An optimal dispatching method for virtual power plants in new energy power systems

Abstract

The present invention relates to a virtual power plant, and in particular to an optimization scheduling method for a virtual power plant in a new energy power system. The objective function of the virtual power plant optimization scheduling and corresponding constraints are determined, a virtual power plant optimization scheduling model is constructed according to the objective function and constraints of the virtual power plant optimization scheduling, and a simulated annealing algorithm is used to solve the virtual power plant optimization scheduling model to obtain an optimization scheduling scheme for the virtual power plant. In the process of determining the objective function of the virtual power plant optimization scheduling, a backward reduction method is used to reduce a wind-photovoltaic power generation scenario to establish the objective function of the virtual power plant optimization scheduling, so as to fully consider the correlation between wind and photovoltaic power generation and reduce the calculation complexity at the same time. The technical scheme provided by the present invention can effectively overcome the defect of the prior art that it is difficult to effectively optimize the scheduling of virtual power plants.

Description

Technical Field

[0001] The present invention relates to a virtual power plant, and in particular to an optimization scheduling method for a virtual power plant in a new energy power system. Background Art

[0002] As energy shortages and environmental pollution become increasingly serious, vigorously developing renewable energy, mainly wind and solar energy, and achieving green, low-carbon and circular development of energy has become an important strategy for the global energy industry. Compared with traditional energy, renewable energy has the advantages of reliability, economy, flexibility and environmental protection, but due to its small capacity, geographical dispersion and random output, it is difficult for the power grid to effectively utilize it. Virtual power plant technology provides an effective way to solve the above problems. It realizes the optimized coordinated control of different types of distributed energy through advanced communication, metering, control and other means, and has gradually become an emerging operation model with strong flexibility, high adaptability and good economy.

[0003] However, since virtual power plants integrate multiple distributed energy sources, and different types of distributed energy sources have different production characteristics, virtual power plants also integrate multiple load resources, and different types of load resources also have different power consumption characteristics. In addition, virtual power plants also integrate a lot of energy storage equipment, and the energy storage cost of energy storage equipment is relatively high, and it is generally necessary to minimize the use of energy storage equipment. It is precisely because of the existence of the above situation that the difficulty of reasonable scheduling of virtual power plants is greatly increased, affecting the resource integration efficiency of virtual power plants.

[0004] Therefore, how to efficiently integrate and dispatch distributed energy to optimize the dispatch of virtual power plants, enable energy equipment, energy storage equipment and load equipment to work better together, improve energy utilization efficiency and reduce energy waste is a technical problem that needs to be solved urgently. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for optimizing the scheduling of virtual power plants in a new energy power system, which can effectively overcome the defect of the prior art that it is difficult to effectively optimize the scheduling of virtual power plants.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An optimal dispatching method for a virtual power plant in a new energy power system, determining an objective function of the optimal dispatching of the virtual power plant and corresponding constraints, constructing an optimal dispatching model of the virtual power plant according to the objective function and constraints of the optimal dispatching of the virtual power plant, solving the optimal dispatching model of the virtual power plant by using a simulated annealing algorithm, and obtaining an optimal dispatching plan of the virtual power plant;

[0010] Among them, in the process of determining the objective function of the optimal scheduling of the virtual power plant, the objective function of the optimal scheduling of the virtual power plant is established by reducing the wind-photovoltaic power generation scenario using the backward reduction method, so as to fully consider the correlation between wind and photovoltaic power generation and reduce the calculation complexity.

[0011] Preferably, the objective function of determining the optimal scheduling of the virtual power plant and the corresponding constraints include:

[0012] S11, establishing a wind-solar output joint distribution function, and generating a wind-solar output initial scenario set according to the wind-solar output joint distribution function;

[0013] S12, using the backward reduction method to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally obtaining the wind-solar output reduction scene set;

[0014] S13. Establishing the objective function of virtual power plant optimization scheduling based on the wind-solar output reduction scenario set;

[0015] S14. Establish constraint conditions for optimal scheduling of virtual power plants based on the power balance relationship.

[0016] Preferably, in S11, a wind-solar output joint distribution function is established, and a wind-solar output initial scenario set is generated according to the wind-solar output joint distribution function, including:

[0017] S111. Establish a wind power output probability model and a photovoltaic output probability model, generate corresponding wind power output probability density function and photovoltaic output probability density function according to the wind power output probability model and the photovoltaic output probability model, and establish a wind-photovoltaic output joint distribution function;

[0018] S112. Generate a large number of wind-solar output initial scene sets with random characteristics using a random sampling method according to the wind-solar output joint distribution function.

[0019] Preferably, in S12, a backward reduction method is used to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally a wind-solar output reduction scene set is obtained, including:

[0020] S121, Kantorovich distance between scenes is used as an evaluation criterion to measure the importance between scenes;

[0021] S122, in the initial set of wind-solar output scenes, the least important scene is eliminated according to the Kantorovich distance between the scenes;

[0022] S123, returning to S121, until the initial wind-solar output scenario set reaches the preset number of scenarios, and finally obtaining the wind-solar output reduction scenario set and the probability of occurrence of each scenario;

[0023] S124. Verify the wind-solar output reduction scenario set to ensure that it can represent the randomness and characteristics of the wind-solar output initial scenario set.

[0024] Preferably, in S121, the Kantorovich distance between scenes is used as an evaluation criterion to measure the importance between scenes, including:

[0025] The Kantorovich distance D between scenes is calculated using the following formula: K (X,Y):

[0026]

[0027] Among them, D K (X,Y) is the Kantorovich distance between scene X and scene Y, It means to find the one that minimizes the expected cost among all possible joint distributions γ, E (X,Y)～γ ||XY|| represents the expected Euclidean distance between scene X and scene Y under the joint distribution γ, P r , P g is the marginal distribution of the joint distribution γ.

[0028] Preferably, in S13, an objective function for optimizing the scheduling of a virtual power plant is established according to a set of wind-solar output reduction scenarios, including:

[0029] The objective function F of the virtual power plant optimal dispatch is expressed as follows:

[0030]

[0031] Among them, P i is the probability of occurrence of the i-th wind-solar output scenario, P j is the probability of occurrence of the j-th electricity price scenario, p j,t is the electricity price in period t under the j-th electricity price scenario, q i,j,t 、c i,j,tare the power trading volume and operating cost of time period t under the jth group of electricity price scenario in the i-th group of wind-solar output scenario, t∈[1,2,…,T], T is the total number of time periods in a day, i∈[1,2,…,n1], n1 is the number of scenarios in the wind-solar output reduction scenario, j∈[1,2,…,n2], n2 is the number of electricity price scenarios.

[0032] Preferably, in S14, the constraint conditions for optimizing the scheduling of the virtual power plant are established according to the power balance relationship, including:

[0033] The constraints for optimal dispatch of virtual power plants are expressed as follows:

[0034] P W (t)+P L (t)+P S (t)+P B (t) = P load (t);

[0035] Among them, P W (t), P L (t) is the wind power generation power and photovoltaic power generation power in time period t, P S (t) is the output power of the energy storage system in time period t. Positive values ​​represent the discharging of the energy storage system, and negative values ​​represent the charging of the energy storage system. B (t) is the exchange power between the power grid and the public grid in time period t. A positive value represents the purchase of power from the public grid, and a negative value represents the sale of power to the public grid. load (t) is the load demand power in time period t.

[0036] Preferably, the method of solving the virtual power plant optimization scheduling model by using a simulated annealing algorithm to obtain the virtual power plant optimization scheduling scheme includes:

[0037] S21, selecting an initial solution as the current solution, and initializing a higher current temperature;

[0038] S22, performing a local search in the neighborhood of the current solution, generating a candidate solution by performing a small random perturbation on the current solution, and calculating the objective function value of the candidate solution;

[0039] S23, comparing the quality of the current solution with the candidate solution, determining whether to accept the candidate solution based on the quality comparison result, and updating the current solution using the candidate solution;

[0040] S24, cooling down the current temperature;

[0041] S25. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22; otherwise, use the current solution as the virtual power plant optimization scheduling plan.

[0042] Preferably, in S23, the quality of the current solution and the candidate solution is compared, whether to accept the candidate solution is determined according to the quality comparison result, and the current solution is updated using the candidate solution, including:

[0043] S231, compare the objective function values ​​of the current solution and the candidate solution. If the objective function value of the candidate solution is larger, accept the candidate solution and use it to update the current solution. Otherwise, proceed to S232;

[0044] S232. Calculate the acceptance probability p according to the current temperature:

[0045]

[0046] Among them, F1 and F2 are the objective function values ​​of the current solution and the candidate solution respectively, T k is the temperature at the kth iteration, that is, the current temperature, the current temperature T k The higher the value, the greater the probability of accepting the candidate solution. k The lower it is, the smaller the probability of accepting the candidate solution;

[0047] S233. Determine whether to accept the candidate solution based on the acceptance probability p, and use the candidate solution to update the current solution to increase the possibility of jumping out of the local optimal solution and exploring the global optimal solution.

[0048] Preferably, in S24, the current temperature is cooled down, including:

[0049] The current temperature T is calculated using the following formula: k Cooling treatment:

[0050] T k+1 =α·T k ;

[0051] Among them, T k is the temperature at the kth iteration, that is, the current temperature, T k+1 is the temperature at the k+1th iteration, α is the temperature attenuation coefficient, which is a constant less than 1 but close to 1. The smaller the temperature attenuation coefficient α is, the faster the temperature drops, and the larger the temperature attenuation coefficient α is, the slower the temperature drops.

[0052] (III) Beneficial effects

[0053] Compared with the prior art, the optimization scheduling method of a virtual power plant in a new energy power system provided by the present invention has the following beneficial effects:

[0054] 1) In the process of determining the objective function of the virtual power plant's optimal scheduling, the objective function of the virtual power plant's optimal scheduling is established by using the backward reduction method to reduce the wind-solar power generation scenario. This can fully consider the correlation between wind and solar power generation, while reducing the computational complexity, providing support for the subsequent accurate and efficient solution of the virtual power plant's optimal scheduling model;

[0055] 2) The simulated annealing algorithm is used to solve the virtual power plant optimization scheduling model and obtain the virtual power plant optimization scheduling plan. The global optimal solution is searched by simulating the process of gradual cooling of the material after heating. In the simulated annealing algorithm, the search range is limited by gradually reducing the probability of accepting a worse solution, thereby shrinking the search space. This makes it possible to solve the virtual power plant optimization scheduling plan more efficiently and ensure the quality of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 It is a schematic diagram of the process of the present invention;

[0058] Figure 2 A schematic diagram of the process of constructing a virtual power plant optimization scheduling model in the present invention;

[0059] Figure 3 The virtual power plant optimization scheduling model is solved in the present invention to obtain a flow chart of the virtual power plant optimization scheduling scheme. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0061] An optimal dispatching method for virtual power plants in a new energy power system, such as Figure 1As shown, the objective function of the virtual power plant optimal scheduling and the corresponding constraints are determined, and a virtual power plant optimal scheduling model is constructed according to the objective function and constraints of the virtual power plant optimal scheduling. The virtual power plant optimal scheduling model is solved by using a simulated annealing algorithm to obtain an optimal scheduling plan for the virtual power plant.

[0062] Among them, in the process of determining the objective function of the optimal scheduling of the virtual power plant, the objective function of the optimal scheduling of the virtual power plant is established by reducing the wind-photovoltaic power generation scenario using the backward reduction method, so as to fully consider the correlation between wind and photovoltaic power generation and reduce the calculation complexity.

[0063] ① Determine the objective function of the virtual power plant optimization scheduling and the corresponding constraints, such as Figure 2 As shown, including:

[0064] S11, establishing a wind-solar output joint distribution function, and generating a wind-solar output initial scenario set according to the wind-solar output joint distribution function;

[0065] S12, using the backward reduction method to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally obtaining the wind-solar output reduction scene set;

[0066] S13. Establishing the objective function of virtual power plant optimization scheduling based on the wind-solar output reduction scenario set;

[0067] S14. Establish constraint conditions for optimal scheduling of virtual power plants based on the power balance relationship.

[0068] 1) In S11, a wind-solar output joint distribution function is established, and a wind-solar output initial scenario set is generated according to the wind-solar output joint distribution function, including:

[0069] S111. Establish a wind power output probability model and a photovoltaic output probability model, generate corresponding wind power output probability density function and photovoltaic output probability density function according to the wind power output probability model and the photovoltaic output probability model, and establish a wind-photovoltaic output joint distribution function;

[0070] S112. Generate a large number of wind-solar output initial scene sets with random characteristics using a random sampling method according to the wind-solar output joint distribution function.

[0071] 2) In S12, the backward reduction method is used to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally the wind-solar output reduction scene set is obtained, including:

[0072] S121, Kantorovich distance between scenes is used as an evaluation criterion to measure the importance between scenes;

[0073] S122, in the initial set of wind-solar output scenes, the least important scene is eliminated according to the Kantorovich distance between the scenes;

[0074] S123, returning to S121, until the initial wind-solar output scenario set reaches the preset number of scenarios, and finally obtaining the wind-solar output reduction scenario set and the probability of occurrence of each scenario;

[0075] S124. Verify the wind-solar output reduction scenario set to ensure that it can represent the randomness and characteristics of the wind-solar output initial scenario set.

[0076] Specifically, S121 uses the Kantorovich distance between scenes as an evaluation criterion to measure the importance of scenes, including:

[0077] The Kantorovich distance D between scenes is calculated using the following formula: K (X,Y):

[0078]

[0079] Among them, D K (X,Y) is the Kantorovich distance between scene X and scene Y, It means to find the one that minimizes the expected cost among all possible joint distributions γ, E (X,Y)～γ ||XY|| represents the expected Euclidean distance between scene X and scene Y under the joint distribution γ, P r , P g is the marginal distribution of the joint distribution γ.

[0080] 3) In S13, the objective function of virtual power plant optimization scheduling is established according to the wind-solar output reduction scenario set, including:

[0081] The objective function F of the virtual power plant optimal dispatch is expressed as follows:

[0082]

[0083] Among them, P i is the probability of occurrence of the i-th wind-solar output scenario, P j is the probability of occurrence of the j-th electricity price scenario, p j,t is the electricity price in period t under the j-th electricity price scenario, q i,j,t 、c i,j,tare the power trading volume and operating cost of time period t under the jth group of electricity price scenario in the i-th group of wind-solar output scenario, t∈[1,2,…,T], T is the total number of time periods in a day, i∈[1,2,…,n1], n1 is the number of scenarios in the wind-solar output reduction scenario, j∈[1,2,…,n2], n2 is the number of electricity price scenarios.

[0084] 4) In S14, constraints for optimal dispatching of virtual power plants are established based on the power balance relationship, including:

[0085] The constraints for optimal dispatch of virtual power plants are expressed as follows:

[0086] P W (t)+P L (t)+P S (t)+P B (t) = P load (t);

[0087] Among them, P W (t), P L (t) is the wind power generation power and photovoltaic power generation power in time period t, P S (t) is the output power of the energy storage system in time period t. Positive values ​​represent the discharging of the energy storage system, and negative values ​​represent the charging of the energy storage system. B (t) is the exchange power between the power grid and the public grid in time period t. A positive value represents the purchase of power from the public grid, and a negative value represents the sale of power to the public grid. load (t) is the load demand power in time period t.

[0088] The above technical scheme, in the process of determining the objective function of the virtual power plant's optimal scheduling, establishes the objective function of the virtual power plant's optimal scheduling by reducing the wind-photovoltaic power generation scenario using the backward reduction method. This can fully consider the correlation between wind and photovoltaic power generation, while reducing the calculation complexity, thus providing support for the subsequent accurate and efficient solution of the virtual power plant's optimal scheduling model.

[0089] ② The simulated annealing algorithm is used to solve the virtual power plant optimization scheduling model and obtain the virtual power plant optimization scheduling scheme, such as Figure 3 As shown, including:

[0090] S21, selecting an initial solution as the current solution, and initializing a higher current temperature;

[0091] S22, performing a local search in the neighborhood of the current solution, generating a candidate solution by performing a small random perturbation on the current solution, and calculating the objective function value of the candidate solution;

[0092] S23, comparing the quality of the current solution with the candidate solution, determining whether to accept the candidate solution based on the quality comparison result, and updating the current solution using the candidate solution;

[0093] S24, cooling down the current temperature;

[0094] S25. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22; otherwise, use the current solution as the virtual power plant optimization scheduling plan.

[0095] 1) In S23, the quality of the current solution and the candidate solution is compared, and whether to accept the candidate solution is determined according to the quality comparison result, and the current solution is updated using the candidate solution, including:

[0096] S231, compare the objective function values ​​of the current solution and the candidate solution. If the objective function value of the candidate solution is larger, accept the candidate solution and use it to update the current solution. Otherwise, proceed to S232;

[0097] S232. Calculate the acceptance probability p according to the current temperature:

[0098]

[0099] Among them, F1 and F2 are the objective function values ​​of the current solution and the candidate solution respectively, T k is the temperature at the kth iteration, that is, the current temperature, the current temperature T k The higher the value, the greater the probability of accepting the candidate solution. k The lower it is, the smaller the probability of accepting the candidate solution;

[0100] S233. Determine whether to accept the candidate solution based on the acceptance probability p, and use the candidate solution to update the current solution to increase the possibility of jumping out of the local optimal solution and exploring the global optimal solution.

[0101] 2) In S24, the current temperature is cooled down, including:

[0102] The current temperature T is calculated using the following formula: k Cooling treatment:

[0103] T k+1 =α·T k ;

[0104] Among them, T k is the temperature at the kth iteration, that is, the current temperature, T k+1 is the temperature at the k+1th iteration, α is the temperature attenuation coefficient, which is a constant less than 1 but close to 1. The smaller the temperature attenuation coefficient α is, the faster the temperature drops, and the larger the temperature attenuation coefficient α is, the slower the temperature drops.

[0105] The above technical solution adopts a simulated annealing algorithm to solve the virtual power plant optimization scheduling model to obtain the virtual power plant optimization scheduling plan, and searches for the global optimal solution by simulating the process of gradual cooling of the material after heating. In the simulated annealing algorithm, the search range is limited by gradually reducing the probability of accepting a worse solution, thereby achieving a contraction of the search space, so that the virtual power plant optimization scheduling plan can be solved more efficiently and the quality of the solution can be ensured.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the scheduling of a virtual power plant in a new energy power system, characterized in that: Determine the objective function of the virtual power plant optimal scheduling and the corresponding constraints, build a virtual power plant optimal scheduling model based on the objective function and constraints of the virtual power plant optimal scheduling, use the simulated annealing algorithm to solve the virtual power plant optimal scheduling model, and obtain the virtual power plant optimal scheduling plan; Among them, in the process of determining the objective function of the virtual power plant optimization scheduling, the objective function of the virtual power plant optimization scheduling is established by using the backward reduction method to reduce the wind-solar power generation scenario, so as to fully consider the correlation between wind and solar power generation and reduce the calculation complexity; The objective function for determining the optimal scheduling of the virtual power plant and the corresponding constraints include: S11, establishing a wind-solar output joint distribution function, and generating a wind-solar output initial scenario set according to the wind-solar output joint distribution function; S12. Use the backward reduction method to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally obtain the wind-solar output reduction scene set, including: The Kantorovich distance between scenes is used as an evaluation criterion to measure the importance between scenes: The Kantorovich distance D between scenes is calculated using the following formula: K (X,Y): Among them, D K (X,Y) is the Kantorovich distance between scene X and scene Y, It means to find the one that minimizes the expected cost among all possible joint distributions γ, E (X,Y)～γ ||XY|| represents the expected Euclidean distance between scene X and scene Y under the joint distribution γ, P r , P g is the marginal distribution of the joint distribution γ; S13. Establish the objective function of virtual power plant optimization scheduling based on the wind-solar output reduction scenario set, including: The objective function F of the virtual power plant optimal dispatch is expressed as follows: Among them, P i is the probability of occurrence of the i-th wind-solar output scenario, P j is the probability of occurrence of the j-th electricity price scenario, p j,t is the electricity price in period t under the j-th electricity price scenario, q i,j,t 、c i,j,t are respectively the power transaction volume and operating cost of time period t under the j-th group of electricity price scenario in the i-th group of wind-solar output scenario, t∈[1,2,...,T], T is the total number of time periods in a day, i∈[1,2,...,n1], n1 is the number of wind-solar output reduction scenarios, j∈[1,2,...,n2], n2 is the number of electricity price scenarios; S14. Establish constraint conditions for optimal scheduling of virtual power plants based on the power balance relationship.

2. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 1, characterized in that: In S11, a wind-solar output joint distribution function is established, and a wind-solar output initial scenario set is generated according to the wind-solar output joint distribution function, including: S111. Establish a wind power output probability model and a photovoltaic output probability model, generate corresponding wind power output probability density function and photovoltaic output probability density function according to the wind power output probability model and the photovoltaic output probability model, and establish a wind-photovoltaic output joint distribution function; S112. Generate a large number of wind-solar output initial scene sets with random characteristics using a random sampling method according to the wind-solar output joint distribution function.

3. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 2, characterized in that: In S12, the backward reduction method is used to gradually eliminate the least important scenes from the initial wind-solar output scene set, and finally the wind-solar output reduction scene set is obtained, including: S121, Kantorovich distance between scenes is used as an evaluation criterion to measure the importance between scenes; S122, in the initial set of wind-solar output scenes, the least important scene is eliminated according to the Kantorovich distance between the scenes; S123, returning to S121, until the initial wind-solar output scenario set reaches the preset number of scenarios, and finally obtaining the wind-solar output reduction scenario set and the probability of occurrence of each scenario; S124. Verify the wind-solar output reduction scenario set to ensure that it can represent the randomness and characteristics of the wind-solar output initial scenario set.

4. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 1, characterized in that: In S14, constraints for optimal dispatching of virtual power plants are established based on the power balance relationship, including: The constraints for optimal dispatch of virtual power plants are expressed as follows: P W (t)+P L (t)+P S (t)+P B (t)=P load (t); Among them, P W (t), P L (t) is the wind power generation power and photovoltaic power generation power in time period t, P S (t) is the output power of the energy storage system in time period t. Positive values ​​represent the discharging of the energy storage system, and negative values ​​represent the charging of the energy storage system. B (t) is the exchange power between the power grid and the public grid during the period t. A positive value represents the purchase of power from the public grid, and a negative value represents the sale of power to the public grid. load (t) is the load demand power in time period t.

5. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 1, characterized in that: The simulated annealing algorithm is used to solve the virtual power plant optimization scheduling model to obtain the virtual power plant optimization scheduling plan, including: S21, selecting an initial solution as the current solution, and initializing a higher current temperature; S22, performing a local search in the neighborhood of the current solution, generating a candidate solution by performing a small random perturbation on the current solution, and calculating the objective function value of the candidate solution; S23, comparing the quality of the current solution with the candidate solution, determining whether to accept the candidate solution based on the quality comparison result, and updating the current solution using the candidate solution; S24, cooling down the current temperature; S25. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22; otherwise, use the current solution as the virtual power plant optimization scheduling plan.

6. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 5, characterized in that: In S23, the quality of the current solution and the candidate solution is compared, and whether to accept the candidate solution is determined according to the quality comparison result, and the current solution is updated using the candidate solution, including: S231, compare the objective function values ​​of the current solution and the candidate solution. If the objective function value of the candidate solution is larger, accept the candidate solution and use it to update the current solution. Otherwise, proceed to S232; S232. Calculate the acceptance probability p according to the current temperature: Among them, F1 and F2 are the objective function values ​​of the current solution and the candidate solution respectively, T k is the temperature at the kth iteration, that is, the current temperature, the current temperature T k The higher the value, the greater the probability of accepting the candidate solution. k The lower it is, the smaller the probability of accepting the candidate solution; S233. Determine whether to accept the candidate solution based on the acceptance probability p, and use the candidate solution to update the current solution to increase the possibility of jumping out of the local optimal solution and exploring the global optimal solution.

7. The method for optimizing the scheduling of virtual power plants in a new energy power system according to claim 5, characterized in that: In S24, the current temperature is cooled down, including: The current temperature T is calculated using the following formula: k Cooling treatment: T k+1 =α·T k ; Among them, T k is the temperature at the kth iteration, that is, the current temperature, T k+1 is the temperature at the k+1th iteration, α is the temperature attenuation coefficient, which is a constant less than 1 but close to 1. The smaller the temperature attenuation coefficient α is, the faster the temperature drops, and the larger the temperature attenuation coefficient α is, the slower the temperature drops.

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