Virtual power plant quantity reporting and quotation method and device, electronic equipment and storage medium

Abstract

The invention discloses a virtual power plant quantity reporting and quoting method and device, electronic equipment and a storage medium, and relates to the technical field of electric power. The method comprises the following steps: determining a target quotation scheme according to a predicted clearing price and a corresponding occurrence probability; obtaining a plurality of building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determining constraint conditions according to the target quotation scheme and the building capacity-price combinations; and adopting a preset optimization tool, the target quotation scheme and the constraint condition to obtain an ideal quotation scheme with maximized ideal income, and performing quantity reporting and quotation according to the ideal quotation scheme. According to the method, by predicting the clearing prices and the corresponding occurrence probabilities in different scenes, the target quotation scheme and the constraint conditions are determined based on the prediction result, and then the ideal quotation scheme with the maximized ideal income is obtained in combination with the preset optimization tool, so that the income of the virtual power plant and the building user is stabilized, and the user experience is improved. And the rationality, flexibility and resource aggregation efficiency of quantity reporting and quotation of the virtual power plant are improved.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method, apparatus, electronic device and storage medium for virtual power plant quantity reporting and pricing. Background Technology

[0002] A virtual power plant is an energy management platform that aggregates distributed power sources (such as photovoltaic and wind power), energy storage systems, and adjustable loads (such as air conditioning and electric vehicles) into a unified and controllable unit based on advanced communication, smart metering, and coordinated control technologies. This unit participates in power system operation and the market. Accurate quantity and price quotes from virtual power plants can more efficiently aggregate power resources and maximize the benefits for participating parties.

[0003] In the current virtual power plant quotation scheme, resources are usually aggregated on a city-by-city basis. When virtual power plants participate in power regulation response, they usually participate in power regulation response based on a fixed subsidy price model. Therefore, the optimization scheme mainly focuses on optimizing the resource allocation scheme within the virtual power plant.

[0004] However, the current virtual power plant quotation scheme is difficult to meet the regulation needs of local areas of the urban power grid. Furthermore, the quotation process focuses too much on price declaration and ignores capacity declaration. The optimization scheme is also relatively simple, resulting in insufficient rationality and flexibility of virtual power plant quotation, unstable revenue, and low resource aggregation efficiency. Summary of the Invention

[0005] The main purpose of this application is to propose a method, apparatus, electronic device and storage medium for virtual power plant quotation, which aims to stabilize the revenue of virtual power plants and building users, and improve the rationality, flexibility and resource aggregation efficiency of virtual power plant quotation.

[0006] In a first aspect, the present invention provides a method for virtual power plant quantity quotation and pricing, comprising:

[0007] Based on historical clearing prices, determine the predicted clearing price for at least one scenario and the probability of occurrence for each predicted clearing price;

[0008] The target pricing scheme is determined based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price;

[0009] The system obtains multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determines constraints based on the target bidding scheme and the building capacity-price combinations. The building users refer to users who are pre-divided into minimum electricity consumption areas, and the constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints.

[0010] Using a preset optimization tool, the target pricing scheme, and the constraints, an ideal pricing scheme is obtained. The ideal pricing scheme is the scheme that maximizes ideal revenue. The ideal pricing scheme includes: multiple virtual power plant capacity-price combinations.

[0011] Provide a quantity quote based on the ideal pricing scheme.

[0012] In an optional implementation, obtaining an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints includes:

[0013] Using a preset optimization tool, the target pricing scheme, and the constraints, the ideal price for each scenario is obtained;

[0014] Based on the constraints, determine multiple segmented ideal prices corresponding to the ideal price;

[0015] Based on the segmented ideal price and the building capacity-price combination, the ideal capacity corresponding to each ideal price is calculated and obtained.

[0016] In an optional implementation, the step of obtaining an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints further includes:

[0017] The ideal pricing scheme is determined based on the ideal price and the ideal capacity corresponding to the ideal price.

[0018] In an optional implementation, after providing a quantity quote based on the obtained ideal quote plan, the method further includes:

[0019] After completing the power regulation response, the revenue of the virtual power plant and the revenue of the building user are calculated according to the preset revenue algorithm, wherein the preset revenue algorithm includes: the virtual power plant revenue algorithm and the building user revenue algorithm;

[0020] The revenue is allocated to the virtual power plant and the building user respectively based on the revenue of the virtual power plant and the revenue of the building user.

[0021] In an optional implementation, the step of calculating and obtaining the revenue of the virtual power plant according to a preset revenue algorithm includes:

[0022] Determine the bidding and allocation status of multiple virtual power plant capacity-price combinations;

[0023] Based on the bidding results, the call-up status, and the capacity of each virtual power plant capacity-price combination, the regulation revenue and reserve revenue of the virtual power plant are calculated and obtained respectively.

[0024] The revenue of the virtual power plant is calculated based on the regulation revenue and the reserve revenue.

[0025] In an optional implementation, the step of calculating and obtaining building user revenue according to a preset revenue algorithm includes:

[0026] Determine the response status of multiple building capacity-price combinations for the building user, and calculate the price difference between each building capacity-price combination and the corresponding virtual power plant capacity-price combination;

[0027] Based on the response status, the capacity and price of the building capacity-price combination, calculate and obtain the response revenue of the building user;

[0028] Based on the response status, the capacity and price difference of the building capacity-price combination, calculate and obtain the additional revenue for the building user.

[0029] The revenue for the building user is calculated based on the response revenue and the additional response revenue.

[0030] In an optional implementation, the plurality of building capacity-price combinations and the plurality of virtual power plant capacity-price combinations are arranged in ascending order of their corresponding prices.

[0031] Secondly, the present invention provides a virtual power plant quantity reporting and pricing device, comprising:

[0032] The prediction module is used to determine the predicted clearing price for at least one scenario and the probability of occurrence of each predicted clearing price based on historical clearing prices.

[0033] The target determination module is used to determine the target pricing scheme based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price;

[0034] The constraint determination module is used to obtain multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determine the constraint conditions according to the target bidding scheme and the building capacity-price combinations. The building users refer to users who are pre-divided into minimum electricity consumption areas. The constraint conditions include: inter-group relationship constraint conditions, capacity constraint conditions, minimum price constraint conditions, winning bid constraint conditions, and scenario constraint conditions.

[0035] The acquisition module is used to obtain an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints. The ideal pricing scheme is the scheme that maximizes ideal revenue. The ideal pricing scheme includes multiple virtual power plant capacity-price combinations.

[0036] The quantity reporting and pricing module is used to report quantities and provide pricing based on the ideal pricing scheme.

[0037] Thirdly, the present invention provides an electronic device, comprising: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of any of the methods described in the foregoing embodiments.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method as described in any of the foregoing embodiments.

[0039] The beneficial effects of this application are:

[0040] The virtual power plant quantity quotation method provided in this application includes: determining a predicted clearing price for at least one scenario and the probability of occurrence of each predicted clearing price based on historical clearing prices; determining a target quotation scheme based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price; obtaining multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determining constraints based on the target quotation scheme and the building capacity-price combinations, wherein the building users represent users in pre-divided minimum electricity consumption areas, and the constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints; obtaining an ideal quotation scheme using a preset optimization tool, the target quotation scheme, and the constraints, wherein the ideal quotation scheme is an ideal profit maximization scheme, wherein the ideal quotation scheme includes: multiple virtual power plant capacity-price combinations; and submitting a quantity quotation based on the obtained ideal quotation scheme. In this embodiment, by predicting the clearing price and the probability of occurrence of the clearing price under different scenarios, the target pricing scheme and constraints are determined based on the predicted clearing price and the probability of occurrence of the clearing price. Then, the target pricing scheme and constraints are input into a preset optimization tool so that the preset optimization tool outputs an ideal pricing scheme that maximizes ideal revenue and includes multiple virtual power plant capacity-price combinations. Finally, the quantity quotation can be completed according to the ideal pricing scheme, thereby stabilizing the revenue of virtual power plants and building users, and improving the rationality, flexibility and resource aggregation efficiency of virtual power plant quantity quotation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of a virtual power plant quantity reporting and pricing method provided in an embodiment of this application;

[0043] Figure 2 This is a schematic diagram of a virtual power plant quantity quotation method provided in another embodiment of this application;

[0044] Figure 3 A schematic diagram of a virtual power plant quantity quotation method provided in another embodiment of this application;

[0045] Figure 4 This is a schematic flowchart of a virtual power plant quantity quotation method provided in another embodiment of the present application;

[0046] Figure 5 A schematic diagram of a virtual power plant quantity reporting and pricing device provided in this application embodiment;

[0047] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0050] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0051] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0052] Current virtual power plant (VPS) quotation schemes typically aggregate resources on a city-by-city basis, rarely focusing on localized adjustments based on specific regions or building users. This resource aggregation and adjustment method struggles to meet the adjustment needs of localized urban power grids. Furthermore, when VPS participate in power regulation response, they usually operate on a fixed subsidy price model. This subsidy price dictates that when VPS seeks to optimize their quotation schemes and increase revenue, they primarily achieve this by optimizing their internal resource allocation. Specifically, during the optimization process, VPS tends to overemphasize price submission optimization while neglecting corresponding capacity submission optimization. The optimization methods are relatively simplistic, resulting in insufficient rationality and flexibility in VPS quotation, unstable revenue, and low resource aggregation efficiency.

[0053] To address the aforementioned issues, the main objective of this application is to propose a virtual power plant quotation method, apparatus, electronic equipment, and storage medium, aiming to stabilize the revenue of virtual power plants and building users, and improve the rationality, flexibility, and resource aggregation efficiency of virtual power plant quotation.

[0054] Figure 1 This is a schematic flowchart of a virtual power plant quantity quotation method according to an embodiment of this application. The execution subject of this method can be, for example, a computer or server of the virtual power plant, or other devices with computing power, but is not limited thereto. Figure 1 As shown, the method includes:

[0055] S101. Determine the predicted clearing price for at least one scenario and the probability of occurrence of each of the predicted clearing prices based on historical clearing prices.

[0056] For example, the aforementioned historical clearing prices can be obtained from publicly available data on past power regulation demand bidding, but are not limited to this. The aforementioned publicly available data on past power regulation demand bidding can be publicly available data on power regulation demand bidding within the past 1 year, the past 2 years, or the past 3 years, but can also be data from other time spans.

[0057] The aforementioned determination of the predicted clearing price under at least one scenario and the probability of occurrence of each of the predicted clearing prices based on historical clearing prices can, for example, refer to predicting the predicted clearing price under at least one scenario and the probability of occurrence of each of the predicted clearing prices using a trained machine learning model. Alternatively, the determination can also refer to predicting the predicted clearing price under at least one scenario and the probability of occurrence of each of the predicted clearing prices using statistical analysis methods, but is not limited to predicting the predicted clearing price under at least one scenario and the probability of occurrence of each of the predicted clearing prices using a trained machine learning model or statistical analysis methods.

[0058] It is understandable that, in addition to using the historical clearing prices, the above-mentioned prediction of the clearing price in at least one scenario and the probability of occurrence of each of the above-mentioned predicted clearing prices can also use relevant seasonal information, temperature information, and other information that is highly correlated with electricity consumption, in order to improve the prediction accuracy of the above-mentioned prediction of the clearing price in at least one scenario and the probability of occurrence of each of the above-mentioned predicted clearing prices.

[0059] For example, the predicted clearing price under at least one of the above scenarios and the probability of each of the above predicted clearing prices occurring can be represented in the form of Table 1 below:

[0060]

[0061] Table 1. Predicted Clearing Prices and Corresponding Probabilities

[0062] Please refer to Table 1, which includes the predicted clearing price under three scenarios and the probability of occurrence of each of the above predicted clearing prices. However, it should be noted that when determining the predicted clearing price under at least one scenario and the probability of occurrence of each of the above predicted clearing prices based on historical clearing prices, the determined predicted clearing price and the probability of occurrence of each of the above predicted clearing prices are not limited to three.

[0063] S102. Based on the above-predicted clearing price and the above-predicted probability of occurrence corresponding to the above-predicted clearing price, determine the target pricing scheme.

[0064] For example, the above target pricing scheme can be used to represent the objective pursued in this bidding process, such as maximizing ideal profit, maximizing the winning bid capacity, or maximizing the winning bid price. Specific targets are not limited here. This target pricing scheme can be used as input to an optimizer or other algorithmic model to indicate its optimization objective, but it is not limited thereto. For example, the target pricing scheme can be expressed as the following formula:

[0065]

[0066] Here, max{} represents the target bidding scheme that aims to maximize ideal profit, maximize the winning bid capacity, or maximize the winning bid price in this bidding process. The formula uses the example of maximizing ideal profit in this bidding process, where π... ω (p clr,ω The clearing price is the predicted clearing price p under scenario ω. clr,ω The probability of occurrence. and Let be the price and the actual winning bid capacity of the m-th capacity-price combination of the virtual power plant under scenario ω, respectively. In this example, it is assumed that there are a total of 3 virtual power plant capacity-price combinations under scenario ω, hence...

[0067] It should be noted that the above formula is also subject to the following constraints:

[0068] ∑ ω π ω (p clr,ω ) = 1,

[0069] This means that the sum of the predicted clearing price occurrences under each scenario is 100%, which is equal to 1.

[0070] It is understood that the above is an example of a target pricing scheme that aims to maximize the ideal profit in this offer. The actual target pricing scheme is not limited to the example above and may take other forms.

[0071] S103. Obtain multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determine the constraints based on the above target quotation scheme and the above building capacity-price combinations. The above building users represent users who pre-divide the minimum electricity consumption area. The above constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints.

[0072] For example, the aforementioned virtual power plant may be a virtual power plant built based on several adjacent buildings. That is, the virtual power plant takes building users as the users of the smallest electricity consumption area. It is understood that taking building users as the users of the smallest electricity consumption area is only one of the possible ways to build a virtual power plant. If necessary, other units can also be used as the smallest electricity consumption area to build a virtual power plant, such as a community or a street. No specific restrictions are imposed here.

[0073] The building users corresponding to the aforementioned virtual power plant can refer, for example, to several adjacent buildings that construct the virtual power plant, with each building being a building user. The multiple building capacity-price combinations submitted by the building users corresponding to the virtual power plant can refer, for example, to the multiple building capacity-price combinations submitted by the building users corresponding to the virtual power plant to the virtual power plant each time the virtual power plant participates in the power regulation response. These building capacity-price combinations can be regarded, for example, as each building user's quotation to the virtual power plant. After receiving the multiple building capacity-price combinations submitted by the building users, the virtual power plant then determines the constraints corresponding to the target quotation scheme based on these building capacity-price combinations.

[0074] For example, assuming there are 7 building users corresponding to the virtual power plant, and each building user can submit a maximum of 3 building capacity-price combinations, then the multiple building capacity-price combinations submitted by the building users corresponding to the virtual power plant can be represented in the form of Table 2 below:

[0075]

[0076]

[0077] Table 2. Building Capacity-Price Combination Diagram

[0078] Please refer to Table 2, which includes the building capacity-price combinations submitted by 7 building users. Building users 1-4 each submitted 3 building capacity-price combinations, while building users 5-7 each submitted 2 building capacity-price combinations. Taking the building capacity-price combination submitted by building user 1 as an example, according to Table 2, during this power regulation response process, building user 1 can provide a power regulation response of 100kW capacity at a price of RMB 1.98 / kWh, a power regulation response of 80kW capacity at a price of RMB 0.80 / kWh, and a power regulation response of 100kW capacity at a price of RMB 1.98 / kWh.

[0079] Continuing with the above assumptions, this bid aims to maximize ideal profits. Taking the example that each virtual power plant submits three virtual power plant capacity-price combinations in each scenario, the aforementioned inter-group relationship constraints can be used to represent the relationship between the three virtual power plant capacity-price combinations submitted by the virtual power plant. For example, these inter-group relationship constraints can be expressed as the following formula:

[0080]

[0081] As can be seen from the examples above, The price is the capacity-electricity price combination of the m-th virtual power plant in scenario ω. This represents the price of the (m+1)th virtual power plant capacity-electricity price combination under scenario ω. From the above formula, we can see that the price of the mth virtual power plant capacity-electricity price combination under scenario ω... That is, the prices of the three virtual power plant capacity-electricity price combinations in scenario ω remain unchanged or increase sequentially.

[0082] Continuing with the above assumptions, this bid aims to maximize ideal returns. For example, in each scenario, there are three virtual power plant capacity-price combinations. The capacity constraint can be used to represent the relationship between the building capacity-price combination and the virtual power plant capacity-price combination. For instance, this capacity constraint can be expressed as the following formula:

[0083]

[0084] Among them, the above Let N be the capacity of the m-th virtual power plant capacity-price combination in scenario ω. N represents the total number of building-power plant capacity-price combinations submitted by the building users corresponding to the virtual power plant in scenario ω. Taking Table 2 as an example, N = 18. Let Q be the capacity of the nth building (building capacity - electricity price combination) in scenario ω. i,n Whether it belongs to the capacity-price combination of the m-th virtual power plant is a variable identifier, and the above p i,n Let Q be the price of the nth building capacity-electricity price combination in scenario ω, which is also the capacity Q of the nth building capacity-electricity price combination in scenario ω. i,n Whether a building belongs to the m-th virtual power plant capacity-price combination is determined by the price p of the n-th building capacity-electricity price combination of the i-th building in scenario ω. i,n Does it meet the requirements? The relationship determines this, and it's understandable that when... When the price is the first virtual power plant capacity-electricity price combination out of the three virtual power plant capacity-electricity price combinations, the above It can be non-existent.

[0085] The above formula is used to constrain: ① The sum of the capacities of the building capacity-electricity price combinations corresponding to the m-th virtual power plant capacity-price combination should be equal to the capacity of the m-th virtual power plant capacity-price combination, that is, the capacity of the m-th virtual power plant capacity-price combination is composed of the capacity of at least one corresponding building capacity-electricity price combination. ② The same Q i,n It can only belong to one virtual power plant capacity-price combination. That is, the capacity of a building capacity-electricity price combination can only be used to form the capacity of one virtual power plant capacity-price combination.

[0086] Continuing with the above assumptions, this bid aims to maximize ideal profits. For example, in each scenario, there are three virtual power plant capacity-price combinations. The aforementioned minimum price constraint can, for example, represent the minimum price of the first virtual power plant capacity-price combination. For instance, this minimum price constraint can be expressed as the following formula:

[0087]

[0088] Among them, the above That is, the minimum price of the first virtual power plant capacity-electricity price combination mentioned above, p. min The lowest price allowed by the current market quotation can be obtained through direct querying. It's understandable that, since the inter-group relationship constraints in the example above have already constrained the prices of the three virtual power plant capacity-price combinations under scenario ω to remain constant or increase sequentially, the minimum price of the first virtual power plant capacity-price combination equals the lowest price p allowed by the current market quotation. min At that time, the prices of the second and third virtual power plant capacity-electricity price combinations will not be lower than the minimum price of the first virtual power plant capacity-electricity price combination.

[0089] Additionally, it's important to note that the corresponding m values ​​for the three virtual power plant capacity-price combinations are 0, 1, and 2, representing the first, second, and third virtual power plant capacity-price combinations, respectively. It is the minimum price of the virtual power plant capacity-electricity price combination when m=0, which is also the minimum price of the first virtual power plant capacity-electricity price combination.

[0090] Continuing with the above assumptions, this bid aims to maximize ideal profits. For example, in each scenario, there are three virtual power plant capacity-price combinations. The above bidding constraints can be used to represent the bidding conditions among the three virtual power plant capacity-price combinations. For instance, these bidding constraints can be expressed as the following formula:

[0091]

[0092] Among them, the above and These are variable identifiers indicating whether the m-th virtual power plant capacity-price combination and the (m+1)-th virtual power plant capacity-price combination in scenario ω have won the bid. For example, as described above... When, it indicates that the m-th virtual power plant capacity-electricity price combination wins the bid in scenario ω, the above When the bid is not successful, it indicates that the m-th virtual power plant capacity-price combination under scenario ω did not win the bid. It can be understood that the bidding outcomes of the m-th and (m+1)-th virtual power plant capacity-price combinations under scenario ω are independent. However, due to the constraints of the inter-group relationships mentioned above, the price of the m-th virtual power plant capacity-price combination under scenario ω... Therefore, when the (m+1)th virtual power plant capacity-price combination in scenario ω wins the bid, the mth virtual power plant capacity-price combination in scenario ω will definitely win the bid, that is, when hour, It must equal 1, and when hour, Or 1.

[0093] The above p clr,ω For the actual clearing price under scenario ω, as can be seen from the above content, the above... For the actual winning bid capacity of the m-th capacity-price combination of the virtual power plant in scenario ω, the above... For the capacity-electricity price combination of the m-th virtual power plant in scenario ω, the above... The price of the m-th virtual power plant capacity-electricity price combination under scenario ω, therefore, the actual winning bid capacity of the m-th virtual power plant capacity-electricity price combination under scenario ω. This is equivalent to winning the bid for the capacity of the m-th virtual power plant capacity-electricity price combination under scenario ω. If the bid for the m-th virtual power plant capacity-electricity price combination under scenario ω is successful, the bid price should be less than or equal to the actual clearing price under scenario ω.

[0094] Continuing with the above assumptions, this bid aims to maximize ideal returns. Taking the example that each virtual power plant submits three virtual power plant capacity-price combinations in each scenario, the scenario constraints can be used to represent the probability of each scenario occurring, i.e., the probability that the clearing price equals the predicted clearing price for each scenario, and the relationship between the ideal bid for the m-th virtual power plant capacity-price combination in scenario ω and the ideal price in scenario ω. For example, this scenario constraint can be expressed as the following formula:

[0095]

[0096] Among them, the above Pr(pclr,ω () represents the actual clearing price p in scenario ω. clr,ω The probability of occurrence, as mentioned above These are the ideal price for the m-th virtual power plant capacity-electricity price combination under scenario ω and the ideal price under scenario ω, respectively.

[0097] It is understandable that when the predicted clearing price for at least one scenario and the probability of occurrence of each of the above-mentioned predicted clearing prices exist in only one scenario, the ideal bid in that scenario can be directly used as the final ideal bid solution. If the predicted clearing price for at least one scenario and the probability of occurrence of each of the above-mentioned predicted clearing prices exist in multiple scenarios, the ideal bids for multiple scenarios can be obtained respectively. At this time, the final ideal bid solution can be obtained by weighting the ideal bids for the above multiple scenarios according to the probability of occurrence of the predicted clearing price in each scenario. However, the above content is only one possible way to determine the ideal bid solution and cannot be used as a limitation on the way to obtain the ideal bid solution.

[0098] S104. Using a preset optimization tool, the above-mentioned target pricing scheme and the above-mentioned constraints, obtain an ideal pricing scheme. The above-mentioned ideal pricing scheme is the ideal profit maximization scheme. The above-mentioned ideal pricing scheme includes: multiple virtual power plant capacity-price combinations.

[0099] For example, the aforementioned preset optimization tool may be an algorithm model such as the aforementioned optimizer, but is not limited thereto. The aforementioned use of the preset optimization tool, the aforementioned target pricing scheme, and the aforementioned constraints to obtain an ideal pricing scheme may, for example, refer to inputting the aforementioned target pricing scheme and the aforementioned constraints into the aforementioned preset optimization tool so that the preset optimization tool finds an ideal pricing scheme that meets the aforementioned constraints according to the target pricing scheme.

[0100] Assuming the ideal pricing scheme includes three virtual power plant capacity-price combinations, the ideal pricing scheme can be represented as shown in Table 3 below:

[0101]

[0102] Table 3. Schematic diagram of ideal pricing scheme

[0103] Please refer to Table 3. The above ideal pricing scheme includes three virtual power plant capacity-price combinations. However, the actual ideal pricing scheme is not limited to three virtual power plant capacity-price combinations and may include other numbers of virtual power plant capacity-price combinations.

[0104] S105. Provide a quantity quote based on the above ideal pricing scheme.

[0105] For example, the quantity quotation is based on the ideal quotation scheme obtained above. That is, the quantity quotation can be completed using the ideal quotation scheme shown in Table 3. In other words, the quantity quotation is also based on multiple virtual power plant capacity-price combinations, with multiple sets of quotation requests. It is understood that the winning bid for each virtual power plant capacity-price combination is independent, but it is understood that when a quotation for a certain virtual power plant capacity-price combination wins the bid, all quotation requests for the virtual power plant capacity-price combinations preceding it will also win the bid. The ideal quotation scheme in Table 3... Taking the case of three virtual power plant capacity-price combinations as an example, if the bid for the third virtual power plant capacity-price combination wins the bid, it means that the clearing price is greater than or equal to 4990 yuan / MWh. In this case, the bids for the first and second virtual power plant capacity-price combinations will also definitely win the bid. Conversely, if the bid for the first virtual power plant capacity-price combination wins the bid, it only means that the clearing price is greater than or equal to 1786 yuan / MWh. The bids for the second and third virtual power plant capacity-price combinations may not necessarily win the bid.

[0106] The virtual power plant quantity quotation method provided in this application includes: determining a predicted clearing price for at least one scenario and the probability of occurrence of each predicted clearing price based on historical clearing prices; determining a target quotation scheme based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price; obtaining multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determining constraints based on the target quotation scheme and the building capacity-price combinations, wherein the building users represent users in pre-divided minimum electricity consumption areas, and the constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints; obtaining an ideal quotation scheme using a preset optimization tool, the target quotation scheme, and the constraints, wherein the ideal quotation scheme is the ideal profit maximization scheme, and the ideal quotation scheme includes: multiple virtual power plant capacity-price combinations; and submitting a quantity quotation based on the obtained ideal quotation scheme. In this embodiment, by predicting the clearing price and the probability of occurrence of the clearing price under different scenarios, the target pricing scheme and constraints are determined based on the predicted clearing price and the probability of occurrence of the clearing price. Then, the target pricing scheme and constraints are input into a preset optimization tool so that the preset optimization tool outputs an ideal pricing scheme that maximizes ideal revenue and includes multiple virtual power plant capacity-price combinations. Finally, the quantity quotation can be completed according to the ideal pricing scheme, thereby stabilizing the revenue of virtual power plants and building users, and improving the rationality, flexibility and resource aggregation efficiency of virtual power plant quantity quotation.

[0107] Figure 2 This is a schematic diagram of a virtual power plant quantity quotation method provided in another embodiment of this application, as shown below. Figure 2 As shown, in Figure 1 Based on the embodiments, the above-mentioned method of obtaining an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints may include:

[0108] S201. Using the preset optimization tool, the above target pricing scheme, and the above constraints, obtain the ideal price for each scenario.

[0109] For example, the ideal quote for each of the above scenarios could refer to the above... Figure 1 In the embodiments When multiple scenarios exist, there can also be multiple ideal quotes for each scenario. The ideal quote for each scenario can be obtained by preset optimization tools, including but not limited to the optimizers mentioned above, but the specific types of preset optimization tools and the specific methods of obtaining them are not limited here.

[0110] S202. Based on the above constraints, determine the multiple segmented ideal prices corresponding to the above ideal price.

[0111] For example, the multiple segmented ideal prices corresponding to the aforementioned ideal price may refer to the above-mentioned Figure 1 In the embodiments Understandably, the number of segmented ideal prices corresponding to the ideal price is related to the number of virtual power plant capacity-price combinations included in the ideal price scheme. For example, if the ideal price scheme includes 3 virtual power plant capacity-price combinations, then each of the above ideal prices can also have 3 segmented ideal prices. Similarly, the multiple segmented ideal prices corresponding to this ideal price can be determined by preset optimization tools, including but not limited to the aforementioned optimizer and other algorithmic models. The specific types of preset optimization tools and the specific determination algorithm logic are not limited here.

[0112] S203. Based on the above segmented ideal price and the above building capacity-price combination, calculate the ideal capacity corresponding to each of the above ideal prices.

[0113] For example, based on the aforementioned segmented ideal quotes and the aforementioned building capacity-price combination, the ideal capacity corresponding to each of the aforementioned ideal quotes is calculated, for example, using the following formula:

[0114]

[0115] Among them, continue to be based on Figure 1 The parameters in the embodiments are explained above. Given the segmented ideal capacity corresponding to the m-th virtual power plant capacity-electricity price combination, by adding the segmented ideal capacities corresponding to multiple simulated power plant capacity-electricity price combinations, which is also equivalent to adding the segmented ideal capacities corresponding to multiple segmented ideal prices, we can determine the ideal capacity corresponding to the aforementioned ideal price.

[0116] It should be noted that the above formula must also satisfy the following constraints:

[0117]

[0118] Among them, the above With the above Similarly, it represents the minimum price of the first virtual power plant capacity-electricity price combination.

[0119] Furthermore, in the above Figure 2 Based on the embodiments, after determining the ideal capacity corresponding to the above-mentioned ideal price, the above-mentioned method of obtaining the ideal price scheme by using a preset optimization tool, the above-mentioned target price scheme, and the above-mentioned constraints may further include:

[0120] Based on the above ideal price and the above ideal capacity corresponding to the above ideal price, the above ideal price scheme is determined.

[0121] For example, the ideal pricing scheme determined based on the ideal price and the ideal capacity corresponding to the ideal price can be as described above. Figure 1 Table 3 in the embodiments includes multiple virtual power plant capacity-price combinations, but is not limited to this.

[0122] In addition, in the aforementioned Figure 1 Based on the embodiments, after obtaining the ideal quotation plan and submitting a quotation, the method may further include:

[0123] After completing the power regulation response, the revenue of the virtual power plant and the revenue of the building user are calculated according to the preset revenue algorithm. The preset revenue algorithm includes the virtual power plant revenue algorithm and the building user revenue algorithm.

[0124] The revenue is allocated to the virtual power plant and the building user respectively based on the revenue of the virtual power plant and the revenue of the building user.

[0125] For example, in addition to obtaining the ideal pricing scheme and submitting the quotation, the revenue is allocated to the virtual power plant and the building user based on the revenue of the virtual power plant and the building user. The target pricing scheme that aims to maximize the ideal revenue in this quotation is input into the preset optimization tool so that when the preset optimization tool searches for the ideal pricing scheme that meets the above constraints according to the target pricing scheme, the preset revenue algorithm can also be input into the preset optimization tool so that when the preset optimization tool searches for the ideal pricing scheme, the ideal revenue is maximized according to the calculation method of the preset revenue algorithm. Of course, when the preset optimization tool searches for the ideal pricing scheme, it can also determine the ideal revenue maximization according to other calculation methods, and is not limited to determining the ideal revenue maximization according to the calculation method of the preset revenue algorithm.

[0126] Figure 3 Please refer to the schematic diagram of the virtual power plant quantity quotation method provided in another embodiment of this application. Figure 3 Optionally, based on the above embodiments, the calculation of virtual power plant revenue according to a preset revenue algorithm may include:

[0127] S301. Determine the bidding and dispatch status of multiple virtual power plant capacity-price combinations of the aforementioned virtual power plants.

[0128] For example, the winning bid for the m-th virtual power plant capacity-price combination can be represented as follows: when For example, it can represent the winning bid for the m-th virtual power plant capacity-price combination. For example, this could indicate that the m-th virtual power plant capacity-price combination was not successfully bid on.

[0129] Similarly, for example, we can assume that the winning bid for the m-th virtual power plant capacity-price combination is represented as follows: when For example, this could represent the m-th virtual power plant capacity-price combination being invoked when... For example, this could indicate that the capacity-price combination of the m-th virtual power plant was not invoked.

[0130] It is understood that the above content is only one possible example, and the actual determination and representation of the bidding and dispatch status of multiple virtual power plant capacity-price combinations may be the same as or different from the content in the above example.

[0131] S302. Based on the above-mentioned bidding results, dispatching results, and capacity of each virtual power plant capacity-price combination, calculate and obtain the control revenue and reserve revenue of the virtual power plant respectively.

[0132] For example, based on the bidding results, dispatch status, and capacity of each virtual power plant capacity-price combination, the regulation revenue and reserve revenue of the virtual power plant are calculated and obtained respectively. For example, they can be calculated using the following formula:

[0133]

[0134] Among them, the above For example, these can represent the control revenue and reserve revenue of the aforementioned virtual power plant, respectively. Let m be the capacity of the m-th virtual power plant capacity-price combination (the capacity corresponding to a set of quoted quantities and prices), and the capacity of the m-th virtual power plant capacity-price combination under scenario ω. Similarly. It's important to note that the above formula assumes there are three virtual power plant capacity-price combinations. If the number of virtual power plant capacity-price combinations changes, the formula will differ. Changes should also be made in sync.

[0135] According to the above formula, the regulation benefits of the aforementioned virtual power plant can be determined. The reserve revenue from the aforementioned virtual power plants is mutually exclusive. That is, if the capacity of the m-th virtual power plant's capacity-electricity-price combination (corresponding to a set of quoted capacity) is utilized, reserve revenue cannot be simultaneously obtained. It must be true, and vice versa. Although each virtual power plant capacity-price combination in equation (2) is multiplied by the reserve price ratio coefficient, this coefficient should only apply to virtual power plant capacity-price combinations that are not invoked.

[0136] In addition, the above formula must also satisfy the following constraints:

[0137]

[0138] That is, the capacity of the m-th virtual power plant capacity-electricity price combination (the capacity corresponding to a set of bids and quotes) must win the bid first before it can be used.

[0139] Of course, the formula for calculating the control revenue and reserve revenue of the virtual power plant is only one possible example, and the actual method for calculating the control revenue and reserve revenue of the virtual power plant is not limited to the above formula.

[0140] S303. Calculate and obtain the revenue of the virtual power plant based on the above-mentioned regulation revenue and reserve revenue.

[0141] For example, the revenue from the aforementioned virtual power plant could be, for instance, the regulation revenue from the aforementioned virtual power plant.

[0142] Figure 4 This is a schematic diagram of a virtual power plant quantity quotation method provided in another embodiment of this application, as shown below. Figure 4 As shown, optionally, based on the foregoing embodiments, the calculation of virtual power plant revenue according to a preset revenue algorithm may include:

[0143] S401. Determine the response status of multiple building capacity-price combinations for the aforementioned building users, and calculate the price difference between each building capacity-price combination and the corresponding virtual power plant capacity-price combination.

[0144] For example, the price difference between the above-mentioned building capacity-price combination and the corresponding above-mentioned virtual power plant capacity-price combination can be calculated by the relationship between "the price of the corresponding above-mentioned virtual power plant capacity-price combination" and "the price of the building capacity-price combination".

[0145] S402. Based on the above response situation, the capacity and price of the above building capacity-price combination, calculate and obtain the response revenue of the above building users.

[0146] For example, the response benefits of the building user are calculated based on the above response situation, the capacity and price of the above building capacity-price combination, for example, using the following formula:

[0147]

[0148] It should be noted that the above formula is based on the example of three virtual power plant capacity-electricity price combinations. If the number of virtual power plant capacity-electricity price combinations changes, the formula will be different. Changes should also be made in sync.

[0149] Among them, the above The response benefits when responding to the i-th building user are as follows: The response benefit when the i-th building user does not respond, and... Figure 1 The content in the embodiments is the same, and the above p i,n Q i,n These represent the price and capacity in the capacity-electricity price combination of the i-th building and the n-th building, respectively. The above s i,m,n Whether the capacity-electricity price combination of the nth building of the i-th building belongs to the capacity-electricity price combination of the m-th virtual power plant is determined by the price p of the capacity-electricity price combination of the nth building of the i-th building. i,n Does it meet the requirements? The relationship determines this, and it's understandable that when... When the price is the first virtual power plant capacity-electricity price combination out of the three virtual power plant capacity-electricity price combinations, the above It can be non-existent.

[0150] Of course, the above formula only represents one possible way to calculate the response revenue of the aforementioned building users. The actual calculation of the response revenue of the aforementioned building users is not limited to the above formula.

[0151] S403. Based on the above response situation and the capacity and price difference of the above building capacity-price combination, calculate and obtain the additional revenue of the above building users.

[0152] For example, the additional revenue for the building user is calculated based on the response situation and the capacity and price difference of the building capacity-price combination. This can be achieved, for instance, using the following formula:

[0153]

[0154] Among them, the above The above provides additional benefits for responding to the i-th building. Additional revenue is provided for responding when the i-th building does not respond.

[0155] Similarly, the above formula assumes there are three virtual power plant capacity-electricity price combinations. If the number of virtual power plant capacity-electricity price combinations changes, then the formula will... Changes should also be made in sync.

[0156] In addition, the formulas S402-S403 above must also satisfy the following constraints:

[0157]

[0158] Compared with the foregoing embodiments similar, It is the minimum price of the first virtual power plant capacity-electricity price combination.

[0159] S404. Calculate and obtain the above-mentioned building user revenue based on the above-mentioned response revenue and the above-mentioned response additional revenue.

[0160] For example, the aforementioned building user revenue may refer to the response revenue when the i-th building user responds. Response benefits when the i-th building user fails to respond Additional benefits of the response when the i-th building responds Additional benefits of response when the i-th building does not respond sum.

[0161] Optionally, based on any of the above embodiments, the plurality of the above building capacity-price combinations and the plurality of the above virtual power plant capacity-price combinations are arranged in ascending order of their corresponding prices.

[0162] For example, this can facilitate determining the relationship between each of the aforementioned virtual power plant capacity-price combinations and the corresponding plurality of building capacity-price combinations.

[0163] Figure 5 This is a schematic diagram of a virtual power plant quantity reporting and pricing device provided in an embodiment of this application. This device can execute the aforementioned virtual power plant quantity reporting and pricing method. The device can be integrated into the aforementioned virtual power plant's computer, server, or other equipment with computing power. Figure 5 As shown, the device may include:

[0164] The prediction module 510 is used to determine the predicted clearing price for at least one scenario and the probability of occurrence of each of the predicted clearing prices based on historical clearing prices.

[0165] The target determination module 520 is used to determine the target pricing scheme based on the above-mentioned predicted clearing price and the above-mentioned probability of occurrence corresponding to the above-mentioned predicted clearing price.

[0166] The constraint determination module 530 is used to obtain multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determine the constraint conditions based on the above target quotation scheme and the above building capacity-price combinations. The above building users represent users who pre-divide the minimum electricity consumption area. The above constraint conditions include: inter-group relationship constraint conditions, capacity constraint conditions, minimum price constraint conditions, winning bid constraint conditions, and scenario constraint conditions.

[0167] The acquisition module 540 is used to obtain an ideal pricing scheme by using a preset optimization tool, the above-mentioned target pricing scheme and the above-mentioned constraints. The ideal pricing scheme is the ideal profit maximization scheme, which includes: multiple virtual power plant capacity-price combinations.

[0168] The quantity reporting and quotation module 550 is used to report quantities and provide quotations based on the above-mentioned ideal quotation scheme.

[0169] The virtual power plant quantity quotation method provided in this application includes: determining a predicted clearing price for at least one scenario and the probability of occurrence of each predicted clearing price based on historical clearing prices; determining a target quotation scheme based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price; obtaining multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determining constraints based on the target quotation scheme and the building capacity-price combinations, wherein the building users represent users in pre-divided minimum electricity consumption areas, and the constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints; obtaining an ideal quotation scheme using a preset optimization tool, the target quotation scheme, and the constraints, wherein the ideal quotation scheme is the ideal profit maximization scheme, and the ideal quotation scheme includes: multiple virtual power plant capacity-price combinations; and submitting a quantity quotation based on the obtained ideal quotation scheme. In this embodiment, by predicting the clearing price and the probability of occurrence of the clearing price under different scenarios, the target pricing scheme and constraints are determined based on the predicted clearing price and the probability of occurrence of the clearing price. Then, the target pricing scheme and constraints are input into a preset optimization tool so that the preset optimization tool outputs an ideal pricing scheme that maximizes ideal revenue and includes multiple virtual power plant capacity-price combinations. Finally, the quantity quotation can be completed according to the ideal pricing scheme, thereby stabilizing the revenue of virtual power plants and building users, and improving the rationality, flexibility and resource aggregation efficiency of virtual power plant quantity quotation.

[0170] Optionally, the acquisition module 540 can be used to obtain the ideal price for each scenario using a preset optimization tool, the target pricing scheme, and the constraints. Based on the constraints, multiple segmented ideal prices corresponding to the ideal price are determined. Based on the segmented ideal prices and the building capacity-price combination, the ideal capacity corresponding to each ideal price is calculated and obtained.

[0171] Optionally, the acquisition module 540 can also be used to determine the ideal pricing scheme based on the ideal price and the ideal capacity corresponding to the ideal price.

[0172] Optionally, the aforementioned virtual power plant quotation device may further include a revenue allocation module, which, after completing the power regulation response, calculates and obtains the virtual power plant revenue and the building user revenue according to a preset revenue algorithm, wherein the preset revenue algorithm includes a virtual power plant revenue algorithm and a building user revenue algorithm. Revenue is then allocated to the virtual power plant and the building user respectively based on the virtual power plant revenue and the building user revenue.

[0173] Optionally, the aforementioned revenue allocation module can be used to determine the bidding results and utilization status of multiple virtual power plant capacity-price combinations for the aforementioned virtual power plant. Based on the bidding results, utilization status, and capacity of each virtual power plant capacity-price combination, the control revenue and reserve revenue of the aforementioned virtual power plant are calculated and obtained respectively. Based on the control revenue and reserve revenue, the revenue of the aforementioned virtual power plant is calculated and obtained.

[0174] Optionally, the revenue distribution module can be specifically used to determine the response status of multiple building capacity-price combinations for the aforementioned building user, and to calculate the price difference between each building capacity-price combination and the corresponding virtual power plant capacity-price combination. Based on the response status, the capacity and price of the building capacity-price combination, the response revenue of the aforementioned building user is calculated and obtained. Based on the response status, the capacity and price difference of the building capacity-price combination, the additional response revenue of the aforementioned building user is calculated and obtained. Based on the response revenue and the additional response revenue, the revenue of the aforementioned building user is calculated and obtained.

[0175] Optionally, multiple of the above-mentioned building capacity-price combinations and multiple of the above-mentioned virtual power plant capacity-price combinations can be arranged in ascending order of their corresponding prices.

[0176] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0177] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device can be a computer, server, or other device with computing power, such as those used in the virtual power plant described above. Figure 6 As shown, the device 600 includes:

[0178] The processor 610, storage medium 620, and bus 630 are connected in communication via bus 630.

[0179] The storage medium 620 stores machine-readable instructions that can be executed by the processor 610. When the electronic device is running, the processor 610 executes the aforementioned machine-readable instructions to perform the aforementioned virtual power plant quotation method.

[0180] It should be understood that, Figure 6 The structure shown is only a schematic diagram of an electronic device; the electronic device may also include components that are larger than those shown. Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown. Figure 6 The components shown can be implemented using hardware, software, or a combination thereof.

[0181] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the virtual power plant quantity quotation method described in the above method embodiments.

[0182] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media include non-transitory computer-readable storage media. The computer-readable storage medium has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0184] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0185] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural transformations made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for virtual power plant quantity reporting and pricing, characterized in that, include: Based on historical clearing prices, determine the predicted clearing price for at least one scenario and the probability of occurrence for each predicted clearing price; The target pricing scheme is determined based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price; The system obtains multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determines constraints based on the target bidding scheme and the building capacity-price combinations. The building users refer to users who pre-divide the minimum electricity consumption area, and the constraints include: inter-group relationship constraints, capacity constraints, minimum price constraints, winning bid constraints, and scenario constraints. Using a preset optimization tool, the target pricing scheme, and the constraints, an ideal pricing scheme is obtained. The ideal pricing scheme is the scheme that maximizes ideal revenue. The ideal pricing scheme includes: multiple virtual power plant capacity-price combinations. Provide a quantity quote based on the ideal pricing scheme.

2. The method according to claim 1, characterized in that, The step of obtaining an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints includes: Using a preset optimization tool, the target pricing scheme, and the constraints, the ideal price for each scenario is obtained; Based on the constraints, determine multiple segmented ideal prices corresponding to the ideal price; Based on the segmented ideal price and the building capacity-price combination, the ideal capacity corresponding to each ideal price is calculated and obtained.

3. The method according to claim 2, characterized in that, The step of obtaining an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints further includes: The ideal pricing scheme is determined based on the ideal price and the ideal capacity corresponding to the ideal price.

4. The method according to claim 1, characterized in that, After submitting a quantity quote based on the obtained ideal quote plan, the method further includes: After completing the power regulation response, the revenue of the virtual power plant and the revenue of the building user are calculated and obtained according to the preset revenue algorithm, wherein the preset revenue algorithm includes: the virtual power plant revenue algorithm and the building user revenue algorithm; The revenue is allocated to the virtual power plant and the building user respectively based on the revenue of the virtual power plant and the revenue of the building user.

5. The method according to claim 4, characterized in that, The step of calculating and obtaining the revenue of the virtual power plant according to the preset revenue algorithm includes: Determine the bidding and allocation status of multiple virtual power plant capacity-price combinations; Based on the bidding results, the call-up status, and the capacity of each virtual power plant capacity-price combination, the regulation revenue and reserve revenue of the virtual power plant are calculated and obtained respectively. The revenue of the virtual power plant is calculated based on the regulation revenue and the reserve revenue.

6. The method according to claim 4, characterized in that, The step of calculating and obtaining building user revenue according to a preset revenue algorithm includes: Determine the response status of multiple building capacity-price combinations for the building user, and calculate the price difference between each building capacity-price combination and the corresponding virtual power plant capacity-price combination; Based on the response status, the capacity and price of the building capacity-price combination, calculate and obtain the response revenue of the building user; Based on the response status, the capacity and price difference of the building capacity-price combination, calculate and obtain the additional revenue for the building user. The revenue for the building user is calculated based on the response revenue and the additional response revenue.

7. The method according to any one of claims 1-6, characterized in that, The various building capacity-price combinations and the various virtual power plant capacity-price combinations are all arranged in ascending order of their corresponding prices.

8. A virtual power plant quantity reporting and pricing device, characterized in that, include: The prediction module is used to determine the predicted clearing price for at least one scenario and the probability of occurrence of each predicted clearing price based on historical clearing prices. The target determination module is used to determine the target pricing scheme based on the predicted clearing price and the probability of occurrence corresponding to the predicted clearing price; The constraint determination module is used to obtain multiple building capacity-price combinations submitted by building users corresponding to the virtual power plant, and determine the constraint conditions according to the target bidding scheme and the building capacity-price combinations. The building users refer to users who pre-divide the minimum electricity consumption area, and the constraint conditions include: inter-group relationship constraint conditions, capacity constraint conditions, minimum price constraint conditions, winning bid constraint conditions, and scenario constraint conditions. The acquisition module is used to obtain an ideal pricing scheme by using a preset optimization tool, the target pricing scheme, and the constraints. The ideal pricing scheme is the scheme that maximizes ideal revenue. The ideal pricing scheme includes multiple virtual power plant capacity-price combinations. The quantity reporting and pricing module is used to report quantities and provide pricing based on the ideal pricing scheme.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1-7.

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