Parameter adjustment method and apparatus for photovoltaic simulation model, device and storage medium

Abstract

The present application relates to a parameter adjustment method for a photovoltaic simulation mode, comprising: acquiring a target capacity input for a photovoltaic simulation model; according to the target capacity and a reference capacity of the photovoltaic simulation model, calculating parameter values of a plurality of basic system parameters adaptive to the target capacity; on the basis of the parameter values of the basic system parameters adaptive to the target capacity, automatically adjusting the basic system parameters of the photovoltaic simulation model; using a PI parameter value of a control system as an optimization object, and using a genetic algorithm to perform iterative calculation on the PI parameter value of the control system until a preset iteration end condition is met, thus obtaining an optimized PI parameter value; and, according to the optimized PI parameter value, updating the current PI parameter of the control system.

Description

Parameter adjustment method, device, equipment and storage medium for photovoltaic simulation model

[0001] Related applications

[0002] This application claims priority to Chinese patent application number 202311590774X, filed on November 27, 2023, entitled “Parameter adjustment method, device, equipment and storage medium for photovoltaic simulation model”, the entire text of which is hereby incorporated by reference. Technical Field

[0003] The present application relates to the field of automation technology, and in particular to a parameter adjustment method, apparatus, computer equipment, storage medium, and computer program product for a photovoltaic simulation model. Background Art

[0004] With the increasing popularity of renewable energy, photovoltaic power generation is currently one of the mainstream forms of renewable energy generation. However, due to varying electricity demand across regions, photovoltaic power plants with varying capacity levels are required. Before research findings can be put into practical production, they must be verified through simulations based on various photovoltaic power generation system simulation platforms. Current simulation studies targeting different targets require frequent changes in the capacity level of the photovoltaic simulation model. Each capacity level adjustment requires manual adjustments to dozens of system parameters and multiple sets of PI parameters (Proportional-Integral controller parameters), resulting in low efficiency.

[0005] Summary of the Invention

[0006] Based on this, it is necessary to provide a parameter adjustment method, device, computer equipment, computer-readable storage medium and computer program product for a photovoltaic simulation model that can improve the efficiency of parameter adjustment in order to address the above technical problems.

[0007] In a first aspect, the present application provides a method for adjusting parameters of a photovoltaic simulation model, comprising:

[0008] Obtaining a target capacity inputted into the photovoltaic simulation model;

[0009] Calculating parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and a benchmark capacity of the photovoltaic simulation model;

[0010] Adjusting basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters that are compatible with the target capacity;

[0011] The PI parameter value of the control system is used as the optimization object, and the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; and

[0012] The current PI parameters of the control system are updated according to the optimized PI parameter values.

[0013] In one embodiment, the calculating, based on the target capacity and the benchmark capacity of the photovoltaic simulation model, parameter values ​​of a plurality of basic system parameters adapted to the target capacity includes:

[0014] Calculating a ratio of the target capacity to the reference capacity to obtain a ratio coefficient; and

[0015] Based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters, the parameter values ​​of the other basic system parameters adapted to the target capacity are calculated.

[0016] In one embodiment, the other system parameters include the number of photovoltaic modules and the number of photovoltaic module strings in the photovoltaic simulation model; and calculating the parameter values ​​of the other basic system parameters adapted to the target capacity based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters includes:

[0017] Calculating the number of photovoltaic modules suitable for the target capacity; and

[0018] The number of photovoltaic module strings suitable for the target capacity is calculated using the following formula:

[0019] Among them, n represents the ratio coefficient, N s ' r With N sr represents the number of photovoltaic modules connected in series in the photovoltaic panels of the photovoltaic simulation model under the target capacity and the benchmark capacity, respectively, N′ pl With N pl They represent the number of photovoltaic module strings connected in parallel in the photovoltaic panel of the photovoltaic simulation model under the target capacity and the benchmark capacity respectively.

[0020] In one embodiment, the other system parameters include a rated output active power reference value, a rated transmission power grid-connected point reference voltage of a single inverter, a grid-connected point bus voltage rating, an inverter AC output side reference voltage, an inverter DC input side reference voltage, a photovoltaic panel rated open-circuit voltage, and a photovoltaic panel rated short-circuit current. The parameter values ​​of the other basic system parameters adapted to the target capacity are calculated based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters, and further include:

[0021] Calculating a grid-connected point reference voltage for a single inverter that is compatible with the target capacity;

[0022] Calculating a grid connection point bus voltage rating corresponding to the target capacity;

[0023] Calculating an inverter AC output side reference voltage that is compatible with the target capacity;

[0024] Calculating a DC input side reference voltage of the inverter that is suitable for the target capacity;

[0025] Calculating a rated open-circuit voltage of the photovoltaic panel corresponding to the target capacity; and

[0026] Calculate the rated short-circuit current of the photovoltaic panel corresponding to the target capacity. The corresponding calculation formula includes:

[0027] Among them, P ref ′ and P ref They represent the rated output active power reference value of the photovoltaic simulation model under target capacity and benchmark capacity, P rated ′ and P rated They represent the rated transmission power of a single inverter of the photovoltaic simulation model under target capacity and benchmark capacity, V base ′ and V base Represent the reference voltage of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V bus ′ and V bus They represent the bus voltage ratings of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V inv_base ′ and V inv_base Represents the AC output side reference voltage of the inverter under target capacity and reference capacity respectively, V dc_base ′ and V dc_base Represents the DC input side reference voltage of the inverter under target capacity and reference capacity respectively, V oc_MPPT ′ and V oc_MPPT They represent the rated open-circuit voltage of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity, respectively. sc_MPPT′ and I sc_MPPT They represent the rated short-circuit current of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity respectively.

[0028] In one embodiment, adjusting the basic system parameters of the photovoltaic simulation model based on parameter values ​​that are compatible with the target capacity includes:

[0029] Determining parameters whose parameter values ​​remain unchanged among basic system parameters of the photovoltaic simulation model;

[0030] Determining parameters to be adjusted among basic system parameters of the photovoltaic simulation model; and

[0031] The parameter to be adjusted is adjusted based on a parameter value adapted to the target capacity.

[0032] In one embodiment, the method further includes: taking the PI parameter value of the control system as the optimization object, iteratively calculating the PI parameter value of the control system using a genetic algorithm until a preset iteration end condition is satisfied and the optimized PI parameter value is obtained.

[0033] A fitness function is pre-built; the fitness function includes:

[0034] Among them, ω1, ω2, and ω3 are three weight coefficients respectively, P is the active power actually output after the photovoltaic simulation model reaches steady state, and P ref is the input capacity target that needs to be adjusted, Q is the reactive power actually output after the photovoltaic simulation model reaches steady state, and Q ref is the reactive power output of the photovoltaic simulation model reference, t stab The time required for the photovoltaic simulation model to reach steady state.

[0035] In one embodiment, the PI parameter value of the control system is used as the optimization object, and the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met, and the optimized PI parameter value is obtained, including:

[0036] Construct multiple initial populations with the set initial values ​​of PI parameters;

[0037] configuring chromosome encoding for each of the initial populations;

[0038] Substituting the current system output active power and reactive power change curves of the photovoltaic simulation model into a pre-built fitness function to calculate the fitness of each of the initial populations;

[0039] Calculating the probability of selecting the next generation corresponding to each of the initial populations according to the fitness;

[0040] placing the selected initial populations into a crossover pool based on the probability of selecting the next generation corresponding to each of the initial populations; and

[0041] The initial population in the crossover pool is subjected to gene crossover and gene mutation processing according to the chromosome coding to obtain a processed new population.

[0042] In one embodiment, the calculating, based on the fitness, the probability of selecting the next generation corresponding to each of the initial populations includes:

[0043] The probability is calculated as follows:

[0044] Among them, f(x i ) represents the fitness of individuals in the i-th initial population, and N represents the size of the entire initial population.

[0045] In one embodiment, after performing gene crossover and gene mutation processing on the selected initial population according to the chromosome coding to obtain a processed new population, the method further includes:

[0046] Determining whether the processed new population meets a preset iteration end condition; and

[0047] If the iteration end condition is not met, return to the step of bringing the current system output active power and reactive power change curves of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each of the initial populations until the preset iteration end condition is met to obtain the optimized PI parameter value.

[0048] In one embodiment, the set initial values ​​of the PI parameters include: the number of optimization objects, the initial population size, and the evolution range limit of the population individuals.

[0049] In one embodiment, the benchmark capacity is the current capacity level of the photovoltaic simulation model.

[0050] In a second aspect, the present application further provides a parameter adjustment device for a photovoltaic simulation model, comprising:

[0051] A target capacity acquisition module, configured to acquire a target capacity inputted into the photovoltaic simulation model;

[0052] A basic parameter calculation module, configured to calculate parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity based on the target capacity and the benchmark capacity of the photovoltaic simulation model;

[0053] a basic parameter adjustment module, configured to adjust basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters adapted to the target capacity;

[0054] A PI parameter calculation module is configured to use the PI parameter value of the control system as an optimization object, and to iteratively calculate the PI parameter value of the control system using a genetic algorithm until a preset iteration end condition is satisfied, thereby obtaining an optimized PI parameter value; the genetic algorithm constructs multiple initial populations using the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the results of the iterative operation of the photovoltaic simulation model as result feedback of the genetic algorithm; and

[0055] The PI parameter updating module is used to update the current PI parameters of the control system according to the optimized PI parameter values.

[0056] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0057] Obtaining a target capacity inputted into the photovoltaic simulation model;

[0058] Calculating parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and a benchmark capacity of the photovoltaic simulation model;

[0059] Adjusting basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters that are compatible with the target capacity;

[0060] The PI parameter value of the control system is used as the optimization object, and the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; and

[0061] The current PI parameters of the control system are updated according to the optimized PI parameter values.

[0062] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0063] Obtaining a target capacity inputted into the photovoltaic simulation model;

[0064] Calculating parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and a benchmark capacity of the photovoltaic simulation model;

[0065] Adjusting basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters that are compatible with the target capacity;

[0066] The PI parameter value of the control system is used as the optimization object, and the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; and

[0067] The current PI parameters of the control system are updated according to the optimized PI parameter values.

[0068] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0069] Obtaining a target capacity inputted into the photovoltaic simulation model;

[0070] Calculating parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and a benchmark capacity of the photovoltaic simulation model;

[0071] Adjusting basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters that are compatible with the target capacity;

[0072] The PI parameter value of the control system is used as the optimization object, and the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; and

[0073] The current PI parameters of the control system are updated according to the optimized PI parameter values.

[0074] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to better describe and illustrate the embodiments and / or examples of the inventions disclosed herein, reference may be made to one or more of the accompanying drawings. The additional details or examples used to describe the accompanying drawings should not be considered to limit the scope of the disclosed inventions, the presently described embodiments and / or examples, and any of the best modes currently understood for these inventions.

[0076] FIG1 is a diagram illustrating an application environment of a parameter adjustment method for a photovoltaic simulation model according to one or more embodiments;

[0077] FIG2 is a schematic flow chart of a method for adjusting parameters of a photovoltaic simulation model according to one or more embodiments;

[0078] FIG3 is a flow chart illustrating steps for optimizing PI parameters using a genetic algorithm according to one or more embodiments;

[0079] FIG4 is a schematic flow chart of a method for automatically adjusting capacity parameters of a photovoltaic simulation model according to another embodiment or embodiments;

[0080] FIG5 is a schematic diagram of a topological model of a photovoltaic power generation system simulation electromagnetic transient model according to one or more embodiments / some embodiments;

[0081] FIG6 is a fitness curve diagram of a genetic algorithm iteration process according to one or more embodiments;

[0082] FIG7 is a graph showing a rated capacity output curve of a photovoltaic simulation model after automatic parameter adjustment is completed in one or more embodiments;

[0083] FIG8 is a structural block diagram of a parameter adjustment device for a photovoltaic simulation model according to one or more embodiments / some embodiments;

[0084] FIG9 is a diagram illustrating the internal structure of a computer device according to one or more embodiments / some embodiments. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0086] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0087] The parameter adjustment method of the photovoltaic simulation model provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. Therein, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the target capacity input from the terminal 102 for the photovoltaic simulation model, and then calculates the parameter values ​​of multiple basic system parameters that are compatible with the target capacity based on the target capacity and the baseline capacity of the photovoltaic simulation model; the server 104 adjusts the basic system parameters of the photovoltaic simulation model based on the parameter values ​​that are compatible with the target capacity. Furthermore, server 104 uses a genetic algorithm to iteratively calculate the control system's PI parameter values, using them as the optimization target until a preset iteration termination condition is met, resulting in an optimized PI parameter value. The genetic algorithm constructs multiple initial populations using the set initial PI parameter values, calculates the fitness of each initial population based on a preset fitness function, and iterates based on each initial population. The preset fitness function is constructed based on the results of the iterative operation of the photovoltaic simulation model, which serves as feedback for the genetic algorithm. Finally, server 104 updates the current PI parameters of the control system based on the optimized PI parameter values. Terminal 102 may be, but is not limited to, various personal computers, laptops, etc. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0088] In an exemplary embodiment, as shown in FIG2 , a parameter adjustment method for a photovoltaic simulation model is provided. This method is described using the server 104 in FIG1 as an example. In other embodiments, the method may also be applied to a terminal. The method includes the following steps S202 to S210 . In particular:

[0089] Step S202: The server obtains a target capacity inputted into a photovoltaic simulation model.

[0090] A photovoltaic simulation model is a mathematical model used to simulate the performance of a solar photovoltaic system. This model is typically based on the physical properties of photovoltaic cells and the system's operating principles, and can be used to predict the system's power generation, efficiency, and performance under different conditions. Target capacity refers to the capacity level that the photovoltaic simulation model must achieve to meet the target demand, given the varying capacity levels of photovoltaic power plants required to meet specific requirements. Furthermore, capacity refers to the total rated effective power of the generators installed in the photovoltaic power plant.

[0091] Optionally, the server obtains a target capacity corresponding to a target demand input into the photovoltaic simulation model.

[0092] In step S204 , the server calculates parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and the reference capacity of the photovoltaic simulation model.

[0093] The baseline capacity refers to the current capacity level of the PV simulation model. Basic system parameters can include the number of PV modules connected in series or the number of PV module strings connected in parallel. Parameter values ​​corresponding to the target capacity refer to the values ​​of the basic system parameters of the PV system at the target capacity.

[0094] Optionally, the server calculates parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity, the current baseline capacity of the photovoltaic simulation model, and a preset formula.

[0095] In step S206 , the server adjusts the basic system parameters of the photovoltaic simulation model based on parameter values ​​that are compatible with the target capacity.

[0096] The adjustment may include updating the current parameter values ​​corresponding to the basic system parameters of the photovoltaic simulation model with the calculated parameter values ​​adapted to the target capacity.

[0097] Optionally, the server adjusts the parameter values ​​of the current basic system parameters of the photovoltaic simulation model based on the calculated basic system parameters and the parameter values ​​corresponding to the target capacity. For example, the server overwrites the current parameter values ​​with the calculated parameter values ​​or uses the calculated parameter values ​​as a reference to adjust the current parameter values ​​so that the capacity of the photovoltaic simulation model is close to the target capacity.

[0098] In step S208, the server uses the PI parameter value of the control system as the optimization object, and adopts a genetic algorithm to iteratively calculate the PI parameter value of the control system until the preset iteration end condition is met, thereby obtaining the optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each initial population according to a preset fitness function, and performs iterative calculation based on each initial population; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm.

[0099] The control system can be used to simulate models of photovoltaic systems and generate control technologies based on simulation results. PI parameters refer to the proportional-integral (PI) controller typically used in both the inner and outer loops to control the inverter. PI controller parameters include the proportional gain (Kp) and integral time constant (Ti), which need to be adjusted and optimized based on the specific inverter and control requirements.

[0100] Fitness, in genetic algorithms, is a measure of the degree to which an individual survives within a population and is used to distinguish between good and bad individuals. Fitness is calculated using a fitness function, also known as an evaluation function, which primarily determines an individual's fitness based on its characteristics.

[0101] Among them, Genetic Algorithm (GA) is a random global search optimization method that simulates phenomena such as replication, crossover, and mutation that occur in natural selection and inheritance. Starting from any initial population (population), through random selection, crossover, and mutation operations, it produces a group of individuals that are more suitable for the environment, allowing the population to evolve to increasingly better areas in the search space. In this way, continuous reproduction and evolution from generation to generation finally converges to a group of individuals (Individuals) that are most adapted to the environment, thereby finding a high-quality solution to the problem.

[0102] Optionally, the server uses four sets of PI parameters in the inner and outer loop controls of the photovoltaic simulation model control system as optimization objects, and employs a genetic algorithm to iteratively calculate the PI parameter values ​​of the control system until a preset iteration termination condition is satisfied, thereby obtaining optimized PI parameter values. The preset condition may be that the rated capacity output of the photovoltaic simulation model controlled by the optimized parameter values ​​satisfies the input target capacity. The genetic algorithm constructs multiple initial populations using the set initial PI parameter values, calculates the fitness of each initial population based on a preset fitness function according to the characteristics of the photovoltaic simulation model, and performs iterative calculations based on each initial population; the preset fitness function is constructed based on the results of the iterative operation of the photovoltaic simulation model as feedback to the genetic algorithm.

[0103] In step S210 , the server updates the current PI parameters of the control system according to the optimized PI parameter values.

[0104] Optionally, the server overwrites the parameter values ​​of the current PI parameters of the control system according to the optimized PI parameter values ​​to complete the update.

[0105] In the parameter adjustment method of the above-mentioned photovoltaic simulation model, the target capacity input for the photovoltaic simulation model is obtained through the server, and the parameter values ​​and multiple basic system parameter values ​​corresponding to the target capacity are calculated according to the target capacity and the benchmark capacity of the photovoltaic model. The basic system parameters of the photovoltaic simulation model are automatically adjusted using the calculated parameter values, thereby realizing automatic adjustment of the photovoltaic simulation model parameters; the server then uses a genetic algorithm to optimize the PI parameters of the control system of the photovoltaic simulation model to obtain the optimized PI parameter values ​​and update the PI parameters, thereby realizing automatic update of the PI parameters of the control system, thereby improving the parameter adjustment efficiency of the photovoltaic simulation model.

[0106] In an exemplary embodiment, the multiple basic system parameters include a ratio coefficient of the target capacity to the baseline capacity and other preset basic system parameters; the server calculates the parameter values ​​of the multiple basic system parameters adapted to the target capacity based on the target capacity and the baseline capacity of the photovoltaic simulation model, including:

[0107] The server calculates the ratio of the target capacity to the baseline capacity to obtain a ratio coefficient; based on the ratio coefficient, the parameter values ​​of other basic system parameters that are adapted to the basic capacity, and the calculation formulas corresponding to other basic system parameters, the server calculates the parameter values ​​of other basic system parameters that are adapted to the target capacity.

[0108] The calculation formula of the ratio coefficient can be:

[0109] Where n is the ratio coefficient, S obj is the target capacity of the input, S base Represents the baseline capacity of the PV simulation model.

[0110] Optionally, the server calculates a ratio of the received target capacity to the current baseline capacity of the PV simulation model to obtain a ratio coefficient. Based on the ratio coefficient, parameter values ​​of other basic system parameters that are compatible with the baseline capacity, and calculation formulas corresponding to the other basic system parameters, the server calculates parameter values ​​of the other basic parameters that are compatible with the target capacity. For example, the server substitutes the ratio coefficient and the parameter values ​​of the basic system parameters corresponding to the baseline capacity into the corresponding basic system parameter formulas to calculate the parameter values ​​of the basic system parameters at the target capacity.

[0111] In this embodiment, the ratio coefficient is obtained by calculation through the server, and the ratio coefficient and other basic system parameters are calculated to obtain other basic system parameter values ​​that are adapted to the target capacity, paving the way for subsequent adjustment of the basic system parameters.

[0112] In an exemplary embodiment, the other system parameters include the number of photovoltaic modules and the number of photovoltaic module strings in the photovoltaic simulation model; the server calculates the parameter values ​​of the other basic system parameters adapted to the target capacity based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters, including:

[0113] The server calculates the number of photovoltaic modules and the number of photovoltaic module strings that are compatible with the target capacity. The corresponding calculation formula includes:

[0114]

[0115] Among them, n represents the ratio coefficient, N s ' r With N sr represents the number of photovoltaic modules connected in series in the photovoltaic panels of the photovoltaic simulation model under target capacity and benchmark capacity, respectively, N′ pl With N pl They represent the number of photovoltaic module strings connected in parallel in the photovoltaic panel of the photovoltaic simulation model under the target capacity and the benchmark capacity respectively.

[0116] Among them, photovoltaic modules refer to devices that convert light energy into electrical energy, also known as solar panels.

[0117] In this embodiment, the server calculates the number of photovoltaic modules and the number of photovoltaic module strings under the target capacity, and proves that under the same light and temperature conditions, the open circuit voltage and short circuit current of the photovoltaic panel become the original times, the rated power becomes n times of the original. At the same time, the impedance of the series module inside the photovoltaic panel becomes the original times, the impedance of the parallel modules becomes the original times, while the external impedance characteristics remain unchanged.

[0118] In an exemplary embodiment, other system parameters include a rated output active power reference value, a rated transmission power grid-connected point reference voltage of a single inverter, a grid-connected point bus voltage rating, an inverter AC output side reference voltage, an inverter DC input side reference voltage, a photovoltaic panel rated open-circuit voltage, and a photovoltaic panel rated short-circuit current. The server calculates parameter values ​​of other basic system parameters adapted to the target capacity based on a ratio coefficient, parameter values ​​adapted to the basic capacity, and calculation formulas corresponding to other basic system parameters, and further includes:

[0119] The server calculates the rated output active power reference value, the rated transmission power of a single inverter, the grid connection point reference voltage, the grid connection point bus voltage rating, the inverter AC output side reference voltage, the inverter DC input side reference voltage, the PV panel rated open-circuit voltage, and the PV panel rated short-circuit current. The corresponding calculation formulas include:

[0120] Among them, P ref ′ and P ref They represent the rated output active power reference value of the photovoltaic simulation model under target capacity and benchmark capacity, P rated ′ and P rated They represent the rated transmission power of a single inverter of the photovoltaic simulation model under target capacity and benchmark capacity, V base ′ and V base They represent the reference voltage of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V bus ′ and V bus They represent the bus voltage ratings of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V inv_base ′ and V inv_base Represents the AC output side reference voltage of the inverter under target capacity and reference capacity respectively, V dc_base ′ and V dc_base Represents the DC input side reference voltage of the inverter under target capacity and reference capacity respectively, V oc_MPPT ′ and V oc_MPPT They represent the rated open-circuit voltage of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity, respectively. sc_MPPT ′ and I sc_MPPT They represent the rated short-circuit current of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity respectively.

[0121] MPPT (Maximum Power Point Tracking) is a controller used in solar photovoltaic systems to ensure that photovoltaic modules output electricity at maximum power. MPPT controllers monitor factors such as light intensity and temperature to automatically adjust the operating point of photovoltaic modules, ensuring they always operate at their maximum power point, thereby improving the system's power generation efficiency.

[0122] In this embodiment, the server further illustrates the formula for calculating parameter values ​​of other basic system parameters that are compatible with the target capacity, thereby paving the way for subsequent adjustment of other basic system parameters.

[0123] In an exemplary embodiment, the server adjusts the basic system parameters of the photovoltaic simulation model based on parameter values ​​that are compatible with the target capacity, including:

[0124] The server determines parameters whose parameter values ​​remain unchanged and parameters whose parameter values ​​are to be adjusted among the basic system parameters of the photovoltaic simulation model; the server adjusts the parameters to be adjusted among the basic parameters of the photovoltaic simulation model based on parameter values ​​adapted to the target capacity.

[0125] The parameters whose parameter values ​​remain unchanged mean that since the external impedance characteristics of the photovoltaic panel remain unchanged, the impedance parameters of other circuits in the system (such as the Boost (DC-DC) circuit and the inverter circuit) do not need to be changed.

[0126] Optionally, the server determines the parameters in the basic system parameters of the photovoltaic simulation model whose parameter values ​​remain unchanged due to the unchanged external impedance characteristics of the photovoltaic panels and the remaining parameter values ​​that need to be adjusted and updated, and then adjusts the parameters in the basic parameters of the photovoltaic simulation model based on the calculated parameter values ​​that are adapted to the target capacity, for example, overwriting the parameter values ​​of the current parameters to be adjusted with the calculated parameter values ​​that are adapted to the target capacity.

[0127] In this embodiment, the server determines the parameters whose parameter values ​​remain unchanged and the parameters to be adjusted in the photovoltaic simulation model, and updates the parameters to be adjusted, so that only the parameters that need to be adjusted are adjusted, avoiding adjusting all parameters, reducing resource usage, and thus improving parameter adjustment efficiency.

[0128] In an exemplary embodiment, the server uses the PI parameter value of the control system as the optimization object and uses a genetic algorithm to iteratively calculate the PI parameter value of the control system until a preset iteration end condition is met. Before obtaining the optimized PI parameter value, the server further includes:

[0129] The server pre-builds the fitness function; the fitness function includes:

[0130] Among them, ω1, ω2, and ω3 are three weight coefficients respectively, P is the active power actually output after the photovoltaic simulation model reaches steady state, and P ref is the input capacity target that needs to be adjusted, Q is the reactive power actually output after the photovoltaic simulation model reaches steady state, and Q ref is the reactive power output of the photovoltaic simulation model reference, t stab The time required for the photovoltaic simulation model to reach steady state.

[0131] Optionally, the server selects different individual combinations from different initial populations as a set of parameter values ​​for the control system PI parameters during each simulation process, and monitors the active power and reactive power change curves output by the system during each simulation of the photovoltaic model, and constructs a fitness function based on the simulation waveform effect of the model.

[0132] In this embodiment, the server constructs a fitness function according to the waveform effect based on the active power and reactive power change curves output by the system during each simulation process, thereby improving the accuracy of the constructed fitness function.

[0133] In an exemplary embodiment, as shown in FIG3 , in step S208, the server uses the PI parameter value of the control system as the optimization object, and uses a genetic algorithm to iteratively calculate the PI parameter value of the control system until a preset iteration end condition is met, and obtains the optimized PI parameter value, which includes the following steps S302 to S310. Among them:

[0134] In step S302, the server constructs multiple initial populations using the set initial values ​​of the PI parameters.

[0135] The initial values ​​of the PI parameters include: the number of optimization objects n, the initial population size N i and evolutionary range limits of individuals in a population.

[0136] Optionally, the server randomly generates n×N PIs within the evolution range of the population individuals using the set PI parameter initial value. i individuals, forming n initial populations.

[0137] Step S304: The server configures chromosome codes for each initial population.

[0138] Among them, chromosome coding mainly includes binary coding, floating point coding and symbol coding.

[0139] Optionally, the server configures chromosome encoding for each individual in the initial population. For example, for each individual, whether the individual feature is selected is encoded using 0 / 1 (0 means not selecting the feature, 1 means selecting the feature), and each individual is represented as a binary string.

[0140] In step S306 , the server brings the current system output active power and reactive power change curves of the photovoltaic simulation model into a pre-built fitness function to calculate the fitness of each initial population.

[0141] Active power is the power in a circuit that can actually do work, typically expressed in watts (W). Reactive power refers to the energy that moves back and forth in a circuit; it doesn't perform any work, but rather flows back and forth. Reactive power typically refers to the energy of the electromagnetic and capacitive fields in a circuit. Reactive power is expressed in VAR (vars).

[0142] Optionally, the server brings the active power and reactive power change curves of the current system output of the photovoltaic simulation model into a pre-built fitness function, and calculates the fitness of each individual in each initial population. The higher the total fitness of the included individuals, the greater the advantage of the population.

[0143] In step S308, the server calculates the probability of selecting the next generation corresponding to each initial population based on the fitness; the probability calculation formula is:

[0144] Among them, represents the fitness of individuals in the i-th initial population, and N represents the size of the entire initial population.

[0145] Optionally, the server calculates the probability of an individual in the initial population being selected as the population of the next generation according to the fitness of each individual in the initial population.

[0146] In step S310 , the server places the selected initial populations into a crossover pool based on the probability of selecting the next generation corresponding to each initial population, and performs gene crossover and gene mutation processing on the initial populations in the crossover pool according to the chromosome encoding to obtain a processed new population.

[0147] The server places the selected initial populations into the crossover pool based on the probability of selection for the next generation corresponding to each initial population. Elite selection and roulette wheel selection are used to select dominant individuals from the parent population for inheritance to the next generation. The server selection operation is used to determine the recombinant or crossover individuals, as well as the number of offspring individuals that the selected individuals will produce. In the elite selection process for each generation, the server retains the individual combination with the highest fitness in the current generation and passes it to the next generation. Roulette wheel selection is a proportional selection method, where the probability of each individual entering the next generation is equal to the ratio of its fitness value to the sum of the fitness values ​​of the individuals in the entire population.

[0148] The crossover pool refers to the collection of crossover operations used to generate new individuals in a genetic algorithm. The crossover pool includes a variety of different crossover methods, each with a certain probability of being selected for the crossover operation. By using a crossover pool, the genetic algorithm can select a different crossover method in each generation, thereby increasing the algorithm's diversity, improving the coverage of the search space, and enhancing the algorithm's global search capabilities.

[0149] Optionally, the server selects individuals from the initial population using an elite selection or roulette wheel selection method based on the probability of selection for the next generation corresponding to each initial population, and places them into a crossover pool. The server then performs gene crossover and gene mutation processing based on the chromosome code of each individual in the initial population to obtain a processed new population. For example, the server randomly exchanges some genes of binary-coded individuals in the initial population in the crossover pool using single-point crossover, two-point and multi-point crossover, uniform crossover, and arithmetic crossover to form new individuals. Individual mutation refers to replacing the gene values ​​at certain loci in the individual code string with the remaining alleles at the loci at a given mutation rate, thereby forming new individuals, and the new individuals are then combined into a new population.

[0150] In this embodiment, the server generates an initial population based on the current PI parameters by using a genetic algorithm, and then selects a better population for the next generation according to the calculated fitness, thereby improving the accuracy.

[0151] In an exemplary embodiment, in step S310, the server places the selected initial population into a crossover pool based on the probability of selecting the next generation corresponding to each initial population, and performs gene crossover and gene mutation processing on the initial population in the crossover pool according to the chromosome encoding to obtain the processed new population, further comprising:

[0152] The server determines whether the processed new population meets the preset iteration end conditions; if the iteration end conditions are not met, the server returns to the step of substituting the current system output active power and reactive power change curves of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each initial population until the preset iteration end conditions are met to obtain the optimized PI parameter value.

[0153] Optionally, the server determines whether the new population after processing meets the iteration end condition. If the iteration end condition is not met, the server returns to the step of bringing the current system output active power and reactive power change curve of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each initial population, until the new population after iterative processing meets the preset iteration end condition, and the optimized PI parameter value is obtained.

[0154] In this embodiment, the server performs iterative processing on the population by adopting an iterative calculation method to obtain the optimal PI parameter value, thereby further improving the accuracy of the control system.

[0155] In an exemplary embodiment, as shown in FIG4 , a method for automatically adjusting capacity parameters of a photovoltaic simulation model is provided, and the specific steps are as follows:

[0156] In this embodiment, a photovoltaic power generation system model as shown in FIG5 was built in PSCAD / EMTDC for simulation testing. As shown in Table 1, the main parameters of the photovoltaic power generation system are provided.

[0157] Table 1

[0158] The implementation process specifically includes the following steps:

[0159] Step 1: The user enters the target capacity S in the prepared program obj In this embodiment, it is set to 2500MW, and the ratio coefficient n of the target capacity to the benchmark capacity is calculated as follows:

[0160] Where S base Indicates the baseline capacity of the PV system model.

[0161] Step 2: The server calculates the basic system parameter values ​​under the target capacity based on the capacity ratio coefficient:

[0162] Where N s ' r With N sr Represents the number of photovoltaic modules connected in series in the photovoltaic panel under target capacity and benchmark capacity, N′ pl With N pl They represent the number of PV module strings connected in parallel in the PV panel at the target capacity and the benchmark capacity, respectively.

[0163] Step 3, therefore, under the same light and temperature conditions, the open circuit voltage and short circuit current of the photovoltaic panel become the original times, the rated power becomes n times of the original. At the same time, the impedance of the series module inside the photovoltaic panel becomes the original times, the impedance of the parallel modules becomes the original times, while the external impedance characteristics remain unchanged.

[0164] Step 4: Since the external impedance characteristics of the photovoltaic panel remain unchanged, the impedance parameters of the remaining circuits in the system (such as the Boost circuit and the inverter circuit) do not need to be changed. The remaining basic parameter values ​​that need to be adjusted and updated by the server are as follows:

[0165] Where, P ref ′ and P ref They represent the rated output active power reference value of the photovoltaic system under the target capacity and the benchmark capacity, respectively. rated ′ and P rated Represent the rated transmission power of a single inverter of the photovoltaic system under target capacity and benchmark capacity respectively, V base ′ and V base Represents the reference voltage of the photovoltaic system grid connection point under target capacity and benchmark capacity, V bus ′ and V bus Represent the bus voltage rating of the photovoltaic system grid connection point under target capacity and benchmark capacity, V inv_base ′ and V inv_base Represents the AC output side reference voltage of the inverter under target capacity and reference capacity respectively, V dc_base ′ and V dc_base Represents the DC input side reference voltage of the inverter under target capacity and reference capacity respectively, V oc_MPPT ′ and V oc_MPPT They represent the rated open-circuit voltage of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity, respectively. sc_MPPT ′ and I sc_MPPT They represent the rated short-circuit current of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity respectively.

[0166] Step 5: After that, the program will interact with the simulation model to automatically update and modify the relevant parameters in the model.

[0167] After the basic system parameters are updated according to the above steps, the server will update the control system PI parameters. This disclosure uses a genetic algorithm (GA) to iteratively update the four sets of PI parameters in the inner and outer loop control.

[0168] Step 6: The server sets the initial genetic parameters necessary for the genetic algorithm to run: the number of optimization objects n = 8, the value type is set to REAL real number class, the population iteration number is set to 400, the initial population size N i =100, the surviving offspring population size is set to 80, the crossover pool population size is set to 40, the elite population size is set to 10, the deviation population ratio is set to 0, the maximum deviation rate is set to 10, the binary part mutation rate is set to 5, the real part mutation rate is set to 5, the pairing method is set to Random, and the population individual evolution range limit is set to 0-5;

[0169] Step 7: The server designs chromosome encoding: There are various methods for designing chromosome encoding, and different encoding methods may be used for each problem, mainly including binary encoding, floating-point encoding, and symbolic encoding. The present disclosure adopts binary encoding, that is, for each individual, whether the individual feature is selected is encoded using 0 / 1 (0 means not to select the feature, 1 means to select the feature), and each individual is represented as a binary string. Since the optimization object of the present disclosure is the four sets of PI parameters in the inner and outer loop control system, they can be directly encoded in binary form without the need for mapping encoding from phenotype to genotype;

[0170] Step 8: The server randomly generates an initial population. The server randomly generates n×N i individuals, forming n initial populations;

[0171] In step 9, the server selects different individual combinations from the initial population of different objects as a set of values ​​for the control system PI parameters during each model simulation. The server monitors the system output active power and reactive power change curves during each model simulation. The server designs the following fitness function based on the simulation waveform effect of the model. The higher the fitness, the greater the advantage of the individual combination:

[0172] Among them, ω1, ω2, ω3 are three weight coefficients, which are given as 1, 0.6 and 0.35 respectively. P is the active power actually output after the system reaches steady state, P ref is the capacity target that needs to be adjusted input by the program, Q is the reactive power actually output after the system reaches steady state, Q ref is the reactive power of the system reference output, which is generally set to 0 in steady state. stab The time required for the system to reach steady state;

[0173] In step 10, the server uses elite selection and roulette wheel selection to select dominant individuals from the parent population for inheritance to the next generation. The selection operation determines the individuals to be recombined or crossed over, and how many offspring individuals the selected individuals will produce.

[0174] Among them, in the elite selection process of each generation, the server retains the individual combination with the highest fitness in this generation and passes it to the next generation, while the roulette wheel selection is a proportional selection method, which makes the probability of each individual entering the next generation equal to the ratio of its fitness value to the sum of the fitness values ​​of individuals in the entire population. The probability of each individual being selected is calculated by the following formula:

[0175] Where, f(xi ) represents the fitness of the i-th individual, and N represents the size of the entire population. Therefore, the higher the fitness, the higher the probability of being selected into the next generation;

[0176] Step 11: The server performs genetic crossover of individuals. The server randomly exchanges some genes of binary-coded individuals in the crossover pool population using methods such as single-point crossover, two-point and multi-point crossover, uniform crossover, and arithmetic crossover to form new individuals.

[0177] Step 12: The server performs genetic individual gene mutation. Individual mutation refers to replacing the gene values ​​at certain loci in the individual code string with the remaining alleles at the loci at a given mutation rate, thereby forming a new individual. Binary coding generally uses locus mutation, (non-)uniform mutation, boundary mutation, and Gaussian approximation mutation to mutate individual genes.

[0178] In step 13, the server determines whether the iteration stopping condition is met. If so, the optimal individual combination value of the current generation is output as the optimal solution of the control system PI parameters under the target capacity; if not, the iteration number is increased by 1, and the server returns to step A4 to continue the iterative inheritance of the population.

[0179] In step 14, after finding the optimal PI control parameters corresponding to the target capacity, the program will interact with the simulation model to automatically update the model control system parameters to achieve automatic adjustment of the photovoltaic simulation model parameters under the target capacity. As shown in FIG6 , a fitness curve diagram of the iterative process of the genetic algorithm of this embodiment is provided. As shown in FIG7 , a rated capacity output curve diagram of the photovoltaic simulation model after the automatic adjustment of the parameters is provided.

[0180] This embodiment solves the problem of automatic matching and updating of various system parameters after the capacity of the photovoltaic simulation model is adjusted. In addition to being applicable to photovoltaic simulation models, the proposed solution is also applicable to other electromagnetic transient simulation models such as wind power generation models and energy storage models.

[0181] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0182] Based on the same inventive concept, embodiments of the present application also provide a photovoltaic simulation model parameter adjustment device for implementing the photovoltaic simulation model parameter adjustment method described above. The solution to the problem provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of the one or more photovoltaic simulation model parameter adjustment device embodiments provided below can be found in the above-mentioned limitations of the photovoltaic simulation model parameter adjustment method, and will not be repeated here.

[0183] In an exemplary embodiment, as shown in FIG8 , a parameter adjustment device 800 for a photovoltaic simulation model is provided, comprising: a target capacity acquisition module 802 , a basic parameter calculation module 804 , a basic parameter adjustment module 806 , a PI parameter calculation module 808 , and a PI parameter update module 810 , wherein:

[0184] The target capacity acquisition module 802 is used to obtain the target capacity inputted into the photovoltaic simulation model;

[0185] The basic parameter calculation module 804 is used to calculate parameter values ​​of multiple basic system parameters that are compatible with the target capacity based on the target capacity and the baseline capacity of the photovoltaic simulation model;

[0186] A basic parameter adjustment module 806 is used to adjust basic system parameters of the photovoltaic simulation model based on parameter values ​​that are compatible with the target capacity;

[0187] The PI parameter calculation module 808 is configured to iteratively calculate the PI parameter values ​​of the control system using a genetic algorithm, using the PI parameter values ​​of the control system as the optimization object, until a preset iteration termination condition is satisfied, thereby obtaining the optimized PI parameter values. The genetic algorithm constructs multiple initial populations using the set PI parameter initial values, calculates the fitness of each initial population according to a preset fitness function, and performs iterative calculations based on each initial population. The preset fitness function is constructed based on the results of the iterative operation of the photovoltaic simulation model as feedback for the genetic algorithm results.

[0188] The PI parameter updating module 810 is used to update the current PI parameters of the control system according to the optimized PI parameter values.

[0189] Furthermore, in one embodiment, the basic parameter calculation module 804 is specifically used to calculate the ratio of the target capacity to the baseline capacity to obtain a ratio coefficient; based on the ratio coefficient, the parameter values ​​of other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to other basic system parameters, the parameter values ​​of other basic system parameters adapted to the target capacity are calculated.

[0190] Furthermore, in one embodiment, the basic parameter calculation module 804 is further configured to calculate the number of photovoltaic modules and the number of photovoltaic module strings that are compatible with the target capacity. The corresponding calculation formula includes:

[0191] Among them, n represents the ratio coefficient, N s ' r With N sr represents the number of photovoltaic modules connected in series in the photovoltaic panels of the photovoltaic simulation model under target capacity and benchmark capacity, respectively, N′ pl With N pl They represent the number of photovoltaic module strings connected in parallel in the photovoltaic panel of the photovoltaic simulation model under the target capacity and the benchmark capacity respectively.

[0192] Furthermore, in one embodiment, the basic parameter calculation module 804 is further specifically configured to calculate a rated output active power reference value corresponding to the target capacity, a rated transmission power grid connection point reference voltage of a single inverter, a grid connection point bus voltage rating, an inverter AC output side reference voltage, an inverter DC input side reference voltage, a photovoltaic panel rated open-circuit voltage, and a photovoltaic panel rated short-circuit current, and the corresponding calculation formulas include:

[0193] Among them, P ref ′ and P ref They represent the rated output active power reference value of the photovoltaic simulation model under target capacity and benchmark capacity, P rated ′ and P rated They represent the rated transmission power of a single inverter of the photovoltaic simulation model under target capacity and benchmark capacity, V base ′ and V base Represent the reference voltage of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V bus ′ and V bus They represent the bus voltage ratings of the photovoltaic simulation model grid connection point under target capacity and benchmark capacity, V inv_base ′ and V inv_base Represents the AC output side reference voltage of the inverter under target capacity and reference capacity respectively, V dc_base ′ and V dc_base Represents the DC input side reference voltage of the inverter under target capacity and reference capacity respectively, V oc_MPPT ′ and V oc_MPPT They represent the rated open-circuit voltage of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity, respectively. sc_MPPT ′ and I sc_MPPT They represent the rated short-circuit current of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity respectively.

[0194] Furthermore, in one embodiment, the basic parameter adjustment module 806 is specifically used to determine the parameters whose parameter values ​​remain unchanged and the parameters whose parameter values ​​are to be adjusted in the basic system parameters of the photovoltaic simulation model; and adjust the parameters to be adjusted in the basic parameters of the photovoltaic simulation model based on the parameter values ​​adapted to the target capacity.

[0195] Furthermore, in one embodiment, the PI parameter calculation module 808 is also used for a pre-built fitness function; the fitness function includes:

[0196] Among them, ω1, ω2, and ω3 are three weight coefficients respectively, P is the active power actually output after the photovoltaic simulation model reaches steady state, and P ref is the input capacity target that needs to be adjusted, Q is the reactive power actually output after the photovoltaic simulation model reaches steady state, and Q ref is the reactive power output of the photovoltaic simulation model reference, t stab The time required for the photovoltaic simulation model to reach steady state.

[0197] Furthermore, in one embodiment, the PI parameter calculation module 808 is specifically configured to construct a plurality of initial populations using the set PI parameter initial values;

[0198] Configure chromosome encoding for each initial population;

[0199] The current system output active power and reactive power change curves of the photovoltaic simulation model are brought into the pre-built fitness function to calculate the fitness of each initial population;

[0200] The probability of selecting the next generation for each initial population is calculated based on fitness; the probability calculation formula is:

[0201] Among them, f(x i ) represents the fitness of individuals in the i-th initial population, and N represents the size of the entire initial population;

[0202] Based on the probability of selecting the next generation corresponding to each initial population, the selected initial population is placed in the crossover pool, and the initial population in the crossover pool is subjected to gene crossover and gene mutation processing according to the chromosome coding to obtain a processed new population.

[0203] Furthermore, in one embodiment, the PI parameter calculation module 808 is further configured to determine whether the processed new population satisfies a preset iteration termination condition;

[0204] If the iteration end condition is not met, the step of returning to the step of bringing the current system output active power and reactive power change curves of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each initial population is repeated until the preset iteration end condition is met to obtain the optimized PI parameter value.

[0205] Each module in the photovoltaic simulation model parameter adjustment device 800 can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0206] In an exemplary embodiment, a computer device is provided, which may be a server. A diagram of its internal structure may be shown in FIG9 . The computer device includes a processor, memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium. The database of the computer device is configured to input a target capacity, store parameter values ​​of basic system parameters corresponding to a baseline capacity, and store parameter value data corresponding to the target capacity. The I / O interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a parameter adjustment method for a photovoltaic simulation model.

[0207] Those skilled in the art will understand that the structure shown in Figure 9 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0208] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0209] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0210] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0211] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0212] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0213] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0214] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for adjusting parameters of a photovoltaic simulation model, characterized in that: A control system applied to a photovoltaic simulation model, the method comprising: Obtaining a target capacity input to the photovoltaic simulation model; According to the target capacity and the reference capacity of the photovoltaic simulation model, parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity are calculated; Adjusting the basic system parameters of the photovoltaic simulation model based on parameter values ​​of the basic system parameters that are compatible with the target capacity; Taking the PI parameter value of the control system as the optimization object, the PI parameter value of the control system is iteratively calculated using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial values, calculates the fitness of each of the initial populations according to a preset fitness function, and performs iterative calculations based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; and The current PI parameters of the control system are updated according to the optimized PI parameter values.

2. The method according to claim 1, characterized in that The multiple basic system parameters include the ratio coefficient of the target capacity to the reference capacity and other preset basic system parameters; The step of calculating parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and the reference capacity of the photovoltaic simulation model includes: Calculating the ratio of the target capacity to the reference capacity to obtain a ratio coefficient; and Based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters, the parameter values ​​of the other basic system parameters adapted to the target capacity are calculated.

3. The method according to claim 2, characterized in that The other system parameters include the number of photovoltaic modules and the number of photovoltaic module strings of the photovoltaic simulation model; the parameter values ​​of the other basic system parameters adapted to the basic capacity based on the ratio coefficient, the other basic system parameters and the basic capacity, and the calculation formula corresponding to the other basic system parameters, calculating the parameter values ​​of the other basic system parameters adapted to the target capacity, including: Calculating the number of photovoltaic modules that are suitable for the target capacity; and The number of photovoltaic module strings corresponding to the target capacity is calculated, and the corresponding calculation formula includes: Where n represents the ratio coefficient, N′ sr With N sr represents the number of photovoltaic modules connected in series in the photovoltaic panels of the photovoltaic simulation model under the target capacity and the benchmark capacity, respectively, N′ pl With N pl They represent the number of photovoltaic module strings connected in parallel in the photovoltaic panels of the photovoltaic simulation model under the target capacity and the benchmark capacity respectively.

4. The method according to claim 2, characterized in that: The other system parameters include a rated output active power reference value, a rated transmission power grid-connected point reference voltage of a single inverter, a grid-connected point bus voltage rating, an inverter AC output side reference voltage, an inverter DC input side reference voltage, a photovoltaic panel rated open-circuit voltage, and a photovoltaic panel rated short-circuit current. The parameter values ​​of the other basic system parameters adapted to the target capacity are calculated based on the ratio coefficient, the parameter values ​​of the other basic system parameters adapted to the basic capacity, and the calculation formulas corresponding to the other basic system parameters, and also include: Calculating a rated output active power reference value corresponding to the target capacity; Calculate the rated transmission power grid connection point reference voltage of a single inverter corresponding to the target capacity; Calculating a grid connection point bus voltage rating corresponding to the target capacity; Calculating an inverter AC output side reference voltage corresponding to the target capacity; Calculating a DC input side reference voltage of the inverter that is compatible with the target capacity; Calculating a rated open circuit voltage of the photovoltaic panel corresponding to the target capacity; and Calculate the rated short-circuit current of the photovoltaic panel corresponding to the target capacity, and the corresponding calculation formula includes: Among them, P ref ′ and P ref represent the rated output active power reference value of the photovoltaic simulation model under the target capacity and the benchmark capacity, respectively, rated ′ and P rated They represent the rated transmission power of a single inverter of the photovoltaic simulation model under the target capacity and the benchmark capacity, respectively. base ′ and V base They represent the reference voltage of the photovoltaic simulation model grid connection point under the target capacity and the reference capacity, V bus ′ and V bus They represent the bus voltage rating of the grid-connected point of the PV simulation model under the target capacity and the benchmark capacity, respectively. inv_base ′ and V inv_base Respectively represent the reference voltage of the inverter AC output side under the target capacity and the reference capacity, V dc_base ′ and V dc_base Respectively represent the reference voltage of the inverter DC input side under the target capacity and the reference capacity, V oc_MPPT ′ and V oc_MPPT Respectively represent the rated open-circuit voltage of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity, I sc_MPPT ′ and I sc_MPPT They represent the rated short-circuit current of the photovoltaic panel set in the MPPT tracking control element at the target capacity and the benchmark capacity respectively.

5. The method according to claim 1, characterized in that: The adjusting of the basic system parameters of the photovoltaic simulation model based on the parameter values ​​that are compatible with the target capacity includes: Determine parameters whose parameter values ​​remain unchanged among the basic system parameters of the photovoltaic simulation model; Determining the parameter values ​​of the basic system parameters of the photovoltaic simulation model to be adjusted; and The parameter to be adjusted is adjusted based on a parameter value that is adapted to the target capacity.

6. The method according to claim 1, characterized in that The method takes the PI parameter value of the control system as the optimization object, uses a genetic algorithm to iteratively calculate the PI parameter value of the control system until a preset iteration end condition is met and the optimized PI parameter value is obtained, and further includes: A fitness function is pre-constructed; the fitness function includes: Among them, ω1, ω2, and ω3 are three weight coefficients, P is the active power actually output after the photovoltaic simulation model reaches a steady state, and P ref is the input capacity target that needs to be adjusted, Q is the reactive power actually output after the photovoltaic simulation model reaches steady state, and Q ref is the reactive power of the reference output of the photovoltaic simulation model, t stab The time required for the photovoltaic simulation model to reach steady state.

7. The method according to claim 1, characterized in that The PI parameter value of the control system is taken as the optimization object, and the PI parameter value of the control system is iteratively calculated by using a genetic algorithm until a preset iteration end condition is met to obtain an optimized PI parameter value, including: Construct multiple initial populations with the set initial values ​​of PI parameters; configuring a chromosome code for each of the initial populations; Substituting the current system output active power and reactive power change curve of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each of the initial populations; Calculate the probability of selecting the next generation corresponding to each of the initial populations according to the fitness; placing the selected initial population into a crossover pool based on the probability of selecting the next generation corresponding to each of the initial populations; and According to the chromosome coding, gene crossover and gene mutation processing is performed on the initial population in the crossover pool to obtain a processed new population.

8. The method according to claim 7, characterized in that The calculating, according to the fitness, the probability of selecting the next generation corresponding to each of the initial populations comprises: The probability is calculated as follows: Among them, f(x i ) represents the fitness of individuals in the i-th initial population, and N represents the size of the entire initial population.

9. The method according to claim 7, characterized in that: After performing gene crossover and gene mutation processing on the selected initial population according to the chromosome code to obtain a processed new population, the method further includes: Determining whether the processed new population meets a preset iteration end condition; and When the iteration end condition is not met, return to the step of bringing the current system output active power and reactive power change curve of the photovoltaic simulation model into the pre-built fitness function to calculate the fitness of each of the initial populations until the preset iteration end condition is met to obtain the optimized PI parameter value.

10. The method according to claim 7, characterized in that The set initial values ​​of PI parameters include: the number of optimization objects, the initial population size and the evolution range limit of the population individuals.

11. The method according to claim 1, characterized in that: The benchmark capacity is the current capacity level of the photovoltaic simulation model.

12. A parameter adjustment device for a photovoltaic simulation model, characterized in that: The device comprises: A target capacity acquisition module, used to acquire a target capacity inputted into the photovoltaic simulation model; A basic parameter calculation module, used to calculate parameter values ​​of a plurality of basic system parameters that are compatible with the target capacity according to the target capacity and the reference capacity of the photovoltaic simulation model; A basic parameter adjustment module, used to adjust the basic system parameters of the photovoltaic simulation model based on parameter values ​​that are compatible with the target capacity; The PI parameter calculation module is used to use the PI parameter value of the control system as the optimization object, and use the genetic algorithm to iteratively calculate the PI parameter value of the control system until the preset iteration end condition is met to obtain the optimized PI parameter value; the genetic algorithm constructs multiple initial populations with the set PI parameter initial value, and calculates the optimal PI parameter value according to the preset fitness function. The fitness of each of the initial populations is iteratively calculated based on each of the initial populations; the preset fitness function is constructed based on the result of the photovoltaic simulation model during iterative operation as the result feedback of the genetic algorithm; The PI parameter updating module is used to update the current PI parameters of the control system according to the optimized PI parameter values.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

Images