A wind-solar-storage combined frequency modulation method, device and computer-readable storage medium

Abstract

The present invention relates to a wind-solar-storage joint frequency regulation method, device and computer-readable storage medium. The method comprises: presetting frequency regulation parameters of a wind-solar-storage system, constructing a wind-solar-storage joint frequency regulation model, and collecting wind-solar unit operation data and grid status data; initializing multi-objective genetic algorithm parameters, generating an initial population according to grid status data, performing selection, crossover and mutation, generating a progeny population, until a maximum number of iterations is reached, and obtaining a non-supporting allocation selection solution set composed of the progeny population after the current iteration; constructing a frequency regulation accuracy objective function and a frequency regulation speed objective function; constructing a solution set matrix, obtaining an ideal solution by using a TOPS IS algorithm, and calculating the relative proximity of all non-supporting allocation selection solutions in the non-supporting allocation selection solution set to the ideal solution, obtaining the non-supporting allocation selection solution with the largest relative proximity as the optimal compromise solution; and frequency regulating the wind-solar-storage system based on the frequency regulation instructions of each frequency regulation source in the wind-solar-storage system represented by the optimal compromise solution.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and in particular to a wind-solar-storage combined frequency modulation method, device and computer-readable storage medium. Background Art

[0002] As renewable and clean energy, wind and solar energy have been valued by all countries. Since my country proposed the "dual carbon" goal of reaching carbon peak in 2030 and carbon neutrality in 2060, the installed capacity and grid-connected capacity of wind and solar power generation have grown rapidly. However, the uncertainty of wind and solar energy also poses challenges to traditional frequency regulation systems. Wind power, photovoltaics and energy storage systems assist thermal power in participating in frequency regulation, which has become a new solution.

[0003] In wind, solar, and storage frequency regulation, due to the influence of weather conditions, it is impossible to perform intraday scheduling at will. It is suitable to declare the frequency regulation plan on the day before, which reduces the requirements for the operation rate; however, wind, solar, and storage data are affected by natural factors such as wind speed and light intensity, and are intermittent and volatile; these factors may cause rapid fluctuations in power output, posing challenges to the stability of the power grid. The scale of wind, solar, and storage data generation and processing continues to expand, and the amount of data is huge; these data may include real-time power output data, weather data, equipment operating status data, etc., which require efficient data processing and analysis. The generation and processing of wind, solar, and storage data are closely related to time and space; for example, power output data in different time periods, wind speed and light data in different geographical locations, etc., all need to be analyzed and processed accordingly.

[0004] Existing wind, solar and storage frequency regulation technologies focus on using virtual inertial control or droop control methods to improve the ability of renewable energy to participate in frequency regulation. They cannot achieve accurate allocation of frequency regulation power to each frequency regulation source while taking into account the volatility of wind, solar and storage data, and cannot reduce frequency regulation time while increasing control accuracy. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the problem that the wind, solar and storage frequency modulation method in the prior art does not take into account the volatility of wind, solar and storage data, resulting in low frequency modulation control accuracy.

[0006] In order to solve the above technical problems, the present invention provides a wind-solar-storage combined frequency modulation method, comprising:

[0007] Preset the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model;

[0008] Use the wind, solar and energy storage joint frequency regulation model to collect wind and solar unit operation data and grid status data;

[0009] Initializing the multi-objective genetic algorithm parameters, generating an initial population according to the power grid state data, performing selection, crossover and mutation, generating a progeny population, until a maximum number of iterations is reached, and obtaining a non-supported selected solution set consisting of the progeny population after the current iteration;

[0010] Construct frequency modulation accuracy objective function and frequency modulation speed objective function;

[0011] Based on the objective function, a solution set matrix is ​​constructed, and the TOPSIS algorithm is used to obtain the ideal solution. The relative proximity between all the non-supporting solutions and the ideal solution in the non-supporting solution set is calculated, and the non-supporting solution with the largest relative proximity is obtained as the optimal compromise solution.

[0012] Based on the frequency modulation instructions of each frequency modulation source in the wind-solar-storage system represented by the optimal compromise solution, the wind-solar-storage system is frequency modulated.

[0013] In one embodiment of the present invention, the preset wind-solar-storage system frequency regulation parameters and the construction of a wind-solar-storage joint frequency regulation model include:

[0014] The frequency modulation parameters of the wind-solar-storage system include the frequency modulation output upper limit and frequency modulation output lower limit, output period, average frequency modulation error, average frequency modulation speed, energy storage SOC upper limit and energy storage SOC lower limit, and energy storage charging efficiency and discharging efficiency of each frequency modulation source in the wind-solar-storage system;

[0015] The wind, solar and energy storage combined frequency regulation model includes:

[0016] The unit output constraint is expressed as: Among them, P i represents the FM output of the ith FM source, P i Indicates the lower limit of the frequency modulation output of the i-th type frequency modulation source, represents the upper limit of the frequency regulation output of the i-th frequency regulation source; i∈{W, P, EES}, W represents the frequency regulation source of the wind turbine, P represents the frequency regulation source of the photovoltaic system, and EES represents the frequency regulation source of the energy storage system;

[0017] Output period constraint, expressed as: Where t represents the frequency modulation source output period, t s Indicates the start time of the frequency modulation source output, t e Indicates the end time of the frequency modulation source output;

[0018] Energy storage SOC constraint, expressed as: Where S(t) represents the SOC value of the energy storage system at the end of the output period t, ΔS(t) represents the change in the SOC value of the energy storage system during the output period t; η c With η d Respectively represent the charging efficiency and discharging efficiency of the energy storage system, Pc With P d They represent the charging power and discharging power of the energy storage system respectively, Δt represents the frequency modulation time step, and E rate Indicates the rated power of the energy storage system; S max With S min Respectively represent the upper limit and lower limit of energy storage SOC;

[0019] The power flow constraints and node voltage constraints are expressed as: -θ j.max ≤θ j,t ≤θ j.max ,θ ref,t =0, Among them, θ l,t With θ j,t They represent the voltage phase angles of node l and node j in the wind-solar-storage joint frequency regulation model, h lj,max represents the power limit of node l and node j, θ j.max represents the phase angle limit, x lj represents the reactance between node l and node j, θ ref,t represents the phase angle of the equilibrium node; G represents the set of units connected to node j, F represents the set of lines starting from node j, and E represents the set of lines ending at node j; f lj,t represents the power flow of the line (l, j) from node l to node j, and the positive and negative values ​​represent the direction of the power flow; D j,t represents the power load demand of node j.

[0020] In one embodiment of the present invention, the wind-solar unit operation data includes the output voltage, output current, output power, and wind turbine speed of the wind turbine unit and the photovoltaic system; the grid status data includes the frequency regulation instructions of each frequency regulation source in the wind-solar storage system, historical regional control errors, and frequency deviations.

[0021] In one embodiment of the present invention, the initialization of multi-objective genetic algorithm parameters, generating an initial population according to the power grid state data, performing selection, crossover and mutation, generating a progeny population, until a maximum number of iterations is reached, and obtaining a non-supported selected solution set consisting of the progeny population after the current iteration, includes:

[0022] Initializing the multi-objective genetic algorithm parameters includes initializing the population size, the maximum number of iterations, the crossover rate, and the mutation rate;

[0023] An initial population including frequency modulation instructions of each frequency modulation source is generated according to the power grid state data, and the frequency modulation instructions of each frequency modulation source are converted into binary codes, and selection, crossover and mutation are performed to generate a sub-generation population; each gene point of the sub-generation population randomly inherits the binary code of the previous generation;

[0024] Until the maximum number of iterations is reached, the offspring population after the current iteration forms a non-supported alternative solution set.

[0025] In one embodiment of the present invention, the step of constructing a frequency modulation accuracy objective function and a frequency modulation speed objective function includes:

[0026] Frequency modulation speed objective function J 1 , expressed as:

[0027] Frequency modulation accuracy objective function J 2 , expressed as:

[0028] Where K is the total number of frequency modulation sources in the wind-solar-storage system, P k is the FM output of the kth FM source, v k is the frequency modulation speed of the kth frequency modulation source, q k is the frequency modulation accuracy of the kth frequency modulation source, k=1,2,...,K.

[0029] In one embodiment of the present invention, the method of constructing a solution set matrix based on the objective function, obtaining an ideal solution using the TOPSIS algorithm, and calculating the relative proximity between all non-supporting alternative solutions in the non-supporting alternative solution set and the ideal solution, and obtaining the non-supporting alternative solution with the largest relative proximity as the optimal compromise solution, includes:

[0030] Based on the frequency modulation accuracy objective function and the frequency modulation speed objective function, the n frequency modulation targets of the m groups of frequency modulation sources are constructed as a solution matrix (x mn ) M*N ;

[0031] Where, N = 2, n = (1, 2); m represents the mth group of frequency modulation sources, that is, the mth non-alternative solution in the non-support alternative solution set, including K frequency modulation source instructions;

[0032] The solution matrix is ​​normalized to obtain a normalized solution matrix (t mn ) M*N , t mn It is expressed as:

[0033] The normalized solution matrix is ​​weighted to obtain a weighted solution matrix (a mn ) M*N , a mn Expressed as: a mn =ω n ×t mn ;ω n represents the weight of the nth frequency modulation target;

[0034] Based on the weighted solution set matrix, calculate the positive ideal solution a + and negative ideal solution a - , expressed as:

[0035]

[0036] Calculate the distance between each non-supported solution and the positive ideal solution and the negative ideal solution in the non-supported solution set to obtain the positive distance Negative distance It is expressed as:

[0037]

[0038] Based on the positive distance and the negative distance, the relative closeness C of each non-supported alternative solution to the ideal solution is calculated. m , expressed as:

[0039]

[0040] Sort all the non-supported alternative solutions from large to small in terms of relative proximity, and take the one with the largest relative proximity as the optimal compromise solution a final , expressed as: a final =a mn ∈max(C m ).

[0041] In one embodiment of the present invention, the wind-solar-storage system includes a wind turbine virtual inertia control system, a photovoltaic system load reduction control system, and an energy storage battery energy management system, which are used to control the wind turbine, the photovoltaic system, and the energy storage system, respectively;

[0042] The frequency modulation of the wind-solar-storage system based on the frequency modulation instruction represented by the optimal compromise solution includes: controlling the wind turbine virtual inertia control system, the photovoltaic system load reduction control system, and the energy storage battery energy management system to execute the frequency modulation instruction of the optimal compromise solution.

[0043] In one embodiment of the present invention, the wind, solar and energy storage combined frequency regulation model is the IEEE 39 model.

[0044] The embodiment of the present invention further provides a wind-solar-storage combined frequency modulation device, comprising:

[0045] Model building module, used to preset the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model;

[0046] The data acquisition module includes a wind turbine operation data acquisition unit and a power grid status data acquisition unit, which is used to collect wind and solar turbine operation data and power grid status data using a wind, solar and energy storage combined frequency modulation model;

[0047] A genetic decision module is used to initialize the parameters of the multi-objective genetic algorithm, generate an initial population according to the power grid state data, perform selection, crossover and mutation, generate a progeny population, and obtain a non-supported allocation solution set composed of the progeny population after the current iteration until the maximum number of iterations is reached; construct a frequency regulation accuracy objective function and a frequency regulation speed objective function; based on the objective function, construct a solution set matrix, use the TOPS IS algorithm to obtain an ideal solution, and calculate the relative proximity of all non-supported allocation solutions in the non-supported allocation solution set to the ideal solution, and obtain the non-supported allocation solution with the largest relative proximity as the optimal compromise solution;

[0048] An execution module is used to adjust the frequency of the wind-solar-storage system based on the frequency modulation instruction represented by the optimal compromise solution.

[0049] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the wind-solar-storage combined frequency modulation method as described above are performed.

[0050] The above technical solution of the present invention has the following advantages compared with the prior art:

[0051] The wind-solar-storage joint frequency modulation method described in the present invention takes into account the volatility and intermittency of wind-solar-storage data, as well as the frequency modulation characteristics of the wind-solar-storage frequency modulation system, and uses a multi-objective genetic algorithm to select a non-supporting configuration selection solution set, and uses the TOPSIS algorithm to obtain the optimal compromise solution that takes into account both frequency modulation accuracy and frequency modulation speed from the non-supporting configuration selection solution set, and controls the wind-solar-storage system to execute the frequency modulation instructions of the optimal compromise solution to achieve wind-solar-storage joint frequency modulation. The present invention uses a genetic decision algorithm to be more likely to find the global optimal solution, which is suitable for wind-solar-storage frequency modulation that cannot be arbitrarily scheduled intraday and needs to declare a frequency modulation plan a day before, thereby improving the accuracy of frequency modulation; the TOPSIS algorithm is used to obtain the optimal compromise solution to achieve the optimal allocation of frequency modulation power between each frequency modulation source, while ensuring the frequency modulation accuracy of the multi-source frequency modulation system, while improving the frequency modulation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0053] Figure 1 It is a flow chart of the steps of the wind-solar-storage combined frequency modulation method provided by the present invention;

[0054] Figure 2 This is a specific implementation step diagram of the wind-solar-storage combined frequency modulation method provided by the present invention;

[0055] Figure 3 It is the IEEE 39 model wiring diagram provided by the present invention;

[0056] Figure 4 It is the output curve diagram of the wind turbine set provided by the present invention;

[0057] Figure 5 It is a historical area control error curve graph provided by the present invention;

[0058] Figure 6 It is a schematic diagram of the multi-objective genetic algorithm process provided by the present invention;

[0059] Figure 7 It is a flowchart of the steps of the TOPSI S algorithm provided by the present invention;

[0060] Figure 8 It is a schematic diagram of the composition of the wind-solar-storage combined frequency modulation device provided by the present invention;

[0061] Fig. 9 It is a schematic diagram of droop control of a wind turbine provided by the present invention;

[0062] Fig.10 It is a schematic diagram of the topological structure of a virtual synchronous machine in the virtual inertia control of a wind turbine provided by the present invention;

[0063] Fig.11 This is a schematic diagram of the photovoltaic load reduction principle provided by the present invention. DETAILED DESCRIPTION

[0064] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0065] Reference Figure 1 As shown, the flow chart of the wind-solar-storage combined frequency modulation method of the present invention includes the following specific steps:

[0066] S101: Preset the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model;

[0067] The wind-solar-storage system includes a wind turbine virtual inertia control system, a photovoltaic system load reduction control system, and an energy storage battery energy management system, which are used to control the wind turbine, photovoltaic system, and energy storage system respectively;

[0068] S102: Using the wind-solar-storage joint frequency regulation model, collect wind-solar unit operation data and grid status data;

[0069] The wind and solar power unit operation data includes the output voltage, output current, output power, and wind turbine speed of the wind turbine unit and the photovoltaic system;

[0070] The grid status data includes frequency regulation instructions of each frequency regulation source in the wind, solar and storage system, historical regional control errors, and frequency deviations;

[0071] S103: Initializing multi-objective genetic algorithm parameters, generating an initial population according to the power grid state data, performing selection, crossover and mutation, generating a progeny population, until a maximum number of iterations is reached, and obtaining a non-supported selected solution set consisting of the progeny population after the current iteration;

[0072] Initializing the multi-objective genetic algorithm parameters includes initializing the population size, the maximum number of iterations, the crossover rate, and the mutation rate;

[0073] An initial population including frequency modulation instructions of each frequency modulation source is generated according to the power grid state data, and the frequency modulation instructions of each frequency modulation source are converted into binary codes, and selection, crossover and mutation are performed to generate a sub-generation population; each gene point of the sub-generation population randomly inherits the binary code of the previous generation;

[0074] Until the maximum number of iterations is reached, the offspring population after the current iteration will form a non-supported selection solution set;

[0075] S104: constructing a frequency modulation accuracy objective function and a frequency modulation speed objective function;

[0076] S105: constructing a solution set matrix based on the objective function, using the TOPSIS algorithm to obtain an ideal solution, and calculating the relative proximity between all the non-supporting configuration alternative solutions in the non-supporting configuration alternative solution set and the ideal solution, and obtaining the non-supporting configuration alternative solution with the largest relative proximity as the optimal compromise solution;

[0077] S106: Based on the frequency modulation instructions of each frequency modulation source in the wind-solar-storage system represented by the optimal compromise solution, the wind-solar-storage system is frequency modulated, including: controlling the wind turbine virtual inertia control system, the photovoltaic system load reduction control system, and the energy storage battery energy management system to execute the frequency modulation instructions of the optimal compromise solution.

[0078] Specifically, in step S101, the frequency regulation parameters of the wind-solar-storage system include the frequency regulation output upper limit and frequency regulation output lower limit, output period, average frequency regulation error, average frequency regulation speed, energy storage SOC upper limit and energy storage SOC lower limit, and energy storage charging efficiency and discharging efficiency of each frequency regulation source in the wind-solar-storage system; constructing a wind-solar-storage joint frequency regulation model includes:

[0079] The unit output constraint is expressed as: Among them, P i represents the FM output of the ith FM source, P i Indicates the lower limit of the frequency modulation output of the i-th type frequency modulation source, represents the upper limit of the frequency regulation output of the i-th frequency regulation source; i∈{W, P, EES}, W represents the frequency regulation source of the wind turbine, P represents the frequency regulation source of the photovoltaic system, and EES represents the frequency regulation source of the energy storage system;

[0080] Output period constraint, expressed as: Where t represents the frequency modulation source output period, t s Indicates the start time of the frequency modulation source output, t e Indicates the end time of the frequency modulation source output;

[0081] Energy storage SOC constraint, expressed as: Where S(t) represents the SOC value of the energy storage system at the end of the output period t, ΔS(t) represents the change in the SOC value of the energy storage system during the output period t; η c With η d Respectively represent the charging efficiency and discharging efficiency of the energy storage system, P c With P d They represent the charging power and discharging power of the energy storage system respectively, Δt represents the frequency modulation time step, and E rate Indicates the rated power of the energy storage system; S max With S min Respectively represent the upper limit and lower limit of energy storage SOC;

[0082] The power flow constraints and node voltage constraints are expressed as: -θ j.max ≤θ j,t ≤θ j.max ,θ ref,t =0, Among them, θ l,t With θ j,t They represent the voltage phase angles of node l and node j in the wind-solar-storage joint frequency regulation model, h lj,max represents the power limit of node l and node j, θ j.max represents the phase angle limit, x lj represents the reactance between node l and node j, θ ref,t represents the phase angle of the equilibrium node; G represents the set of units connected to node j, F represents the set of lines starting from node j, and E represents the set of lines ending at node j; f lj,t represents the power flow of the line (l, j) from node l to node j, and the positive and negative values ​​represent the direction of the power flow; D j,t represents the power load demand of node j.

[0083] The non-dominated alternative solution set refers to the solution in which the individuals in an evolutionary population are not dominated by other individuals in multiple objective functions. Due to the existence of multiple objective functions, no solution in this set is absolutely better than another, but all are better than feasible solutions outside the solution set. Non-dominated solutions refer to a solution that is not inferior to another solution in all objectives in a multi-objective optimization problem, and is better than another solution in at least one objective. The non-dominated solutions constitute the alternative solution set.

[0084] Specifically, in steps S104 and S105, based on the constructed frequency modulation accuracy objective function and frequency modulation speed objective function, a solution set matrix of the TOPSIS algorithm is constructed to obtain the optimal compromise solution, which specifically includes:

[0085] Frequency modulation speed objective function J 1 , expressed as:

[0086] Frequency modulation accuracy objective function J 2 , expressed as:

[0087] Where K is the total number of frequency modulation sources in the wind-solar-storage system, P k is the FM output of the kth FM source, v k is the frequency modulation speed of the kth frequency modulation source, q k is the frequency modulation accuracy of the kth frequency modulation source, k = 1, 2, ..., K;

[0088] Based on the frequency modulation accuracy objective function and the frequency modulation speed objective function, the n frequency modulation targets of the m groups of frequency modulation sources are constructed as a solution matrix (x mn ) M*N ;

[0089] Where, N = 2, n = (1, 2); m represents the mth group of frequency modulation sources, that is, the mth non-alternative solution in the non-support alternative solution set, including K frequency modulation source instructions;

[0090] The solution matrix is ​​normalized to obtain a normalized solution matrix (t mn ) M*N , t mn It is expressed as:

[0091] The normalized solution matrix is ​​weighted to obtain a weighted solution matrix (a mn ) M*N , a mn Expressed as: a mn =ω n ×t mn ;ω n represents the weight of the nth frequency modulation target;

[0092] Based on the weighted solution set matrix, calculate the positive ideal solution a + and negative ideal solution a - , expressed as:

[0093]

[0094] Calculate the distance between each non-supported solution and the positive ideal solution and the negative ideal solution in the non-supported solution set to obtain the positive distance Negative distance It is expressed as:

[0095]

[0096] Based on the positive distance and the negative distance, the relative closeness C of each non-supported alternative solution to the ideal solution is calculated. m , expressed as:

[0097]

[0098] Sort all the non-supported alternative solutions from large to small in terms of relative proximity, and take the one with the largest relative proximity as the optimal compromise solution a final , expressed as: a final =a mn ∈max(C m ).

[0099] TOPSIS is a superior and inferior solution distance method, which is a sorting algorithm that approaches the ideal solution. It can make full use of the information of the original data. The selected compromise solution follows the principle of being closest to the positive ideal solution and farthest from the negative ideal solution. Its result can accurately reflect the gap between the evaluation schemes. In a multi-source frequency modulation system, the positive ideal solution has the maximum frequency modulation speed and frequency modulation accuracy, while the negative ideal solution is the opposite.

[0100] The wind, solar, and storage combined frequency modulation method of the present invention takes into account the frequency modulation characteristics of the wind, solar, and storage frequency modulation system, and obtains the optimal compromise solution that takes into account both frequency modulation accuracy and frequency modulation speed through a multi-objective genetic algorithm, so as to control the wind, solar, and storage multi-source system to execute the frequency modulation instructions of the optimal compromise solution; optimizes the distribution of frequency modulation power among the frequency modulation sources, and improves the frequency modulation accuracy and frequency modulation speed of the multi-source frequency modulation system.

[0101] Reference Figure 2 As shown, based on the above embodiment, in this embodiment, based on Figure 3 The IEEE 39 model shown in the figure builds a wind, solar and energy storage combined frequency regulation model to perform frequency regulation, specifically including:

[0102] S201: Set the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model.

[0103] The frequency regulation parameters of the wind-solar-storage system include the upper and lower limits of the frequency regulation output of each frequency regulation source, the average frequency regulation error, and the average frequency regulation speed parameters as shown in Table 1.

[0104] Table 1 Wind and solar frequency modulation source parameter settings

[0105]

[0106] In this embodiment, the upper limit of the frequency modulation output of the two energy storage systems is 12MW, the lower limit of the frequency modulation output is -12MW, and the charging and discharging efficiency η c , η d It is 0.94 and the capacity is 14MW·h.

[0107] In this embodiment, the multi-source frequency regulation model includes unit output constraints, output period constraints and energy storage SOC constraints:

[0108]

[0109] Among them, P W , P P , P ESS They are the output of wind, solar, storage and frequency modulation modules respectively. P W , P P , P ESS are the lower output limits of the corresponding modules, are the output limits of the corresponding modules respectively. t represents the output period of the photovoltaic system, t s The time when the photovoltaic system starts to produce power; t e is the time when the photovoltaic system stops outputting power. S and ΔS represent the SOC value and change of the energy storage unit respectively; P c , P d Represent the charging power and discharging power of the energy storage unit respectively; Δt is the frequency modulation time step; E rate Represents the rated power of the energy storage unit; S min , S max Represent the lower and upper limits of SOC respectively.

[0110] In a multi-source frequency regulation system, the power flow constraints and node voltage constraints of the power grid system must also be considered:

[0111] -θ j.max ≤θ j,t ≤θ j.max θ ref,t =0;

[0112]

[0113] Among them, θ l,t ,θ j,t is the voltage phase angle at point l, j; θ j,max is the phase angle limit; θ ref,t is the equilibrium node phase angle; f lj,t is the power flow of line (l, j), and the positive and negative values ​​indicate the direction of the flow; D j,tis the power load demand of node j; G is the set of units connected to node j; F and E are the sets of lines with node j as the starting point and end point, respectively.

[0114] S202: The data acquisition module collects data from the power supply side and the grid side.

[0115] The data on the power supply side and the grid side include the frequency regulation instructions of the automatic power generation system, historical regional control errors, frequency deviations, the output of thermal power units on the power supply side, etc. The time resolution of the data is 5 minutes.

[0116] Reference Figure 4 As shown, it is the output curve of wind turbine; Figure 5 As shown, it is the historical regional control error curve;

[0117] S203: The decision module determines the multi-objective genetic algorithm parameters and randomly generates an initial population.

[0118] The parameters include population size, maximum number of iterations, crossover rate, mutation rate, etc. The population size is set to 100, the maximum number of iterations is 200, the crossover rate is 0.6, and the mutation rate is 0.01.

[0119] S204: The population forms a offspring population through selection, crossover and mutation.

[0120] Reference Figure 6 As shown, it is a schematic diagram of the multi-objective genetic algorithm process of the present invention; in this embodiment, the initial population and offspring population of the multi-objective genetic algorithm refer to the frequency modulation output combination of wind power, photovoltaics and energy storage. The frequency modulation output combination of the three is converted into a binary code, which is the population in the genetic algorithm. The multi-objective genetic algorithm requires selection, crossover, and mutation to form offspring. The frequency modulation power decision represented by each individual is binary-encoded to form a gene point. Individuals are randomly selected for crossover, and each gene point of the offspring randomly inherits the code of the previous generation during crossover.

[0121] During the crossover process, each point has a certain probability of mutation. The purpose of the mutation operation is to prevent the corresponding decision of the offspring from falling into the local optimum. By simulating the selection, crossover and mutation of genes, the encoding gradually changes, representing the change of the FM source output combination, until the maximum number of iterations is reached, and then the non-supported solution set is selected in the offspring population.

[0122] S205: Repeat step S204 until the maximum number of iterations is reached to obtain a non-supported solution set.

[0123] In this embodiment, after selection, crossover, and mutation to the 200th generation, the frequency modulation strategy corresponding to the non-dominated offspring is used as the non-dominated solution of the algorithm. A non-dominated solution refers to a solution that is not inferior to another solution in all objectives and is superior to another solution in at least one objective in a multi-objective optimization problem. The non-dominated solutions constitute the alternative solution set.

[0124] S206: Calculate the optimal compromise solution using the TOPSIS algorithm.

[0125] In this embodiment, TOPSIS is a sorting algorithm that approximates the ideal solution. The compromise solution selected follows the principle of being closest to the positive ideal solution and farthest from the negative ideal solution. In a multi-source frequency modulation system, the positive ideal solution has the maximum frequency modulation speed and frequency modulation accuracy, while the negative ideal solution is the opposite. Figure 7 As shown, the specific steps of the TOPSIS algorithm include:

[0126] Step S206-1, setting the frequency modulation target function;

[0127] The frequency modulation objective function is:

[0128]

[0129] Among them, v W 、v P 、v ESS are the average frequency modulation speeds of wind turbines, photovoltaic systems, and energy storage systems, respectively, and q W ,q P ,q ESS S are the average frequency regulation accuracy of wind turbines, photovoltaic systems, and energy storage systems.

[0130] Step S206-2, generating and normalizing the disaggregation matrix;

[0131] Normalize the solution matrix (xmn)M*2:

[0132]

[0133] Step S206-3, calculating a weighted normalized solution matrix;

[0134] The weighted normalized solution matrix is ​​calculated as:

[0135] a mn =ω n ×t mn ,m=1,2,...,M,n=1,2;

[0136] Step S206-4, determining a positive ideal solution a+ and a negative ideal solution a-;

[0137] The calculation method of positive ideal solution a+ and negative ideal solution a- is:

[0138]

[0139] Step S206-5, calculating the distance between the candidate solution and the positive and negative ideal solutions;

[0140] The calculation method of the distance between the alternative solution am and the positive and negative ideal solutions is:

[0141]

[0142] Step S206-6, calculating the relative closeness of the candidate solutions;

[0143] The relative closeness of the alternative solution am to the ideal solution is calculated as follows:

[0144]

[0145] Step S206-7, sorting the alternatives according to the relative proximity, and determining the optimal compromise solution.

[0146] The optimal compromise solution is calculated as:

[0147] a final =a∈max(C m );

[0148] S207: The execution module controls the wind, solar and storage system to execute the frequency modulation instruction of the best compromise solution.

[0149] Specifically, the present invention can adopt a method of wind, solar and storage combined frequency regulation to assist the thermal power frequency regulation system in participating in secondary frequency regulation, and adopt a multi-objective genetic algorithm to comprehensively consider the frequency regulation speed and frequency regulation accuracy, and optimize the allocation of wind, solar and storage system frequency regulation power.

[0150] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the wind-solar-storage combined frequency modulation method as described above are performed to perform frequency modulation of the wind-solar-storage combined system.

[0151] Based on the above embodiments, the present invention provides a wind-solar-storage combined frequency regulation device, referring to Figure 8 The figure is a schematic diagram of the composition of the wind, solar and storage combined frequency regulation device, which specifically includes:

[0152] Model building module 100, used to preset the frequency regulation parameters of the wind-solar-storage system and build a wind-solar-storage joint frequency regulation model;

[0153] The data acquisition module 200 includes a wind turbine operation data acquisition unit and a power grid status data acquisition unit, which is used to collect wind and solar turbine operation data and power grid status data using a wind, solar and energy storage combined frequency modulation model;

[0154] The genetic decision module 300 is used to initialize the multi-objective genetic algorithm parameters, generate an initial population according to the power grid state data, perform selection, crossover and mutation, generate a progeny population, and obtain a non-supported allocation solution set composed of the progeny population after the current iteration until the maximum number of iterations is reached; construct a frequency regulation accuracy objective function and a frequency regulation speed objective function; based on the objective function, construct a solution set matrix, use the TOPS IS algorithm to obtain an ideal solution, and calculate the relative proximity of all non-supported allocation solutions in the non-supported allocation solution set to the ideal solution, and obtain the non-supported allocation solution with the largest relative proximity as the optimal compromise solution;

[0155] The execution module 400 is used to adjust the frequency of the wind-solar-storage system based on the frequency modulation instruction represented by the optimal compromise solution.

[0156] The wind-solar-storage combined frequency modulation device of this embodiment is used to implement the aforementioned wind-solar-storage combined frequency modulation method. Therefore, the specific implementation methods of the wind-solar-storage combined frequency modulation device can be seen in the implementation method part of the wind-solar-storage combined frequency modulation method in the previous text. For example, the model building module 100 and the data acquisition module 200 are respectively used to implement steps S101 and S102 in the aforementioned wind-solar-storage combined frequency modulation method; the genetic decision module 300 is used to implement steps S103, S104 and S105 in the aforementioned wind-solar-storage combined frequency modulation method; the execution module 400 is used to implement step S106 in the aforementioned wind-solar-storage combined frequency modulation method. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part.

[0157] Specifically, the data acquisition module includes a thermal power unit operation data acquisition device and a power grid state data acquisition device. The thermal power unit operation data acquisition device collects data such as the output voltage, output current, output power, and steam turbine speed of the thermal power unit in real time. The power grid state data acquisition device collects and calculates data such as power grid frequency deviation and regional control error in real time. The data collected in the data acquisition module is transmitted to the genetic decision module. The multi-objective genetic algorithm parameters and objective functions are set in the genetic decision module, and the initial population is randomly generated. The non-dominated solution set is formed by iteration through selection, crossover, and mutation. The TOPS IS algorithm is used to calculate the relative proximity between the alternative solution and the ideal solution, select the optimal compromise solution, and input the optimal compromise solution into the execution module. The execution module includes a wind turbine virtual inertia control system, a photovoltaic system load reduction control system, and an energy storage battery energy management system. The execution module controls the frequency modulation output of the wind turbine, photovoltaic system, and energy storage system according to the optimal compromise solution input by the genetic decision module, completes the secondary frequency modulation optimization control, and improves the frequency modulation accuracy and frequency modulation speed of the secondary frequency modulation.

[0158] Specifically, the execution module executes the optimal frequency regulation instruction by controlling the virtual inertia control and droop control system of the wind turbine, the photovoltaic load reduction control system and the energy management system of the energy storage battery.

[0159] Reference Fig. 9 The figure shows the schematic diagram of wind turbine droop control. In the wind power system, in order to ensure that the power system has sufficient rotational inertia, the wind turbine achieves the frequency regulation function of the synchronous machine by combining the inverter with the virtual inertia control algorithm. Fig.10 As shown in FIG. 1 , a schematic diagram of the topological structure of the virtual synchronous machine in the virtual inertia control of a wind turbine set is shown; the virtual synchronous machine achieves control characteristics similar to those of a synchronous generator by embedding the rotor motion equations, reactive power droop control, and electromagnetic control algorithms of the synchronous generator into the inverter.

[0160] Reference Fig.11 The figure shows the principle diagram of photovoltaic load shedding. In the photovoltaic system, in order to ensure the maximum power generation efficiency, the photovoltaic system usually operates in the maximum power point tracking mode. However, in this mode, if the grid frequency drops, the photovoltaic system cannot provide active support. Therefore, the photovoltaic system can have a certain amount of spare capacity by deviating from the maximum power point through load shedding control.

[0161] In the energy storage system, the energy storage battery energy management system is used to perform real-time monitoring, fault diagnosis, and charge and discharge control of lithium battery energy storage.

[0162] The wind-solar-storage joint frequency modulation method described in the present invention takes into account the volatility and intermittency of wind-solar-storage data, as well as the frequency modulation characteristics of the wind-solar-storage frequency modulation system, and uses a multi-objective genetic algorithm to select a non-supporting configuration selection solution set, and uses the TOPSIS algorithm to obtain the optimal compromise solution that takes into account both frequency modulation accuracy and frequency modulation speed from the non-supporting configuration selection solution set, and controls the wind-solar-storage system to execute the frequency modulation instructions of the optimal compromise solution to achieve wind-solar-storage joint frequency modulation. The present invention uses a genetic decision algorithm to be more likely to find the global optimal solution, which is suitable for wind-solar-storage frequency modulation that cannot be arbitrarily scheduled intraday and needs to declare a frequency modulation plan a day before, thereby improving the accuracy of frequency modulation; the TOPSIS algorithm is used to obtain the optimal compromise solution to achieve the optimal allocation of frequency modulation power between each frequency modulation source, while ensuring the frequency modulation accuracy of the multi-source frequency modulation system, while improving the frequency modulation speed.

[0163] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0164] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0165] 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.

[0166] 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.

[0167] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A wind, solar and energy storage combined frequency modulation method, characterized in that: include: Preset the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model; Use the wind, solar and energy storage joint frequency regulation model to collect wind and solar unit operation data and grid status data; Initializing the multi-objective genetic algorithm parameters, generating an initial population according to the power grid state data, performing selection, crossover and mutation, generating a progeny population, until a maximum number of iterations is reached, and obtaining a non-supported selected solution set consisting of the progeny population after the current iteration; Construct frequency modulation accuracy objective function and frequency modulation speed objective function; Based on the objective function, a solution set matrix is ​​constructed, and the TOPSIS algorithm is used to obtain the ideal solution. The relative proximity between all the non-supporting solutions and the ideal solution in the non-supporting solution set is calculated, and the non-supporting solution with the largest relative proximity is obtained as the optimal compromise solution, including: Based on the frequency modulation accuracy objective function and the frequency modulation speed objective function, the n frequency modulation targets of the m groups of frequency modulation sources are constructed as a solution matrix (x mn ) M*N ; Where N = 2, n = (1, 2); m represents the mth group of frequency modulation sources, that is, the mth non-supported configuration option in the non-supported configuration option set, including K frequency modulation source instructions; The solution matrix is ​​normalized to obtain a normalized solution matrix (t mn ) M*N , t mn It is expressed as: The normalized solution matrix is ​​weighted to obtain a weighted solution matrix (a mn ) M*N , a mn Expressed as: a mn =ω n ×t mn ;ω n represents the weight of the nth frequency modulation target; Based on the weighted solution set matrix, calculate the positive ideal solution a + and negative ideal solution a - , expressed as: Calculate the distance between each non-supported solution and the positive ideal solution and the negative ideal solution in the non-supported solution set to obtain the positive distance Negative distance It is expressed as: Based on the positive distance and the negative distance, the relative closeness C of each non-supported alternative solution to the ideal solution is calculated. m , expressed as: Sort all the non-supported alternative solutions from large to small in terms of relative proximity, and take the one with the largest relative proximity as the optimal compromise solution a final , expressed as: a final =a mn ∈max(C m ); Based on the frequency modulation instructions of each frequency modulation source in the wind-solar-storage system represented by the optimal compromise solution, the wind-solar-storage system is frequency modulated.

2. The wind-solar-storage combined frequency modulation method according to claim 1 is characterized in that: The preset wind-solar-storage system frequency modulation parameters and the construction of a wind-solar-storage joint frequency modulation model include: The frequency modulation parameters of the wind-solar-storage system include the frequency modulation output upper limit and frequency modulation output lower limit, output period, average frequency modulation error, average frequency modulation speed, energy storage SOC upper limit and energy storage SOC lower limit, and energy storage charging efficiency and discharging efficiency of each frequency modulation source in the wind-solar-storage system; The wind, solar and energy storage combined frequency regulation model includes: The unit output constraint is expressed as: Among them, P i represents the FM output of the ith FM source, P i Indicates the lower limit of the frequency modulation output of the i-th type frequency modulation source, represents the upper limit of the frequency regulation output of the i-th frequency regulation source; i∈{W, P, EES}, W represents the frequency regulation source of the wind turbine, P represents the frequency regulation source of the photovoltaic system, and EES represents the frequency regulation source of the energy storage system; Output period constraint, expressed as: Where t represents the frequency modulation source output period, t s Indicates the start time of the frequency modulation source output, t e Indicates the end time of the FM source output; Energy storage SOC constraint, expressed as: Where S(t) represents the SOC value of the energy storage system at the end of the output period t, ΔS(t) represents the change in the SOC value of the energy storage system during the output period t; η c With η d Respectively represent the charging efficiency and discharging efficiency of the energy storage system, P c With P d They represent the charging power and discharging power of the energy storage system respectively, Δt represents the frequency modulation time step, and E rate Indicates the rated power of the energy storage system; S max With S min Respectively represent the upper limit and lower limit of energy storage SOC; The power flow constraints and node voltage constraints are expressed as: -θ j.max ≤θ j,t ≤θ j.max ,θ ref,t =0, Among them, θ l,t With θ j,t They represent the voltage phase angles of node l and node j in the wind-solar-storage joint frequency regulation model, h lj,max represents the power limit of node l and node j, θ j.max represents the phase angle limit, x lj represents the reactance between node l and node j, θ ref,t represents the phase angle of the equilibrium node; G represents the set of units connected to node j, F represents the set of lines starting from node j, and E represents the set of lines ending at node j; f lj,t represents the power flow of the line (l, j) from node l to node j, and the positive and negative values ​​represent the direction of the power flow; D j,t represents the power load demand of node j.

3. The wind-solar-storage combined frequency modulation method according to claim 1 is characterized in that: The wind-solar unit operation data includes the output voltage, output current, output power, and wind turbine speed of the wind turbine unit and the photovoltaic system; the grid status data includes the frequency modulation instructions of each frequency modulation source in the wind-solar storage system, historical regional control errors, and frequency deviations.

4. The wind-solar-storage combined frequency modulation method according to claim 1 is characterized in that: The initialization of the multi-objective genetic algorithm parameters, generating an initial population according to the power grid state data, performing selection, crossover and mutation, generating a progeny population, until the maximum number of iterations is reached, and obtaining a non-supported selected solution set consisting of the progeny population after the current iteration, includes: Initializing the multi-objective genetic algorithm parameters includes initializing the population size, the maximum number of iterations, the crossover rate, and the mutation rate; An initial population including frequency modulation instructions of each frequency modulation source is generated according to the power grid state data, and the frequency modulation instructions of each frequency modulation source are converted into binary codes, and selection, crossover and mutation are performed to generate a sub-generation population; each gene point of the sub-generation population randomly inherits the binary code of the previous generation; Until the maximum number of iterations is reached, the offspring population after the current iteration forms a non-supported alternative solution set.

5. The wind-solar-storage combined frequency modulation method according to claim 1 is characterized in that: The constructing of the frequency modulation accuracy objective function and the frequency modulation speed objective function includes: The frequency modulation speed objective function J1 is expressed as: The frequency modulation accuracy objective function J2 is expressed as: Where K is the total number of frequency modulation sources in the wind-solar-storage system, P k is the FM output of the kth FM source, v k is the frequency modulation speed of the kth frequency modulation source, q k is the frequency modulation accuracy of the kth frequency modulation source, k=1,2,...,K.

6. The wind, solar and energy storage combined frequency modulation method according to claim 1 is characterized in that: The wind-solar-storage system includes a wind turbine virtual inertia control system, a photovoltaic system load reduction control system, and an energy storage battery energy management system, which are used to control the wind turbine, photovoltaic system, and energy storage system respectively; The frequency modulation of the wind-solar-storage system based on the frequency modulation instruction represented by the optimal compromise solution includes: controlling the wind turbine virtual inertia control system, the photovoltaic system load reduction control system, and the energy storage battery energy management system to execute the frequency modulation instruction of the optimal compromise solution.

7. The wind, solar and energy storage combined frequency modulation method according to claim 1 is characterized in that: The wind, solar and energy storage combined frequency regulation model is the IEEE39 model.

8. A wind, solar and energy storage combined frequency modulation device, characterized in that: include: Model building module, used to preset the frequency regulation parameters of the wind, solar and storage system and build a wind, solar and storage joint frequency regulation model; The data acquisition module includes a wind turbine operation data acquisition unit and a power grid status data acquisition unit, which is used to collect wind and solar turbine operation data and power grid status data using a wind, solar and energy storage combined frequency modulation model; The genetic decision module is used to initialize the multi-objective genetic algorithm parameters, generate an initial population according to the power grid state data, perform selection, crossover and mutation, generate a progeny population, until the maximum number of iterations is reached, and obtain a non-supported allocation solution set composed of the progeny population after the current iteration; construct a frequency regulation accuracy objective function and a frequency regulation speed objective function; based on the objective function, construct a solution set matrix, use the TOPSIS algorithm to obtain an ideal solution, and calculate the relative proximity of all non-supported allocation solutions in the non-supported allocation solution set to the ideal solution, and obtain the non-supported allocation solution with the largest relative proximity as the optimal compromise solution, including: Based on the frequency modulation accuracy objective function and the frequency modulation speed objective function, the n frequency modulation targets of the m groups of frequency modulation sources are constructed as a solution matrix (x mn ) M*N ; Where N = 2, n = (1, 2); m represents the mth group of frequency modulation sources, that is, the mth non-supported configuration option in the non-supported configuration option set, including K frequency modulation source instructions; The solution matrix is ​​normalized to obtain a normalized solution matrix (t mn ) M*N , t mn It is expressed as: The normalized solution matrix is ​​weighted to obtain a weighted solution matrix (a mn ) M*N , a mn Expressed as: a mn =ω n ×t mn ;ω n represents the weight of the nth frequency modulation target; Based on the weighted solution set matrix, calculate the positive ideal solution a + and negative ideal solution a - , expressed as: Calculate the distance between each non-supported solution and the positive ideal solution and the negative ideal solution in the non-supported solution set to obtain the positive distance Negative distance It is expressed as: Based on the positive distance and the negative distance, the relative closeness C of each non-supported alternative solution to the ideal solution is calculated. m , expressed as: Sort all the non-supported alternative solutions from large to small in terms of relative proximity, and take the one with the largest relative proximity as the optimal compromise solution a final , expressed as: a final =a mn ∈max(C m ); An execution module is used to adjust the frequency of the wind-solar-storage system based on the frequency modulation instruction represented by the optimal compromise solution.

9. 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 wind-solar-storage combined frequency modulation method as described in any one of claims 1 to 7 are performed.

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