Locating and sizing optimization method, device and equipment for distributed photovoltaic grid connection and storage medium

Abstract

The invention discloses a distributed photovoltaic grid-connected locating and sizing optimization method, a distributed photovoltaic grid-connected locating and sizing optimization device, distributed photovoltaic grid-connected locating and sizing optimization equipment and a storage medium. Constructing a corresponding first target function, a second target function, a third target function and corresponding constraint conditions by taking the maximum capacity, the minimum network loss and the minimum voltage sensitivity of the distributed photovoltaic access power distribution network as targets; and according to constraint conditions, solving the first target function, the second target function and the third target function to obtain a distributed photovoltaic grid-connected locating and sizing optimization scheme when the distributed photovoltaic access power distribution network has the maximum capacity, the minimum network loss and the lowest voltage sensitivity. And optimizing the locating and sizing of the distributed photovoltaic grid connection according to the locating and sizing optimization scheme. According to the invention, the locating and sizing of the distributed photovoltaic grid connection can be optimized, and the power supply reliability and the electric energy quality of the distributed photovoltaic grid connection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a method, device, equipment and storage medium for optimizing site selection and capacity setting for distributed photovoltaic grid connection. Background Art

[0002] In the context of energy conservation, emission reduction and environmental protection, distributed photovoltaics have developed rapidly and have begun to be connected to the distribution network in large quantities. However, the access of distributed photovoltaics has changed the grid structure of the distribution network, making the power grid operation more complicated; photovoltaic access has changed the short-circuit current, causing the relay protection device to malfunction easily, making the power supply reliability lower; in addition, photovoltaics are connected to the grid through power electronic components, which will also have a certain impact on the power quality of the traditional power system, such as voltage and frequency. In order to make the distribution network run more safely and reliably, the site selection and capacity determination of distributed photovoltaic grid connection can be scientifically optimized to ensure stable grid connection of distributed photovoltaics, but there is no scientific optimization method for site selection and capacity determination of distributed photovoltaic grid connection in the existing technology. Summary of the invention

[0003] The present invention provides a method, device, equipment and storage medium for optimizing site selection and capacity determination for distributed photovoltaic grid connection, so as to solve the technical problem that there is no scientific optimization method for site selection and capacity determination for distributed photovoltaic grid connection in the prior art.

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a method for optimizing the site selection and capacity setting of distributed photovoltaic grid-connected power plants, comprising:

[0005] Obtain the access mode and grid-connected node type of distributed photovoltaic grid-connected;

[0006] According to the access mode and the type of grid-connected node, a corresponding first objective function is constructed with the goal of maximizing the capacity of the distributed photovoltaic access distribution network, a corresponding second objective function is constructed with the goal of minimizing the network loss of the distributed photovoltaic access distribution network, and a corresponding third function is constructed with the goal of minimizing the voltage sensitivity of the distributed photovoltaic access distribution network, and constraints corresponding to the first objective function, the second objective function and the third objective function are constructed according to the access mode and the type of grid-connected node;

[0007] According to the constraints, the first objective function, the second objective function and the third objective function are solved respectively to obtain an optimization plan for the site selection and capacity determination of distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest. Then, the site selection and capacity determination of distributed photovoltaic grid connection is optimized according to the optimization plan for site selection and capacity determination.

[0008] As a preferred solution, according to the constraint conditions, the first objective function, the second objective function and the third objective function are solved respectively to obtain a site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of the distributed photovoltaic access distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including:

[0009] According to the constraint conditions 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 of multiple objective functions;

[0010] According to fuzzy decision making, the feasible solutions of the objective function in the Pareto front solution set are calculated, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and a site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained according to the multi-objective comprehensive optimal solution when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest.

[0011] As a preferred solution, according to the constraint conditions 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 of multiple objective functions, including:

[0012] Acquire distribution network data, and construct an initial population according to 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] According to the initial population, the photovoltaic access position is changed to generate a corresponding first offspring population, and the photovoltaic capacity is changed to generate a corresponding second offspring population;

[0014] 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;

[0015] The merged population is quickly non-dominated sorted according to the fitness, and the best front is selected in the merged population according to the sorting result, and a new population is generated according to the selected best front, until the iteration reaches a preset number of iterations or the iteration target converges, and a Pareto front solution set containing multiple feasible solutions of the objective function is obtained.

[0016] As a preferred solution, the feasible solutions of the objective function in the Pareto front solution set are calculated according to the fuzzy decision, and a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, including:

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

[0018] According to the evaluation index, the evaluation index level and the weight of the evaluation index, a single factor fuzzy evaluation is performed on the feasible solution of the objective function in the Pareto frontier solution set, the membership degree of each evaluation index level is calculated according to the feasible solution of the objective function, and the corresponding fuzzy relationship matrix is ​​obtained according to the membership degree;

[0019] According to the fuzzy relationship matrix and the preset weight vector, a corresponding fuzzy comprehensive evaluation result vector is obtained, and then according to 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 solution, the first objective function is:

[0021] f1=maxf(P pv )=P pv ;

[0022] P pv ≤P pv.s ;

[0023] Among them, P pv.s Report capacity for users of distributed photovoltaics;

[0024] The second objective function is:

[0025]

[0026] Where N is the number of power sources in the distribution network; P Gi is the active power generated by the i-th power source; M is the load number of the distribution network; P Li is the active power consumed by the jth load;

[0027] The third objective function is:

[0028]

[0029] Among them, ΔQ i is the reactive power change of distributed photovoltaic access node i; ΔV j is the voltage change of node j; n is the distribution network node except the balancing node.

[0030] As a preferred solution, the constraint conditions include: power flow constraint, node voltage over-limit constraint, wide-band oscillation constraint, relay protection constraint, thermal stability constraint, short-circuit current constraint and reverse load rate constraint;

[0031] The power flow constraint is:

[0032]

[0033] Among them, P is and Q is are respectively the active and reactive injection amounts of node i; U i is the voltage amplitude of node i; j∈i is all nodes directly connected to node i; G ij and B ij are the corresponding real and imaginary parts of the node admittance matrix respectively; θ ij is the phase difference between the nodes at both ends of branch ij;

[0034] The node voltage limit constraint is:

[0035]

[0036] Where ΔU is the line voltage drop, P Li is the random variable of node i load; μ i is the expected value of the load over a period of time; is the normal distribution variance of load over a period of time;

[0037] The broadband oscillation constraint is:

[0038] Δf=ff N =m(P D -P ref );

[0039] -0.5≤Δf≤0.5;

[0040] Among them, f N is the system rated frequency; P D is the output active power of photovoltaic; P ref Reference active power for photovoltaic grid-connected control;

[0041] The relay protection constraints are:

[0042]

[0043]

[0044] Among them, E s is the system phase potential; Z f is the line impedance between the fault point and the grid connection point; Z S is the system equivalent impedance; Z AB is the impedance of line AB; P N and U N are the photovoltaic rated power and the grid-connected point rated voltage respectively; K′ rel K″ is the reliability factor of current stage I protection; rel is the reliability factor of current stage II protection;

[0045] The thermal stability constraint is:

[0046]

[0047] 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 is the line short-circuit current; I L is the thermal stability current limit of the conductor; S N is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; U N is the rated voltage of the transformer; K θ is the ambient temperature correction coefficient; p is the transformer load rate;

[0048] The short-circuit current constraint is:

[0049]

[0050] Among them, I m is the breaking current of the switch;

[0051] The reverse load rate constraint is:

[0052]

[0053] Among them, P D Output for distributed photovoltaic; P L is the sum of all loads downstream of the PV access point; S e It is the rated current carrying capacity of the upstream line of the PV access point or the rated capacity of the upstream transformer.

[0054] Based on the above embodiment, another embodiment of the present invention provides a distributed photovoltaic grid-connected site selection and capacity optimization device, including: an access mode and node type acquisition module, an objective function construction module and a site selection and capacity optimization module;

[0055] The access mode and node type acquisition module is used to acquire the access mode and grid-connected node type of distributed photovoltaic grid-connected;

[0056] The objective function construction module is used to construct a corresponding first objective function based on the access mode and the grid-connected node type with the goal of maximizing the capacity of the distributed photovoltaic access to the distribution network, construct a corresponding second objective function based on the goal of minimizing the network loss of the distributed photovoltaic access to the distribution network, and construct a corresponding third function based on the goal of minimizing the voltage sensitivity of the distributed photovoltaic access to the distribution network, and construct constraint conditions corresponding to the first objective function, the second objective function and the third objective function based on the access mode and the grid-connected node type;

[0057] The site selection and capacity determination optimization module is used to solve the first objective function, the second objective function and the third objective function respectively according to the constraints, to obtain the site selection and capacity determination optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, and then optimize the site selection and capacity determination of distributed photovoltaic grid connection according to the site selection and capacity determination optimization plan.

[0058] As a preferred solution, according to the constraint conditions, the first objective function, the second objective function and the third objective function are solved respectively to obtain a site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of the distributed photovoltaic access distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including:

[0059] According to the constraint conditions 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 of multiple objective functions;

[0060] According to fuzzy decision making, the feasible solutions of the objective function in the Pareto front solution set are calculated, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and a site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained according to the multi-objective comprehensive optimal solution when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest.

[0061] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the site selection and sizing optimization method for distributed photovoltaic grid-connected connection described in the above invention embodiments is implemented.

[0062] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the site selection and sizing optimization method for distributed photovoltaic grid-connected connection described in the above invention embodiment.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention provides a method for optimizing the site selection and capacity of distributed photovoltaic grid-connected, obtaining the access mode and grid-connected node type of distributed photovoltaic grid-connected; according to the access mode and the grid-connected node type, constructing a corresponding first objective function with the maximum capacity of distributed photovoltaic access to the distribution network as the goal, constructing a corresponding second objective function with the minimum network loss of distributed photovoltaic access to the distribution network as the goal, constructing a corresponding third function with the minimum voltage sensitivity of distributed photovoltaic access to the distribution network as the goal, and constructing constraints corresponding to the first objective function, the second objective function and the third objective function according to the access mode and the grid-connected node type; according to the constraints, respectively solving the first objective function, the second objective function and the third objective function, and obtaining a site selection and capacity optimization scheme for distributed photovoltaic grid-connected when the capacity of distributed photovoltaic access to the distribution network is the maximum, the network loss is the minimum and the voltage sensitivity is the lowest, and then optimizing the site selection and capacity of distributed photovoltaic grid-connected according to the site selection and capacity optimization scheme. The present invention combines the access mode and node type of distributed photovoltaic grid-connected, constructs objective functions and conditional constraints of maximum grid-connected capacity, minimum network loss and lowest voltage sensitivity, and solves the constructed objective functions to obtain the optimal solution, thereby obtaining a site selection and sizing optimization scheme for distributed photovoltaic grid-connected. The site selection and sizing optimization scheme has better globality, is more in line with the needs of distributed photovoltaic grid-connected, and can improve the power supply reliability and power quality of distributed photovoltaic grid-connected. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a method for optimizing site selection and capacity determination for distributed photovoltaic grid connection provided by one embodiment of the present invention;

[0066] Figure 2 It is the equivalent circuit diagram of the power supply line of the distribution network;

[0067] Figure 3 It is a schematic diagram of a simple distribution network connected to distributed power sources;

[0068] Figure 4 This is a schematic diagram of a simple distribution network model containing distributed electric volts;

[0069] Figure 5 It is a structural schematic diagram of a distributed photovoltaic grid-connected site selection and capacity optimization device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0072] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0073] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0074] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0075] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0076] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0077] Embodiment 1

[0078] Please refer to Figure 1 , is a flow chart of a method for optimizing the site selection and capacity of distributed photovoltaic grid-connected power plants provided by an embodiment of the present invention, comprising the following specific steps:

[0079] S1. Obtain the access mode and grid-connected node type of distributed photovoltaic grid-connected;

[0080] Specifically, the site selection and capacity optimization method for distributed photovoltaic grid connection of the present invention specifically includes the following steps:

[0081] S1. Determine the access method of distributed photovoltaic, which adopts the photovoltaic single grid point access method to connect to any 220V single-phase bus under the molded case switch of the low-voltage distribution network;

[0082] Determine the type of distributed photovoltaic grid-connected nodes; confirm the type of photovoltaic grid-connected nodes based on the distributed photovoltaic grid-connected mode and its operation;

[0083] The main types of photovoltaic grid-connected are PV, PV (Q) and PQ nodes. For distributed photovoltaics connected to medium and low voltage distribution networks, the inverter control strategy of PV nodes / PV (Q) nodes is droop V / F control to ensure that the voltage and frequency at the grid connection point are constant. For this type of photovoltaic model, it can be regarded as a PV node. The active power reference value is the rated output of the photovoltaic power plant, and the voltage reference value is the connected voltage level. If the voltage amplitude of the PV node is not equal to the set voltage amplitude, it is necessary to adjust the reactive power of the PV node. The method of injecting current into the PV node is used to generate a certain amount of reactive power so that the voltage amplitude of the PV node reaches the reference value.

[0084] The increased reactive power and the corresponding injected current of the reactive power are:

[0085]

[0086] In the formula is 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, so the PV node injection current is obtained as: is the total injected current at the PV node obtained at the kth iteration.

[0087] In addition, since the adjustable reactive power of the PV node is limited, it is necessary to determine whether the reactive power of the node exceeds the limit. When the reactive power of the PV node exceeds the capacity of the reactive compensation device, the PV node will be converted to a PQ node. The reactive power conversion limit at the PV (Q) node is:

[0088] In the formula, is the load reactive power of the kth power flow at the PV node; is the reactive capacity of the reactive compensation device;

[0089] For the photovoltaic power station connected to the medium-voltage distribution network, the inverter control strategy of the PQ node is droop PQ control to ensure the reliability of the photovoltaic power supply to the feeder load. For this type of photovoltaic model, it can be equivalent to a constant power load model and regarded as a PQ node. The current direction is the direction of injection into the distribution network. The apparent power is: S = -P-jQ. The injection current of the photovoltaic power station to the access node is:

[0090]

[0091] Where: is the voltage of the PQ type photovoltaic access node obtained by power flow iteration; S PQ is the distributed photovoltaic apparent power.

[0092] S2. According to the access mode and the type of grid-connected node, a corresponding first objective function is constructed with the maximum capacity of the distributed photovoltaic access distribution network as the goal, a corresponding second objective function is constructed with the minimum network loss of the distributed photovoltaic access distribution network as the goal, and a corresponding third function is constructed with the minimum voltage sensitivity of the distributed photovoltaic access distribution network as the goal, and constraints corresponding to the first objective function, the second objective function and the third objective function are constructed according to the access mode and the type of grid-connected node;

[0093] Preferably, the first objective function is:

[0094] f1=maxf(P pv )=P pv ;

[0095] P pv ≤P pv.s ;

[0096] Among them, P pv.s Report capacity for users of distributed photovoltaics;

[0097] The second objective function is:

[0098]

[0099] Where N is the number of power sources in the distribution network; P Gi is the active power generated by the i-th power source; M is the load number of the distribution network; P Li is the active power consumed by the jth load;

[0100] The third objective function is:

[0101]

[0102] Among them, ΔQ i is the reactive power change of distributed photovoltaic access node i; ΔV j is the voltage change of node j; n is the distribution network node except the balancing node.

[0103] Preferably, the constraint conditions include: power flow constraint, node voltage over-limit constraint, wide-band oscillation constraint, relay protection constraint, thermal stability constraint, short-circuit current constraint and reverse load rate constraint;

[0104] The power flow constraint is:

[0105]

[0106] Among them, P is and Q is are respectively the active and reactive injection amounts of node i; U i is the voltage amplitude of node i; j∈i is all nodes directly connected to node i; G ij and B ij are the corresponding real and imaginary parts of the node admittance matrix respectively; θ ij is the phase difference between the nodes at both ends of branch ij;

[0107] The node voltage limit constraint is:

[0108]

[0109] Where ΔU is the line voltage drop, P Li is the random variable of node i load; μ i is the expected value of the load over a period of time; is the normal distribution variance of load over a period of time;

[0110] The broadband oscillation constraint is:

[0111] Δf=ff N =m(P D -P ref );

[0112] -0.5≤Δf≤0.5;

[0113] Among them, f N is the system rated frequency; P D is the output active power of photovoltaic; P ref Reference active power for photovoltaic grid-connected control;

[0114] The relay protection constraints are:

[0115]

[0116]

[0117] Among them, E s is the system phase potential; Z f is the line impedance between the fault point and the grid connection point; Z S is the system equivalent impedance; Z AB is the impedance of line AB; P N and U N are the photovoltaic rated power and the grid-connected point rated voltage respectively; K′ rel K″ is the reliability factor of current stage I protection; rel is the reliability factor of current stage II protection;

[0118] The thermal stability constraint is:

[0119]

[0120] 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 is the line short-circuit current; I L is the thermal stability current limit of the conductor; S N is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; U N is the rated voltage of the transformer; K θ is the ambient temperature correction coefficient; p is the transformer load rate;

[0121] The short-circuit current constraint is:

[0122]

[0123] Among them, I m is the breaking current of the switch;

[0124] The reverse load rate constraint is:

[0125]

[0126] Among them, P D Output for distributed photovoltaic; P Lis the sum of all loads downstream of the PV access point; S e It is the rated current carrying capacity of the upstream line of the PV access point or the rated capacity of the upstream transformer.

[0127] S2. According to the access mode and the grid-connected node type, a corresponding objective function and its constraints are constructed; 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, wide-band oscillation constraints, relay protection constraints, thermal stability constraints, short-circuit current constraints and reverse load rate constraints.

[0128] The objective functions are to maximize the capacity of distributed photovoltaic access to the distribution network, minimize network loss, and minimize voltage sensitivity. The specific objective functions are as follows:

[0129] Considering that distributed photovoltaic power generation is connected to the grid with unity power factor and ignoring the impact of photovoltaic reactive power, the first objective function is:

[0130] f1=maxf(P pv )=P pv P pv ≤P pv.s ;

[0131] Where: P pv.s Report capacity for distributed photovoltaic users;

[0132] The access of distributed photovoltaic power generation will cause the power flow of the original distribution network to change, resulting in changes in the distribution transformer and line losses. The second objective function is:

[0133]

[0134] Where: N is the number of power sources in the distribution network; P Gi is the active power generated by the i-th power source; M is the load number of the distribution network; P Li The active power consumed by the jth load;

[0135] The reactive power injected by distributed photovoltaics will cause changes in the voltage of each node in the distribution network. The voltage changes of each node in the system are measured based on the voltage sensitivity. The third objective function is:

[0136]

[0137] Where: ΔQ i is the reactive power change of distributed photovoltaic access node i; ΔV j is the voltage change of node j; n is the distribution network node except the balancing node

[0138] The constraints are:

[0139] Power flow constraints:

[0140] Flow balance is when distributed photovoltaics inject power into the distribution network, the power balance between each node and line is maintained to ensure the stability of the system. Therefore, the distributed photovoltaic access needs to meet the flow equation constraint:

[0141]

[0142] Where: P is , Q is are respectively the active and reactive injection amounts of node i; U i is the voltage amplitude of node i; j∈i represents all nodes directly connected to node i; G ij , B ij are the corresponding real and imaginary parts of the node admittance matrix respectively; θ ij is the phase difference between the two end nodes of branch ij.

[0143] Node voltage limit constraint:

[0144] The connection and disconnection of distributed photovoltaics can easily lead to high and low voltages at the nodes of the distribution network. Figure 2 , is the equivalent circuit diagram of the power supply line of the distribution network, such as Figure 2 As shown, R and X are line resistance reactance; P+jQ is the power injected into the node by the system power supply through the line transmission.

[0145] Figure 2 In the case of photovoltaic power generation, the photovoltaic power source is connected to the grid near the load, and the surrounding load is compensated according to its access capacity, which reduces the transmission capacity of the distribution network power supply line, reduces the line voltage drop ΔU, and raises the node voltage around the grid connection point. When the photovoltaic access capacity is too large or the line is lightly loaded, the node voltage is raised too much, resulting in high voltage. On the contrary, when the photovoltaic power source is off-grid or the line is overloaded, the line voltage drop increases, causing the node voltage to decrease, and the voltage deviation exceeds the allowable value, resulting in low voltage. When the node voltage exceeds the voltage deviation limit specified in GB / T 12325-2008, and the high and low voltage duration exceeds 60s, it means that the node voltage exceeds the limit. The voltage limit constraint is:

[0146]

[0147] From the above, we can know that the high and low voltage phenomenon is related to the node load, so it is necessary to process the load of each node in the distribution network. As the load has the characteristics of randomness, the processing method is to use the normal distribution to approximate random values ​​to reflect the uncertainty of the load, that is:

[0148]

[0149] Where: P Li is the random variable of node i load; μ iis the expected value of the load over a period of time, taking 60% of the capacity of the distribution transformer at the node; The variance of the normal distribution of load over a period of time is set. The maximum value of the 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 calculated variance is 0.21278.

[0150] Broadband oscillation constraints:

[0151] Since the inertia of photovoltaic power generation is smaller than that of generators, and the photovoltaic inverter responds quickly during the process of grid connection and off-grid connection, the damping effect of the photovoltaic power generation system is insufficient, and broadband oscillation is prone to occur. In order to suppress oscillation, a suitable control strategy is required to improve the frequency stability of the system. The droop control is used to set the active droop control coefficient m to constrain the oscillation frequency. According to the frequency deviation regulations of GB / T12325-2008, the broadband oscillation constraint is:

[0152] Δf=ff N =m(PD-P ref ) -0.5≤Δf≤0.5;

[0153] Where: f N is the system rated frequency; P D is the output active power of photovoltaic; P ref It is the reference active power for photovoltaic grid-connected control.

[0154] Relay protection constraints:

[0155] Distributed photovoltaic access to medium and low voltage distribution networks generally uses three-stage current protection as the main protection. The impact of photovoltaic access on short-circuit current is auxiliary and reverse, which puts a test on the setting value and sensitivity of the original protection. Please refer to Figure 3 , is a schematic diagram of a simple distribution network connected to distributed power sources, such as Figure 3 As shown, 1 to 5 are circuit breakers equipped with three-stage current protection; distributed photovoltaic is connected at bus B.

[0156] When a fault occurs in the downstream line of the grid connection point, the photovoltaic power generation will increase the short-circuit current downstream, which will cause the fault line circuit breaker current protection stage I and II to malfunction. Therefore, it is necessary to constrain the maximum photovoltaic access capacity under the original setting value. First, consider the impact of the photovoltaic power generation current to recalculate the protection setting value; then, the photovoltaic power generation is shut down, and the setting value is recalculated to meet the minimum protection range of protection stage I to constrain the photovoltaic capacity. Figure 3 Taking the current stage I protection of medium protection 2 as an example, the setting value is:

[0157]

[0158] Where: E s is the system phase potential; Zf Z is the line impedance between the fault point and the grid connection point; S is the system equivalent impedance; Z AB is the impedance of line AB; P N , U N is the photovoltaic rated power and the grid-connected point rated voltage; K′ rel is the reliability factor of current stage I protection.

[0159] While meeting the protection range constraints of current segment I, the sensitivity requirements of current segment II must also be met. The current segment II protection constraints on photovoltaic capacity are:

[0160]

[0161] Where: K″ rel It is the reliability coefficient of current stage II protection.

[0162] When a fault occurs in an adjacent feeder, the impact of photovoltaics on the short-circuit current is reverse, and the reverse current may cause the upstream line protection to malfunction. Therefore, the photovoltaic capacity is constrained by using the maximum reverse short-circuit current that photovoltaics can provide to be less than the minimum protection setting value of the three-stage protection of the upstream line of the grid connection point, that is, the short-circuit current is less than the setting value of the current stage III protection. Figure 3 Taking the current stage III protection of medium protection 1 as an example, according to GB / T19964-2012, the short-circuit current output by the photovoltaic power station to the grid shall not be greater than 1.5 times its rated current. The protection constraints for the photovoltaic upstream circuit breaker are:

[0163]

[0164] The photovoltaic capacity constrains the protection selectivity and sensitivity of each circuit breaker, and the minimum value is taken as the constraint limit of the relay protection on the photovoltaic capacity.

[0165] Thermal stability constraints:

[0166] Distributed photovoltaic access to the distribution network reduces the transmission capacity of the line and the load rate of the upper transformer under normal working conditions, and there will be no thermal stability limit problem. In the case of short circuit, the above relay protection constraint analysis shows that the impact of photovoltaic on short-circuit current is auxiliary increase and reverse. The auxiliary increase short-circuit current is likely to cause the downstream line to overload, while the reverse short-circuit current is likely to cause the transformer to overload. According to DL / T 5222-2005 and GB 1094-2008, the thermal stability constraints for lines and transformers are:

[0167]

[0168] 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 is the line short-circuit current; I Lis the thermal stability current limit of the conductor; S N is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; U N is the rated voltage of the transformer; K θ is the ambient temperature correction coefficient; p is the transformer load rate.

[0169] Short circuit current constraint:

[0170] After the distributed photovoltaic system is connected to the distribution network, the short-circuit current of the system does not exceed the short-circuit current limit, that is, the maximum photovoltaic short-circuit current is less than the interrupting current of the switch or circuit breaker to ensure the reliable operation of the circuit breaker. The short-circuit current constraint is

[0171]

[0172] In the formula, I m is the breaking current of the switch.

[0173] Reverse load rate constraint:

[0174] The access of distributed power sources to the distribution network will change the grid structure of the distribution network and make the power flow operation complicated. Figure 4 , is a schematic diagram of a simple distribution network model with distributed power. Figure 4 For example, when the output of the distributed photovoltaic connected to node m is greater than the total load value of the line behind m, the photovoltaic power source will return power to the head end of the distribution network, causing the power flow of the line upstream of the photovoltaic node to reverse.

[0175] Therefore, the reverse load rate is used to represent the local photovoltaic consumption capacity, and the reverse load rate constraint is:

[0176]

[0177] Where P D Output for distributed photovoltaic; P L is the sum of all loads downstream of the PV access point; S e It is the rated current carrying capacity of the upstream line of the PV access point or the rated capacity of the upstream transformer.

[0178] S3. According to the constraints, the first objective function, the second objective function and the third objective function are solved respectively to obtain an optimization plan for the site selection and capacity determination of distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, and then the site selection and capacity determination of distributed photovoltaic grid connection is optimized according to the optimization plan for site selection and capacity determination.

[0179] Preferably, according to the constraints, the first objective function, the second objective function and the third objective function are solved respectively to obtain the site selection and sizing optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including: according to 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 multiple feasible solutions of objective functions; according to fuzzy decision-making, the feasible solutions of the objective function in the Pareto front solution set are calculated, and a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and according to the multi-objective comprehensive optimal solution, the site selection and sizing optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest is obtained.

[0180] Preferably, according to 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 of multiple objective functions, including: obtaining distribution network data, and constructing an initial population according to the distribution network data, the 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, the photovoltaic access position is changed to generate a corresponding first offspring population, and the photovoltaic capacity is changed to generate a corresponding second offspring population; the first offspring population and the second offspring population are merged to generate a corresponding merged population and the fitness of each individual in the merged population is calculated; the merged population is quickly non-dominated sorted according to the fitness, and the best front is selected in the merged population according to the sorting result, and a new population is generated according to the selected best front, until the iteration reaches a preset number of iterations or the iteration target reaches convergence, so as to obtain a Pareto front solution set containing feasible solutions of multiple objective functions.

[0181] Preferably, the feasible solutions of the objective function in the Pareto front solution set are calculated according to fuzzy decision making, and a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, including: 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 according to the evaluation indicators, evaluation indicator levels and evaluation indicator weights, calculating the membership of each evaluation indicator level according to the feasible solution of the objective function, and obtaining a corresponding fuzzy relationship matrix according to the membership; obtaining a corresponding fuzzy comprehensive evaluation result vector according to the fuzzy relationship matrix and a preset weight vector, and then selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function according to the fuzzy comprehensive evaluation result vector.

[0182] S3. Solve the objective function: Adopt NSGA-Ⅱ and fuzzy decision-making combined algorithm, according to the objective function and constraints, use NSGA-Ⅱ to solve the Pareto frontier solution set, and then calculate the feasible solutions in the Pareto frontier solution set according to fuzzy decision-making to select the multi-objective comprehensive optimal solution.

[0183] The specific steps of calculation using NSGA-Ⅱ algorithm are as follows:

[0184] S301: Acquire distribution network data and build an initial population;

[0185] Among them, the specific data involved in "obtaining distribution network data" mainly includes:

[0186] (1) Grid topology data: including the connection relationship of all nodes, such as node number, line number, line length, etc. These data are used to construct the mathematical model of the grid; (2) Load data: the load demand of each node, including load type, load power, load curve, etc. These data are used to simulate the operating status of the distribution network; (3) Line parameters: such as the rated capacity, loss rate, resistance, reactance, etc. of the line. These data are used to calculate the operating performance of the line; (4) Photovoltaic power generation data: including the capacity, efficiency, maximum output power, etc. of photovoltaic power generation equipment. These data are used to simulate the access of photovoltaic power generation; (5) Economic parameters: such as investment cost, operating cost, electricity price, etc. These data are used to calculate the economic benefits of the distribution network.

[0187] The specific method of constructing the initial population is as follows:

[0188] (1) Random initialization: According to 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, and the initial capacity of each photovoltaic power generation equipment is set at the same time; (2) Genetic operation: Based on the results of the above random initialization, a certain number of initial populations are generated through the selection, crossover and mutation operations in the genetic algorithm. Among them, the selection operation is used to select individuals with higher fitness from the existing population; the crossover operation is used to exchange certain genes between individuals to generate new individuals; the mutation operation is used to introduce randomness in individuals to increase the diversity of the population; (3) Fitness calculation: According to the characteristics of photovoltaic power generation equipment, the operating status of the distribution network and economic benefits and other factors, the fitness value of each individual is calculated for subsequent selection operations.

[0189] S302: Produce offspring populations by changing photovoltaic access locations and capacities through selection, crossover and mutation;

[0190] S303: merging the above two populations and calculating fitness;

[0191] Specifically, it involves merging the individuals in 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 goal. Its role is to provide a basis for selection and mutation for the genetic algorithm, thereby promoting the retention and evolution of excellent individuals in the merged population and enhancing the convergence and optimization effect of the algorithm.

[0192] S304: Classify the merged large population using fast non-dominated sorting;

[0193] Among them, Rapid Non-Domination Sorting (RNS) is a sorting method for multi-objective optimization problems. It is mainly used to deal with the classification of non-dominated solution sets (Pareto optimal solution sets) in multi-objective optimization problems. First, the merged population is regarded as the object to be classified; secondly, by comparing the objective function values ​​of individuals in the population two by two, the non-dominated solutions are identified and classified into different Pareto frontiers; then, this process is iterated, and the classification is continuously refined until all individuals are assigned to the corresponding Pareto frontiers; finally, these Pareto frontiers are output, which is the solution set classification result under the multi-objective optimization of the problem.

[0194] S305: Select the best frontier in the large population according to the sorting result and the crowding calculation to form a new population;

[0195] Steps to calculate congestion:

[0196] (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 of each individual: For each target dimension, calculate the crowding 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 of each individual in a certain target dimension is the sum of its distances to the adjacent individuals on both sides; (3) Comprehensive calculation: Add up the crowding of all target dimensions to obtain the total crowding of the individual in all target dimensions.

[0197] Select the best frontier based on the sorting results and crowding:

[0198] (1) Fast Non-dominated Sorting: First, the merged population is sorted using the fast non-dominated sorting algorithm to obtain non-dominated levels.

[0199] (2) Selecting first-level non-dominated individuals: Select first-level non-dominated individuals as the current population. These individuals have no other individuals dominating them in the target space.

[0200] (3) Select individuals based on crowding: In the same non-dominated level, individuals are selected based on their crowding. At the same time, individuals with higher crowding are selected, because high crowding means that these individuals are more dispersed in the target space, which can better maintain the diversity of the population.

[0201] (4) Forming a new population: The selected individuals are organized into a new population as candidate individuals for the next generation.

[0202] 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 between multiple objectives.

[0203] S306: Determine whether the iteration reaches the number of iterations or the target reaches convergence;

[0204] Fuzzy decision making is adopted to calculate single-objective fuzzy evaluation and multi-index comprehensive evaluation, and compare the comprehensive evaluation vector to obtain the optimal planning result. The specific steps are as follows:

[0205] (1) Determine the evaluation indicators, the number of which is m;

[0206] (2) Determine the level of evaluation indicators, the number is o;

[0207] (3) Determine the weight of the evaluation index A = (a1, a2, ..., a m ), where ∑a m =1,am >0;

[0208] (4) Perform single-factor fuzzy evaluation, calculate the membership of each indicator level for each optimal solution in the optimal solution set, and obtain the fuzzy relationship matrix R, which is an m-row and o-column matrix, and its membership function is as follows:

[0209]

[0210] Where: f ij is the membership degree of the jth optimal solution to the i-th evaluation index function value; f imin is the lower limit of the value of the i-th evaluation index function; f imax is the upper limit of the value of the i-th evaluation index function. There are three evaluation index functions in this paper, among which the first index is the maximum access capacity, the second index is the minimum network loss, and the third index is the lowest voltage sensitivity;

[0211] (5) Multi-index comprehensive evaluation: The weight vector A and the fuzzy relationship matrix R are used to synthesize the fuzzy comprehensive evaluation result vector B, that is, B = A·R, where the solution with the largest value in B is the optimal solution;

[0212] In the case of meeting multiple indicators, the fuzzy comprehensive evaluation result vector B is synthesized from R and weight vector A corresponding to each solution in the optimal solution set, and the one with the largest B value is found from multiple optimal solutions as the optimal solution.

[0213] S4. The optimal solution is analyzed and verified by examples, including the analysis and verification of PQ equivalent photovoltaic planning and PV equivalent photovoltaic planning. By comparing and analyzing different photovoltaic equivalent nodes and verifications, it is concluded that: based on the PQ equivalent photovoltaic planning results, the access capacity is larger, the photovoltaic access capacity ensures the power supply of the entire feeder load at the access node, and the impact on the distribution network after access is more obvious. With the increase of access capacity, the network loss decreases and the voltage sensitivity increases, which is in line with the actual situation. Based on the PV equivalent photovoltaic planning results, the access capacity is smaller, the photovoltaic access capacity ensures that the power quality of the grid connection point meets the requirements, and the impact on the distribution network after access is smaller.

[0214] S5. According to actual application requirements, apply the optimal solution to distributed photovoltaic grid-connected systems and optimize the site selection and capacity determination of distributed photovoltaic grid-connected systems.

[0215] It can be seen that the present invention provides a method for optimizing the site selection and sizing of distributed photovoltaic grid-connected power plants. The present invention optimizes the site selection and sizing of distributed photovoltaic grid-connected power plants by integrating multi-objective functions and constraints, combining NSGA-Ⅱ and fuzzy decision-making algorithms. Compared with the prior art, the present invention has the advantages of improving the globality and adaptability of the algorithm, and effectively solving the limitations of the prior art of single target, insufficient constraints and poor algorithm performance. Thus, while improving the stability of distributed photovoltaic grid-connected power plants and reducing short-circuit current to reduce the risk of false operation of relay protection, the stability of voltage and frequency is enhanced, and the power supply reliability and power quality are significantly improved.

[0216] Embodiment 2

[0217] Please refer to Figure 5 , is a schematic diagram of the structure of a distributed photovoltaic grid-connected site selection and capacity optimization device provided by an embodiment of the present invention, the device comprising: an access mode and node type acquisition module, an objective function construction module and a site selection and capacity optimization module;

[0218] The access mode and node type acquisition module is used to acquire the access mode and grid-connected node type of distributed photovoltaic grid-connected;

[0219] The objective function construction module is used to construct a corresponding first objective function based on the access mode and the grid-connected node type with the goal of maximizing the capacity of the distributed photovoltaic access to the distribution network, construct a corresponding second objective function based on the goal of minimizing the network loss of the distributed photovoltaic access to the distribution network, and construct a corresponding third function based on the goal of minimizing the voltage sensitivity of the distributed photovoltaic access to the distribution network, and construct constraint conditions corresponding to the first objective function, the second objective function and the third objective function based on the access mode and the grid-connected node type;

[0220] The site selection and capacity determination optimization module is used to solve the first objective function, the second objective function and the third objective function respectively according to the constraints, to obtain the site selection and capacity determination optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, and then optimize the site selection and capacity determination of distributed photovoltaic grid connection according to the site selection and capacity determination optimization plan.

[0221] Preferably, according to the constraints, the first objective function, the second objective function and the third objective function are solved respectively to obtain the site selection and sizing optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including: according to 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 multiple feasible solutions of objective functions; according to fuzzy decision-making, the feasible solutions of the objective function in the Pareto front solution set are calculated, and a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and according to the multi-objective comprehensive optimal solution, the site selection and sizing optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest is obtained.

[0222] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0223] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0224] Embodiment 3

[0225] Accordingly, an embodiment of the present invention provides an electronic device, comprising 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, the site selection and sizing optimization method for distributed photovoltaic grid-connected devices described in the above-mentioned embodiment of the invention is implemented.

[0226] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0227] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0228] Embodiment 4

[0229] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the site selection and sizing optimization method for distributed photovoltaic grid-connected connection described in the above-mentioned embodiment of the invention.

[0230] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0231] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0232] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing site selection and capacity determination for distributed photovoltaic grid connection, characterized in that: include: Obtain the access mode and grid-connected node type of distributed photovoltaic grid-connected; According to the access mode and the type of grid-connected node, a corresponding first objective function is constructed with the goal of maximizing the capacity of the distributed photovoltaic access distribution network, a corresponding second objective function is constructed with the goal of minimizing the network loss of the distributed photovoltaic access distribution network, and a corresponding third function is constructed with the goal of minimizing the voltage sensitivity of the distributed photovoltaic access distribution network, and constraints corresponding to the first objective function, the second objective function and the third objective function are constructed according to the access mode and the type of grid-connected node; According to the constraints, the first objective function, the second objective function and the third objective function are solved respectively to obtain an optimization plan for the site selection and capacity determination of distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest. Then, the site selection and capacity determination of distributed photovoltaic grid connection is optimized according to the optimization plan for site selection and capacity determination.

2. The site selection and capacity optimization method for distributed photovoltaic grid connection according to claim 1, characterized in that: According to the constraint conditions, the first objective function, the second objective function and the third objective function are solved respectively to obtain a site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including: According to the constraint conditions 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 of multiple objective functions; According to fuzzy decision making, the feasible solutions of the objective function in the Pareto front solution set are calculated, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and a site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained according to the multi-objective comprehensive optimal solution when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest.

3. The site selection and capacity optimization method for distributed photovoltaic grid connection according to claim 2, characterized in that: According to the constraint conditions 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 of multiple objective functions, including: Acquire distribution network data, and construct an initial population according to 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; According to the initial population, the photovoltaic access position is changed to generate a corresponding first offspring population, and the photovoltaic capacity is changed 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; The merged population is quickly non-dominated sorted according to the fitness, and the best front is selected in the merged population according to the sorting result, and a new population is generated according to the selected best front, until the iteration reaches a preset number of iterations or the iteration target converges, and a Pareto front solution set containing multiple feasible solutions of the objective function is obtained.

4. The site selection and capacity optimization method for distributed photovoltaic grid connection according to claim 2, characterized in that: The step of calculating feasible solutions of the objective function in the Pareto frontier solution set according to fuzzy decision making and selecting a multi-objective comprehensive optimal solution from the feasible solutions of the objective function comprises: Obtaining preset evaluation indicators, evaluation indicator levels, and evaluation indicator weights; According to the evaluation index, the evaluation index level and the evaluation index weight, a single factor fuzzy evaluation is performed on the feasible solution of the objective function in the Pareto front solution set, the membership degree of each evaluation index level is calculated according to the feasible solution of the objective function, and the corresponding fuzzy relationship matrix is ​​obtained according to the membership degree; According to the fuzzy relationship matrix and the preset weight vector, a corresponding fuzzy comprehensive evaluation result vector is obtained, and then according to the fuzzy comprehensive evaluation result vector, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function.

5. The method for optimizing site selection and capacity determination for distributed photovoltaic grid connection according to claim 1, characterized in that: The first objective function is: f1=maxf(P pv )=P pv ; P pv ≤P pv.s ; Among them, P pv.s Report capacity for users of distributed photovoltaics; The second objective function is: Where N is the number of power sources in the distribution network; P Gi is the active power generated by the i-th power source; M is the load number of the distribution network; P Li is the active power consumed by the jth load; The third objective function is: Among them, ΔQ i is the reactive power change of distributed photovoltaic access node i; ΔV j is the voltage change of node j; n is the distribution network node except the balancing node.

6. The method for optimizing site selection and capacity determination for distributed photovoltaic grid connection according to claim 5, characterized in that: The constraints include: power flow constraint, node voltage over-limit constraint, wide-band oscillation constraint, relay protection constraint, thermal stability constraint, short-circuit current constraint and reverse load rate constraint; The power flow constraint is: Among them, P is , and Q is are respectively the active and reactive injection amounts of node i; U i is the voltage amplitude of node i; j∈i is all nodes directly connected to node i; GG ij and B ij are the corresponding real and imaginary parts of the node admittance matrix respectively; θ ij is the phase difference between the nodes at both ends of branch ij; The node voltage limit constraint is: Where ΔU is the line voltage drop, P Li is the random variable of node i load; μ i is the expected value of the load over a period of time; is the normal distribution variance of load 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 is the system rated frequency; P D is the output active power of photovoltaic; P ref Reference active power for photovoltaic grid-connected control; The relay protection constraints are: Among them, E s is the system phase potential; Z f is the line impedance between the fault point and the grid connection point; Z S is the system equivalent impedance; Z AB is the impedance of line AB; P N and U N are the photovoltaic rated power and the grid-connected point rated voltage respectively; K′ rel K″ is the reliability factor of current stage I protection; rel is the reliability factor of 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 is the line short-circuit current; I L is the thermal stability current limit of the conductor; S N is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; U N is the rated voltage of the transformer; K θ is the ambient temperature correction coefficient; p is the transformer load rate; The short-circuit current constraint is: Among them, I m is the breaking current of the switch; The reverse load rate constraint is: Among them, P D Output for distributed photovoltaic; P L is the sum of all loads downstream of the PV access point; S e It is the rated current carrying capacity of the upstream line of the PV access point or the rated capacity of the upstream transformer.

7. A distributed photovoltaic grid-connected site selection and capacity optimization device, characterized in that: include: Access mode and node type acquisition module, objective function construction module and site selection and capacity optimization module; The access mode and node type acquisition module is used to acquire the access mode and grid-connected node type of the distributed photovoltaic grid; The objective function construction module is used to construct a corresponding first objective function based on the access mode and the grid-connected node type with the goal of maximizing the capacity of the distributed photovoltaic access to the distribution network, construct a corresponding second objective function based on the goal of minimizing the network loss of the distributed photovoltaic access to the distribution network, and construct a corresponding third function based on the goal of minimizing the voltage sensitivity of the distributed photovoltaic access to the distribution network, and construct constraint conditions corresponding to the first objective function, the second objective function and the third objective function based on the access mode and the grid-connected node type; The site selection and capacity determination optimization module is used to solve the first objective function, the second objective function and the third objective function respectively according to the constraints, to obtain the site selection and capacity determination optimization plan for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, and then optimize the site selection and capacity determination of distributed photovoltaic grid connection according to the site selection and capacity determination optimization plan.

8. The site selection and capacity optimization device for distributed photovoltaic grid connection according to claim 7, characterized in that: According to the constraint conditions, the first objective function, the second objective function and the third objective function are solved respectively to obtain a site selection and capacity optimization scheme for distributed photovoltaic grid connection when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized and the voltage sensitivity is lowest, including: According to the constraint conditions 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 of multiple objective functions; According to fuzzy decision making, the feasible solutions of the objective function in the Pareto front solution set are calculated, a multi-objective comprehensive optimal solution is selected from the feasible solutions of the objective function, and a site selection and capacity optimization scheme for distributed photovoltaic grid connection is obtained according to the multi-objective comprehensive optimal solution when the capacity of distributed photovoltaic access to the distribution network is maximized, the network loss is minimized, and the voltage sensitivity is lowest.

9. An electronic device, characterized in that: The method comprises 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, the method for optimizing the site selection and sizing of distributed photovoltaic grid-connected devices as described in any one of claims 1 to 6 is implemented.

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

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