A virtual power plant optimization scheduling method based on a particle swarm algorithm

Abstract

The present application relates to a kind of virtual power plant optimization scheduling method based on particle swarm algorithm, to obtain optimal virtual power plant scheduling scheme as goal, using particle swarm optimization algorithm to optimize virtual power plant scheduling scheme, including: S1, obtain the power generation power of all units in virtual power plant and related load power;S2, input the related parameters of particle swarm optimization algorithm;S3, initialize the position and speed of particle;S4, calculate the fitness value of each particle;S5, update the speed and position of particle, update individual optimal and group optimal;S6, recalculate the fitness value of each particle;S7, judge whether reach the number of iteration termination, yes, output optimization result, otherwise go to next step;S8, judge whether the fitness value of particle is unchanged for m times of iteration, no, return S5, yes, update particle speed;S9, to particle is carried out SCR calculation and selects out inferior particle;S10, respectively update the value of each dimension of inferior particle, then return S4 continue iteration.The method is conducive to improve virtual power plant scheduling effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of comprehensive energy system scheduling optimization, and particularly relates to a virtual power plant optimal scheduling method based on a particle swarm algorithm. BACKGROUND

[0002] New energy power generation has an undeniable disadvantage, that is, the uncertainty of power transmission power, and in addition, new energy power generation, including wind power generation and photovoltaic power generation, accounts for a higher and higher proportion in power system generation, thus greatly affecting the power system, especially the power fluctuation, which causes the frequency of the power grid to fluctuate, and if wind power generation and photovoltaic power generation are directly connected to the grid, it will pose a certain threat to the stability of the power system, power quality and frequency quality, and therefore, it is of practical significance to consider how to effectively suppress the output power of new energy power generation, reduce the impact of its fluctuation on the power system, and improve the operation capability of wind farms and photovoltaic farms, for the large-scale development and application of new energy power generation.

[0003] At present, in order to suppress the original power generated by renewable energy power generation, the main method adopted is to use virtual power plant technology to balance energy supply and demand by integrating and optimizing distributed energy resources, so as to maximize the rational use of renewable energy. However, the model has the characteristics of high-dimensional variables, nonlinearity and complex constraints. Although it can be converted into a mixed integer linear programming model through linearization, there are too many decision variables to be converted in the model, and therefore other methods need to be sought for model solving. SUMMARY

[0004] The present application aims to provide a virtual power plant optimal scheduling method based on a particle swarm algorithm, which is beneficial to improve the scheduling effect of the virtual power plant.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: a virtual power plant optimal scheduling method based on a particle swarm algorithm, taking obtaining an optimal virtual power plant scheduling scheme as the target, using a particle swarm optimization algorithm to optimize the virtual power plant scheduling scheme, including the following steps:

[0006] S1, obtaining the power generation power of all units in the virtual power plant and the related load power and performing data preprocessing;

[0007] S2, inputting the related parameters of the particle swarm optimization algorithm, including the population size, the iteration number and the inertia weight; each particle in the particle swarm optimization algorithm represents a scheduling scheme;

[0008] S3, initializing the position and speed of the particle;

[0009] S4, calculating the fitness value of each particle and recording the archive;

[0010] S5, update the velocity and position of the particle, update the individual optimal and the group optimal;

[0011] S6, recalculate the fitness value of each particle and record the archive;

[0012] S7, judge whether the number of iterations is reached, yes, go to step S11, otherwise go to step S8;

[0013] S8, judge whether the fitness value of the particle is not changed in the continuous m iterations, otherwise return to step S5, yes, update the velocity of each particle;

[0014] S9, perform self-conductivity SCR calculation on the particle, and select the inferior particle from the value of SCR;

[0015] S10, generate a random number for each dimension of the inferior particle, if the generated random number is less than the mutation value of the particle dimension, replace the value of the inferior particle in the dimension with the value of the corresponding dimension of the optimal individual particle, and then return to step S4 to continue iteration;

[0016] S11, end the optimization process, and output the optimal virtual power plant scheduling scheme.

[0017] Further, in step S1, when data preprocessing is performed, abnormal parameters are screened out and several groups of 24-hour parameters are randomly selected as checking data.

[0018] Further, in step S4, the fitness value of each particle is calculated by using the fitness function as formula (1):

[0019] f=R VPP -C VPP (1)

[0020] In the formula, f is the benefit of the virtual power plant VPP; R VPP and C VPP are the total revenue and total cost of the internal operation of the VPP, respectively;

[0021] Among them, the total operation cost is expressed as:

[0022]

[0023] In the formula, C F is the operation cost of the thermal power unit; C WT and are the operation and maintenance cost and the wind penalty cost of the wind turbine, respectively; C PV and are the operation and maintenance cost and the light penalty cost of the photovoltaic unit, respectively; C ESS is the operation and maintenance cost of the energy storage device; C gridthe cost of electricity for VPP to interact with the upper grid; C P2G the operation cost of P2G device; C CS the carbon sequestration cost of VPP; a f , b f and c f are fuel cost coefficients, P G,t is the total output power of thermal power units at time period t; δ WT and δ PV are the operation cost coefficients of wind power and photovoltaic power, P WT,t and P PV,t are the power generation of wind power units and photovoltaic units at time period t; and are the penalty cost factors of abandoned wind and abandoned light; P WT,pre,t and P PV,pre,t are the predicted output power of wind power and photovoltaic at time period t; δ ESS is the operation and maintenance cost factor of energy storage device, and are the charging and discharging power of energy storage device at time period t; δ buy,t and δ sell,t are the purchase and sale prices of electricity of VPP to external grid; P grid,buy,t and P grid,sell,t are the power purchased and sold by VPP to the upper grid at time period t; δ P2G is the operation cost coefficient of P2G, P P2G,t is the P2G energy consumption at time period t; δ CCS is the cost of sequestration of unit carbon dioxide; N CCS,t is the amount of carbon dioxide that needs to be sequestered at time period t, as shown in equation (3):

[0024]

[0025] In the formula, N P2G,t is the amount of carbon dioxide consumed by P2G at time period t; η C is the amount of carbon dioxide required to produce unit natural gas;

[0026] The total operating revenue is represented as:

[0027]

[0028] In the formula, R SE,t is the revenue of VPP from selling electricity at time period t; δ sell,t is the price of electricity sold by VPP; P VPP,t is the planned output power of VPP at time period t. is the natural gas production at time period t; λ P2Gis the electric conversion efficiency; H g is the heating value of natural gas; S g is the price of unit natural gas; R SG,t is the benefit of participating in the natural gas market. P GN,t is the net output power of the thermal power unit in period t; P CC,t is the total energy consumption required by the carbon capture device in period t; P bas and P opt,t are the basic energy consumption and operating energy consumption of the carbon capture device in period t, respectively; P CO2 is the energy consumption for processing unit carbon dioxide; P GC,t is the carbon capture energy consumption provided by the thermal power unit.

[0029] Further, in step S5, the velocity and position of the particle are updated according to formulas (7) and (8), and the individual optimal and global optimal are updated;

[0030] v l,n+1 = wv l,n + c1r1(P best,l -x l,n ) + c2r2(G best,n -x l,tn ) (7)

[0031]

[0032]

[0033] wherein v l,n+1 is the velocity of particle l in the next iteration n+1; v l,t＝n is the velocity of particle l in the current iteration n, w is the inertia weight of particle velocity change, controlling the degree of preservation of particle velocity; c1 and c2 are acceleration coefficients, representing the individual learning factor and social learning factor, respectively, controlling the degree of convergence of the particle to the individual optimal and global optimal; r1 and r2 are random numbers in the interval [0, 1]; P best,l is the individual optimal position found by particle l so far; G best,n is the global optimal position found by the entire particle swarm so far; x l,n+1 is the position of particle l in the next iteration n+1; x l,n is the velocity of particle l in the current iteration n; p n is the inertia weight of particle position change; p max and p min are the maximum and minimum values of the inertia weight, respectively; R1 controls the amplitude of the sine and cosine functions, which can be adjusted according to the iteration process; a is a constant set; R2, R3 and R4 represent three random numbers, which are subject to uniform distribution; N is the total number of iterations.

[0034] Further, in step S8, when the position of the particle is changed and the fitness value of the particle is unchanged in continuous m times of iteration, the velocity of each particle is updated according to the velocity correction formula of formula (10);

[0035]

[0036] wherein, v l,n+1 represents the velocity of the lth particle in the next iteration n+1, l represents the lth particle, n represents the current iteration, and m represents the set number of continuous iterations.

[0037] Further, in step S9, in order to avoid falling into local optimization in the process of searching for the optimal solution, the self-conductivity of the particle is calculated according to formula (11);

[0038]

[0039] wherein, SCR(l,n) is the ratio of the individual optimal fitness change and the global optimal individual fitness change in the first q iterations of the lth particle in the nth iteration; f() represents the fitness function.

[0040] Further, in step S10, the value of the dimension of the detected poor particle is changed by a certain probability, so that the poor particle escapes from the current poor area; when the value of SCR is 0 or infinite, it indicates that the current is in a local optimal state; based on the idea of genetic mutation, the positions of the screened poor particles and the global optimal particle are exchanged with a certain probability.

[0041] The application also provides a virtual power plant optimization scheduling system for realizing the above method, comprising:

[0042] A data acquisition module is configured to acquire the power generation of all units in the virtual power plant and the related load power and perform data preprocessing.

[0043] A particle swarm optimization module is configured to perform virtual power plant scheduling optimization by using a particle swarm optimization algorithm based on the acquired data.

[0044] A self-conductivity calculation module is configured to be called by the particle swarm optimization module, to perform self-conductivity calculation on the particles, and to select poor particles according to the value of SCR.

[0045] The application also provides a computer device, comprising at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, realize the above method.

[0046] The application further provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions realize the method when executed by a processor.

[0047] Compared with the prior art, the application has the following beneficial effects: the application provides a virtual power plant optimal scheduling method based on a particle swarm algorithm, the method optimizes the particle position updating method of the PSO algorithm by adopting the SCA algorithm, in addition, in order to develop the local search capability of the algorithm in the later iteration, a reverse search strategy is adopted for the local optimal particle, in the continuous iteration process, when the position of the particle changes and the fitness value stagnates, the current speed is used as the vector sum of the speeds of the previous m iterations to intervene, the application keeps the advantages of the PSO algorithm, reduces the probability of falling into a local optimum by increasing the diversity of the population, and improves the efficiency and accuracy of the algorithm in searching for an optimal solution, the method can obtain better optimal scheduling effect when applied to the optimal scheduling of the virtual power plant, and therefore, the application has strong practicability and broad application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The figure is a flowchart of the implementation of the particle swarm optimization algorithm of the embodiment of the application.

[0049] Figure 2 The figure is a schematic diagram of the reverse search strategy in the particle swarm optimization algorithm of the embodiment of the application.

[0050] Figure 3 The figure is a comparison of the solution results of the high-dimensional test function of the embodiment of the application.

[0051] Figure 4 The figure is a structure block diagram of the virtual power plant of the embodiment of the application.

[0052] Figure 5 The figure is the original power and load power of wind power and photovoltaic power in the virtual power plant of the embodiment of the application in four typical days.

[0053] Figure 6 The figure is the solution result of the virtual power plant of the embodiment of the application in the third typical day. DETAILED DESCRIPTION

[0054] The application will be further described below in combination with the drawings and embodiments.

[0055] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0056] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0057] As shown in Figures 1-2 , the embodiment provides a virtual power plant optimization scheduling method based on a particle swarm algorithm, to obtain an optimal virtual power plant scheduling scheme, and a particle swarm optimization algorithm is used to optimize the virtual power plant scheduling scheme, including the following steps:

[0058] S1, obtaining the power generation of all units in the virtual power plant and the related load power and performing data preprocessing.

[0059] During the data preprocessing process, abnormal parameters are screened out and several groups of 24-hour parameters are randomly selected as calculation data.

[0060] S2, inputting related parameters of the particle swarm optimization algorithm, including population size, iteration number and inertia weight, etc.

[0061] In the particle swarm optimization algorithm, each particle represents a scheduling scheme, that is, the power generation and power consumption of all units in the virtual power plant within a scheduling period (in this embodiment, 24 hours is set as a scheduling period).

[0062] S3, initializing the position and speed of the particle.

[0063] S4, calculating the fitness value of each particle and recording and archiving.

[0064] Specifically, the fitness value of each particle is calculated by using the fitness function of formula (1):

[0065] f = R VPP -C VPP (1)

[0066] In the formula, f is the benefit of the virtual power plant (VPP); R VPP and C VPP are the total revenue and total cost of the internal operation of the VPP, respectively.

[0067] Among them, the total operation cost is represented as:

[0068]

[0069] In the formula, C F is the operation cost of the thermal power unit; CWT and are the operation and maintenance cost and the penalty cost of curtailment of wind power, respectively; C PV and are the operation and maintenance cost and the penalty cost of curtailment of photovoltaic power, respectively; C ESS is the operation and maintenance cost of energy storage equipment; C grid is the electricity cost of VPP interacting with the upper grid; C P2G is the operation cost of P2G equipment; C CS is the carbon sequestration cost of VPP; a f , b f and c f are the fuel cost coefficients, P G,t is the total output power of thermal power at time period t; δ WT and δ PV are the operation cost coefficients of wind power and photovoltaic power, respectively, P WT,t and P PV,t are the power generation of wind power and photovoltaic power at time period t, respectively; and are the penalty cost factors of curtailment of wind power and photovoltaic power, respectively; P WT,pre,t and P PV,pre,t are the predicted output power of wind power and photovoltaic power at time period t, respectively; δ ESS is the operation and maintenance cost factor of energy storage equipment, and are the charge and discharge power of energy storage equipment at time period t, respectively; δ buy,t and δ sell,t are the purchase and sale prices of VPP to and from the external grid; P grid,buy,t and P grid,sell,t are the power purchased and sold by VPP to and from the upper grid at time period t, respectively; δ P2G is the operation cost coefficient of P2G, P P2G,t is the energy consumption of P2G at time period t; δ CCS is the cost of sequestration of one unit of carbon dioxide; N CCS,t is the amount of carbon dioxide that needs to be sequestered at time period t, as shown in equation (3):

[0070]

[0071] wherein, N P2G,t is the amount of carbon dioxide consumed by P2G at time period t; η C is the amount of carbon dioxide required to produce one unit of natural gas;

[0072] The total operation revenue is represented as:

[0073]

[0074] Where R SE,t is the electricity sales revenue of VPP in period t; δ sell,t P is the price of electricity sold by VPP; VPP,t is the planned output power of VPP during period t. is the natural gas production in period t; P2G is the electrical conversion efficiency; H g is the calorific value of natural gas; S g is the price of natural gas per unit; R SG,t The income from participating in the natural gas market. GN,t is the net output power of the thermal power unit during period t; P CC,t is the total energy consumption of the carbon capture equipment during period t; P bas and P opt,t are the basic energy consumption and operating energy consumption of carbon capture in period t respectively; P CO2 is the energy consumption per unit of carbon dioxide treatment; P GC,t Energy consumption of carbon capture provided to thermal power units.

[0075] S5. Update the speed and position of the particles according to formulas (7) and (8), and update the individual optimum and group optimum.

[0076] v l,n+1 =wv l,n +c1r1(P best,l -x l,n )+c2r2(G best,n -x l,tn ) (7)

[0077]

[0078]

[0079] Among them, v l,n+1 is the velocity of particle l at the next iteration n+1; v l,t＝n is the speed of particle l in the current iteration n, w is the inertia weight, which controls the degree of particle speed retention and affects the global search ability of the algorithm; c1 and c2 are acceleration coefficients, representing the individual learning factor and the social learning factor, respectively, which control the degree to which the particle approaches the individual optimum and the global optimum; r1 and r2 are random numbers in the interval [0,1], which add randomness to the algorithm; P best,l is the individual optimal position found by particle l so far; G best,n is the global optimal position found by the entire particle swarm so far; x l,n+1 is the position of particle l at the next iteration n+1; x l,n is the velocity of particle l at the current iteration n; ρ n is the inertia weight; ρmax and p min respectively are the maximum and minimum values of the inertia weight; R1 controls the amplitude of the sine and cosine functions, which can be flexibly adjusted according to the iteration process; a is a constant set to 2 in this embodiment; R2, R3 and R4 represent three random numbers subject to uniform distribution; and N is the total number of iterations.

[0080] S6, recalculate the fitness value of each particle and record the archive.

[0081] S7, determine whether the number of iterations has reached the termination iteration, if the number of iterations has reached the termination iteration, go to step S11, if not, go to step S8 for the next operation.

[0082] S8, if the number of iterations has not reached the termination iteration, further determine whether the fitness value of the particle has not changed in the last m iterations, if not, return to step S5, update the speed and position of the particle according to formulas (7) and (8), update the individual optimal and global optimal, if yes, update the speed of each particle; specifically, when the position of the particle changes and the fitness value of the particle does not change in the last m iterations, update the speed of each particle according to the speed correction formula of formula (10):

[0083]

[0084] wherein v l,n+1 represents the speed of the lth particle in the next iteration n+1, l represents the lth particle, n represents the current iteration, and m represents the set number of consecutive iterations.

[0085] S9, calculate the self-conduction rate (SCR) of the particle, and select the inferior particle from the value of the SCR.

[0086] Specifically, in order to avoid falling into local optimum in the process of finding the optimal solution, the self-conduction rate of the particle is calculated according to formula (11);

[0087]

[0088] wherein SCR(l, n) is the ratio of the change of the individual optimal fitness value to the change of the global optimal individual fitness value in the last q iterations of the lth particle in the nth iteration; f() represents the fitness function.

[0089] S10, generate a random number for each dimension of the inferior particle, if the generated random number is less than the mutation value of the particle dimension, replace the value of the inferior particle in the corresponding dimension with the value of the corresponding dimension of the optimal individual particle, and then return to step S4 for iteration.

[0090] S11, ending the optimization process, outputting the optimization result, that is, the optimal virtual power plant scheduling scheme.

[0091] Specifically, the detected poor particles change the value of the particle part dimension by a certain probability, so that the poor particles escape from the current poor area; when the value of SCR is 0 or infinity, it indicates that the current is in a local optimal state; based on the idea of genetic mutation, the positions of the screened poor particles and the global optimal particles are exchanged by a certain probability.

[0092] Before applying the method of the embodiment, simulation analysis is performed by using Matlab software. In order to verify the effectiveness and rationality of the method, the method is compared with a traditional PSO algorithm, a differential evolution algorithm (DE) and a grey wolf optimizer algorithm (GWO). In order to simulate the high dimensionality and nonlinearity of the virtual power plant model, three 30-dimensional test functions shown in formula (12) are selected for testing. From the iteration curve trends of the three algorithms, it can be seen that, compared with the other three solving algorithms, the method converges at the fastest speed and the smallest value when solving the test functions, which indicates that the convergence speed of the method is the fastest and the convergence accuracy is the highest in the comparison algorithms, proving that the method has higher solving accuracy and solving speed. Figure 3

[0093] In order to verify the application of the algorithm in the virtual power plant, a virtual power plant model as shown in formula (13) is constructed on the Matlab software, and the original power generated by the four typical days of wind power and photovoltaic power and the load power in the virtual power plant as shown in formula (14) are input into the virtual power plant model.

[0094] Figure 4 Figure 5

[0095] Figure 6 The solving result of the third typical day in the virtual power plant is shown in formula (15). The power generated by the thermal power unit is more, and the carbon emission is less, which is due to the fact that the carbon capture device in the virtual power plant absorbs most of the carbon dioxide generated by the thermal power unit. In addition, under the incentive of the carbon-green certificate coupling transaction mechanism, the virtual power plant increases the utilization of renewable energy in the system in order to reduce the cost, and sells the excess power in the system to the external power grid to earn profits. It can be seen that the method can be well applied to the optimal scheduling of the virtual power plant.

[0096] The embodiment also provides a virtual power plant optimal scheduling system for implementing the above method, which comprises a data acquisition module, a particle swarm optimization module and a self-permeability calculation module. ​​​

[0097] The data collection module is configured to collect power generation of all units in the virtual power plant and related load power and perform data preprocessing.

[0098] The particle swarm optimization module is configured to perform virtual power plant dispatching optimization based on the collected data by using a particle swarm optimization algorithm.

[0099] The self-conductivity calculation module is configured to be called by the particle swarm optimization module, to perform self-conductivity calculation on particles, and to select inferior particles from the values of the SCR.

[0100] The embodiment further provides a computer device, including at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method described above.

[0101] The embodiment further provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions, when executed by a processor, implement the method described above.

[0102] Those skilled in the art should understand that embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0103] 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 flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams 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 devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device implemented in the flowcharts and / or block diagrams. Figure One The device that implements the function specified in one flow or multiple flows and / or blocks Figure One The device that implements the function specified in one flow or multiple flows and / or blocks

[0104] These computer program instructions can also be stored in a computer readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction devices, which implement the flowcharts and / or block diagrams. Figure One The device that implements the function specified in one flow or multiple flows and / or blocksFigure One the function(s) specified in the block or blocks.

[0105] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flows Figure One the flow(s) or flows and / or block(s) Figure One the function(s) specified in the block or blocks.

[0106] The above description is only the preferred embodiment of the present application, not other forms of the present application, any skilled in the art can use the above disclosed technical content to change or modify as equivalent embodiments of equivalent changes. But any simple modification, equivalent change and modification of the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A virtual power plant optimization scheduling method based on a particle swarm algorithm, characterized in that, To obtain the optimal virtual power plant scheduling scheme, a particle swarm optimization algorithm is used to optimize the virtual power plant scheduling scheme, including the following steps: S1, obtain the power generation of all units in the virtual power plant and the related load power and perform data preprocessing; S2, input the related parameters of the particle swarm optimization algorithm, including the population size, the number of iterations and the inertia weight; each particle in the particle swarm optimization algorithm represents a scheduling scheme; S3, initialize the position and velocity of the particle; S4, calculate the fitness value of each particle and record it; S5, update the velocity and position of the particle, update the individual optimal and group optimal; S6, recalculate the fitness value of each particle and record it; S7, determine whether the number of iterations has reached the termination iteration, yes, go to step S11, otherwise go to step S8; S8, determine whether the fitness value of the particle has not changed for m consecutive iterations, no, return to step S5, yes, update the velocity of each particle; S9, calculate the self-conductivity SCR of the particle, and select the inferior particle from the value of SCR; S10, generate a random number for each dimension of the inferior particle, if the generated random number is less than the mutation value of the particle dimension, replace the value of the inferior particle in the dimension with the corresponding value of the optimal individual particle, and then return to step S4 for iteration; S11, end the optimization process and output the optimization result, which is the optimal virtual power plant scheduling scheme; In step S5, the velocity and position of the particle are updated according to formulas (7) and (8), and the individual optimal and group optimal are updated; v l,n+1 = wv l,n + c1r1(P best,l - x l,n ) + c2r2(G best,n - x l,tn ) (7) where v l,n+1 is the velocity of particle l in the next iteration n+1; v l,t＝n is the velocity of particle l in the current iteration n, w is the inertia weight of the velocity change of the particle, which controls the degree of preservation of the particle velocity; c1 and c2 are acceleration coefficients, which represent the individual learning factor and the social learning factor respectively, and control the degree of convergence of the particle to the individual optimum and the global optimum; r1 and r2 are random numbers in the interval [0, 1]; P best,l is the individual optimum position found by particle l so far; G best,n is the global optimum position found by the entire particle group so far; x l,n+1 is the position of particle l in the next iteration n+1; x l,n is the velocity of particle l in the current iteration n; p n is the inertia weight of the position change of the particle; p max and p min are the maximum and minimum values of the inertia weight respectively; R1 controls the amplitude of the sine and cosine functions, which can be adjusted according to the iteration process; a is a constant set; R2, R3 and R4 represent three random numbers, which are subject to uniform distribution; N is the total number of iterations; In step S8, when the position of the particle changes and the fitness value of the particle does not change for m consecutive iterations, the velocity of each particle is updated according to the velocity correction formula of formula (10); where v l,n+1 represents the velocity of the lth particle at the next iteration n+1, l represents the lth particle, n represents the current iteration, and m represents the set number of consecutive iterations.

2. The virtual power plant optimization scheduling method based on the particle swarm algorithm according to claim 1, characterized in that, In step S1, when performing data preprocessing, abnormal parameters are filtered out and several groups of 24-hour parameters are randomly selected as calculation data.

3. The virtual power plant optimization scheduling method based on particle swarm algorithm according to claim 1, characterized in that, In step S4, the fitness value of each particle is calculated using the fitness function of formula (1): f = R VPP -C VPP (1) In the formula, f is the benefit of the virtual power plant VPP; R VPP and C VPP are the total revenue and total cost of the internal operation of the VPP, respectively; Wherein, the total running cost is represented as: wherein C F is the operation cost of thermal power units; C WT and are the operation and maintenance cost and the penalty cost of curtailment of wind power, respectively; C PV and are the operation and maintenance cost and the penalty cost of curtailment of photovoltaic power, respectively; C ESS is the operation and maintenance cost of energy storage devices; C grid is the electricity cost of VPP interacting with the upper grid; C P2G is the operation cost of P2G devices; C CS is the carbon sequestration cost of VPP; a f , b f and c f are fuel cost coefficients, respectively; P G,t is the total output power of thermal power units in period t; δ WT and δ PV are the operation cost coefficients of wind power and photovoltaic power, respectively; P WT,t and P PV,t are the power generation of wind power units and photovoltaic power units in period t, respectively; and are the penalty cost factors of curtailment of wind power and photovoltaic power, respectively; P WT,pre,t and P PV,pre,t are the predicted output power of wind power and photovoltaic power in period t, respectively; δ ESS is the operation and maintenance cost factor of energy storage devices, and are the charging and discharging power of energy storage devices in period t, respectively; δ buy,t and δ sell,t are the purchase and sale prices of VPP to the external grid; P grid,buy,t and P grid,sell,t are the power purchased and sold by VPP to the upper grid in period t, respectively; δ P2G is the operation cost coefficient of P2G, P P2G,t is the energy consumption of P2G in period t; δ CCS is the cost of sequestration of one unit of carbon dioxide; N CCS,t is the amount of carbon dioxide that needs to be sequestered in period t, as shown in equation (3): where N P2G,t is the amount of carbon dioxide consumed by P2G in period t; η C is the amount of carbon dioxide required to produce one unit of natural gas; The total running income is represented as: wherein R SE,t is the revenue of VPP from selling electricity in time period t; δ sell,t is the price of electricity sold by VPP; P VPP,t is the planned output power of VPP in time period t; is the natural gas production in time period t; λ P2G is the electricity conversion efficiency; H g is the heating value of natural gas; S g is the price of unit natural gas; R SG,t is the revenue from participating in the natural gas market; P GN,t is the net output power of thermal power unit in time period t; P CC,t is the total energy consumption required by carbon capture equipment in time period t; P bas and P ope,t are the carbon capture basic energy consumption and operating energy consumption in time period t, respectively; P CO2 is the energy consumption for processing unit carbon dioxide; R GC,t is the carbon capture energy consumption provided by thermal power unit.

4. The virtual power plant optimization scheduling method based on particle swarm algorithm according to claim 1, characterized in that, In step S9, in order to avoid falling into local optimum in the process of finding the optimal solution, the self-conductivity of the particle is calculated according to formula (11); Wherein, SCR(l,n) is the ratio of the individual optimal fitness change and the global optimal individual fitness change in the first q iterations of the lth particle in the nth iteration; f() represents the fitness function.

5. The virtual power plant optimization scheduling method based on particle swarm algorithm according to claim 1, characterized in that, In step S10, the value of part of the dimensions of the detected inferior particle is changed with a certain probability to make the inferior particle escape from the current inferior area; when the value of SCR is 0 or infinity, it indicates that the current is in a local optimal state; based on the idea of genetic mutation, the positions of the selected inferior particle and the global optimal particle are exchanged with a certain probability.

6. A virtual power plant optimization scheduling system for implementing the method of any one of claims 1-5, characterized by It includes: A data acquisition module for acquiring the power generation of all units in the virtual power plant and the related load power and performing data preprocessing; A particle swarm optimization module for virtual power plant scheduling optimization based on the collected data using a particle swarm optimization algorithm; and A self-conductivity calculation module is configured to be called by the particle swarm optimization module, to perform self-conductivity calculation on the particles, and to select a poor particle from the numerical values of the SCR.

7. A computer device, comprising: The method comprises: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-5.

8. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method of any of claims 1-5.

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