Method and device for improving distributed photovoltaic accommodation capacity based on low-voltage distribution network

Abstract

The present invention provides a method and device for improving the acceptance capacity of distributed photovoltaics based on a low-voltage distribution network. The method includes: determining the electrical parameters of the low-voltage distribution network; calculating a global optimal solution according to the electrical parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity; wherein the global optimal solution at least includes a decision variable value and a fitness function value corresponding to the decision variable value. The present invention solves the problems of large investment and insufficient flexibility in the prior art when improving the acceptance capacity of distributed photovoltaics, thereby effectively improving the acceptance capacity of distributed photovoltaics in the low-voltage distribution network and giving full play to the power generation benefit of photovoltaic power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and particularly to a method and device for improving the acceptance capacity of distributed photovoltaics based on low-voltage distribution networks. Background Art

[0002] In the power system, the distribution network is an important terminal link, directly supplying electric energy to various users. With the development of the new power system, the proportion of new energy is continuously increasing, and the installed capacity and access ratio of distributed photovoltaics in low-voltage distribution networks will continue to increase. As a clean energy source, the high proportion of photovoltaics connected to low-voltage distribution networks will bring huge power generation benefits. However, due to its own uncertainty, it may bring a series of problems that endanger the safe and stable operation of the distribution network, such as voltage over-limit, line overload, and reduced power quality. Therefore, in order to avoid the occurrence of the above problems, it is particularly important to accurately evaluate the acceptance capacity of photovoltaics in low-voltage distribution networks. Particularly importantly, in order to further improve the power generation benefit of photovoltaics, it is urgent to improve the acceptance capacity of distributed photovoltaics based on accurate evaluation.

[0003] Voltage over-limit is the main factor restricting the improvement of the acceptance capacity of photovoltaics in low-voltage distribution networks. Existing methods for alleviating voltage rise include transformer tap adjustment, line transformation, distribution network reconfiguration, adding energy storage, reactive power compensation, etc. Among them, by adjusting the transformer tap, the voltage of each node on the feeder can be reduced during photovoltaic power generation. However, since the transformer tap cannot be changed multiple times in a short period, it means that there will be a large voltage drop under heavy load conditions at night when there is no photovoltaic power generation, which is not conducive to the stable operation of the system, and this method lacks flexibility. The line transformation method mainly improves the voltage by changing the resistance and reactance parameters of the line. This method has a long construction and transformation period and large investment. The distribution network reconfiguration method mainly adjusts the power flow distribution by closing the tie switches and sectional switches, and then improves the voltage. However, the topological structure in low-voltage distribution networks is relatively simple, lacking the condition basis for reconfiguration. The methods of adding energy storage and reactive power compensation increase the forward power flow from the perspectives of active power and inductive reactive power respectively to increase the degree of voltage drop. However, the two methods have the disadvantages of large investment and insufficient flexibility respectively.

[0004] Therefore, how to provide a method for improving the acceptance capacity of distributed photovoltaics based on low-voltage distribution networks on the basis of accurate evaluation is an urgent problem to be solved at present. Summary of the Invention

[0005] The present invention provides a method and device for improving the acceptance capacity of distributed photovoltaics based on a low-voltage distribution network to at least partially solve the technical problem of difficult improvement of the acceptance capacity of photovoltaics in the prior art. To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a preamble to the subsequent detailed description.

[0006] According to a first aspect of an embodiment of the present invention, there is provided a method for improving the acceptance capacity of distributed photovoltaics based on a low-voltage distribution network, the method comprising:

[0007] Determine the electrical parameters of the low-voltage distribution network;

[0008] Calculate the global optimal solution according to the electrical parameters and a pre-constructed particle swarm optimization algorithm, and use the global optimal solution as the maximum photovoltaic acceptance capacity;

[0009] Wherein, the global optimal solution at least includes the decision variable value and the fitness function value corresponding to the decision variable value.

[0010] Optionally, before determining the electrical parameters of the low-voltage distribution network, it further includes:

[0011] Set the initial parameters of the particle swarm optimization algorithm.

[0012] Optionally, the initial parameters of the particle swarm optimization algorithm at least include the population size, the number of iterations, the learning factor, and the inertia weight.

[0013] Optionally, the determining the electrical parameters of the low-voltage distribution network specifically includes:

[0014] Determine the topological structure of the low-voltage distribution network, the line type and resistance and reactance parameters, the load level of each node, the capacity and power factor of the distributed photovoltaic inverter, and the access node.

[0015] Optionally, calculating the global optimal solution according to the electrical parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity specifically includes:

[0016] Determine the decision variables and construct a fitness function that satisfies the voltage constraint;

[0017] Randomly select the initial photovoltaic active power output according to the upper limit of the decision variable to initialize the population;

[0018] Based on the photovoltaic inverter control algorithm, calculate the photovoltaic reactive power output value;

[0019] Calculate the node voltages in the initialized population based on the forward-backward substitution power flow method and the PV inductive reactive power output value;

[0020] Calculate the fitness function of each particle in the initialized population, and determine the current individual extreme value of each particle and the current global optimal solution of the particle swarm.

[0021] Optionally, after determining the current individual extreme value of each particle and the current global optimal solution of the particle swarm, it includes:

[0022] Update the velocity and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm;

[0023] When the number of iterations reaches the preset maximum number of iterations, use the current global optimal solution as the maximum PV acceptance capacity output.

[0024] Optionally, determine the decision variables, specifically including:

[0025] Take the active power output of each distributed PV as the decision variable;

[0026] Among them, the upper limit of the decision variable is the maximum active power output of the distributed PV at the minimum power factor, and the lower limit of the decision variable is 0.

[0027] Optionally, the constructed fitness function f(X) that satisfies the voltage constraint is:

[0028]

[0029] Among them, X is the set of decision variables of each particle, P pv,i is the active power output of the i-th distributed PV; N pv is the number of distributed PVs connected to the low-voltage distribution network; f(X) is the fitness function; V i is the voltage of node i; V MAX is the maximum upper limit value allowed for the low-voltage distribution network voltage.

[0030] Optionally, the steps of calculating the PV inductive reactive power output value based on the PV inverter control algorithm include: obtaining the calculation formula for the power factor of the i-th distributed PV as:

[0031]

[0032] Among them, is the power factor of the i-th distributed PV, A 1 is the maximum power factor set for the distributed PV, P pv,i is the active power output of the i-th distributed PV, A 2The minimum power factor set for distributed photovoltaics, P 1 The demarcation value for starting the power factor adjustment, P 2 The demarcation value for the photovoltaics to operate at the minimum power factor.

[0033] Optionally, the step of calculating the reactive power output value of the photovoltaic inductance based on the photovoltaic inverter control algorithm further includes: obtaining the calculation formula for the reactive power output value of the i-th distributed photovoltaic as:

[0034]

[0035] where Q pv,i is the reactive power output value of the i-th distributed photovoltaic, and S i is the capacity of the i-th distributed photovoltaic.

[0036] Optionally, the step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm includes: obtaining the calculation formula for the updated moving speed of each particle as:

[0037]

[0038] where is the updated moving speed of the particle, is the moving speed of the particle before update, ω is the inertia weight, C 1 and C 2 are learning factors, rand is a random number between the interval [0,1], P pb is the individual extreme value, P gb is the global optimal solution, is the position of the particle before update.

[0039] Optionally, the step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm further includes: obtaining the calculation formula for the updated position of each particle as:

[0040]

[0041] where is the updated position of the particle, is the position of the particle before update.

[0042] According to the second aspect of the embodiments of the present application, the present invention provides a device for improving the acceptance capacity of distributed photovoltaics based on a low-voltage distribution network. The device includes:

[0043] A parameter determination unit for determining the electrical parameters of the low-voltage distribution network;

[0044] An optimal solution calculation unit is configured to calculate a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and use the global optimal solution as the maximum photovoltaic acceptance capacity.

[0045] Wherein, the global optimal solution at least includes a decision variable value and a fitness function value corresponding to the decision variable value.

[0046] Optionally, the parameter determination unit is further configured to:

[0047] Set the initial parameters of the particle swarm optimization algorithm, where the initial parameters of the particle swarm optimization algorithm at least include population size, number of iterations, learning factor, and inertia weight.

[0048] According to the third aspect of the embodiments of the present application, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of any of the above methods are implemented.

[0049] According to the fourth aspect of the embodiments of the present application, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0050] According to the fifth aspect of the embodiments of the present application, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0051] The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

[0052] The method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network provided by the present invention determines the power parameters of the low-voltage distribution network, calculates a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and uses the global optimal solution as the maximum photovoltaic acceptance capacity. This method uses a method for improving the distributed photovoltaic acceptance capacity of a low-voltage distribution network based on photovoltaic inverter control and particle swarm algorithm, which can effectively alleviate the problem of voltage over-limit, solve the problems of large investment and insufficient flexibility in improving the distributed photovoltaic acceptance capacity in the prior art, thereby effectively improving the distributed photovoltaic acceptance capacity of the low-voltage distribution network and giving full play to the photovoltaic power generation benefit.

[0053] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Description of the Drawings

[0054] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0055] Figure 1 One of the flowcharts of a method for improving the acceptance capacity of distributed photovoltaic power based on a low-voltage distribution network provided by an embodiment of the present invention;

[0056] Figure 2 Another flowchart of a method for improving the acceptance capacity of distributed photovoltaic power based on a low-voltage distribution network provided by an embodiment of the present invention;

[0057] Figure 3 Another flowchart of a method for improving the acceptance capacity of distributed photovoltaic power based on a low-voltage distribution network provided by an embodiment of the present invention;

[0058] Figure 4 A flowchart of a method for improving the acceptance capacity of distributed photovoltaic power based on a low-voltage distribution network provided by the present invention in a specific usage scenario;

[0059] Figure 5 The topological structure diagram of the low-voltage distribution network provided by the present invention;

[0060] Figure 6 A comparison chart of the improvement effects of the method provided by the present invention and the traditional method on the acceptance capacity;

[0061] Figure 7 The structural block diagram of a device for improving the acceptance capacity of distributed photovoltaic power based on a low-voltage distribution network provided by an embodiment of the present invention;

[0062] Figure 8 The structural schematic diagram of the electronic device provided by the present invention. Detailed implementation manners

[0063] The following description and the accompanying drawings fully disclose specific embodiments herein, enabling those skilled in the art to practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents of the claims. In this document, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a structure, device or equipment comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such structure, device or equipment. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the structure, device or equipment comprising the element. The embodiments herein are described in a progressive manner, with each embodiment highlighting the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0064] In this document, the orientation or positional relationships indicated by the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing this document and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on this application. In the description of this document, unless otherwise specified and defined, the terms "mounted", "connected" and "coupled" shall be understood in a broad sense. For example, they can be mechanical connections or electrical connections, or the internal communication of two elements. They can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0065] In this document, unless otherwise stated, the term "plurality" means two or more.

[0066] In this document, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B.

[0067] In this document, the term "and / or" is an associative relationship describing an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0068] Without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0069] Aiming at the problem that it is difficult to improve the photovoltaic acceptance capacity of the existing method, the present invention uses the particle swarm optimization algorithm to evaluate the photovoltaic acceptance capacity, and uses the method based on the power factor control of the photovoltaic inverter ( control) to alleviate the voltage over-limit problem, thereby improving the distributed photovoltaic acceptance capacity of the low-voltage distribution network.

[0070] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network provided by an embodiment of the present invention.

[0071] In a specific embodiment, a method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network provided by the present invention includes the following steps:

[0072] Step S101: Determine the electrical parameters of the low-voltage distribution network. The electrical parameters of the low-voltage distribution network specifically include the topological structure of the low-voltage distribution network, the line model and resistance-reactance parameters, the load level of each node, the capacity of the distributed photovoltaic inverter, the power factor, and the access node.

[0073] Step S102: Calculate the global optimal solution according to the electrical parameters and the pre-constructed particle swarm optimization algorithm, and use the global optimal solution as the maximum photovoltaic acceptance capacity; wherein, the global optimal solution at least includes the decision variable value and the fitness function value corresponding to the decision variable value.

[0074] In order to improve the algorithm accuracy of the particle swarm optimization algorithm and make the algorithm more suitable for the specific field of the low-voltage distribution network, as Figure 2 shown, before determining the electrical parameters of the low-voltage distribution network, the following steps are further included:

[0075] Step S201: Set the initial parameters of the particle swarm optimization algorithm.

[0076] Specifically, the initial parameters of the particle swarm optimization algorithm at least include the population size, the number of iterations, the learning factor, and the inertia weight.

[0077] In some embodiments, as Figure 3 shown, calculating the global optimal solution according to the electrical parameters and the pre-constructed particle swarm optimization algorithm specifically includes the following steps:

[0078] Step S301: Determine the decision variable and construct a fitness function that satisfies the voltage constraint;

[0079] Among them, determining the decision variable specifically includes:

[0080] Take the active power output of each distributed photovoltaic as the decision variable;

[0081] Among them, the upper limit of the decision variable is the maximum active power output of the distributed photovoltaic under the minimum power factor, and the lower limit of the decision variable is 0.

[0082] Construct the fitness function f(X) that satisfies the voltage constraint as:

[0083]

[0084] Among them, X is the set of decision variables of each particle, P pv,i is the active power output of the i-th distributed photovoltaic; N pv is the number of distributed photovoltaics connected to the low-voltage distribution network; f(X) is the fitness function; V i is the voltage of node i; V MAX is the maximum upper limit value allowed for the low-voltage distribution network voltage.

[0085] That is to say, take the active power output of each distributed photovoltaic as the decision variable, the upper limit of the variable is the maximum active power output of the distributed photovoltaic under the minimum power factor, and the lower limit of the variable is 0; as shown in formula (1), construct the fitness function that satisfies the voltage constraint as the sum of the active power outputs of each distributed photovoltaic.

[0086] Step S302: Randomly select the initial photovoltaic active power output according to the upper limit of the decision variable to initialize the population.

[0087] Step S303: Based on the photovoltaic inverter control algorithm, calculate the photovoltaic reactive power output value. The photovoltaic inverter can achieve active and reactive decoupling. Adopt the photovoltaic inverter control, adjust the inverter power factor according to the size of the photovoltaic active power output, so as to calculate the photovoltaic reactive power output value.

[0088] Specifically, use the following formula to calculate the power factor of the i-th distributed photovoltaic:

[0089]

[0090] Among them, is the power factor of the i-th distributed photovoltaic, A 1 is the maximum power factor set for the distributed photovoltaic, P pv,i is the active power output of the i-th distributed photovoltaic, A 2 is the minimum power factor set for the distributed photovoltaic, P 1 is the demarcation value for starting power factor adjustment, P 2 is the demarcation value for the photovoltaic to operate at the minimum power factor.

[0091] Calculate the reactive power output value Q of the i-th distributed photovoltaic using the following formula pv,i :

[0092]

[0093] where S i is the capacity of the i-th distributed photovoltaic. It should be understood that, in principle, the active power P, reactive power Q, and capacity S form a triangle. The angle size is derived from the cosine angle size, and then using the relationship between the S capacity size and the angle, the reactive power Q can be obtained.

[0094] Step S304: Calculate the node voltages in the initial population based on the forward-backward substitution power flow method and the photovoltaic inductive reactive power output value.

[0095] Step S305: Calculate the fitness function of each particle in the initial population, and determine the current individual extreme value of each particle and the current global optimal solution of the particle swarm.

[0096] Furthermore, after determining the current individual extreme value of each particle and the current global optimal solution of the particle swarm, it includes:

[0097] Update the moving speed and position of each particle according to the current individual extreme value and the current global optimal solution;

[0098] Specifically, use the following formula to update the moving speed of each particle:

[0099]

[0100] where is the updated moving speed, is the moving speed before update, ω is the inertia weight, C 1 and C 2 are learning factors, rand is a random number between the interval [0,1], P pb is the individual extreme value, P gb is the global optimal solution, is the particle position before update.

[0101] Specifically, use the following formula to update the position of each particle:

[0102]

[0103] where is the updated particle position, is the particle position before update.

[0104] When the number of iterations reaches the preset maximum number of iterations, the current global optimal solution is output as the maximum photovoltaic acceptance capacity. Specifically, it is determined whether the termination condition, that is, the maximum number of iterations, is reached. If so, the optimal solution is output. Otherwise, it returns to step S301, and steps S301 - S305 are repeated.

[0105] In the above specific implementation manner, the method for improving the distributed photovoltaic acceptance capacity based on a low - voltage distribution network provided by the present invention determines the electrical parameters of the low - voltage distribution network, calculates the global optimal solution according to the electrical parameters and a pre - constructed particle swarm optimization algorithm, and uses the global optimal solution as the maximum photovoltaic acceptance capacity. This method uses a method for improving the distributed photovoltaic acceptance capacity of a low - voltage distribution network based on photovoltaic inverter control and particle swarm algorithm, which can effectively alleviate the problem of voltage over - limit, solve the problems of large investment and insufficient flexibility in improving the distributed photovoltaic acceptance capacity in the prior art, thereby effectively improving the distributed photovoltaic acceptance capacity of the low - voltage distribution network and giving full play to the photovoltaic power generation benefit.

[0106] Next, taking a specific usage scenario as an example, the implementation process of the method for improving the distributed photovoltaic acceptance capacity based on a low - voltage distribution network provided by the present invention is briefly described.

[0107] As Figure 4 shown, in this usage scenario, the method for improving the distributed photovoltaic acceptance capacity based on a low - voltage distribution network includes the following steps:

[0108] Step S1: Set the initial parameters of the particle swarm optimization algorithm, where the learning factors C 1 、C 2 both take the classical value of 2, the initial inertia weight takes the classical value of 0.9, the termination inertia weight takes the classical value of 0.4, and the remaining parameters are adjusted according to the actual problem;

[0109] Step S2: Input the topological structure parameters of the low - voltage distribution network, as Figure 5 shown; input the active load parameters, as shown in Table 1, and the load power factor of each node is 0.85; input the capacity, power factor of the distributed photovoltaic inverter and its specific access nodes, as shown in Table 2.

[0110] Table 1 Active load parameters of each node

[0111] Node Load type Load size / KW 0 Slack node 0 1 PQ 11 2 PQ 0 3 PQ 17 4 PQ 14 5 PQ 12 6 PQ 0 7 PQ 15 8 PQ 10

[0112] Table 2 Related parameters of the distributed photovoltaic inverter

[0113] Label Distributed PV inverter capacity / KW Power factor range Connection node 1 100 0.90-1.00 1 2 50 0.90-1.00 5 3 150 0.90-1.00 7

[0114] Step S3: Select decision variables and construct a fitness function;

[0115] X = [Ppv,1 , P pv,2 , P pv,3

[0116]

[0117] Step S4: Calculate the upper limit of the variable, randomly select the initial photovoltaic active power output, and initialize the population;

[0118] Step S5; Adopt the control based on the photovoltaic inverter control, adjust the inverter power factor according to the actual photovoltaic active power output, and then adjust the distributed photovoltaic reactive power value. A 1 Take 1, A 2 Take 0.9, P 1 is the demarcation value for starting the power factor adjustment, take 0.7S i , P 2 is the demarcation value for the photovoltaic to operate at the minimum power factor, take 0.9S i , S i Refer to the data in Table 2;

[0119]

[0120]

[0121] Step S6: Perform power flow calculation based on the forward-backward substitution power flow method, determine the voltage of each node, judge whether the voltage constraint is satisfied, accurately calculate the fitness function of each particle, and then find the current individual extreme value of each particle and the current global optimal solution of the entire particle swarm.

[0122] Step S7: Update the moving speed and position of each particle, and continuously update the individual extreme value and the global optimal solution.

[0123] Step S8: Output the optimal solution (specifically including: the value of the decision variable and the corresponding fitness function value), that is, the maximum photovoltaic acceptance capacity.

[0124] Compare the method for improving the distributed photovoltaic acceptance capacity of the low-voltage distribution network based on the photovoltaic inverter control and the particle swarm algorithm with the optimal solution obtained by the traditional method for the distributed photovoltaic acceptance capacity of the low-voltage distribution network. It can be found that the method proposed in the present invention can significantly improve the distributed photovoltaic acceptance capacity of the low-voltage distribution network, and the results are as Figure 6 shown. It can be seen that by using the method for improving the distributed photovoltaic acceptance capacity of the low-voltage distribution network based on the photovoltaic inverter control and the particle swarm algorithm, the problem of voltage over-limit can be effectively alleviated, the disadvantages of large investment and lack of flexibility of other methods can be made up for, the distributed photovoltaic acceptance capacity of the low-voltage distribution network can be effectively improved, and the photovoltaic power generation benefit can be fully exerted.​

[0125] In addition to the above methods, the present invention provides a device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network, as Figure 7 shown, the device includes:

[0126] A parameter determination unit 701, configured to determine the power parameters of the low-voltage distribution network;

[0127] An optimal solution calculation unit 702, configured to calculate a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and use the global optimal solution as the maximum photovoltaic acceptance capacity;

[0128] Wherein, the global optimal solution at least includes a decision variable value and a fitness function value corresponding to the decision variable value.

[0129] Optionally, before determining the power parameters of the low-voltage distribution network, it further includes:

[0130] Setting the initial parameters of the particle swarm optimization algorithm.

[0131] Optionally, the initial parameters of the particle swarm optimization algorithm at least include a population size, an iteration number, a learning factor, and an inertia weight.

[0132] Optionally, the determination of the power parameters of the low-voltage distribution network specifically includes:

[0133] Determining the topological structure of the low-voltage distribution network, the line model and resistance-reactance parameters, the load level of each node, the capacity of the distributed photovoltaic inverter, the power factor, and the access node.

[0134] Optionally, calculating a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity specifically includes:

[0135] Determining decision variables and constructing a fitness function that satisfies voltage constraints;

[0136] Randomly selecting an initial photovoltaic active power output according to the variable upper limit of the decision variable to initialize the population;

[0137] Calculating the photovoltaic reactive power output value based on the photovoltaic inverter control algorithm;

[0138] Calculating the node voltages in the initialized population based on the forward-backward substitution power flow method and the photovoltaic reactive power output value;

[0139] Calculating the fitness functions of the particles in the initialized population and determining the current individual extreme values of the particles and the current global optimal solution of the particle swarm.

[0140] Optionally, determine the current individual extreme value of each particle and the current global optimal solution of the particle swarm, and then include:

[0141] Update the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm;

[0142] When the number of iterations reaches the preset maximum number of iterations, output the current global optimal solution as the maximum PV acceptance capacity.

[0143] Optionally, determine the decision variables, specifically including:

[0144] Take the active power output of each distributed PV as the decision variable;

[0145] Among them, the upper limit of the decision variable is the maximum active power output of the distributed PV at the minimum power factor, and the lower limit of the decision variable is 0.

[0146] Optionally, the constructed fitness function f(X) that satisfies the voltage constraint is:

[0147]

[0148] Among them, X is the set of decision variables of each particle, P pv,i is the active power output of the i-th distributed PV; N pv is the number of distributed PVs connected to the low-voltage distribution network; f(X) is the fitness function; V i is the voltage of node i; V MAX is the maximum upper limit value allowed for the low-voltage distribution network voltage.

[0149] Optionally, the steps of calculating the PV inductive reactive power output value based on the PV inverter control algorithm include obtaining the calculation formula for the power factor of the i-th distributed PV as:

[0150]

[0151] Among them, is the power factor of the i-th distributed PV, A 1 is the maximum power factor set for the distributed PV, P pv,i is the active power output of the i-th distributed PV, A 2 is the minimum power factor set for the distributed PV, P 1 is the demarcation value for starting the power factor adjustment, P 2 is the demarcation value for the PV to operate at the minimum power factor.

[0152] Optionally, the step of calculating the reactive power output value of the PV inductive reactive power based on the PV inverter control algorithm further includes: obtaining the calculation formula for the reactive power output value of the i-th distributed PV as:

[0153]

[0154] where Q pv,i is the reactive power output value of the i-th distributed PV, and S i is the capacity of the i-th distributed PV.

[0155] Optionally, the step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm includes: obtaining the calculation formula for the updated moving speed of each particle as:

[0156]

[0157] where is the updated moving speed of the particle, is the moving speed before update, ω is the inertia weight, C 1 and C 2 are the learning factors, rand is a random number between the interval [0, 1], P pb is the individual extreme value, P gb is the global optimal solution, is the particle position before update.

[0158] Optionally, the step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm further includes: obtaining the calculation formula for the updated position of each particle as:

[0159]

[0160] where is the updated particle position, is the particle position before update.

[0161] In the above specific implementation manner, the distributed PV acceptance capacity improvement device based on the low-voltage distribution network provided by the present invention determines the electrical parameters of the low-voltage distribution network, calculates the global optimal solution according to the electrical parameters and the pre-constructed particle swarm optimization algorithm, and uses the global optimal solution as the maximum PV acceptance capacity. This method uses a method for improving the distributed PV acceptance capacity of a low-voltage distribution network based on PV inverter control and particle swarm algorithm, which can effectively alleviate the problem of voltage over-limit, solve the problems of large investment and insufficient flexibility in improving the distributed PV acceptance capacity in the prior art, thereby effectively improving the distributed PV acceptance capacity of the low-voltage distribution network and giving full play to the power generation benefit of photovoltaic power generation.

[0162] Figure 8 An entity structure schematic diagram of an electronic device is exemplified, as Figure 8 shown. The electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communications interface 820, and the memory 830 complete mutual communication through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute a method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network. The method includes: determining the power parameters of the low-voltage distribution network; calculating a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity; where the global optimal solution at least includes a decision variable value and a fitness function value corresponding to the decision variable value.

[0163] In addition, when the logical instructions in the above-mentioned memory 830 can be implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0164] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network provided by the above-mentioned various methods. The method includes: determining the power parameters of the low-voltage distribution network; calculating a global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity; where the global optimal solution at least includes a decision variable value and a fitness function value corresponding to the decision variable value.

[0165] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network provided by the above-mentioned various methods. The method includes: determining the electrical parameters of the low-voltage distribution network; calculating the global optimal solution according to the electrical parameters and a pre-constructed particle swarm optimization algorithm, and using the global optimal solution as the maximum photovoltaic acceptance capacity; wherein the global optimal solution at least includes the decision variable value and the fitness function value corresponding to the decision variable value.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0167] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network, characterized in that, the method includes: Determine the electrical parameters of the low-voltage distribution network; According to the electrical parameters and the pre-constructed particle swarm optimization algorithm, calculate the global optimal solution, and use the global optimal solution as the maximum photovoltaic acceptance capacity, including: determine the decision variables, and construct a fitness function that satisfies the voltage constraint; according to the upper limit of the decision variables, randomly select the initial photovoltaic active power output to initialize the population; based on the photovoltaic inverter control algorithm, calculate the photovoltaic reactive power output value; based on the forward-backward substitution power flow method and the photovoltaic reactive power output value, calculate the node voltages in the initialized population; calculate the fitness function of each particle in the initialized population, and determine the current individual extreme value of each particle and the current global optimal solution of the particle swarm; wherein, the global optimal solution includes at least the decision variable value and the fitness function value corresponding to the decision variable value; Determine the decision variables, specifically including: using the active power output of each distributed photovoltaic as the decision variable; wherein, the upper limit of the decision variable is the maximum active power output of the distributed photovoltaic under the minimum power factor, and the lower limit of the decision variable is 0; The structure satisfies the fitness function under voltage constraints as follows: ， Among them, is the set of decision variables for each particle, ; is the active power output of the th distributed photovoltaic; is the number of distributed photovoltaics connected to the low-voltage distribution network; is the fitness function; is the voltage of node ; is the maximum upper limit allowed for the low-voltage distribution network voltage.

2. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 1, characterized in that, before determining the electrical parameters of the low-voltage distribution network, it further includes: Set the initial parameters of the particle swarm optimization algorithm.

3. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 2, characterized in that, The initial parameters of the particle swarm optimization algorithm include at least the population size, the number of iterations, the learning factor, and the inertia weight.

4. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 1, characterized in that, The determination of the electrical parameters of the low-voltage distribution network specifically includes: Determine the topological structure of the low-voltage distribution network, the line model and resistance-reactance parameters, the load level of each node, the capacity and power factor of the distributed photovoltaic inverter, and the access node.

5. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 1, characterized in that, After determining the current individual extreme value of each particle and the current global optimal solution of the particle swarm, it includes: Update the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm; When the number of iterations reaches the preset maximum number of iterations, use the current global optimal solution as the maximum photovoltaic acceptance capacity output.

6. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 5, characterized in that, The steps of calculating the photovoltaic inductive reactive power output value based on the photovoltaic inverter control algorithm include: obtaining the calculation formula for the power factor of the nth distributed photovoltaic is: ， Among them, is the power factor of the th distributed photovoltaic, is the maximum power factor set for the distributed photovoltaic, is the active power output of the th distributed photovoltaic, is the minimum power factor set for the distributed photovoltaic, is the demarcation value for starting the power factor adjustment, is the demarcation value for the photovoltaic to operate at the minimum power factor.

7. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 5, characterized in that, The step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm includes: obtaining the calculation formula for the updated moving speed of each particle as: ， Among them, is the moving speed of the particle after update, is the moving speed of the particle before update, is the inertia weight, and are learning factors, is a random number between the interval [0, 1], is the individual extreme value, is the global optimal solution, is the particle position before update.

8. The method for improving the acceptance capacity of distributed photovoltaic power in a low-voltage distribution network according to claim 7, It is characterized in that The step of updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm further includes: The calculation formula for obtaining the updated position of each particle is: ， Among them, is the position of the particle after update, is the position of the particle before update.

9. A device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network It is characterized in that The device includes: A parameter determination unit for determining the power parameters of the low-voltage distribution network; An optimal solution calculation unit for calculating the global optimal solution according to the power parameters and a pre-constructed particle swarm optimization algorithm, and taking the global optimal solution as the maximum photovoltaic acceptance capacity, including: determining decision variables and constructing a fitness function that satisfies voltage constraints; randomly selecting an initial photovoltaic active power output according to the upper limit of the variables of the decision variables to initialize the population; calculating the photovoltaic reactive power output value based on the photovoltaic inverter control algorithm; calculating the node voltages in the initialized population based on the forward-backward load flow method and the photovoltaic reactive power output value; calculating the fitness function of each particle in the initialized population, and determining the current individual extreme value of each particle and the current global optimal solution of the particle swarm; Wherein, the global optimal solution at least includes the decision variable value and the fitness function value corresponding to the decision variable value; Determining the decision variables specifically includes: using the active power output of each distributed photovoltaic as the decision variable; wherein, the upper limit of the variables of the decision variable is the maximum active power output of the distributed photovoltaic under the minimum power factor, and the lower limit of the variables of the decision variable is 0; The structure satisfies the fitness function under voltage constraints as follows: ， Among them, is the set of decision variables for each particle, ; is the active power output of the th distributed photovoltaic; is the number of distributed photovoltaics connected to the low-voltage distribution network; is the fitness function; is the voltage of node ; is the maximum upper limit allowed for the low-voltage distribution network voltage.

10. The device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network according to claim 9 It is characterized in that The parameter determination unit is further used for: Setting the initial parameters of the particle swarm optimization algorithm, and the initial parameters of the particle swarm optimization algorithm at least include the population size, the number of iterations, the learning factor, and the inertia weight.

11. The device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network according to claim 9 It is characterized in that Determining the power parameters of the low-voltage distribution network specifically includes: Determining the topological structure of the low-voltage distribution network, the line model and resistance-reactance parameters, the load level of each node, the capacity and power factor of the distributed photovoltaic inverter, and the access node.

12. The device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network according to claim 9 It is characterized in that After the optimal solution calculation unit determines the current individual extreme value of each particle and the current global optimal solution of the particle swarm, it includes: Updating the moving speed and position of each particle according to the current individual extreme value of each particle and the current global optimal solution of the particle swarm; When the number of iterations reaches the preset maximum number of iterations, output the current global optimal solution as the maximum photovoltaic acceptance capacity.

13. The device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network according to claim 9 It is characterized in that In the optimal solution calculation unit, the steps of calculating the photovoltaic reactive power output value based on the photovoltaic inverter control algorithm include: obtaining the calculation formula for the power factor of the ， Among them, is the power factor of the th distributed photovoltaic, is the maximum power factor set for the distributed photovoltaic, is the th active power output of the distributed photovoltaic, is the minimum power factor set for the distributed photovoltaic, is the demarcation value for starting the power factor adjustment, is the demarcation value for the photovoltaic to operate at the minimum power factor.

14. The device for improving the distributed photovoltaic acceptance capacity based on a low-voltage distribution network according to claim 13 It is characterized in that In the optimal solution calculation unit, the step of calculating the photovoltaic inductive reactive power output value based on the photovoltaic inverter control algorithm further includes: obtaining the reactive power output value calculation formula of the nth distributed photovoltaic is: ， Among them, is the , capacity of the nth distributed photovoltaic.

15. A non-transitory computer-readable storage medium, on which a computer program is stored It is characterized in that When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

16. A computer program product, comprising a computer program, wherein, when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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