Method and device for distributed energy to participate in virtual power plant scheduling and medium

Abstract

The invention provides a method for distributed energy to participate in virtual power plant scheduling, and the method comprises the following steps: S1, obtaining the types and number of all distributed energy in a virtual power plant, and determining the adjustable capacity and adjustable cost of each distributed energy; s2, the virtual power plant receives a power grid dispatching demand, and an internal balanced dispatching model of the virtual power plant is established; s3, utilizing a dynamic optimization algorithm to calculate scheduling strategies of different types of distributed energy sources in the virtual generation plant; and S4, substituting the scheduling strategies of different types of distributed energy calculated in the step S3 into the internal balanced scheduling model of the virtual power plant established in the step S2 so as to schedule all distributed energy in the virtual power plant. And various types of distributed power generation resources in the virtual power generation plant are enabled to participate in cooperative scheduling.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant scheduling, and specifically relates to a method, device and medium for multiple types of distributed energy to participate in balanced scheduling of virtual power plants. Background Art

[0002] In recent years, the development focus of power systems in various countries is shifting towards clean and decentralized types. Distributed energy has the characteristics of reasonable energy efficiency, low loss, less pollution, flexible operation, and good system economy, which can effectively promote the low-carbon and environmentally friendly operation of the power system.

[0003] The following technical problems exist in the actual operation of distributed energy: (1) The layout of new distributed generation resources is scattered and small in scale. When connected to the grid, it is difficult for them to participate in the power system dispatching and power market, and they cannot fully play their positive role;

[0004] (2) Some users use distributed generation technology as the basis, relying mainly on decentralized resources or users' small power stations to realize the integrated operation of internal power supply and load in microgrids, and connect to the main grid or operate independently through coordinated control with the main grid. At present, some methods aggregate distributed power sources into virtual power plants, and treat virtual power plants as a whole power source to participate in the operation and dispatching control of the power grid. However, there is a lack of methods to evenly aggregate various distributed generation resources in virtual power plants.

[0005] Therefore, there is an urgent need for a method for distributed energy to participate in virtual power plant scheduling, so that all types of distributed power generation resources within the virtual power plant can participate in coordinated scheduling. Summary of the invention

[0006] The purpose of the present invention is to provide a method for distributed energy to participate in virtual power plant scheduling, so as to solve the problem of balanced scheduling within the virtual power plant.

[0007] The technical solution of the present invention is a method for distributed energy to participate in virtual power plant scheduling, comprising the following steps:

[0008] S1: Obtain the types and quantities of all distributed energy resources in the virtual power plant, and determine the adjustable capacity P of each distributed energy resource si and adjustable cost cost; S2: the virtual power plant receives the grid dispatching demand and establishes an internal equilibrium dispatching model of the virtual power plant;

[0009] S3: Using dynamic optimization algorithms, calculate the dispatch strategies of different types of distributed energy in the virtual power plant;

[0010] S4: The dispatching strategies of different types of distributed energy calculated in step S3 are brought into the internal balanced dispatching model of the virtual power plant established in S2, so as to dispatch all distributed energy in the virtual power plant.

[0011] Preferably, the overall objective function of the balanced scheduling model in S2 is:

[0012]

[0013] Among them, F cost represents the total cost of each distributed energy participating in the regulation of the virtual power plant; C is the total number of distributed energy within the virtual power plant, which is a fixed constant, and cost i is the cost of distributed generation resource i, which is a constant, and cost i >0;

[0014] The overall objective function is the minimum value of the overall cost: min(F cost ).

[0015] Preferably, the balanced scheduling model also includes another objective function: the green distributed energy output power of the virtual power plant is maximized, that is,

[0016]

[0017] Among them, P clean is the total output of green distributed energy in the virtual power plant, n is the number of green distributed energy in the virtual power plant, is the output power of the jth green distributed energy source.

[0018] Preferably, the constraints calculated by the balanced scheduling model include:

[0019] 1) System power balance constraints:

[0020]

[0021] C is the total number of distributed energy resources within the virtual power plant, which is a fixed constant. c is the power generated by the cth resource; and satisfies p out <p s , the p s is the adjustable capacity of the virtual power plant, is the maximum value that can be output from the virtual power plant, and is a limiting constant.

[0022] 2) Distributed generation resource output constraints: Each distributed generation resource output Pc must be within the range of its dispatchable power, that is:

[0023] 0 <P c ≤P si

[0024] Among them, P si It is the adjustable maximum value of each distributed power source, which takes the maximum rated value under safe operating conditions.

[0025] Preferably, the dynamic optimization algorithm in S2 is a breadth-uniform algorithm, and the specific steps include the following:

[0026] S3.1: Input initialization parameters, population size initial size N, minimum size NM, merging distance r', core particle set KL size N', maximum number of iterations TL, TL is initially set to an integer of 500 to 1000, particle mass range [m1, m2], initially set to [1, 25];

[0027] Among them, any particle is represented by:

[0028] pa i = {P1, ...P c ...P C}

[0029] Where P c is the power generated by the cth resource, pa i is the i-th particle, and its total number is N;

[0030] According to the constraints, pa i = {P1, ...P c ...P C}, must be full 0 <P c ≤P si , randomly generate an initialization particle in the solution space, a second-layer particle, with the initial particle as the center, randomly generate 2 particles in a sphere with a radius of 3 times r', and similarly, a third-layer self-particle is further generated with the second-layer particle as the center. Finally, an initial population S of size N is generated, and each particle in the population is assigned a random value in the particle mass range [m1, m2], and the initialization iteration count t=1 is set;

[0031] S3.2: Calculate the dominance relationship of particles in group S based on the Pareto relation, create a new core particle set KL, and copy the non-inferior solution to the core particle set KL;

[0032] S3.3: Traverse all particles and calculate the Pareto dominance relationship between them.

[0033] If the particle pa i and pa j There is a dominance relationship between them. The particle g* with the largest gravitational force is selected from the core particle set KL, and the dominated particle pa i and pa j , according to the following formula, move towards the core solution g* and the dominant solution;

[0034]

[0035] Otherwise, select g* with the largest gravitational force from the core particle set KL, and the non-dominated particle pa i and pa j , according to the following formula, move;

[0036]

[0037] Among them, α is a random value (0, 1), G * is the mass gravitational coefficient, R ij is the Euclidean distance between particles, m is the particle mass, and θ is the random position, which ensures that the solution particles try to explore more space while moving;

[0038] S3.4: Calculate the distance between particles. If the distance between particles is close enough to be smaller than the merging distance and the number of particles is greater than the minimum size NM, the particles are merged and the mass of the merged particles is updated according to the following formula;

[0039] m new =m i +m j

[0040] Among them, m new is the mass of the new particle after merging, m i , m j are the masses of the two particles respectively, otherwise, proceed to the next step;

[0041] S3.5: Update the particle positions, calculate the dominance relationship of particles in the group based on the Pareto relationship, and update the core particle set according to the non-inferior solution sorting;

[0042] S3.6: If the number of iterations TL is not met, return to step 3, otherwise, execute the next step until the number of iterations TL is reached;

[0043] S3.7: Output the solution in the core particle set, and the output solution is a candidate solution of the optimal solution.

[0044] Preferably, the solution output in S4 is a candidate solution of the optimal solution and is brought into the objective function, that is, the minimum overall cost and the maximum output power of green distributed energy, to obtain the final scheduling strategy.

[0045] A computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for distributed energy to participate in virtual power plant scheduling is implemented based on any one of the above-mentioned items.

[0046] A computer-readable storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, a method for distributed energy to participate in virtual power plant scheduling is implemented based on any one of the above-mentioned items.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) All types of distributed generation resources within the virtual power plant are involved in coordinated scheduling. Within the range of their controllable capacity, the control cost of each distributed generation resource is calculated so that each distributed generation resource is controlled relatively evenly.

[0049] (2) Give full play to the benefits of green distributed power sources, and the method is simple and easy to expand, which can effectively control the dispatching cost;

[0050] (3) When performing dynamic optimization calculations, the mass of particles can enhance their mutual attraction. By optimizing the merging mechanism of particle mass, the mass of aggregated particles can be changed, their movement speed can be increased, and the convergence speed of the algorithm can be accelerated. The stratified initialization distribution of the particle swarm is optimized to make the particles more widely and evenly distributed in the solution space, providing a good basic condition for quickly exploring non-inferior solutions. By adding a particle merging mechanism, when the particles meet the merging conditions, the number of particles after merging is reduced, thereby reducing the number of algorithm cycles and further accelerating the calculation speed.

[0051] (4) The algorithm can obtain multiple optimal solutions, and the appropriate power combination can be selected according to the actual weather and equipment operating status, which is more flexible;

[0052] The present invention can help coordinate the scheduling of various types of distributed power generation resources within a virtual power plant, give full play to the benefits of each distributed power source, and the method is simple and easy to expand, and can effectively control the scheduling cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the present invention;

[0054] Figure 2 Flowchart for dynamic optimization calculation. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] As attached Figure 1As shown, a method for distributed energy to participate in virtual power plant scheduling includes the following steps:

[0057] S1: Obtain the types and quantities of all distributed energy resources in the virtual power plant, and determine the adjustable capacity P of each distributed energy resource si and adjustable cost cost; where P si is the adjustable maximum value of each distributed power source, taking the maximum rated value under the safe operating state, cost is the cost of distributed power generation resources, its value is a constant, cost>0; confirm the distributed energy within the aggregation range of the virtual power plant, the type of distributed energy can be distributed photovoltaic, wind power, electric vehicles, controllable loads, energy storage systems, etc.

[0058] S2: The virtual power plant receives grid dispatching requirements and establishes an internal equilibrium dispatching model for the virtual power plant;

[0059] S3: Using dynamic optimization algorithms, calculate the dispatch strategies of different types of distributed energy in the virtual power plant;

[0060] S4: The dispatching strategies of different types of distributed energy calculated in step S3 are brought into the internal balanced dispatching model of the virtual power plant established in S2, so as to dispatch all distributed energy in the virtual power plant, so that the virtual power plant can efficiently participate in the unified dispatching of the power grid.

[0061] According to the grid dispatch instruction, that is, the power demand, the power p provided by the virtual power plant to the grid system is determined out . p out Less than the maximum adjustable capacity p of the virtual power plant s . p s is the maximum value that can be output from the virtual power plant, which is a limiting constant. Those skilled in the art can calculate it according to the specific system construction situation.

[0062] The power p supplied to the grid system out It is composed of various distributed power sources and is expressed as

[0063]

[0064] Where C is the total number of distributed energy resources within the virtual power plant, which is a fixed constant, and P i is the power emitted by the i-th resource.

[0065] The control center of the virtual power plant dispatches and allocates power based on the operating cost of each distributed power generation resource, its adjustable capacity and green energy power.

[0066] According to the adjustable power of each distributed generation resource and its adjustment cost, the overall objective function of the virtual power plant is formed.

[0067]

[0068] Among them, F cost represents the total cost of each distributed energy participating in the regulation of the virtual power plant; C is the total number of distributed energy within the virtual power plant, which is a fixed constant; cost i is the cost of distributed generation resource i, which is usually a constant over a period of time, and cost i >0, this data can be calculated by technicians in this field according to the specific system construction situation.

[0069] Therefore, one of the objective functions of the internal scheduling optimization of this method is:

[0070] min(F cost )

[0071] At the same time, in order to give full play to the benefits of green distributed power sources and effectively control the dispatching cost, another objective function is introduced. The green distributed energy output power of the virtual power plant is maximized, that is,

[0072]

[0073] Where n is the number of green distributed energy resources in the virtual power plant, is the output power of the jth green distributed energy source, such as photovoltaic and wind energy. clean It is the sum of the green distributed energy output of the virtual power plant.

[0074] During operation, the following constraints must be followed:

[0075] 1) System power balance constraint: All distributed generation resources must always meet the system's requirements for the power generation of the virtual power plant during the scheduling solution process, namely:

[0076]

[0077] 2) Distributed generation resource output constraints: Each distributed generation resource output Pc must be within the range of its dispatchable power, that is:

[0078] 0 <P c ≤P si

[0079] Among them, P si It is the maximum adjustable value of each distributed power source, and its value is usually the maximum rated value under safe operating conditions.

[0080] See also Figure 2 , the solution process of the balanced power generation strategy of distributed power sources within the virtual power plant:

[0081] S3.1: Input initialization parameters, population size initial size N, minimum size NM, merging distance r', core particle set KL size, N', N, NM, r', N'. There is no universal optimization for these values. They need to be adjusted by technicians in this field according to the specific data situation each time they are used. The maximum number of iterations TL is usually adjusted according to the data scale. TL is initially set to an integer of 500 to 1000. The particle mass range [m1, m2] is mainly used to differentiate particles and is initially set to [1, 25].

[0082] Among them, any particle is represented by: pa i = {P1, ...P c ...P C}, which includes the power of all distributed energy resources in the virtual power plant;

[0083] Where P c is the power generated by the cth resource, pa i is the i-th particle, and its total number is N; according to the constraints, pa i = {P1, ...P c ...P C}, must satisfy 0 <P c ≤P si , that is, within the constraint range, randomly generate different power combinations; randomly generate an initialization particle in the solution space, a second-layer particle, with the initial particle as the center, randomly generate 2 particles in a sphere with a radius of 3 times r', and similarly, the three-layer self-particle is further generated with the second-layer particle as the center. Finally, an initial group S with a scale of N is generated, and each particle in the group is assigned a random value within the particle mass range [m1, m2], and the initialization iteration count t=1 is set;

[0084] S3.2: Calculate the dominance relationship of particles in group S based on the Pareto relation, create a new core particle set KL, and copy the non-inferior solution to the core particle set KL;

[0085] S3.3: Traverse all particles and calculate the Pareto dominance relationship between them.

[0086] If the particle pa i and pa j There is a dominance relationship between them. The particle g* with the largest gravitational force is selected from the core particle set KL, and the dominated particle pa i and pa j , according to the following formula, move towards the core solution g* and the dominant solution;

[0087]

[0088] Otherwise, select g* with the largest gravitational force from the core particle set KL, and the non-dominated particle pa i and pa j , according to the following formula, move;

[0089]

[0090] Among them, α is a random value (0, 1), G * is the mass gravitational coefficient, R ij is the Euclidean distance between particles, m is the particle mass, and θ is the random position, which ensures that the solution particles try to explore more space while moving;

[0091] S3.4: Calculate the distance between particles. If the distance between particles is close enough to be smaller than the merging distance and the number of particles is greater than the minimum size NM, the particles are merged and the mass of the merged particles is updated according to the following formula;

[0092] m new =m i +m j

[0093] Among them, m new is the mass of the new particle after merging, m i , m j are the masses of the two particles respectively, otherwise, proceed to the next step;

[0094] S3.5: Update the particle positions, calculate the dominance relationship of particles in the group based on the Pareto relationship, and update the core particle set according to the non-inferior solution sorting;

[0095] S3.6: If the number of iterations TL is not met, return to step 3, otherwise, execute the next step until the number of iterations TL is reached;

[0096] S3.7: Finally, the set of optimal solutions (Pareto surface) is formed, and the solutions in the core particle set are output as candidate solutions of the optimal solution. Each particle in the core particle set represents the power output of all distributed energy sources; that is, different power combinations are obtained.

[0097] The algorithm can obtain multiple optimal solutions, and can select the appropriate power combination according to the actual weather and the operating status of the equipment, which is more flexible. For example, when the weather is not very good, particles with lower photovoltaic power are selected;

[0098] In S4, according to the actual situation (weather, equipment operating status), a candidate solution from the optimal solution is selected and brought into the objective function, that is, the minimum overall cost and the maximum output power of green distributed energy, to obtain the final scheduling strategy. Since virtual power plants are not real power plants, not all power generation is delivered to the power grid. They contain a variety of types and even adjustable loads, and the electricity of virtual power plants is generally supplied to the power grid on demand, and some are even used internally. For example, some large steel mills’ self-provided power plants will inevitably bring about cost changes if they change their own electricity consumption for the power grid, so an objective function needs to be established.

[0099] Ultimately, the adjustable capacity of each distributed power source is solved and they all participate in grid dispatching in a unified manner.

[0100] Taking a virtual power plant in a certain city as an example, the virtual power plant includes 1 microgrid, 2 distributed power sources and 1 controllable load cluster. The method for distributed energy to participate in the virtual power plant scheduling includes the following specific steps:

[0101] First, determine the adjustable capacity P of each distributed energy source si , the distributed adjustable capacity within the virtual power plant is shown in the following table:

[0102]

[0103] In the next dispatch period, the power grid system needs the virtual power plant to provide capacity p out =100MW.

[0104] The calculation steps of the method for distributed energy to participate in virtual power plant dispatch are as follows:

[0105] Input initialization parameters, initial population size N = 50, minimum size NM = 25, merging distance r' = 5, core particle set KL size N' = 15, maximum number of iterations TL = 1000, particle mass range [10,25].

[0106] 1) According to the constraints, an initialization particle is randomly generated in the solution space. The second layer of particles is randomly generated in a sphere with a radius of 3 times r' with the initial particle as the center. Similarly, the third layer of particles is further generated with the second layer of particles as the center. Finally, an initial population S of size N is generated. Each particle in the population is assigned a random value in the particle mass range [10,25], and the initialization iteration count t=1 is set.

[0107] 2) Based on the Pareto relationship, the dominance relationship of particles in group S is calculated, a new core set KL is created, and the non-inferior solutions are copied to the core particle solution set KL.

[0108] 3) Traverse all particles and calculate the Pareto dominance relationship between them. If particle p i and pj There is a dominance relationship between them. From the set KL, select the particle g* with the largest gravitational force and the dominated particle p i and p j , according to the following formula, move towards the core solution g* and the dominant solution.

[0109]

[0110] Otherwise, select g* with the largest gravitational force from the set KL, and the non-dominated particle p i and p j , according to the following formula, move the movement.

[0111]

[0112] Among them, α is a random value (0, 1), G * is the mass gravitational coefficient, R ij is the Euclidean distance between particles, m is the mass of the particle, and θ is the random position, which ensures that the solution particles try to explore more space while moving.

[0113] 4) Calculate the distance between particles. When the distance between particles is close enough to be smaller than the merging distance and the number of particles is greater than the minimum scale NM, the particles are merged and the mass of the merged particles is updated according to the following formula.

[0114] m new =m i +m j

[0115] 5) Update the particle positions and calculate the dominance relationship of particles in the group based on the Pareto relationship. At the same time, update the core particle set according to the non-inferior solution sorting.

[0116] 6) Return to step 3 until the number of iterations TL is reached.

[0117] 7) Output the solution in the core particle set and derive the final scheduling strategy based on the objective function.

[0118] Finally, the adjustment strategy for each distributed power source is determined as shown in the following table.

[0119]

[0120] The present invention enables all types of distributed power generation resources within the virtual power plant to participate in the coordinated scheduling. Within the adjustable capacity range, by calculating the control cost of each distributed power generation resource, each distributed power generation resource is relatively balanced and regulated; the benefits of green distributed power sources are fully utilized, and the method is simple and easy to expand, and the scheduling cost can be effectively controlled; when performing dynamic optimization calculations, the quality of particles can enhance their mutual attraction, and by optimizing the merging mechanism of particle quality, the quality of aggregated particles can be changed, their movement speed can be increased, and the convergence speed of the algorithm can be accelerated; the layered initialization distribution of particle groups is optimized, so that particles are more widely and evenly distributed in the solution space, providing good basic conditions for rapidly exploring non-inferior solutions; by adding a particle merging mechanism, when the particles meet the merging conditions, the number of particles after merging is reduced, thereby reducing the number of algorithm cycles and further accelerating the calculation speed.

[0121] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solution disclosed in the present invention, technicians in this field can make some substitutions and deformations to some technical features therein according to the disclosed technical content without creative labor, and these substitutions and deformations are all within the protection scope of the present invention.

Claims

1. A method for distributed energy to participate in virtual power plant scheduling, characterized in that: The following steps are involved: S1: Obtain the types and quantities of all distributed energy resources in the virtual power plant, and determine the adjustable capacity P of each distributed energy resource si and adjustable cost cost; S2: The virtual power plant receives grid dispatching requirements and establishes an internal equilibrium dispatching model for the virtual power plant; S3: Using dynamic optimization algorithms, calculate the dispatch strategies of different types of distributed energy in the virtual power plant; S4: The dispatching strategies of different types of distributed energy calculated in step S3 are brought into the internal balanced dispatching model of the virtual power plant established in S2, so as to dispatch all distributed energy in the virtual power plant.

2. The method for distributed energy to participate in virtual power plant scheduling according to claim 1 is characterized in that: The overall objective function of the balanced scheduling model in S2 is: Among them, F cost represents the total cost of each distributed energy participating in the regulation of the virtual power plant; C is the total number of distributed energy within the virtual power plant, which is a fixed constant, and cost i is the cost of distributed generation resource i, which is a constant, and cost i >0; The overall objective function is the minimum value of the overall cost: min(F cost ).

3. The method for distributed energy to participate in virtual power plant scheduling according to claim 2 is characterized in that: The balanced scheduling model also includes another objective function: the green distributed energy output power of the virtual power plant is maximized, that is, Among them, P clean is the total output of green distributed energy in the virtual power plant, n is the number of green distributed energy in the virtual power plant, is the output power of the jth green distributed energy source.

4. The method for distributed energy to participate in virtual power plant scheduling according to claim 3 is characterized in that: The constraints calculated by the balanced scheduling model include: 1) System power balance constraints: C is the total number of distributed energy resources within the virtual power plant, which is a fixed constant. c is the power generated by the cth resource; and satisfies p out <p s , the p s is the adjustable capacity of the virtual power plant, is the maximum value that can be output from the virtual power plant, and is a limiting constant. 2) Distributed generation resource output constraints: Each distributed generation resource output Pc must be within the range of its dispatchable power, that is: 0<P c ≤P si Among them, P si It is the adjustable maximum value of each distributed power source, which is the maximum rated value under safe operating conditions.

5. The method for distributed energy to participate in virtual power plant scheduling according to claim 1 is characterized in that: The dynamic optimization algorithm in S2 is a breadth-uniform algorithm. The specific steps include: the following: S3.1: Input initialization parameters, population size initial size N, minimum size NM, merging distance r', core particle set KL size N', maximum number of iterations TL, TL is initially set to an integer of 500 to 1000, particle mass range [m1, m2], initially set to [1, 25]; Among them, any particle is represented by: on i ={P1,...P c ...P C } Where P c is the power generated by the cth resource, pa i is the i-th particle, and its total number is N; According to the constraints, pa i = {P1, ...P c ...P C }, must be full 0 <P c ≤P si , randomly generate an initialization particle in the solution space, a second-layer particle, with the initial particle as the center, randomly generate 2 particles in a sphere with a radius of 3 times r', and similarly, a third-layer self-particle is further generated with the second-layer particle as the center. Finally, an initial population S of size N is generated, and each particle in the population is assigned a random value in the particle mass range [m1, m2], and the initialization iteration count t=1 is set; S3.2: Calculate the dominance relationship of particles in group S based on the Pareto relation, create a new core particle set KL, and copy the non-inferior solution to the core particle set KL; S3.3: Traverse all particles and calculate the Pareto dominance relationship between them. If the particle pa i and pa j There is a dominance relationship between them. The particle g* with the largest gravitational force is selected from the core particle set KL, and the dominated particle pa i and pa j , according to the following formula, move towards the core solution g* and the dominant solution; Otherwise, select g* with the largest gravitational force from the core particle set KL, and the non-dominated particle pa i and pa j , according to the following formula, move; Among them, α is a random value (0, 1), G * is the mass gravitational coefficient, R ij is the Euclidean distance between particles, m is the particle mass, and θ is the random position, which ensures that the solution particles try to explore more space while moving; S3.4: Calculate the distance between particles. If the distance between particles is close enough to be smaller than the merging distance and the number of particles is greater than the minimum size NM, the particles are merged and the mass of the merged particles is updated according to the following formula; m new =m i +m j Among them, m new is the mass of the new particle after merging, m i , m j are the masses of the two particles respectively, otherwise, proceed to the next step; S3.5: Update the particle positions, calculate the dominance relationship of particles in the group based on the Pareto relationship, and update the core particle set according to the non-inferior solution sorting; S3.6: If the number of iterations TL is not met, return to step 3, otherwise, execute the next step until the number of iterations TL is reached; S3.7: Output the solution in the core particle set, and the output solution is a candidate solution of the optimal solution.

6. The method for distributed energy to participate in virtual power plant scheduling according to claim 4 is characterized in that: In S4, an optimal candidate solution is selected and brought into the objective function, i.e., the minimum overall cost and the maximum output power of green distributed energy, to obtain the final scheduling strategy.

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it is based on the method for distributed energy to participate in virtual power plant scheduling as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it is based on the method for distributed energy to participate in virtual power plant scheduling as described in any one of claims 1-6.

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