A distributed photovoltaic operation optimization method, device, equipment and medium

Abstract

The present invention belongs to the field of distributed photovoltaics, and specifically relates to a distributed photovoltaic operation optimization method, device, equipment and medium. The present invention constructs an operation optimization problem containing a distributed photovoltaic distribution network, adopts an improved adaptive particle swarm algorithm, optimizes and solves the objective function based on constraints, and obtains the optimal power generation power of each photovoltaic panel of the distributed photovoltaic. The characteristics of the distributed photovoltaic power generation system and the operation requirements of the power grid are comprehensively considered, and the power generation efficiency of the distributed photovoltaic power generation system can be effectively improved, energy waste can be reduced, and the adjustable and controllable capabilities of distributed photovoltaics can be improved, providing strong technical support for the widespread application of distributed photovoltaic power generation systems.

Description

Technical Field

[0001] The present invention belongs to the field of distributed photovoltaics, and specifically relates to a distributed photovoltaic operation optimization method, device, equipment and medium. Background Art

[0002] In order to achieve the "dual carbon" goal and accelerate energy transformation, the scale of new energy installed capacity in the distribution network has grown rapidly, and the proportion of renewable energy power generation connected to the grid, represented by photovoltaics and wind power, has continued to rise. At the same time, with the large-scale development of multiple flexible loads such as electric vehicles, the distribution network has undergone profound changes in form, transforming into an active distribution network with the characteristics of random fluctuations in source and load and multiple interactions, which brings challenges to the high-reliability operation of the distribution network. At present, distributed power sources are often connected to the distribution network in a high-density and decentralized manner, showing the characteristics of many points and wide areas, and high local penetration. Due to the grid connection of large-scale distributed power sources, the distribution network has prominent problems such as flow return and voltage over-limit. It is necessary to effectively regulate the distributed power sources of the distribution network. Active distribution networks need to improve the system's ability to absorb a large number of intermittent renewable energy by regulating multiple types of distributed power sources in the system under the premise of ensuring the safe and stable operation of the system. The distributed power optimization scheduling strategy is the core technology and important means to implement active management of distributed power sources and achieve network security and economic operation.

[0003] Due to changes in environmental factors such as light intensity and temperature, the power generation of photovoltaic panels will fluctuate, resulting in instability in grid voltage and current, which in turn affects the operational safety and stability of the entire grid. In addition, the access of distributed photovoltaic grid-connected systems also places higher demands on the dispatching and management of the grid. Traditional power dispatching strategies are often difficult to adapt to the characteristics of distributed photovoltaic power generation systems, resulting in low energy utilization efficiency and even energy waste.

[0004] Therefore, how to optimize the operation of distributed photovoltaic power generation systems, improve power generation efficiency, and reduce the negative impact on the power grid is a hot topic in current research. Existing distributed photovoltaic operation optimization methods mainly focus on the selection of photovoltaic panels, installation angles, array layout, etc., while how to optimize the overall system level, especially considering grid constraints and scheduling strategies, to achieve the optimal operation of distributed photovoltaic power generation systems, is still an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed photovoltaic operation optimization method, device, equipment and medium to solve the problem that traditional power dispatching strategies in the prior art are often difficult to adapt to distributed photovoltaic power generation systems, resulting in low energy utilization efficiency and even energy waste.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a distributed photovoltaic operation optimization method, comprising:

[0008] Constructing an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions;

[0009] An improved adaptive particle swarm algorithm is used to optimize and solve the objective function based on constraint conditions to obtain the optimal power generation power of each photovoltaic panel in the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2.

[0010] Furthermore, the objective function specifically includes the following:

[0011] minf=ω1f1-ω2f2

[0012]

[0013]

[0014] Among them, ω1 and ω2 represent the weight coefficients of the two objective functions respectively; M is the number of photovoltaic users participating in the regulation, N is the total number of nodes in the distribution network area, l is the set of nodes connected to node n; i nl is the current in the branch connecting node n and node l, r nl is the resistance of the branch connecting node n and node l. J is the number of key nodes in the distribution network, U j represents the voltage amplitude of the key node j, and U0 represents the node voltage reference value.

[0015] Furthermore, after obtaining the optimal power generation power of each photovoltaic panel of the distributed photovoltaic system, the power of the photovoltaic panel is regulated based on a distributed photovoltaic intelligent monitoring and control device pre-installed on the photovoltaic panel; wherein the distributed photovoltaic intelligent monitoring and control device is also used to collect operating data of the photovoltaic panel when solving the objective function.

[0016] Furthermore, the constraints specifically include: power balance constraints, distributed photovoltaic power generation constraints, node voltage constraints and line current constraints, which are specifically expressed as follows:

[0017] 1) Power balance constraints:

[0018]

[0019] Among them, P G,i , Q G,i represents the active power and reactive power generated by node i, P L,i , QL,i Represent the active power and reactive power of the load at node i, U i , U j Represent the voltage amplitude of node i and node j respectively, G ij , B ij Represent the conductance and susceptance of the line between node i and node j, δ ij Represents the power angle of branch ij;

[0020] 2) Distributed photovoltaic power generation constraints

[0021] 0≤P pv ≤P max

[0022] Q min ≤Q pv ≤Q max

[0023] Among them, P pv , Q pv Represent the active power and reactive power of distributed photovoltaic power generation respectively; P max Indicates the maximum active power of distributed photovoltaic power generation; Q min and Q max They represent the upper and lower limits of the reactive power of distributed photovoltaic power generation respectively;

[0024] 3) Node voltage constraints

[0025] U min ≤U i ≤U max

[0026] Among them, U max is the upper limit of node voltage, U min is the lower limit of node voltage;

[0027] 4) Line current constraints

[0028] I ij |≤I ij.max

[0029] Among them, I ij represents the current in the line between node i and node j, I ij.max Indicates the maximum current carrying capacity allowed for the line between node i and node j.

[0030] Furthermore, based on the inertia weight ω and the learning factors c1 and c2, the particle velocity and position update formulas are optimized, including:

[0031]

[0032]

[0033] in, is the component of the velocity vector of particle i in the jth dimension at the tth iteration, r1 and r2 are random numbers between (0,1), represents the j component of the single best position component of particle i in t iterations, represents the j component of the global optimal position component of particle i in the tth iteration, t represents the number of iterations; ω t , Indicates the inertia weight and learning factor of each iteration value after adaptively changing according to the particle fitness value; is the component of the position vector of particle i at the tth iteration in the jth dimension.

[0034] Furthermore, an improved adaptive particle swarm algorithm is used to optimize and solve the objective function based on constraints, including:

[0035] Input: Population initialization x=x1,x2,...,x n ;

[0036] Output: particle velocity v and position x;

[0037] 1) Population initialization: The initial position of the particle x = x1, x2, ..., x n ;

[0038] 2) Calculate the individual fitness f(x i );

[0039] 3) Calculate the individual's historical optimal position The historical best position of the group And the corresponding optimal value of Fb t ;

[0040] 4) Update all particles according to the final improved particle update formula to obtain the iterated particle speed and position;

[0041] 5) Determine the number of iterations. If the maximum number of iterations is reached, end the optimization process. If the maximum number of iterations is not reached, proceed to step 2).

[0042] Furthermore, the number of particle groups is set to the number of controllable photovoltaic panels, and the maximum speed of particle movement v max Constrained by the photovoltaic power generation, the maximum distance that the particle changes at each update is the actual maximum power generation of the photovoltaic panel, and the minimum speed of the particle movement v min Set to 0; the result of the optimization solution is the optimal power generation power of each photovoltaic panel for each photovoltaic user.

[0043] In a second aspect, the present invention provides a distributed photovoltaic operation optimization device, comprising:

[0044] An optimization problem construction module is used to construct an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions;

[0045] The optimization problem solving module is used to use an improved adaptive particle swarm algorithm to optimize and solve the objective function based on constraints to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2.

[0046] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned distributed photovoltaic operation optimization method.

[0047] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed photovoltaic operation optimization method as described above is implemented.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] The present invention constructs an operation optimization problem involving a distributed photovoltaic distribution network, adopts an improved adaptive particle swarm algorithm, optimizes and solves the objective function based on constraint conditions, and obtains the optimal power generation power of each photovoltaic panel of the distributed photovoltaic system. The characteristics of the distributed photovoltaic power generation system and the operation requirements of the power grid are comprehensively considered, and the power generation efficiency of the distributed photovoltaic power generation system can be effectively improved, energy waste can be reduced, and the adjustable and controllable capabilities of distributed photovoltaics can be improved, providing strong technical support for the widespread application of distributed photovoltaic power generation systems. The distributed photovoltaic operation optimization device, electronic device, and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology section.

[0050] The present invention proposes an adaptive particle swarm algorithm, which uses an adaptive inertia weight and a variable learning factor method to adaptively improve the particle swarm algorithm. The weights of global search and local search can be changed at different stages of the search, effectively improving the global search capability of the algorithm.

[0051] The present invention installs a photovoltaic monitoring device on the photovoltaic panel, which can monitor the photovoltaic operation status and obtain data such as voltage, current, and power of the photovoltaic operation to perform distributed photovoltaic operation optimization analysis. The device has the function of quickly shutting down the photovoltaic panel and can realize remote control of the power generation power of the photovoltaic panel. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0053] Figure 1 A schematic diagram of a distributed photovoltaic operation optimization method according to an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of the solution of the particle swarm algorithm in the embodiment of the present invention;

[0055] Figure 3 This is a structural block diagram of a distributed photovoltaic operation optimization device according to an embodiment of the present invention;

[0056] Figure 4 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0058] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0059] Example 1

[0060] The present invention proposes a distributed photovoltaic operation optimization method based on an adaptive particle swarm algorithm. Firstly, a photovoltaic operation monitoring device is installed on a photovoltaic panel to collect photovoltaic operation data and remotely control the power generation of the photovoltaic panel. Secondly, the distributed photovoltaic operation optimization problem is decomposed into an objective function and constraint conditions to facilitate solving the optimization problem. Then, on the basis of the basic particle swarm algorithm, the adaptive inertia weight is improved, the learning factor is improved by considering the search characteristics, and the particle swarm update formula is adjusted to have an adaptive characteristic. Finally, the distributed photovoltaic operation optimization problem is optimized and solved by using the improved adaptive particle swarm algorithm to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic.

[0061] like Figure 1 As shown, a distributed photovoltaic operation optimization method includes:

[0062] S1. Constructing an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions;

[0063] S2. Using an improved adaptive particle swarm algorithm, based on constraints, the objective function is optimized and solved to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2.

[0064] This scheme comprehensively considers the characteristics of the distributed photovoltaic system and the operation requirements of the power grid. By optimizing the objective function, it can ensure that the power grid can maintain stable operation after connecting to the distributed photovoltaic system, and reduce the fluctuation and instability of the power grid caused by the access of the photovoltaic system. By optimizing the operation of the distributed photovoltaic distribution network, each photovoltaic panel can achieve the optimal power generation, thereby improving the energy efficiency of the entire system. In addition, the improved adaptive particle swarm algorithm has a strong global search capability and convergence speed, which can cope with the complex operating environment and variable constraints of the distributed photovoltaic system, and ensure the accuracy and reliability of the optimization results.

[0065] Specifically, in step S1, considering the actual operation situation of the distributed photovoltaic distribution network, the optimization objective function is set to minimize the regional distribution network loss and minimize the voltage deviation of key nodes in the region, which can be expressed as:

[0066]

[0067]

[0068] Among them, ω1 and ω2 represent the weight coefficients of the two objective functions respectively; M is the number of photovoltaic users participating in the regulation, N is the total number of nodes in the distribution network area, l is the set of nodes connected to node n; i nl is the current in the branch connecting node n and node l, r nl is the resistance of the branch connecting node n and node l. J is the number of key nodes in the distribution network, U j represents the voltage amplitude of the key node j, U0 represents the node voltage reference value, and for the convenience of calculation and analysis, the node voltage amplitude is expressed in the form of per-unit value.

[0069] Considering the different emphasis on the two objective functions in practical problems, the two objective functions are weighted and combined, and the comprehensive objective function is as follows:

[0070] minf=ω1f1-ω2f2

[0071] Among them, ω1 and ω2 represent the weight coefficients of the two objective functions. When ω1>ω2, it means that in the optimization process, the main optimization goal is to minimize the distribution network loss, and the system economy is emphasized; when ω1<ω2, it means that the main optimization goal is to minimize the node voltage deviation, and the system safety is emphasized.

[0072] Specifically, in step S1, in order to solve the distributed photovoltaic operation optimization problem, the constraints should meet the following aspects:

[0073] 1) Power balance constraints:

[0074] The distribution network must meet active power balance and reactive power balance during normal operation, namely:

[0075]

[0076] Among them, P G,i , Q G,i represents the active power and reactive power generated by node i, P L,i , Q L,i Represent the active power and reactive power of the load at node i, U i , U j Represent the voltage amplitude of node i and node j respectively, G ij , B ij Represent the conductance and susceptance of the line between node i and node j, δ ij Represents the power angle of branch ij;

[0077] 2) Distributed photovoltaic power generation constraints

[0078] 0≤P pv ≤P max

[0079] Q min ≤Q pv ≤Q max

[0080] Among them, P pv , Q pv Represent the active power and reactive power of distributed photovoltaic power generation respectively; P max Indicates the maximum active power of distributed photovoltaic power generation; Q min and Q max They represent the upper and lower limits of the reactive power of distributed photovoltaic power generation respectively;

[0081] 3) Node voltage constraints

[0082] When the distribution network is operating normally, the voltage at each node cannot exceed the specified upper and lower limits, namely:

[0083] U min ≤U i ≤U max

[0084] Among them, U max is the upper limit of node voltage, U min is the lower limit of node voltage;

[0085] 4) Line current constraints

[0086] To ensure line safety, the current flowing through the line cannot exceed its maximum allowable current carrying capacity, that is:

[0087] I ij |≤I ij.max

[0088] Among them, I ij represents the current in the line between node i and node j, I ij.max Indicates the maximum current carrying capacity allowed for the line between node i and node j.

[0089] Preferably, after the step of obtaining the optimal power generation power of each photovoltaic panel of the distributed photovoltaic system, the power of the photovoltaic panel is regulated based on a distributed photovoltaic intelligent monitoring and control device pre-installed on the photovoltaic panel; wherein the distributed photovoltaic intelligent monitoring and control device is also used to collect the operating data of the photovoltaic panel when solving the objective function.

[0090] Specifically, the distributed photovoltaic intelligent monitoring and control device is installed on each photovoltaic panel, mainly realizing the functions of operating data monitoring, collection, transmission and power generation control. The photovoltaic panel operation data collected includes voltage, current, power generation, real-time power and temperature data. The data collection frequency is 10 minutes and sent to the server through the built-in wireless network card. The distributed photovoltaic intelligent monitoring and control device has the function of quickly shutting down and starting the photovoltaic panel. It can remotely issue instructions through the server to control the shutdown and start status of the photovoltaic panel, and combine the photovoltaic panel operation data for distributed photovoltaic operation optimization.

[0091] Specifically, in step S2, based on the inertia weight ω and the learning factors c1 and c2, the particle velocity and position update formulas are optimized, including:

[0092]

[0093]

[0094] in, is the component of the velocity vector of particle i in the jth dimension at the tth iteration, r1 and r2 are random numbers between (0,1), represents the j component of the single best position component of particle i in t iterations, represents the j component of the global optimal position component of particle i in the tth iteration, t represents the number of iterations; ω t , Indicates the inertia weight and learning factor of each iteration value after adaptively changing according to the particle fitness value; is the component of the position vector of particle i at the tth iteration in the jth dimension.

[0095] Specifically, an improved adaptive particle swarm algorithm is used to optimize and solve the objective function based on constraints, including:

[0096] Input: Population initialization x=x1,x2,...,x n ;

[0097] Output: particle velocity v and position x;

[0098] 1) Population initialization: The initial position of the particle x = x1, x2, ..., x n ;

[0099] 2) Calculate the individual fitness f(x i );

[0100] 3) Calculate the individual's historical optimal position The historical best position of the group And the corresponding optimal value of Fb t ;

[0101] 4) Update all particles according to the final improved particle update formula to obtain the iterated particle speed and position;

[0102] 5) Determine the number of iterations. If the maximum number of iterations is reached, end the optimization process. If the maximum number of iterations is not reached, proceed to step 2).

[0103] Specifically, in the above scheme, the number of particle groups is set to the number of controllable photovoltaic panels, and the maximum speed v of particle movement is max Constrained by the photovoltaic power generation, the maximum distance that the particle changes at each update is the actual maximum power generation of the photovoltaic panel, and the minimum speed of the particle movement v min Set to 0; the result of the optimization solution is the optimal power generation power of each photovoltaic panel for each photovoltaic user.

[0104] In the optional scheme, the objective function set in this scheme can be solved by particle swarm algorithm. The basic steps of particle swarm algorithm are as follows:

[0105] Input: initial position and initial velocity x, v of the particle

[0106] Output: Global optimal solution

[0107] 1) Particle swarm initialization: set initial parameters, randomly generate particles within a predetermined range, and assign initial positions and velocities to the particles;

[0108] 2) Calculate the fitness value of each particle in the group based on the objective function and the current position of the particle;

[0109] 3) Obtain the speed and position update formula based on the particle's best historical position and the group's best position, and calculate the particle's updated speed and position;

[0110] The particle update formula is as follows:

[0111]

[0112]

[0113] in, is the component of the velocity vector of particle i in the jth dimension at the tth iteration, is the component of the position vector of particle i at the tth iteration in the jth dimension, represents the j component of the single best position component of particle i in t iterations, Represents the j component of the global best position component of particle i in t iterations. r1 and r2 are random numbers between (0,1), and c1 and c2 are the step sizes set to adjust the direction of the optimal solution, that is, the learning factor.

[0114] 4) Determine whether the search stop condition is met. If the stop condition is met, directly proceed to step 5). Otherwise, proceed to step 2) to continue updating the particle position;

[0115] 5) Output the global optimal solution and the algorithm ends.

[0116] In some other optional schemes, an adaptive particle swarm algorithm can also be used to solve the objective function set in this scheme.

[0117] It should be noted that:

[0118] 1) Improved adaptive weighted particle swarm algorithm

[0119] Based on the improved particle swarm algorithm with linearly decreasing inertia weight, the traditional particle update formula is modified and ω is added as the inertia weight. The particle velocity update formula is as follows:

[0120]

[0121]

[0122] Where t represents the number of iterations, ω max Indicates the maximum inertia weight setting, ω minIndicates the minimum value of the set inertia weight. As the number of iterations increases, the inertia coefficient gradually decreases from the maximum value to the minimum value. In the early stage of the search, the particle inertia weight ω value is large, and the particle is not easily affected by the local optimal solution, which is conducive to global search. In the later stage of the search, the particle inertia weight ω value is small, and the particle's own historical optimal position and the population's global historical optimal position have a greater relative influence, which is more conducive to local search.

[0123] In order to balance the global search in the early stage of the search and the local search capabilities in the later stage of the search, the ω that gradually decreases with the number of iterations is adaptively optimized as shown below.

[0124]

[0125] Among them, ω min and ω max are the preset minimum and maximum inertia coefficients, represents the fitness of particle i at the tth iteration, represents the average fitness value of all particles at the tth iteration, It is the minimum fitness of all particles at the tth iteration.

[0126] 2) Improved learning factor

[0127] The particle update formula is modified using a similar idea to the adaptive inertia weighted particle swarm algorithm. The modified learning factor expression is as follows:

[0128]

[0129]

[0130] Among them, c min and c max are the preset minimum and maximum learning factors, Indicates the learning factor c1 at the tth iteration. The smaller the fitness of the current particle, the closer it is to the optimal solution. At this time, the particle needs more local search, the c1 value is larger, and the c2 value is smaller. The larger the fitness of the current particle, the farther it is from the optimal solution. At this time, the particle needs more global search, the c1 value is smaller, and the c2 value is larger.

[0131] For the inertia weight ω and the learning factors c1 and c2, corresponding to the adaptive characteristics of the adaptive particle swarm algorithm, the particle velocity and position update formulas are adjusted as shown below.

[0132]

[0133]

[0134] Among them, ω t, It indicates the inertia weight and learning factor after each iteration value is adaptively changed according to the particle fitness value.

[0135] like Figure 2 As shown, in the preferred embodiment, the above-adjusted adaptive particle swarm algorithm is used to solve the objective function. The speed and position of the particles in the adaptive particle swarm algorithm and the corresponding parameters of each iteration change at any time. The algorithm flow is as follows:

[0136] Input: Population initialization x=x1,x2,...,x n

[0137] Output: particle velocity v and position x

[0138] 1) Population initialization: The initial position of the particle x = x1, x2, ..., x n ;

[0139] 2) Calculate the individual fitness f(x i );

[0140] 3) Calculate the individual's historical optimal position The historical best position of the group And the corresponding optimal value of Fb t ;

[0141] 4) Update all particles according to the final improved particle update formula to obtain the iterated particle speed and position;

[0142]

[0143]

[0144] 5) Determine the number of iterations. If the maximum number of iterations is reached, end the optimization process. If the maximum number of iterations is not reached, proceed to step 2).

[0145] The above scheme provides an adaptive particle swarm algorithm, which is applied to the distributed photovoltaic operation optimization problem. The number of particle swarms N is set to the number of controllable photovoltaic panels. In order to ensure the solution effect and time, considering the complexity of the problem, the number of particle swarms is set in the range of [100,200]. The specific value can be adjusted according to the actual situation. The maximum speed v of particle movement max The maximum distance that a particle can change at each update is the actual maximum power generated by the photovoltaic panel, and the minimum speed v of the particle movement is min Set to 0. The result of the optimization solution is the optimal power generation of each photovoltaic panel for each photovoltaic user.

[0146] Example 2

[0147] like Figure 3 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a distributed photovoltaic operation optimization device, including:

[0148] An optimization problem construction module is used to construct an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions;

[0149] The optimization problem solving module is used to use an improved adaptive particle swarm algorithm to optimize and solve the objective function based on constraints to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2.

[0150] Example 3

[0151] like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a distributed photovoltaic operation optimization method in the above embodiment; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0152] The memory 101 can be used to store a computer program 103 , and the processor 102 implements the steps of a distributed photovoltaic operation optimization method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101 .

[0153] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may 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 (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0154] At least one processor 102 may be a central processing unit (CPU), or 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. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0155] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a distributed photovoltaic operation optimization method, and the processor 102 can execute the plurality of instructions to implement:

[0156] S1. Constructing an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions;

[0157] S2. Using an improved adaptive particle swarm algorithm, based on constraints, the objective function is optimized and solved to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2.

[0158] Example 4

[0159] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. 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. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0160] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed photovoltaic operation optimization method, characterized in that: include: Constructing an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions; An improved adaptive particle swarm algorithm is used to optimize and solve the objective function based on the constraint conditions to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2; The objective function specifically includes the following: minf=ω1f1-ω2f2 Among them, ω1 and ω2 represent the weight coefficients of the two objective functions respectively; M is the number of photovoltaic users participating in the regulation, N is the total number of nodes in the distribution network area, l is the set of nodes connected to node n; i nl is the current in the branch connecting node n and node l, r nl is the resistance of the branch connecting node n and node l; J is the number of key nodes in the distribution network, U j represents the voltage amplitude of the key node j, and U0 represents the node voltage reference value; Based on the inertia weight ω and the learning factors c1 and c2, the particle velocity and position update formulas are optimized, including: in, is the component of the velocity vector of particle i in the jth dimension at the tth iteration, r1 and r2 are random numbers between (0,1), represents the j component of the single best position component of particle i in t iterations, represents the j component of the global optimal position component of particle i in the tth iteration, t represents the number of iterations; ω t , Indicates the inertia weight and learning factor of each iteration value after adaptively changing according to the particle fitness value; is the component of the position vector of particle i at the tth iteration in the jth dimension; The improved adaptive particle swarm algorithm is used to optimize the objective function based on the constraints, including: Input: Population initialization x=x1,x2,...,x n ; Output: particle velocity v and position x; 1) Population initialization: The initial position of the particle x = x1, x2, ..., x n ; 2) Calculate the individual fitness f(x i ); 3) Calculate the individual's historical optimal position The historical best position of the group And the corresponding optimal value of Fb t ; 4) Update all particles according to the final improved particle update formula to obtain the iterated particle speed and position; 5) Determine the number of iterations. If the maximum number of iterations is reached, the optimization process ends. If the maximum number of iterations is not reached, proceed to step 2); The number of particle swarms is set to the number of controllable photovoltaic panels, and the maximum speed of particle movement v max Constrained by the photovoltaic power generation, the maximum distance that the particle changes at each update is the actual maximum power generation of the photovoltaic panel, and the minimum speed of the particle movement v min Set to 0; the result of the optimization solution is the optimal power generation power of each photovoltaic panel for each photovoltaic user.

2. The distributed photovoltaic operation optimization method according to claim 1, characterized in that: After obtaining the optimal power generation power of each photovoltaic panel of the distributed photovoltaic system, the power of the photovoltaic panel is regulated based on the distributed photovoltaic intelligent monitoring and control device pre-installed on the photovoltaic panel; wherein the distributed photovoltaic intelligent monitoring and control device is also used to collect the operating data of the photovoltaic panel when solving the objective function.

3. The distributed photovoltaic operation optimization method according to claim 1, characterized in that: The constraints are specifically expressed as follows: 1) Power balance constraints: Among them, P G,i , Q G,i represents the active power and reactive power generated by node i, P L,i , Q L,i Represent the active power and reactive power of the load at node i, U i , U j Represent the voltage amplitude of node i and node j respectively, G ij , B ij Represent the conductance and susceptance of the line between node i and node j, δ ij Represents the power angle of branch ij; 2) Distributed photovoltaic power generation constraints 0≤P pv ≤P max Q min ≤Q pv ≤Q max Among them, P pv , Q pv Represent the active power and reactive power of distributed photovoltaic power generation respectively; P max Indicates the maximum active power of distributed photovoltaic power generation; Q min and Q max They represent the upper and lower limits of the reactive power of distributed photovoltaic power generation respectively; 3) Node voltage constraints IN min ≤U i ≤U max Among them, U max is the upper limit of node voltage, U min is the lower limit of node voltage; 4) Line current constraints |I ij |≤I ij.max Among them, I ij represents the current in the line between node i and node j, I ij.max Indicates the maximum current carrying capacity allowed for the line between node i and node j.

4. A distributed photovoltaic operation optimization device, characterized in that: include: An optimization problem construction module is used to construct an operation optimization problem of a distributed photovoltaic distribution network; wherein the operation optimization problem is decomposed into an objective function and constraint conditions; The optimization problem solving module is used to optimize and solve the objective function based on the constraint conditions by using the improved adaptive particle swarm algorithm to obtain the optimal power generation of each photovoltaic panel of the distributed photovoltaic system; wherein, in the improved adaptive particle swarm algorithm, the particle velocity and position update formula are optimized based on the inertia weight ω and the learning factors c1 and c2; The objective function specifically includes the following: min f=ω1f1-ω2f2 Among them, ω1 and ω2 represent the weight coefficients of the two objective functions respectively; M is the number of photovoltaic users participating in the regulation, N is the total number of nodes in the distribution network area, l is the set of nodes connected to node n; i nl is the current in the branch connecting node n and node l, r nl is the resistance of the branch connecting node n and node l; J is the number of key nodes in the distribution network, U j represents the voltage amplitude of the key node j, and U0 represents the node voltage reference value; Based on the inertia weight ω and the learning factors c1 and c2, the particle velocity and position update formulas are optimized, including: in, is the component of the velocity vector of particle i in the jth dimension at the tth iteration, r1 and r2 are random numbers between (0,1), represents the j component of the single best position component of particle i in t iterations, represents the j component of the global optimal position component of particle i in the tth iteration, t represents the number of iterations; ω t , Indicates the inertia weight and learning factor of each iteration value after adaptively changing according to the particle fitness value; is the component of the position vector of particle i at the tth iteration in the jth dimension; The improved adaptive particle swarm algorithm is used to optimize the objective function based on the constraints, including: Input: Population initialization x=x1,x2,...,x n ; Output: particle velocity v and position x; 1) Population initialization: The initial position of the particle x = x1, x2, ..., x n ; 2) Calculate the individual fitness f(x i ); 3) Calculate the individual's historical optimal position The historical best position of the group And the corresponding optimal value of Fb t ; 4) Update all particles according to the final improved particle update formula to obtain the iterated particle speed and position; 5) Determine the number of iterations. If the maximum number of iterations is reached, the optimization process ends. If the maximum number of iterations is not reached, proceed to step 2); The number of particle swarms is set to the number of controllable photovoltaic panels, and the maximum speed of particle movement v max Constrained by the photovoltaic power generation, the maximum distance that the particle changes at each update is the actual maximum power generation of the photovoltaic panel, and the minimum speed of the particle movement v min Set to 0; the result of the optimization solution is the optimal power generation power of each photovoltaic panel for each photovoltaic user.

5. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed photovoltaic operation optimization method as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed photovoltaic operation optimization method according to any one of claims 1 to 3 is implemented.

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