Virtual power plant aggregation method and related equipment

Abstract

One or more embodiments of the invention provide a virtual power plant aggregation method and related equipment. The method comprises the following steps: constructing a virtual power plant aggregation model of a virtual power plant and distributed resources according to a bilateral matching mechanism so as to determine a potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each distributed resource and the preference of the distributed resource for each virtual power plant; solving an optimal solution of the virtual power plant aggregation model by taking virtual power plant aggregation benefit maximization as a target to obtain a virtual power plant aggregation scheme; the virtual power plant aggregation scheme is an optimal matching relationship between the virtual power plants and the distributed resources when the benefits of all the virtual power plants are maximized. According to the application, the energy utilization rate and the virtual power plant aggregation benefit can be effectively improved, the energy loss is reduced, the environmental pollution is reduced, and the reliability and safety of energy supply can also be improved.

Description

Technical Field

[0001] One or more embodiments of the present application relate to the technical field of power system management, and in particular, to a virtual power plant aggregation method and related equipment. Background Art

[0002] Virtual power plant aggregation technology is a power system management method that integrates advanced information communication, artificial intelligence, data analysis and automatic control technologies. It aggregates dispersed distributed energy resources (such as photovoltaics, wind turbines, energy storage equipment and controllable loads) into a coordinated virtual power source through a software platform to achieve optimized control and economic dispatch of the power system. This technology can improve the flexibility and reliability of the power grid, promote the efficient use of renewable energy, and participate in power market transactions. It is one of the key technologies for realizing smart grids and energy Internet.

[0003] Although the aggregation methods in related technologies have achieved resource aggregation and optimization to a certain extent, they ignore the uniqueness and differences of distributed resources, limiting the maximization of the aggregation effect. In addition, these methods lack a guarantee mechanism for fairness and stability, which may lead to the risk of fluctuations or even instability in the power market. Summary of the invention

[0004] In view of this, an object of one or more embodiments of the present application is to propose a virtual power plant aggregation method and related equipment to solve the problems raised by the background technology.

[0005] Based on the above objectives, one or more embodiments of the present application provide a virtual power plant aggregation method, including: According to the bilateral matching mechanism, a virtual power plant aggregation model of the virtual power plant and the distributed resources is constructed to determine the potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each of the distributed resources and the preference of the distributed resources for each of the virtual power plants; With the goal of maximizing the benefits of virtual power plant aggregation, the optimal solution of the virtual power plant aggregation model is solved to obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the best matching relationship between the virtual power plant and the distributed resources when the benefits of all virtual power plants are maximized.

[0006] Optionally, constructing a virtual power plant aggregation model of a virtual power plant and distributed resources according to the bilateral matching mechanism includes executing the following steps: The distributed resource sends an invitation message to all the virtual power plants; In response to receiving the invitation message, the virtual power plant calculates the marginal effect contribution value of the distributed resource; The virtual power plant determines the intended distributed resource according to a preset method, and sends an acceptance message to the intended distributed resource; The intended distributed resource calculates a preference value of the virtual power plant in response to receiving the acceptance message; The intended distributed resource determines the intended virtual power plant according to a preset method and sends a matching message to the intended virtual power plant.

[0007] Optionally, the marginal effect contribution value is calculated as follows: ; in, express The fluctuation value of the electricity price of the virtual power plant at time represents the output value of the virtual power plant, represents the positive coefficient of deviation of the electricity price of the virtual power plant, represents the load adjustment cost of the virtual power plant after adding the distributed energy, represents the negative deviation coefficient of the electricity price of the virtual power plant, represents the time step, Represents the time step identifier.

[0008] Optionally, the calculation formula of the preference value is: ; in, represents the demand degree of the distributed resources for the virtual power plant, , represents the resource amount of the distributed resource, represents the existing resource amount of the virtual power plant, Indicates the number of matches. represents the tolerance of the virtual power plant to the distributed resources, represents the resource weight coefficient of the virtual power plant, Indicates the resource adjustment factor.

[0009] Optionally, the step of solving the optimal solution of the virtual power plant aggregation model with the goal of maximizing the aggregation benefit of the virtual power plant includes: Taking the maximization of the aggregated benefits of virtual power plants as the goal, an objective function is established; Iteratively calculating the objective function until the objective function result converges; The objective function result is used as the optimal solution, and the objective function result satisfies the preset constraint conditions.

[0010] Optionally, the objective function is: ; in, Indicates The benefits of a virtual power plant, , represents the electricity sales revenue of the virtual power plant, represents the subsidy income obtained by the virtual power plant, represents the reward income obtained by the virtual power plant, Indicates The economic cost of a virtual power plant, , represents the power generation cost of the virtual power plant, represents the operation and maintenance cost of the virtual power plant, Indicates The environmental cost of a virtual power plant, , represents the carbon emission cost of the virtual power plant, represents the wastewater treatment cost of the virtual power plant, Represents the number of virtual power plants.

[0011] Optionally, the constraint conditions include: ; in, Indicates The power generation capacity of a virtual power plant, represents the power demand of the virtual power plant, represents the power loss of the virtual power plant during the transmission process, represents the minimum power generation capacity of the virtual power plant. represents the maximum power generation capacity of the virtual power plant, represents the charging power of the energy storage equipment in the virtual power plant, and They represent the minimum charging power and maximum charging power of the energy storage device respectively. represents the capacity constraint of the energy storage equipment of the virtual power plant, and Respectively indicate the maximum and minimum values ​​of the energy storage device capacity.

[0012] Based on the same inventive concept, one or more embodiments of the present application further provide an aggregation device for a virtual power plant, including: A first computing module is configured to construct a virtual power plant aggregation model of a virtual power plant and distributed resources according to a bilateral matching mechanism to determine a potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on a marginal benefit obtained by the virtual power plant from each of the distributed resources and a preference of the distributed resources for each of the virtual power plants; The second computing module is configured to solve the optimal solution of the virtual power plant aggregation model with the goal of maximizing the aggregation benefit of the virtual power plant, and obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the optimal matching relationship between the virtual power plant and the distributed resources when the benefits of all the virtual power plants are maximized.

[0013] Based on the same inventive concept, one or more embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the virtual power plant aggregation method as described in any one of the above items is implemented.

[0014] Based on the same inventive concept, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the virtual power plant aggregation methods described above.

[0015] From the above, it can be seen that the virtual power plant aggregation method provided by one or more embodiments of the present application constructs a virtual power plant aggregation model of virtual power plants and distributed resources based on a bilateral matching mechanism to determine the potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each of the distributed resources and the preference of the distributed resources for each of the virtual power plants; with the goal of maximizing the aggregation benefit of the virtual power plant, the optimal solution of the virtual power plant aggregation model is solved to obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the optimal matching relationship between the virtual power plant and the distributed resources when the benefits of all the virtual power plants are maximized.

[0016] The method proposed in this application aims to identify the potential matching relationship between virtual power plants and distributed resources based on the dual interests of the two, and ensure the stability of the matching process by adopting a bilateral matching mechanism. Furthermore, with the maximization of aggregation benefits as the optimization goal, the differences in resource distribution and demand characteristics in each region are fully considered, and the best matching solution is screened out from all possible matching relationships. Through this application, energy utilization and virtual power plant aggregation benefits can be effectively improved, and energy losses and environmental pollution can be further reduced, and the reliability and safety of energy supply can be improved.

[0017] The virtual power plant aggregation device, electronic device and computer-readable storage medium provided in the present application are all capable of implementing the steps of the above-mentioned virtual power plant aggregation method, and therefore also have the beneficial effects of the above-mentioned virtual power plant aggregation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate one or more embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic flow chart of a method for aggregating a virtual power plant according to one or more embodiments of the present application; Figure 2 A schematic diagram of the structure of an aggregated virtual power plant of one or more embodiments of the present application; Figure 3 A schematic diagram of the distribution of virtual power plants and distributed resources in a power system according to an embodiment of the present application; Figure 4 A schematic diagram of the test results of the preference of distributed energy resources for virtual power plants in one embodiment of the present application; Figure 5 A schematic diagram of energy utilization test results of a virtual power plant aggregation method in one embodiment of the present application; Figure 6 A schematic diagram of the hardware structure of an electronic device according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] As described in the background technology, virtual power plant aggregation technology integrates power system management methods with advanced information communication, artificial intelligence, data analysis and automation control technologies. Through a software platform, it aggregates dispersed distributed energy resources (such as photovoltaics, wind turbines, energy storage equipment and controllable loads) into a coordinated virtual power source to achieve optimized control and economic dispatch of the power system.

[0023] Among them, the aggregation of multi-regional virtual power plants is an important direction for the development of virtual power plant technology. It integrates distributed energy resources in different geographical regions to achieve energy complementarity and optimized scheduling among regions, which not only enhances the regulation flexibility and economy of the power grid, but also improves the system's responsiveness and market participation. It provides strong technical support for the intelligent and green transformation of the power system, and is a key strategy for achieving efficient energy utilization and power market optimization.

[0024] In order to improve the aggregation effect of multi-regional virtual power plants, various virtual power plant aggregation schemes are proposed in related technologies with the goal of maximizing aggregation benefits. However, these virtual power plant aggregation schemes cannot fully consider the uniqueness and differences of distributed resources, resulting in unsatisfactory aggregation effects.

[0025] Therefore, relevant technologies have proposed to use deep learning technology, communication technology, etc. to assist in solving the best aggregation plan. The first solution proposes to build a virtual power plant aggregation model by analyzing the response characteristics of distributed resources before and after the aggregation of virtual power plants, and realize the aggregation of virtual power plants. The second solution proposes to monitor the status of supply-side resources in real time, and build a model to configure resources under the premise of considering the response of the demand side. The third solution proposes to first analyze the characteristics of virtual power plants and resources, and then establish a virtual power plant aggregation model to solve the virtual power plant aggregation plan. However, the above technical solutions all have problems such as poor aggregation effect.

[0026] In the process of implementing this application, the applicant found that the reason why the relevant technologies have problems such as poor aggregation effect is that the relevant technologies do not take into account the characteristics and differences of different distributed resources, and the obtained virtual power plant aggregation solutions also lack fairness and stability.

[0027] Therefore, the applicant proposes a virtual power plant aggregation method of the present application. Figure 1 The aggregation method of a virtual power plant of one or more embodiments of the present application comprises the following steps: Step S101: constructing a virtual power plant aggregation model of virtual power plants and distributed resources according to a bilateral matching mechanism to determine a potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each of the distributed resources and the preference of the distributed resources for each of the virtual power plants; Step S102: With the goal of maximizing the benefits of virtual power plant aggregation, the optimal solution of the virtual power plant aggregation model is solved to obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the optimal matching relationship between the virtual power plant and the distributed resources when the benefits of all the virtual power plants are maximized.

[0028] In order to improve the fairness and stability of the virtual power plant aggregation solution, before solving the optimal matching solution between the virtual power plant and the distributed resources, this application first determines the potential matching relationship that satisfies the wishes of the virtual power plant and the distributed resources based on the bilateral matching mechanism.

[0029] In the process of implementing the present application, considering that the delayed acceptance algorithm (also known as the Gale-Shapley algorithm) can be used to solve the stable matching problem, the algorithm can form a stable match between two groups of objects through a series of proposal and rejection operations. The applicant, based on the above-mentioned Gale-Shapley algorithm, has proposed a bilateral matching mechanism after improvement to improve the stability of the virtual power plant aggregation scheme.

[0030] It should be noted that in the bilateral matching of the present application, the matching relationship between virtual power plants and distributed resources can be many-to-many, that is, any virtual power plant can be matched with one or more distributed resources, and any distributed resource can also be matched with one or more virtual power plants.

[0031] In an embodiment of the present application, a virtual power plant aggregation model of a virtual power plant and distributed resources is constructed according to a bilateral matching mechanism, which may specifically include executing the following steps: Step S201: the above-mentioned distributed resources send an invitation message to all the above-mentioned virtual power plants; Step S202: the virtual power plant calculates the marginal effect contribution value of the distributed resources in response to receiving the invitation message; Step S203: the virtual power plant determines the intended distributed resource according to a preset method, and sends an acceptance message to the intended distributed resource; Step S204: the intended distributed resource calculates a preference value of the virtual power plant in response to receiving the acceptance message; Step S205: The intended distributed resources determine the intended virtual power plant according to a preset method, and send a matching message to the intended virtual power plant.

[0032] It should be noted that in the above embodiment, the invitation message is actively sent by the distributed resources. In the embodiment of the present application, the invitation message can also be actively sent by the virtual power plant. As long as the bilateral matching mechanism proposed in the present application is followed, the invitation messages sent by different executors will not affect the protection scope of the present invention.

[0033] The following describes the bilateral matching mechanism of the present application in detail by taking the sending of invitation messages by distributed resources as an example.

[0034] In step S201, the distributed resource sends an invitation message to all virtual power plants, which means that the distributed resource sends an invitation message to all virtual power plants that have not refused to match (the distributed resource has not marked the virtual power plant as refused to match, or the distributed resource has not been marked by the virtual power plant as refused to match). The invitation message can be sent to all virtual power plants simultaneously or sequentially.

[0035] Those skilled in the art may also adopt other methods to send the above-mentioned invitation message. As long as the different sending methods can achieve the purpose of attempting to match each distributed resource with all virtual power plants, the different sending methods will not affect the protection scope of the present invention.

[0036] The invitation message in step S201 is a message inviting the other party (ie, the virtual power plant receiving the invitation message) to match itself. When the virtual power plant receives the invitation message, it can select the intended distributed resources according to the preset method, that is, select the distributed resources that meet the requirements of the virtual power plant.

[0037] In an embodiment of the present application, the marginal effect value provided by the distributed resources for the virtual power plant can be used as an indicator to measure the beneficial effect of the distributed resources on the virtual power plant. Therefore, in step S202, the present application first calculates the marginal effect of the distributed resource that sends the invitation message.

[0038] By using quantitative indicators to guide the decision-making of virtual power plants in selecting distributed resources, the matching process between virtual power plants and distributed resources can be made more scientific and reasonable. In the application documents of this application, the marginal effect contribution value represents the contribution of distributed resources to the marginal effect of virtual power plants. The marginal effect contribution value reflects the additional benefits brought by the addition of distributed resources to virtual power plants. By calculating the marginal effect contribution value, it can be clarified which distributed resources have made greater contributions to improving the overall benefits of virtual power plants.

[0039] In the embodiment of the present application, the above marginal effect contribution value can be determined according to the price fluctuation of the power market and the output value of the virtual power plant. Based on the real-time changing price fluctuation of the power market and the output value of the virtual power plant, the marginal contribution value can dynamically reflect the value of distributed resources at different time points, making the matching process more flexible and more adaptable.

[0040] In addition, the calculation of the above marginal effect contribution value can also take into account factors such as electricity price deviation coefficient and load adjustment cost, so as to evaluate the reduced costs and increased revenues of the virtual power plant after the addition of distributed resources, that is, to more comprehensively evaluate the economic benefits of the virtual power plant after the addition of distributed resources.

[0041] Specifically, in the embodiment of the present application, the calculation formula of the marginal effect contribution value can be: ; in, express The fluctuation value of the electricity price of the virtual power plant at time represents the output value of the virtual power plant, represents the positive coefficient of deviation of the electricity price of the virtual power plant, represents the load adjustment cost of the virtual power plant after adding the distributed energy, represents the negative deviation coefficient of the electricity price of the virtual power plant, represents the time step, Represents the time step identifier. It can be understood as, Indicates any moment, represents the total time step, Moment superposition Time step, that is, get the time step identifier , used to indicate different time periods.

[0042] In power system and virtual power plant scheduling, time is usually divided into smaller time periods (such as hours, minutes, etc.), and the length of each time period is This time interval is used to calculate the changes in different time periods, such as fluctuations in electricity prices, changes in load, etc. Each time period is identified by a time step (in this application ) can help calculate and analyze in different time periods. By accumulating these changes, the total effect or total cost of the entire scheduling period can be obtained.

[0043] Setting the marginal effect contribution value as a quantitative indicator has the following beneficial effects: ensuring that the resources that can best improve the overall benefits of the virtual power plant are given priority in the virtual power plant aggregation, thereby optimizing resource allocation and improving the utilization rate of distributed resources; improving the stability of the power system, reducing power supply fluctuations caused by resource mismatch, and maintaining the stable operation of the power grid; accurately measuring the contribution of each resource will help maximize the economic benefits of the virtual power plant, including electricity sales revenue and related subsidies, and further improve the economic benefits of the power system; using a single quantitative indicator (marginal contribution value) to evaluate and select resources simplifies the complex matching decision-making process and improves operational efficiency; it can be updated in real time to ensure rapid response to dynamic changes in the power market and timely adjustment of resource allocation strategies.

[0044] Those skilled in the art may also use other indicators to measure the value of distributed resources. As long as the indicators they choose can achieve the corresponding effects, they are within the protection scope of this application.

[0045] In an embodiment of the present application, the virtual power plant may use the marginal effect contribution value calculated in step S202 as a basis for whether to select a distributed resource as an intended distributed resource.

[0046] In step S203, after receiving the invitation messages sent by all distributed resources, the virtual power plant can select the intended distributed resource from them according to a preset method; it can also determine whether the corresponding distributed resources are intended distributed resources in the order in which the invitation messages are received. For example, after receiving the invitation messages sent by all distributed resources that have not rejected the match (the distributed resource is not marked as refusing to match, or is not marked as refusing to match by the distributed resource), the virtual power plant can determine the intended distributed resource according to a preset method. Alternatively, after receiving an invitation message, the distributed resource corresponding to the invitation message can be compared with the current intended distributed resource (if there is no distributed resource with a matching relationship at present, the distributed resource can be accepted as the intended distributed resource by default), and determine whether to use the current distributed resource as the intended distributed resource according to a preset method.

[0047] As described above, a virtual power plant can be matched with one or more distributed resources. Therefore, the intended distributed resources in step S203 can also be one or more.

[0048] In an embodiment of the present application, the above preset method may be to select one or more distributed resources with the largest marginal effect contribution value. The present application does not limit the above preset method, and different preset methods, as long as they can achieve the corresponding goals, are within the protection scope of the present application.

[0049] It should be noted that after making a judgment, the virtual power plant should accept or reject the corresponding distributed resource. For example, it should send an acceptance message when accepting, and mark the distributed resource as rejected when rejecting, and update this mark when necessary. This action means that the virtual power plant has made a judgment on whether it is willing to match the distributed resource, so that the distributed resource will no longer send an invitation message to the virtual power plant that has been marked as rejected.

[0050] In addition to the above steps, the bilateral matching mechanism of the present application also requires the intended distributed resources to counter-select the virtual power plant.

[0051] In an embodiment of the present application, the preference value can be used as a basis for the distributed resource to counter-select the virtual power plant. Therefore, step S204 of the present application calculates the preference value of the virtual power plant. In the present application, the preference value refers to a quantitative representation of the degree of preference of the distributed resource for different virtual power plants in the decision-making process. It reflects the intensity of the preference of the distributed resource for a certain virtual power plant relative to other virtual power plants, and is usually used to describe the preference order of the distributed resource when making decisions among multiple virtual power plants.

[0052] Based on the preference value, it is possible to identify distributed resources that are more suitable for matching with specific virtual power plants, thereby achieving optimal resource allocation and improving the operating efficiency and benefits of the overall system. In addition, by comprehensively considering various influencing factors, the preference value helps to form a more stable and reliable matching result and reduce matching errors caused by a single factor.

[0053] In an embodiment of the present application, the above-mentioned preference value can be calculated based on multiple factors such as the demand degree of distributed resources for the virtual power plant, the resource weight coefficient of the virtual power plant, the resource quantity, the existing resource quantity, the number of matches and the tolerance. This multi-dimensional consideration can more comprehensively reflect the matching suitability between distributed resources and virtual power plants.

[0054] In addition, this application also introduces dynamic parameters such as matching times and tolerance when calculating the preference value, so that the preference value can be adjusted according to the actual situation, thereby adapting to the changes of virtual power plants and distributed resources at different times and under power market conditions.

[0055] Specifically, in the embodiment of the present application, the calculation formula of the above preference value can be: ; in, represents the demand degree of the distributed resources for the virtual power plant, , represents the resource amount of the distributed resource, represents the existing resource amount of the virtual power plant, Indicates the number of matches. represents the tolerance of the virtual power plant to the distributed resources, represents the resource weight coefficient of the virtual power plant, Indicates the resource adjustment factor.

[0056] In this application, the resource weight coefficient of the virtual power plant is involved in the formula for calculating the preference value of distributed resources for the virtual power plant. Coefficients that play an adjustment role in resource-related calculations, such as adjusting the relative importance of distributed resources of different types or sources in the process of calculating preference values ​​or aggregation benefits, so as to better achieve goals such as optimal resource allocation of virtual power plants.

[0057] The setting of preference values ​​has the following beneficial effects: the multi-dimensional preference value calculation can more accurately reflect the relationship between distributed resources and virtual power plants, thereby improving the accuracy of matching; the dynamically adjusted preference values ​​enable the system to better adapt to changes in the external environment, such as fluctuations in electricity demand, changes in the availability of distributed energy resources, etc., thereby enhancing the flexibility of the power system; by optimizing resource allocation, it ensures that resources are allocated to virtual power plants that can best play their role, thereby improving the overall resource utilization efficiency; taking into account factors such as the resource weight coefficient and existing resource volume of virtual power plants, the setting of preference values ​​helps to achieve relatively fair resource allocation among different virtual power plants; the quantified preference value indicators help to simplify the complex matching decision-making process, making virtual power plants more intuitive and efficient when selecting distributed resources.

[0058] In the embodiments of the present application, other indicators may be selected as the basis for the intended distributed resource deselection. As long as the selection of different indicators can achieve the corresponding goals, they are all within the protection scope of the present application.

[0059] As mentioned above, a distributed resource can select one or more potential virtual power plants.

[0060] Therefore, in step S205, a distributed resource can reversely select one or more potential virtual power plants. When any distributed resource and any virtual power plant reach an agreement with each other, it can be considered that the distributed resource and the virtual power plant have a potential matching relationship.

[0061] In the embodiment of the present application, the above preset method can be one or more virtual power plants with the largest preference value. The present application does not limit the above preset method, and different preset methods, as long as they can achieve the corresponding goals, are within the protection scope of the present application.

[0062] It should be noted that after the distributed resource makes a judgment, it should take an action to accept or reject the corresponding virtual power plant. For example, it may send a matching message or mark a rejection of the match, and update the mark if necessary. This action means that the distributed resource has made a judgment on whether it is willing to match the virtual power plant, so that the virtual power plant will no longer send an invitation message to the distributed resource that has been marked.

[0063] It can be understood that after matching according to the above-mentioned bilateral matching mechanism, any distributed resource determines the matching relationship (rejection or intention) with all virtual power plants, and any virtual power plant also determines the matching relationship (rejection or intention) with all distributed resources. The bilateral matching mechanism of the present application ensures that each virtual power plant / distributed resource has the right to choose to accept or reject distributed resources / virtual power plants. Through the bilateral matching mechanism, the stability of the matching results between virtual power plants and distributed resources is guaranteed while virtual power plants pursue the maximization of aggregation benefits. It solves the problem in related technologies that it is difficult for virtual power plants and distributed resources to form a stable matching relationship because only the maximization of electricity market benefits is considered.

[0064] However, in the process of implementing this application, the applicant found that the above bilateral matching can only achieve a preliminary matching between virtual power plants and distributed resources. This process mainly focuses on the matching relationship between a single virtual power plant and distributed resources, and lacks a global optimization perspective.

[0065] Therefore, the technical solution of this application further considers the overall benefits and various cost factors of multiple virtual power plants on the basis of the above potential matching relationship. By setting the objective function and constraints, the aggregation scheme can achieve the optimal allocation of resources between virtual power plants in multiple regions to ensure the maximization of overall benefits.

[0066] It can be understood that the virtual power plant aggregation model obtained based on the bilateral matching mechanism mainly focuses on the matching relationship between the virtual power plant and distributed resources. The aggregation scheme, on this basis, takes into account the economic, environmental, operation and maintenance costs and benefits of the virtual power plant, providing a more comprehensive optimization solution.

[0067] Therefore, this application constructs a virtual power plant aggregation based on the potential matching relationship between virtual power plants and distributed resources, so as to comprehensively consider multi-dimensional factors and more comprehensively reflect the operation status and benefits of virtual power plants. The above multi-dimensional factors may include one or more of economic costs, environmental costs, electricity sales revenue, subsidies, rewards, power generation costs, operation and maintenance costs, carbon emission costs and sewage treatment costs.

[0068] The objective function and constraints of the virtual power plant aggregation scheme can be dynamically adjusted according to actual conditions to adapt to changes in the power market and distributed energy resources. This flexibility enables the aggregation model to better cope with complex and changing market environments and maximize sustainable benefits. Specifically, by building an aggregation model, the resources of the virtual power plant can be more effectively utilized, resource waste can be reduced, and energy utilization efficiency can be improved; by optimizing the use and scheduling of energy storage equipment, the supply and demand of electricity can be better balanced and power losses can be reduced.

[0069] In step S102, the optimal solution of the virtual power plant aggregation model is solved with the goal of maximizing the virtual power plant aggregation benefit, which may include: Step S301: Establishing an objective function with the goal of maximizing the aggregated benefit of the virtual power plant; Step S302: iteratively calculating the above objective function until the objective function result converges; Step S303: taking the objective function result as the optimal solution, and the objective function result satisfies the preset constraint conditions.

[0070] It can be understood that in order to solve the virtual power plant aggregation scheme that maximizes the aggregation benefit, the setting of objective functions and constraints is particularly important.

[0071] It should be noted that the setting of the objective function should not only consider the benefits of a single virtual power plant, but also comprehensively consider the overall benefits of all virtual power plants to achieve global optimization.

[0072] In the embodiment of the present application, the objective function of step S301 can be set by comprehensively considering multiple factors such as the economic cost, environmental cost, electricity sales revenue, subsidies, rewards, power generation cost, operation and maintenance cost, carbon emission cost and sewage treatment cost of the virtual power plant, so as to fully reflect the operation status and benefits of the virtual power plant. In addition, the objective function not only needs to focus on the benefits of a single virtual power plant, but also needs to comprehensively consider the overall benefits of all virtual power plants, so as to achieve the optimal allocation of resources between virtual power plants in multiple regions by setting the objective function, and ensure the maximization of overall benefits.

[0073] Specifically, the above objective function can be set as: ; in, Indicates The benefits of a virtual power plant, , represents the electricity sales revenue of the virtual power plant, represents the subsidy income obtained by the virtual power plant, represents the reward income obtained by the virtual power plant, Indicates The economic cost of a virtual power plant, , represents the power generation cost of the virtual power plant, represents the operation and maintenance cost of the virtual power plant, Indicates The environmental cost of a virtual power plant, , represents the carbon emission cost of the virtual power plant, represents the wastewater treatment cost of the virtual power plant, Represents the number of virtual power plants.

[0074] By introducing environmental costs and carbon emission costs, environmental protection and social responsibility are taken into consideration, which is in line with the concept of sustainable development.

[0075] It should be noted that parameters such as economic cost and electricity sales revenue in the objective function can be adjusted according to real-time changes in the electricity market, so that the virtual power plant can flexibly respond to market fluctuations and optimize resource allocation.

[0076] Determining the best matching relationship between virtual power plants and distributed resources by solving the optimal solution of the objective function in the above manner has the following beneficial effects: it can optimize resource allocation, ensure the rational allocation of resources among multiple virtual power plants, reduce resource waste, and improve energy utilization efficiency; by comprehensively considering various costs and benefits, the objective function helps to balance the power generation and electricity demand of virtual power plants, reduce power losses, and ensure the stable operation of the power grid; the objective function maximizes the overall benefits of virtual power plants to ensure that virtual power plants obtain maximum benefits in economic activities, including electricity sales revenue, subsidies and rewards; by introducing environmental costs and carbon emission costs, the objective function encourages virtual power plants to reduce carbon emissions and sewage emissions, and promote environmental protection and sustainable development; the parameters in the objective function can be adjusted according to market changes, so that virtual power plants can flexibly respond to fluctuations in the electricity market and optimize resource allocation.

[0077] The objective function of this application maximizes the aggregate benefits of virtual power plants from the following aspects: by optimizing factors such as the economic cost, electricity sales revenue, subsidies and rewards of virtual power plants, the objective function ensures that virtual power plants obtain maximum benefits in economic activities; by introducing environmental costs and carbon emission costs, the objective function encourages virtual power plants to reduce carbon emissions and sewage emissions, and promote environmental protection and sustainable development; by comprehensively considering the impact of the operation of virtual power plants on society, the objective function ensures that virtual power plants meet social responsibilities and public needs while providing electricity services; by optimizing the power generation capacity and electricity demand of virtual power plants, the objective function ensures the stable operation of the power grid and reduces the risk of power loss and power shortages.

[0078] The convergence result obtained by iterative calculation in step S302 is the optimal solution of the objective function. In the embodiment of the present application, the number of iterations can also be set, and the optimal solution of the objective function is determined when the number of iterations is met and the result converges.

[0079] The constraints in step S303 need to ensure the feasibility and stability of the virtual power plant in actual operation. In an embodiment of the present application, the constraints set by the aggregation model may include power generation, power demand, power loss, minimum and maximum power generation, charging and capacity constraints of energy storage devices, etc.

[0080] Specifically, the constraint condition of step S303 can be set as: ; in, Indicates The power generation capacity of a virtual power plant, represents the power demand of the virtual power plant, represents the power loss of the virtual power plant during the transmission process, represents the minimum power generation capacity of the virtual power plant. represents the maximum power generation capacity of the virtual power plant, represents the charging power of the energy storage equipment in the virtual power plant, and They represent the minimum charging power and maximum charging power of the energy storage device respectively. represents the capacity constraint of the energy storage equipment of the virtual power plant, and Respectively indicate the maximum and minimum values ​​of the energy storage device capacity.

[0081] The above constraints are set mainly for the following considerations: the constraints set the virtual power plant's power generation, power demand, power loss, minimum and maximum power generation parameters. These parameters are set to ensure that the virtual power plant does not exceed its physical and technical limitations during operation, thereby ensuring the stable operation of the power system; by setting the charging power and capacity constraints of the energy storage equipment, the use of the energy storage equipment can be optimized to ensure that it can provide sufficient power support during peak hours and charge during off-peak hours, thereby achieving effective use of resources.

[0082] By setting the above constraints, the optimal matching relationship can also meet the bilateral matching principle. The reason is that the constraints ensure the technical feasibility and economic rationality of the virtual power plant during operation, thereby ensuring the feasibility of the bilateral matching results. For example, by setting constraints on power generation and electricity demand, it is ensured that the virtual power plant can meet the electricity demand and avoid overload operation, that is, the feasibility of the matching results is ensured; the constraints optimize the use of energy storage equipment and power scheduling to ensure the rational allocation of resources among multiple virtual power plants, thereby improving resource utilization efficiency. This optimization of resource allocation helps to achieve the goal of bilateral matching, that is, to maximize the overall benefits of the virtual power plant.

[0083] The final virtual power plant aggregation scheme can be expressed as: ; in, Indicates that the virtual power plant Demand parameters under state, Indicates that the virtual power plant The demand under the state, Indicates that the virtual power plant Demand parameters under state, Indicates that the virtual power plant The demand under the state, Represents an aggregate function, represents the number of iterations. Status and Each state represents the operating state of the virtual power plant under a specific condition or demand. The state can correspond to high demand, high load or peak hour operation. In this state, the virtual power plant may need to adjust its generation and energy storage strategies to meet the higher power demand, and the various components of the virtual power plant (such as distributed energy resources, energy storage equipment, etc.) may operate at a higher efficiency or capacity. The state can correspond to low demand, low load, or off-peak operating conditions. In this state, the virtual power plant may have more flexibility to adjust its generation and storage strategies to optimize cost and efficiency, and individual components of the virtual power plant may operate at lower efficiency or capacity to save energy and cost. During peak electricity demand periods, the virtual power plant may switch to state to maximize its power generation and supply capabilities; during periods of low electricity demand, the virtual power plant may switch to By flexibly switching between different states, virtual power plants can better adapt to changes in the electricity market and improve overall operational efficiency and economic benefits.

[0084] In short, the virtual power plant aggregation method provided in this application establishes a bilateral matching mechanism for multi-regional virtual power plants based on the Gale-Shapley algorithm, calculates the marginal contribution value of distributed resources after joining the virtual power plant, calculates the preference value of distributed resources for the virtual power plant, determines the potential matching relationship between the virtual power plant and the distributed resources, and constructs a virtual power plant bureau and model; under the premise of considering the maximization of the aggregation benefit of the virtual power plant, sets the corresponding objective function and constraints, and solves the constructed aggregation model to obtain the virtual power plant aggregation result.

[0085] The technical solution of the present application takes into account the characteristics and differences of distributed resources through the classification and preference calculation of distributed resources, the calculation of marginal contribution values, etc.; and ensures the fairness and stability of the matching and aggregation process through the application of the Gale-Shapley algorithm, the setting of constraints, and the iterative optimization process.

[0086] Specifically, in the bilateral matching mechanism, resources are selected by calculating the marginal contribution value of distributed resources after joining the virtual power plant, which reflects the different impacts of different resources on the benefits of the virtual power plant, that is, taking into account the characteristics and differences of resources. The preference of different types of distributed energy resources for virtual power plants is calculated. This process also takes into account the differences of various types of resources in terms of response speed, climbing ability, power output, etc.

[0087] The Gale-Shapley algorithm itself is a stable matching algorithm that can ensure a stable matching relationship between multi-regional virtual power plants and distributed resources, and ensure the fairness and stability of the matching process. The application of this algorithm in this application ensures that both virtual power plants and distributed resources have the right to choose to accept or reject. This two-way selection mechanism helps to achieve fair matching. When constructing the aggregation model, this application sets constraints such as the power generation power, power demand, power loss, minimum and maximum power generation of the virtual power plant. These constraints help to ensure the stability and safety of the operation of the virtual power plant. At the same time, constraints are also set for the charging power and capacity of the energy storage equipment, which helps to balance the supply and demand relationship of different distributed resources and ensure the stability of the system. When solving the aggregation model, the optimal solution is continuously approached through an iterative optimization process. This process helps to discover and solve potential problems of uneven resource allocation, thereby ensuring the fairness and stability of resource allocation.

[0088] In order to verify the beneficial effects of the technical solution of this application, the applicant also conducted an experimental verification. In this experiment, the applicant used the method designed by this application as method 1, the multi-modal flexible resource aggregation model of virtual power plants based on grey target theory and spectral clustering as method 2, and the distributed 5G base station adjustable resource aggregation technology and application based on virtual power plants as method 3.

[0089] The experiment includes 10 virtual power plant areas, each of which contains a varying number of distributed energy resources, ranging from 50 to 100. The distribution of virtual power plants and distributed resources in the power system of the experimental area is as follows: Figure 3 Among them, the maximum power of distributed energy resources is less than 5MW, the response speed is less than 30s, and the climbing ability is 0.5MW / min.

[0090] The applicant carried out experimental tests under the above-mentioned experimental environment. The specific experimental parameters are shown in Table 1.

[0091] Table 1

[0092] During the experiment, the applicant used the method of this application to first obtain the distributed energy resource data of the virtual power plant, and divided the distributed energy resources into four types: photovoltaic, energy storage, wind power and thermal power, and then calculated the preference degree of different categories of distributed energy resources for virtual power plants.

[0093] The preference of distributed resources for virtual power plants is as follows Figure 4 As shown, thermal power type distributed resources have a higher preference for virtual power plant No. 4, photovoltaic type distributed resources have a higher preference for virtual power plant No. 8, and energy storage type distributed resources have a higher preference for virtual power plant No. 2. Based on the above calculated preference, the experiment was carried out.

[0094] In the experiment, the above three methods were used to aggregate distributed energy resources and virtual power plants, and the aggregation effects of the three methods were compared by comparing the amount of distributed energy resources remaining after aggregation. The specific statistical results are shown in Table 2.

[0095] Table 2

[0096] As shown in Table 2, after aggregation using method 1, the amount of distributed energy resources remaining in the virtual power plant is relatively small, indicating that the method provided in this application can effectively utilize the energy resources of the virtual power plant and improve energy utilization efficiency. After aggregation using methods 2 and 3, the amount of distributed energy resources remaining is relatively large, indicating that the utilization effect of energy resources is not good. Therefore, the method proposed in this application is better in aggregation in practical applications.

[0097] In order to further verify the effects of the above three methods in practical applications, the applicant also compared the polymerization performance of the three methods using energy utilization as an evaluation indicator.

[0098] The energy utilization rates of the three methods are as follows: Figure 5 As shown, the test results show that for different virtual power plants, after polymerization, method 1 has a higher energy utilization rate, all above 95%, and has better polymerization performance. The energy utilization rates of method 2 and method 3 are relatively low. Therefore, the method designed by the present invention has better polymerization performance in practical applications.

[0099] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.

[0100] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an aggregation device for a virtual power plant. Figure 2 As shown, the device comprises: The first calculation module 11 is configured to construct a virtual power plant aggregation model of the virtual power plant and the distributed resources according to a bilateral matching mechanism to determine a potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each of the distributed resources and the preference of the distributed resources for each of the virtual power plants; The second computing module 12 is configured to solve the optimal solution of the virtual power plant aggregation model with the goal of maximizing the virtual power plant aggregation benefit, and obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the optimal matching relationship between the virtual power plant and the distributed resources when the benefits of all the virtual power plants are maximized.

[0102] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing one or more embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0103] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0104] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0105] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0106] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0107] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0108] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0109] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0110] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0111] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0112] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0113] A person of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0114] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the well-known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided figures. In addition, the device may be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0115] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0116] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of this disclosure.

Claims

1. A virtual power plant aggregation method, characterized in that: include: According to the bilateral matching mechanism, a virtual power plant aggregation model of the virtual power plant and the distributed resources is constructed to determine the potential matching relationship between the virtual power plant and the distributed resources; The bilateral matching mechanism is established based on the marginal benefit obtained by the virtual power plant from each of the distributed resources and the preference of the distributed resources for each of the virtual power plants; With the goal of maximizing the benefits of virtual power plant aggregation, the optimal solution of the virtual power plant aggregation model is solved to obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the best matching relationship between the virtual power plant and the distributed resources when the benefits of all virtual power plants are maximized.

2. The method according to claim 1, characterized in that The construction of a virtual power plant aggregation model of a virtual power plant and distributed resources according to the bilateral matching mechanism includes executing the following steps: The distributed resource sends an invitation message to all the virtual power plants; In response to receiving the invitation message, the virtual power plant calculates the marginal effect contribution value of the distributed resource; The virtual power plant determines the intended distributed resource according to a preset method, and sends an acceptance message to the intended distributed resource; The intended distributed resource calculates a preference value of the virtual power plant in response to receiving the acceptance message; The intended distributed resource determines the intended virtual power plant according to a preset method and sends a matching message to the intended virtual power plant.

3. The method according to claim 2, characterized in that The calculation formula of the marginal effect contribution value is: ； in, express The fluctuation value of the electricity price of the virtual power plant at time represents the output value of the virtual power plant, represents the positive coefficient of deviation of the electricity price of the virtual power plant, represents the load adjustment cost of the virtual power plant after adding the distributed energy, represents the negative deviation coefficient of the electricity price of the virtual power plant, represents the time step, Represents the time step identifier.

4. The method according to claim 2, characterized in that: The calculation formula of the preference value is: ； in, represents the demand degree of the distributed resources for the virtual power plant, , represents the resource amount of the distributed resource, represents the existing resource amount of the virtual power plant, Indicates the number of matches. represents the tolerance of the virtual power plant to the distributed resources, represents the resource weight coefficient of the virtual power plant, Indicates the resource adjustment factor.

5. The method according to claim 1, characterized in that The method of solving the optimal solution of the virtual power plant aggregation model with the goal of maximizing the virtual power plant aggregation benefit includes: Taking the maximization of the aggregated benefits of virtual power plants as the goal, an objective function is established; Iteratively calculating the objective function until the objective function result converges; The objective function result is used as the optimal solution, and the objective function result satisfies the preset constraint conditions.

6. The method according to claim 5, characterized in that The objective function is: ； in, Indicates The benefits of a virtual power plant, , represents the electricity sales revenue of the virtual power plant, represents the subsidy income obtained by the virtual power plant, represents the reward income obtained by the virtual power plant, Indicates The economic cost of a virtual power plant, , represents the power generation cost of the virtual power plant, represents the operation and maintenance cost of the virtual power plant, Indicates The environmental cost of a virtual power plant, , represents the carbon emission cost of the virtual power plant, represents the wastewater treatment cost of the virtual power plant, Represents the number of virtual power plants.

7. The method according to claim 5, characterized in that The constraints include: ； in, Indicates The power generation capacity of a virtual power plant, represents the power demand of the virtual power plant, represents the power loss of the virtual power plant during the transmission process, represents the minimum power generation capacity of the virtual power plant. represents the maximum power generation capacity of the virtual power plant, represents the charging power of the energy storage equipment in the virtual power plant, and They represent the minimum charging power and maximum charging power of the energy storage device respectively. represents the capacity constraint of the energy storage equipment of the virtual power plant, and Respectively indicate the maximum and minimum values ​​of the energy storage device capacity.

8. A virtual power plant aggregation device, characterized in that: include: A first computing module is configured to construct a virtual power plant aggregation model of a virtual power plant and distributed resources according to a bilateral matching mechanism to determine a potential matching relationship between the virtual power plant and the distributed resources; the bilateral matching mechanism is established based on a marginal benefit obtained by the virtual power plant from each of the distributed resources and a preference of the distributed resources for each of the virtual power plants; The second computing module is configured to solve the optimal solution of the virtual power plant aggregation model with the goal of maximizing the aggregation benefit of the virtual power plant, and obtain a virtual power plant aggregation plan; the virtual power plant aggregation plan is the optimal matching relationship between the virtual power plant and the distributed resources when the benefits of all the virtual power plants are maximized.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute any one of claims 1 to 7.

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