resulting in high voltage. Conversely, when the PV is off-grid or the line is heavily loaded, the increased line voltage drop causes the node voltage to decrease, exceeding the allowable voltage deviation value, resulting in low voltage. If the node voltage exceeds the voltage deviation limit specified in GB / T 12325-2008, and the duration of high and low voltage exceeds 60 seconds, it indicates that the node voltage is over-limit. The voltage over-limit constraint is as follows:

[0114] As mentioned above, high and low voltage phenomena are related to node loads, therefore, it is necessary to address the load characteristics of each node in the distribution network. Given the random nature of the load, the approach is to use a normal distribution to approximate random values ​​to reflect the load's uncertainty, i.e.:

[0115] In the formula: P Li Let μ be a random variable representing the load at node i; i The expected load over a period of time is taken as 60% of the capacity of the distribution transformer at that node; The variance of the normal distribution of load over a period of time is calculated. The maximum node load is set to 95% of the distribution transformer capacity. When the load exceeds 80% of the distribution transformer capacity, it is considered an overload state. The probability of overload does not exceed 5%. Under this condition, the variance is calculated to be 0.21278.

[0116] Wideband oscillation constraint:

[0117] Because photovoltaic (PV) power systems have less inertia than generators, and PV inverters respond quickly during grid connection and disconnection, the damping effect of PV power generation systems is insufficient, making them prone to broadband oscillations. To suppress these oscillations, appropriate control strategies are needed to improve system frequency stability. A droop control is employed, with an active power droop control coefficient m constraining the oscillation frequency. According to the frequency deviation specifications in GB / T 12325-2008, the broadband oscillation constraint is: Δf = ff N =m(P D -P ref -0.5 ≤ Δf ≤ 0.5;

[0118] In the formula: f N The system's rated frequency; P D The active power output of photovoltaics; P ref It serves as a reference for photovoltaic grid-connected control.

[0119] Relay protection constraints:

[0120] Distributed photovoltaic (PV) power grids connected to medium and low voltage distribution networks typically rely on three-stage current protection as the primary protection method. The impact of PV connection on short-circuit current is both amplifying and reversing effect, posing a challenge to the settings and sensitivity of existing protection systems. Please refer to Figure 3, which shows a simplified distribution network diagram with distributed power sources. In Figure 3, 1-5 represent circuit breakers equipped with three-stage current protection; the distributed PV power grid is connected at busbar B.

[0121] When a fault occurs on a line downstream of the grid connection point, the photovoltaic (PV) power contributes to the short-circuit current downstream, causing maloperation of the current protection stages I and II of the circuit breaker on the faulty line. Therefore, it is necessary to constrain the maximum PV capacity under the original settings. First, the influence of the PV-assisted current needs to be considered, and the protection settings need to be recalculated. Then, the PV power is taken out of operation, and the settings are recalculated to meet the minimum protection range of protection stage I, thus constraining the PV capacity. Taking the current stage I protection of protection 2 in Figure 3 as an example, the setting value is:

[0122] In the formula: E s Z is the system phase potential; f Z represents the line impedance between the fault point and the grid connection point. S Z is the equivalent impedance of the system; AB P is the impedance of line AB; N U N For photovoltaic rated power and grid connection point rated voltage; K′ rel This is the reliability coefficient for current stage I protection.

[0123] While meeting the protection range constraints of current stage I, the sensitivity requirements of current stage II must also be met. The current stage II protection constraint on photovoltaic capacity is as follows:

[0124] In the formula: K″ rel This is the reliability coefficient for the current stage II protection.

[0125] When a fault occurs on an adjacent feeder, the photovoltaic (PV) system's effect on the short-circuit current is reversed, and this reverse current may cause the upstream line protection to malfunction. Therefore, the PV capacity is constrained by the fact that the maximum reverse short-circuit current provided by the PV system is less than the minimum protection setting value of the three-stage protection system of the upstream line at the grid connection point, i.e., the short-circuit current is less than the setting value of the current stage III protection. Taking the current stage III protection of protection 1 in Figure 3 as an example, according to GB / T 19964-2012, the short-circuit current output by the PV power station to the grid should not exceed 1.5 times its rated current. The constraint on the upstream circuit breaker protection of the PV system is as follows:

[0126] The photovoltaic capacity constrains the selectivity and sensitivity of each circuit breaker protection. The minimum value among these constraints is the constraint limit of the relay protection on the photovoltaic capacity.

[0127] Thermal stability constraints:

[0128] Distributed photovoltaic (PV) grid integration reduces the transmission capacity of lines and the load rate of upstream transformers under normal operating conditions, without causing thermal stability over-limit issues. Under short-circuit conditions, the above relay protection constraints analyze the impact of PV on short-circuit current as both amplifying and reverse current. Amplifying short-circuit current can easily cause downstream line overload, while reverse short-circuit current can easily cause transformer overload. According to DL / T 5222-2005 and GB 1094-2008, the thermal stability constraints for lines and transformers are as follows:

[0129] In the formula: S is the cross-sectional area of ​​the conductor; C is the thermal stability coefficient of the conductor; T is the short-circuit time; I ij For line short-circuit current; I L For conductor thermal stability current limit; S N U is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; N K is the rated voltage of the transformer. θ is the ambient temperature correction factor; p is the transformer load rate.

[0130] Short-circuit current constraint:

[0131] After distributed photovoltaic (PV) power is connected to the distribution network, the system short-circuit current does not exceed the short-circuit current limit. That is, the maximum short-circuit current of the PV system is less than the breaking current of the switch or circuit breaker, ensuring reliable operation of the circuit breaker. The short-circuit current constraint is...

[0132] In the formula, I m This is the interrupting current of the switch.

[0133] Reverse load factor constraint:

[0134] The integration of distributed generation into the distribution network alters the network structure, complicating power flow operations. Referring to Figure 4, a simplified distribution network model incorporating distributed photovoltaics, as an example, when the output of the distributed photovoltaic system connected to node m exceeds the total load of the lines downstream of node m, the photovoltaic power source will feed power back to the head end of the distribution network, causing the power flow of the lines upstream of the photovoltaic node to reverse.

[0135] Therefore, the reverse load factor is used to represent the local photovoltaic grid integration capacity, and the reverse load factor constraint is:

[0136] In the formula, P D Powering distributed photovoltaic systems; P L S is the sum of all loads downstream of the photovoltaic grid connection point; e This refers to the rated current carrying capacity of the upstream line or the rated capacity of the upstream transformer at the photovoltaic access point.

[0137] S3. Based on the constraints, solve the first objective function, the second objective function, and the third objective function respectively to obtain the optimal site selection and capacity setting scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, optimize the site selection and capacity setting of distributed photovoltaic grid connection according to the optimal site selection and capacity setting scheme.

[0138] Preferably, the step of solving the first objective function, the second objective function, and the third objective function according to the constraints to obtain the distributed photovoltaic grid connection location and capacity optimization scheme that maximizes the capacity of distributed photovoltaic grid connection, minimizes network loss, and minimizes voltage sensitivity includes: solving the first objective function, the second objective function, and the third objective function according to the constraints and the preset NSGA-II algorithm to obtain a Pareto front solution set containing multiple feasible solutions of objective functions; calculating the feasible solutions of objective functions in the Pareto front solution set according to fuzzy decision, selecting a multi-objective comprehensive optimal solution from the feasible solutions of objective functions, and obtaining the distributed photovoltaic grid connection location and capacity optimization scheme that maximizes the capacity of distributed photovoltaic grid connection, minimizes network loss, and minimizes voltage sensitivity according to the multi-objective comprehensive optimal solution.

[0139] Preferably, the step of solving the first objective function, the second objective function, and the third objective function according to the constraints and the preset NSGA-II algorithm to obtain a Pareto front solution set containing feasible solutions to multiple objective functions includes: acquiring distribution network data, and constructing an initial population based on the distribution network data, constraints, the first objective function, the second objective function, and the third objective function; wherein, the distribution network data includes: power grid topology data, power grid load data, power grid line parameters, photovoltaic power generation data, and power grid economic parameters; according to the initial population, changing the photovoltaic access location to generate a corresponding first offspring population, and changing the photovoltaic capacity to generate a corresponding second offspring population; merging the first offspring population and the second offspring population to generate a corresponding merged population and calculating the fitness of each individual in the merged population; performing fast non-dominated sorting on the merged population based on the fitness, and selecting the best front in the merged population based on the sorting results, generating a new population based on the selected best front, until the iteration reaches a preset number of iterations or the iteration objective reaches convergence, thereby obtaining a Pareto front solution set containing feasible solutions to multiple objective functions.

[0140] Preferably, the step of calculating feasible solutions of the objective function in the Pareto front solution set based on fuzzy decision, and selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function, includes: obtaining preset evaluation indicators, evaluation indicator levels, and evaluation indicator weights; performing single-factor fuzzy evaluation on the feasible solutions of the objective function in the Pareto front solution set based on the evaluation indicators, evaluation indicator levels, and evaluation indicator weights; calculating the membership degree of each evaluation indicator level based on the feasible solutions of the objective function, and obtaining the corresponding fuzzy relation matrix based on the membership degree; obtaining the corresponding fuzzy comprehensive evaluation result vector based on the fuzzy relation matrix and the preset weight vector; and then selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function based on the fuzzy comprehensive evaluation result vector.

[0141] S3. Solve the objective function: Using the combined NSGA-II and fuzzy decision algorithm, based on the objective function and constraints, use NSGA-II to solve for the Pareto front solution set, and then use fuzzy decision to calculate the feasible solutions in the Pareto front solution set to select the multi-objective comprehensive optimal solution.

[0142] The specific steps for calculation using the NSGA-II algorithm are as follows:

[0143] S301: Acquire distribution network data and establish an initial population;

[0144] The specific data involved in "obtaining distribution network data" mainly includes:

[0145] (1) Power grid topology data: including the connection relationship of all nodes, such as node number, line number, line length, etc., which are used to construct the mathematical model of the power grid; (2) Load data: the load demand of each node, including load type, load power, load curve, etc., which are used to simulate the operation status of the distribution network; (3) Line parameters: such as the rated capacity, loss rate, resistance, reactance, etc., which are used to calculate the operation performance of the line; (4) Photovoltaic power generation data: including the capacity, efficiency, maximum output power of photovoltaic power generation equipment, etc., which are used to simulate the access of photovoltaic power generation; (5) Economic parameters: such as investment cost, operating cost, electricity price, etc., which are used to calculate the economic benefits of the distribution network.

[0146] The specific method for establishing the initial population is as follows:

[0147] (1) Random initialization: Based on the characteristics of photovoltaic power generation equipment, a certain number of locations are randomly selected at each node of the distribution network and allocated to photovoltaic power generation equipment. At the same time, the initial capacity of each photovoltaic power generation equipment is set. (2) Genetic operation: Based on the results of the above random initialization, a certain number of initial populations are generated through selection, crossover and mutation operations in the genetic algorithm. Among them, the selection operation is used to select individuals with high fitness from the existing population; the crossover operation is used to exchange some genes between individuals to generate new individuals; the mutation operation is used to introduce randomness into individuals and increase the diversity of the population. (3) Fitness calculation: Based on the characteristics of photovoltaic power generation equipment, the operating status of the distribution network and economic benefits, the fitness value of each individual is calculated and used for subsequent selection operations.

[0148] S302: Generate offspring populations by altering the photovoltaic access location and capacity through selection, crossover, and mutation;

[0149] S303: Merge the two populations mentioned above and calculate their fitness;

[0150] Specifically, this involves merging individuals from two populations into a new population according to a certain strategy and calculating the fitness of each individual. Fitness calculation evaluates the quality of individuals by defining a function, which is usually designed based on the problem objective. Its role is to provide a basis for selection and mutation in the genetic algorithm, thereby promoting the preservation and evolution of superior individuals in the merged population and enhancing the convergence and optimization effect of the algorithm.

[0151] S304: Use fast non-dominated sorting to classify the merged large population;

[0152] Rapid Non-Dominion Sorting (RNS) is a sorting method for multi-objective optimization problems. It primarily deals with the classification of non-dominated solution sets (Pareto optimal solution sets) in multi-objective optimization problems. First, the merged population is considered as the object to be classified. Second, by comparing the objective function values ​​of individuals in the population pairwise, non-dominated solutions are identified and assigned to different Pareto fronts. Then, this process is iterated, continuously refining the classification until all individuals are assigned to their corresponding Pareto fronts. Finally, these Pareto fronts are output, which represent the classification results of the solution set under multi-objective optimization.

[0153] S305: Based on the ordination results and crowding, the best frontier is selected from the large population to form a new population;

[0154] Steps for calculating congestion:

[0155] (1) Determine the target dimension: First, determine the target dimension in the multi-objective optimization problem, that is, the number of decision variables in the problem; (2) Calculate the crowding degree of each individual: For each target dimension, calculate the crowding degree of each individual in that dimension. Select an individual as a reference point, which is located on a boundary in the target space. For each reference point, calculate the distance of other individuals in the target space. The crowding degree of each individual in a certain target dimension is the sum of its distances to the individuals on both sides; (3) Comprehensive calculation: Add up the crowding degrees of all target dimensions to obtain the total crowding degree of the individual in all target dimensions.

[0156] Select the best frontier based on the sorting results and crowding:

[0157] (1) Fast Non-Domination Sorting: First, the merged population is sorted using the fast non-dominated sorting algorithm to obtain non-dominated levels.

[0158] (2) Select first-level non-dominated individuals: Select first-level non-dominated individuals as the current population. These individuals are not dominated by other individuals in the target space.

[0159] (3) Select individuals based on crowding: Within the same non-dominant level, individuals are selected based on their crowding. Individuals with higher crowding are selected because higher crowding means that these individuals are more dispersed in the target space, which can better maintain population diversity.

[0160] (4) Forming a new population: Selected individuals are grouped into a new population as candidates for the next generation.

[0161] In this way, the NSGA-II algorithm can effectively find a set of approximate Pareto optimal solutions in multi-objective optimization problems, which provide a good balance among multiple objectives.

[0162] S306: Determine whether the iteration count has been reached or the objective has converged;

[0163] Fuzzy decision-making is employed, and the optimal planning result is obtained by calculating single-objective fuzzy evaluation and multi-index comprehensive evaluation, and comparing the comprehensive evaluation vector. The specific steps are as follows:

[0164] (1) Determine the evaluation indicators, with m being the number of evaluation indicators;

[0165] (2) Determine the evaluation index levels, with a quantity of 0;

[0166] (3) Determine the weights of the evaluation indicators A = (a1, a2, ..., a...) m ), where ∑a m =1, am >0;

[0167] (4) Perform single-factor fuzzy evaluation. For each optimal solution in the optimal solution set, calculate the membership degree of each index level to obtain the fuzzy relation matrix R. R is an m-row o-column matrix, and its membership function is as follows:

[0168] In the formula: f ij f is the membership degree of the j-th optimal solution to the i-th evaluation index function value; i min f is the lower bound of the value of the i-th evaluation index function; i max This represents the upper limit of the value of the i-th evaluation index function. This paper uses three evaluation index functions: the first is the maximum access capacity, the second is the minimum network loss, and the third is the lowest voltage sensitivity.

[0169] (5) Multi-index comprehensive evaluation: use the weight vector A and the fuzzy relation matrix R to synthesize the fuzzy comprehensive evaluation result vector B, that is, B = A·R, where the scheme with the largest value in B is the optimal scheme;

[0170] Under the condition of meeting multiple indicators, the fuzzy comprehensive evaluation result vector B is synthesized from the R corresponding to each solution in the optimal solution set and the weight vector A. The one with the largest B value among multiple optimal solutions is selected as the optimal solution.

[0171] S4. The obtained optimal scheme is analyzed and verified through case studies, including the analysis and verification of photovoltaic planning based on PQ equivalent and PV equivalent. By comparing and analyzing different photovoltaic equivalent nodes and verification, it is found that: the photovoltaic planning result based on PQ equivalent has a larger access capacity, which ensures the power supply of the entire feeder load at the access node. After access, the impact on the distribution network is more significant. Moreover, as the access capacity increases, the network loss decreases and the voltage sensitivity increases, which is consistent with the actual situation. The photovoltaic planning result based on PV equivalent has a smaller access capacity, which ensures that the power quality at the grid connection point meets the requirements. After access, the impact on the distribution network is smaller.

[0172] S5. Based on actual application needs, apply the optimal solution to distributed photovoltaic grid connection and optimize the site selection and capacity of distributed photovoltaic grid connection.

[0173] Therefore, this invention provides a method for optimizing the site selection and capacity of distributed photovoltaic grid connection. By integrating multiple objective functions and constraints, and combining NSGA-II and fuzzy decision algorithms, this invention optimizes the site selection and capacity of distributed photovoltaic grid connection. Compared with existing technologies, its advantages lie in improving the globality and adaptability of the algorithm, effectively solving the limitations of single objective, insufficient constraints and poor algorithm performance in existing technologies. Thus, while improving the stability of distributed photovoltaic grid connection and reducing short-circuit current to reduce the risk of relay protection maloperation, it also enhances the stability of voltage and frequency, significantly improving power supply reliability and power quality.

[0174] Example 2

[0175] Please refer to Figure 5, which is a structural schematic diagram of a distributed photovoltaic grid-connected site selection and capacity optimization device according to an embodiment of the present invention. The device includes: an access method and node type acquisition module, an objective function construction module, and a site selection and capacity optimization module.

[0176] The access method and node type acquisition module is used to acquire the access method and grid connection node type of distributed photovoltaic grid connection;

[0177] The objective function construction module is used to construct a first objective function with the maximum capacity of distributed photovoltaic power grid access as the objective, a second objective function with the minimum network loss of distributed photovoltaic power grid access as the objective, and a third objective function with the minimum voltage sensitivity of distributed photovoltaic power grid access as the objective, based on the access method and the grid connection node type. The module also constructs the constraints corresponding to the first objective function, the second objective function and the third objective function based on the access method and the grid connection node type.

[0178] The site selection and capacity optimization module is used to solve the first objective function, the second objective function, and the third objective function according to the constraints, respectively, to obtain the site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, the site selection and capacity of distributed photovoltaic grid connection are optimized according to the site selection and capacity optimization scheme.

[0179] Preferably, the step of solving the first objective function, the second objective function, and the third objective function according to the constraints to obtain the distributed photovoltaic grid connection location and capacity optimization scheme that maximizes the capacity of distributed photovoltaic grid connection, minimizes network loss, and minimizes voltage sensitivity includes: solving the first objective function, the second objective function, and the third objective function according to the constraints and the preset NSGA-II algorithm to obtain a Pareto front solution set containing multiple feasible solutions of objective functions; calculating the feasible solutions of objective functions in the Pareto front solution set according to fuzzy decision, selecting a multi-objective comprehensive optimal solution from the feasible solutions of objective functions, and obtaining the distributed photovoltaic grid connection location and capacity optimization scheme that maximizes the capacity of distributed photovoltaic grid connection, minimizes network loss, and minimizes voltage sensitivity according to the multi-objective comprehensive optimal solution.

[0180] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0181] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0182] Example 3

[0183] Accordingly, embodiments of the present invention provide an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the distributed photovoltaic grid-connected addressing and capacity optimization method described in the above embodiments of the invention.

[0184] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0185] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0186] Example 4

[0187] Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the distributed photovoltaic grid-connected site selection and capacity optimization method described in the above embodiments of the invention.

[0188] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0189] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0190] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the site selection and capacity of distributed photovoltaic grid connection, characterized in that, include: Obtain the grid connection method and grid connection node type for distributed photovoltaic systems; Based on the access method and the grid connection node type, a first objective function is constructed with the goal of maximizing the capacity of distributed photovoltaic power grid access, a second objective function is constructed with the goal of minimizing the network loss of distributed photovoltaic power grid access, and a third objective function is constructed with the goal of minimizing the voltage sensitivity of distributed photovoltaic power grid access. Constraints are also constructed for the first, second, and third objective functions based on the access method and the grid connection node type. Based on the constraints, the first objective function, the second objective function, and the third objective function are solved respectively to obtain the optimal site selection and capacity setting scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, the site selection and capacity setting of distributed photovoltaic grid connection are optimized according to the optimal site selection and capacity setting scheme.

2. The site selection and capacity optimization method for distributed photovoltaic grid connection as described in claim 1, characterized in that, The steps involve solving the first, second, and third objective functions based on the constraints to obtain an optimized site selection and capacity setting scheme for distributed photovoltaic (PV) grid connection that maximizes the capacity of PV grid connection, minimizes network losses, and minimizes voltage sensitivity. This includes: Based on the constraints and the preset NSGA-II algorithm, the first objective function, the second objective function, and the third objective function are solved respectively to obtain a Pareto front solution set containing feasible solutions for multiple objective functions; The feasible solutions of the objective function in the Pareto front solution set are calculated based on fuzzy decision-making. A multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function. Based on the multi-objective comprehensive optimal solution, the site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized.

3. The site selection and capacity optimization method for distributed photovoltaic grid connection as described in claim 2, characterized in that, The first objective function, the second objective function, and the third objective function are solved according to the constraints and the preset NSGA-II algorithm, respectively, to obtain a Pareto front solution set containing feasible solutions to multiple objective functions, including: Acquire distribution network data, and construct an initial population based on the distribution network data, constraints, a first objective function, a second objective function, and a third objective function; wherein, the distribution network data includes: power grid topology data, power grid load data, power grid line parameters, photovoltaic power generation data, and power grid economic parameters; Based on the initial population, the photovoltaic access location is changed to generate a corresponding first offspring population, and the photovoltaic capacity is changed to generate a corresponding second offspring population. The first and second offspring populations are merged to generate a corresponding merged population, and the fitness of each individual in the merged population is calculated. The merged population is sorted rapidly according to the fitness, and the best front is selected from the merged population according to the sorting results. A new population is generated according to the selected best front until the iteration reaches the preset number of iterations or the iteration objective reaches convergence, thus obtaining a Pareto front solution set containing multiple feasible solutions of the objective function.

4. The site selection and capacity optimization method for distributed photovoltaic grid connection as described in claim 2, characterized in that, The step of calculating feasible solutions of the objective function in the Pareto front solution set based on fuzzy decision, and selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function, includes: Obtain the preset evaluation indicators, evaluation indicator levels, and evaluation indicator weights; Based on the evaluation indicators, evaluation indicator levels, and evaluation indicator weights, a single-factor fuzzy evaluation is performed on the feasible solutions of the objective function in the Pareto front solution set. The membership degree of each evaluation indicator level is calculated based on the feasible solutions of the objective function, and the corresponding fuzzy relation matrix is ​​obtained based on the membership degree. Based on the fuzzy relation matrix and the preset weight vector, the corresponding fuzzy comprehensive evaluation result vector is obtained. Then, based on the fuzzy comprehensive evaluation result vector, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function.

5. The site selection and capacity optimization method for distributed photovoltaic grid connection as described in claim 1, characterized in that, The first objective function is: f1 = max f(P) pv ) = P pv ; P pv ≤P pv.s ; Among them, P pv.s For distributed photovoltaic users' installed capacity; The second objective function is: Where N is the number of power sources in the distribution network; P Gi P represents the active power generated by the i-th power source; M represents the number of loads in the distribution network; Li This represents the active power consumed by the j-th load. The third objective function is: Where, ΔQ i ΔV represents the reactive power change at distributed photovoltaic access node i. j Let n be the voltage change at node j; n is the distribution network node excluding the balancing node.

6. The site selection and capacity optimization method for distributed photovoltaic grid connection as described in claim 5, characterized in that, The constraints include: power flow constraints, node voltage over-limit constraints, broadband oscillation constraints, relay protection constraints, thermal stability constraints, short-circuit current constraints, and reverse load rate constraints. The power flow constraint is: Among them, P is , and Q is These represent the active power injection and reactive power injection at node i, respectively; U i Let G be the voltage magnitude of node i; j∈i are all nodes directly connected to node i; ij and B ij These are the real and imaginary parts of the nodal admittance matrix, respectively; θ ij Let be the phase difference between the two nodes of branch ij; The node voltage over-limit constraint is as follows: Where ΔU is the line voltage drop, P Li Let μ be a random variable representing the load at node i; i This represents the expected load over a given period of time. The variance of the load normal distribution over a period of time; The broadband oscillation constraint is: Δf=f-f N =m(P D -P ref ); -0.5≤Δf≤0.5; Among them, f N The system's rated frequency; P D P represents the output active power of the photovoltaic system. ref The reference active power for photovoltaic grid-connected control; The relay protection constraint is: Among them, E s Z is the system phase potential; f Z represents the line impedance between the fault point and the grid connection point. S Z is the equivalent impedance of the system; AB P is the impedance of line AB; N and U N These are the rated power of the photovoltaic system and the rated voltage at the grid connection point, respectively; K′ rel K″ is the reliability coefficient for current stage I protection. rel The reliability coefficient of the current stage II protection; The thermal stability constraint is: Where S is the cross-sectional area of ​​the conductor; C is the thermal stability coefficient of the conductor; T is the short-circuit time; I ij For line short-circuit current; I L For conductor thermal stability current limit; S N U is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; N K is the rated voltage of the transformer. θ ρ is the ambient temperature correction factor; p is the transformer load rate. The short-circuit current constraint is: Among them, I m For the interrupting current of the switch; The reverse load rate constraint is: Among them, P D Powering distributed photovoltaic systems; P L S is the sum of all loads downstream of the photovoltaic grid connection point; e This refers to the rated current carrying capacity of the upstream line or the rated capacity of the upstream transformer at the photovoltaic access point.

7. A site selection and capacity optimization device for distributed photovoltaic grid connection, characterized in that, include: The module includes modules for obtaining access methods and node types, constructing objective functions, and optimizing site selection and capacity. The access method and node type acquisition module is used to acquire the access method and grid connection node type of distributed photovoltaic grid connection; The objective function construction module is used to construct a first objective function with the maximum capacity of distributed photovoltaic power grid access as the objective, a second objective function with the minimum network loss of distributed photovoltaic power grid access as the objective, and a third objective function with the minimum voltage sensitivity of distributed photovoltaic power grid access as the objective, based on the access method and the grid connection node type. The module also constructs the constraints corresponding to the first objective function, the second objective function and the third objective function based on the access method and the grid connection node type. The site selection and capacity optimization module is used to solve the first objective function, the second objective function, and the third objective function according to the constraints, respectively, to obtain the site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized. Then, the site selection and capacity of distributed photovoltaic grid connection are optimized according to the site selection and capacity optimization scheme.

8. The distributed photovoltaic grid-connected site selection and capacity optimization device as described in claim 7, characterized in that, The steps involve solving the first, second, and third objective functions based on the constraints to obtain an optimized site selection and capacity setting scheme for distributed photovoltaic (PV) grid connection that maximizes the capacity of PV grid connection, minimizes network losses, and minimizes voltage sensitivity. This includes: Based on the constraints and the preset NSGA-II algorithm, the first objective function, the second objective function, and the third objective function are solved respectively to obtain a Pareto front solution set containing feasible solutions for multiple objective functions; The feasible solutions of the objective function in the Pareto front solution set are calculated based on fuzzy decision-making. A multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function. Based on the multi-objective comprehensive optimal solution, the site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained when the capacity of distributed photovoltaic grid connection is maximized, the network loss is minimized, and the voltage sensitivity is minimized.

9. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the distributed photovoltaic grid-connected addressing and capacity optimization method as described in any one of claims 1 to 6.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the distributed photovoltaic grid-connected site selection and capacity optimization method as described in any one of claims 1 to 6.

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