Virtual power plant energy integration method and system based on bidding mechanism

Abstract

The present invention discloses a virtual power plant energy integration method and system based on a bidding mechanism. The method comprises the following steps: obtaining energy blocks by analyzing resource attributes in a virtual power plant, obtaining energy adjustable space according to the energy supply and demand relationship of each energy block, and associating resource characteristics with the energy adjustable space to obtain an energy elasticity space portrait; obtaining the regulation potential sequence and demand compliance sequence of the energy block according to an internal bidding mechanism, dynamically optimizing the resource allocation within the energy block in combination with the regulation potential factor and the demand compliance factor to obtain an in-plant resource dynamic allocation strategy; determining an out-of-plant resource dynamic allocation strategy between virtual power plants according to an external bidding mechanism and an in-plant resource dynamic allocation strategy; integrating the resources within each virtual power plant through the in-plant resource dynamic allocation strategy and the out-of-plant resource dynamic allocation strategy to reconstruct an energy elasticity space portrait, thereby achieving accurate matching and efficient utilization of the energy of the virtual power plant and ensuring its efficient, safe and economical operation.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant control, and in particular to a virtual power plant energy integration method and system based on a bidding mechanism. Background Art

[0002] With the transformation of energy structure and the development of smart grid, virtual power plant, as a new energy management mode, integrates distributed energy resources to achieve efficient utilization and flexible dispatch of energy, which is of great significance to improving the stability and economy of power system. However, a major challenge faced by virtual power plant is the diversity and complexity of resource types, including distributed generation, energy storage system, controllable load and other resources. These resources have different technical characteristics, operation constraints and economic attributes, which makes resource allocation difficult and it is difficult to achieve accurate matching and efficient utilization of resources.

[0003] Chinese patent, publication number: CN 116562567 A, publication date: August 8, 2023, relates to a virtual power plant aggregation control method considering power auxiliary services. The method is based on the aggregation control architecture of the virtual power plant, and is divided into a three-layer structure according to the main body. Among them, the user layer is responsible for managing a large number of heterogeneous resources, and provides quotations and quantities to the virtual power plant layer according to the optimization objectives; after receiving the quotations and quantities from the user layer, the virtual power plant layer submits the quotations and quantities to the distribution network after optimization scheduling; the distribution network layer provides the quotation and quantity information provided by the virtual power plant layer, combines the distributed power sources for joint clearing, and then selects and calls layer by layer. The scheme can dynamically allocate energy within the plant. When the overall supply and demand relationship in the plant is unbalanced, its energy allocation strategy faces failure.

[0004] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to address the problem that the resource types of virtual power plants in the prior art are complex, which leads to great difficulty in resource allocation. A virtual power plant energy integration method and system based on a bidding mechanism are proposed. The resource attributes in the virtual power plant are analyzed, resource characteristics are extracted and spatialized to obtain energy blocks, and the energy adjustable space is obtained according to the energy supply and demand relationship of each energy block. The resource characteristics are associated with the energy adjustable space to obtain an energy elasticity space portrait; the regulation potential sequence and demand compliance sequence of each energy block are obtained according to the internal bidding mechanism, and the resource allocation in the energy block is dynamically optimized in combination with the regulation potential factor and the demand compliance factor to obtain the in-plant resource dynamic allocation strategy belonging to each energy block; the out-of-plant resource dynamic allocation strategy between each virtual power plant is determined according to the external bidding mechanism and the in-plant resource dynamic allocation strategy; the resources in each virtual power plant are integrated through the in-plant resource dynamic allocation strategy and the out-of-plant resource dynamic allocation strategy to reconstruct the energy elasticity space portrait, so as to achieve accurate matching and efficient utilization of virtual power plant resources and ensure efficient, safe and economical operation of the virtual power plant.

[0006] In a first aspect, a technical solution provided in an embodiment of the present invention is a virtual power plant energy integration method based on a bidding mechanism, comprising the following steps:

[0007] S1. Analyze the energy attributes in the virtual power plant, extract energy characteristics and spatialize them to obtain energy blocks;

[0008] S2. Obtain the energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain the energy elasticity space portrait;

[0009] S3. Obtain the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimize the energy elasticity spatial portrait within the energy block by combining the regulation potential factor and demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block;

[0010] S4. Determine the off-site energy dynamic configuration strategy between virtual power plants based on the external bidding mechanism and the on-site energy dynamic configuration strategy; S5. Integrate the energy of virtual power plants based on the on-site energy dynamic configuration strategy and the off-site energy dynamic configuration strategy to obtain a reconstructed energy elasticity space portrait.

[0011] Preferably, the method of analyzing the energy attributes in the virtual power plant, extracting the energy characteristics and spatializing them to obtain energy blocks comprises the following steps:

[0012] S11. Obtain the energy radiation area corresponding to each energy unit according to the historical energy supply and demand relationship; analyze the energy radiation area through a clustering algorithm to obtain an energy clustering space;

[0013] S12. Obtain the energy characteristics and regional outlines of various energy sources in the energy clustering space to construct energy blocks.

[0014] Preferably, the step of performing cluster analysis on energy radiation areas by using a clustering algorithm to obtain an energy clustering space comprises the following steps:

[0015] S111, extracting the load entity corresponding to each energy radiation area, determining the location information of the load entity, and taking the energy transmission amount and energy transmission direction obtained by each load entity within a unit time scale as energy flow information;

[0016] S112, determining the center position information of the energy radiation area according to the minimum rectangle method in combination with the position information corresponding to the load entity, and determining the target energy flow information of the energy radiation area according to the mean value of the energy transmission amount;

[0017] S113. Use K-means algorithm to combine center location information and target energy flow information to perform cluster analysis on energy radiation areas to determine energy clustering space.

[0018] Preferably, the step of obtaining an energy adjustable spatial sequence according to the energy supply and demand relationship of each energy block, and associating the energy characteristics with the energy adjustable spatial sequence to obtain an energy elasticity spatial portrait comprises the following steps:

[0019] S21. According to the long-time scale prediction algorithm, the energy supply of each energy unit in the energy block on the time scale is obtained and an energy supply sequence is constructed, and the energy demand of each load entity on the time scale is obtained and an energy demand sequence is constructed; S22. According to the energy supply and demand relationship of the corresponding sequence positions of the energy supply sequence and the energy demand sequence, the energy adjustable space is determined, and the energy adjustable space sequence on the time scale is constructed according to the energy adjustable space;

[0020] S23. Obtain the energy type and corresponding energy flow information corresponding to the sequence position of the energy adjustable spatial sequence; and construct an energy elasticity spatial portrait based on the energy flow information and energy type.

[0021] Preferably, the step of obtaining the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimizing the energy elasticity space portrait within the energy block in combination with the regulation potential factor and the demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block comprises the following steps:

[0022] S31. According to the energy supply and demand relationship, the energy adjustable space sequence is cut to obtain an energy positive adjustable space sequence and an energy negative adjustable space sequence;

[0023] S32, obtaining the first supply price and the first adjustable energy amount of the unit energy corresponding to each sequence position in the energy forward adjustable space sequence; and obtaining the first adjustment potential factor of the corresponding sequence position according to the first electricity selling price, the first adjustable energy amount and the first electricity selling price elasticity factor, and thereby obtaining the first adjustment potential sequence of the corresponding energy block;

[0024] S33, obtaining the first electricity purchase price and the first energy demand of each unit energy corresponding to each sequence position in the negative adjustable energy space sequence; and obtaining the first demand catering factor of the corresponding sequence position according to the first electricity purchase price, the first energy demand and the first electricity purchase price elasticity factor, and thereby obtaining the first demand catering sequence of the corresponding energy block;

[0025] S34, according to the supply priority principle, sequentially configure the first demand catering factor corresponding to the first regulation potential sequence to obtain the first in-plant energy matching chain;

[0026] Simultaneously, according to the principle of demand priority, the first regulation potential factor corresponding to the first demand catering sequence is configured in turn to obtain the second in-plant energy matching chain;

[0027] S35. Based on the first in-plant energy matching chain and the second in-plant energy matching chain, the energy type and energy flow information in the energy elasticity space portrait corresponding to the energy block are updated to obtain the in-plant energy dynamic configuration strategy.

[0028] Preferably, the step of cutting the energy adjustable space sequence according to the energy supply and demand relationship to obtain the energy positive adjustable space sequence and the energy negative adjustable space sequence comprises the following steps:

[0029] S311, removing the sequence position of the energy adjustable space sequence whose energy supply corresponding to the energy block is equal to the energy demand, to obtain the target energy adjustable space sequence;

[0030] S312, sequentially extracting the sequence positions of the target energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the energy positive adjustable space sequence;

[0031] S313 , sequentially extracting the sequence positions of the target energy adjustable space sequence where the energy supply is less than the energy demand to obtain the negative energy adjustable space sequence.

[0032] Preferably, the method of determining the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy comprises the following steps:

[0033] S41, determining the energy supply and demand matching situation in the factory according to the dynamic energy configuration strategy in the factory to obtain the positive adjustable space sequence of the remaining energy and the negative adjustable space sequence of the remaining energy;

[0034] S42, obtaining the second supply price and the second adjustable energy quantity of the unit energy corresponding to each sequence position in the positive adjustable space sequence of the remaining energy; and obtaining the second adjustment potential factor of the corresponding sequence position according to the second electricity selling price, the second adjustable energy quantity and the second electricity selling price elasticity factor, and thereby obtaining the second adjustment potential sequence of the corresponding virtual power plant;

[0035] S43, obtaining the second electricity purchase price and the second energy demand of the unit energy corresponding to each sequence position in the negative adjustable space sequence of the remaining energy; and obtaining the second demand catering factor of the corresponding sequence position according to the second electricity purchase price, the second energy demand and the second electricity purchase price elasticity factor, and thereby obtaining the second demand catering sequence of the corresponding energy block;

[0036] S44, according to the supply priority principle, sequentially configure the corresponding second demand catering factors for the second regulation potential sequence to obtain the first off-site energy matching chain;

[0037] Simultaneously, according to the demand priority principle, the corresponding second regulation potential factors are sequentially configured for the second demand catering sequence to obtain the second off-site energy matching chain;

[0038] S45. Based on the first off-site energy matching chain and the second off-site energy matching chain, the energy type and energy flow information in the energy elastic space portraits in the virtual power plant are updated for the second time to obtain the off-site energy dynamic configuration strategy.

[0039] Preferably, the method of determining the matching of energy supply and demand in the plant according to the dynamic configuration strategy of energy in the plant to obtain a positive adjustable spatial sequence of residual energy and a negative adjustable spatial sequence of residual energy comprises the following steps:

[0040] S411, determining the adjustable spatial sequence of remaining energy corresponding to each virtual power plant according to the energy supply and demand relationship and the dynamic energy configuration strategy within the plant;

[0041] S412, eliminating the sequence positions of the remaining energy adjustable space sequence whose energy supply corresponding to the virtual power plant is equal to the energy demand, to obtain the target remaining energy adjustable space sequence;

[0042] S413, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the remaining energy positive adjustable space sequence;

[0043] S414, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence where the energy supply is less than the energy demand to obtain the remaining energy negative adjustable space sequence.

[0044] As a preferred method, the virtual power plant energy is integrated according to the in-plant energy dynamic configuration strategy and the out-plant energy dynamic configuration strategy, including the following steps:

[0045] S51, obtaining first energy types and first energy flow information of a first in-plant energy matching chain and a second in-plant energy matching chain corresponding to each energy block in the in-plant energy dynamic configuration strategy;

[0046] S52, obtaining the second energy type and the second energy flow information in the first off-site energy matching chain and the second off-site energy matching chain corresponding to each virtual power plant in the off-site energy dynamic configuration strategy;

[0047] S53, modifying the energy type and energy flow information in the energy elasticity spatial portrait corresponding to each energy block according to the first energy type, the first energy flow information, the second energy type and the second energy flow information to obtain a reconstructed energy elasticity spatial portrait.

[0048] In a second aspect, a technical solution provided in an embodiment of the present invention is a virtual power plant energy integration system, which is applicable to a virtual power plant energy integration method based on a bidding mechanism, including:

[0049] Analysis module: Analyzes the energy attributes in the virtual power plant, extracts energy characteristics and spatializes them to obtain energy blocks;

[0050] Construction module: Obtain the energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain the energy elasticity space portrait;

[0051] The first configuration module: obtains the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimizes the energy elasticity spatial portrait within the energy block by combining the regulation potential factor and demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block;

[0052] The second configuration module: determines the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy;

[0053] Reconstruction module: Integrate the energy of the virtual power plant according to the dynamic configuration strategy of in-plant energy and the dynamic configuration strategy of out-plant energy to obtain a reconstructed energy elasticity space portrait.

[0054] In the third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of a virtual power plant energy integration method based on a bidding mechanism are implemented.

[0055] In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of a virtual power plant energy integration method based on a bidding mechanism are implemented.

[0056] The present invention has at least the following substantial effects:

[0057] (1) In order to solve the resource allocation problem caused by the complexity of resource types in virtual power plants, this application deeply analyzes the energy attributes in virtual power plants, extracts and spatializes energy characteristics, forms energy blocks, and then determines the adjustable space of energy according to the supply and demand conditions of each energy block. By combining resource characteristics with energy adjustable space to construct an energy elasticity space portrait, the resource allocation problem is effectively solved, and the accurate matching and efficient utilization of virtual power plant resources are achieved;

[0058] (2) In order to optimize the internal resource allocation of virtual power plants, this application proposes a strategy based on the internal bidding mechanism. This strategy obtains the regulation potential sequence and demand compliance sequence of each energy block, and combines the regulation potential factor and demand compliance factor to dynamically optimize the energy elasticity space portrait within the energy block, thereby generating a dynamic energy allocation strategy within the plant belonging to each energy block, effectively solving the resource allocation optimization problem and realizing the efficient, safe and economical operation of the internal resources of the virtual power plant;

[0059] (3) In order to solve the problem of dynamic configuration of off-site resources between virtual power plants, this application proposes to combine the external bidding mechanism with the dynamic configuration strategy of on-site energy to determine the dynamic configuration strategy of off-site energy. By integrating on-site and off-site strategies, the energy elasticity space portrait is reconstructed, which not only solves the problem of dynamic configuration of off-site resources, but also realizes the optimal configuration and efficient utilization of resources between virtual power plants.

[0060] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0062] Figure 1 It is a flow chart of the virtual power plant energy integration method based on bidding mechanism of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0064] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0065] Embodiment 1

[0066] like Figure 1 The flowchart of the virtual power plant energy integration method based on the bidding mechanism is shown, which includes the following steps:

[0067] S1. Analyze the energy attributes in the virtual power plant, extract energy characteristics and spatialize them to obtain energy blocks; specifically, the steps include:

[0068] S11. Obtain the energy radiation area corresponding to each energy unit according to the historical energy supply and demand relationship; analyze the energy radiation area through a clustering algorithm to obtain an energy clustering space;

[0069] S12. Obtain the energy characteristics and regional outlines of various energy sources in the energy clustering space to construct energy blocks.

[0070] Furthermore, the clustering analysis of the energy radiation area by a clustering algorithm to obtain the energy clustering space includes the following steps:

[0071] S111, extracting the load entity corresponding to each energy radiation area, determining the location information of the load entity, and taking the energy transmission amount and energy transmission direction obtained by each load entity within a unit time scale as energy flow information;

[0072] S112, determining the center position information of the energy radiation area according to the minimum rectangle method in combination with the position information corresponding to the load entity, and determining the target energy flow information of the energy radiation area according to the mean value of the energy transmission amount;

[0073] S113. Use K-means algorithm to combine center location information and target energy flow information to perform cluster analysis on energy radiation areas to determine energy clustering space.

[0074] In this embodiment, in view of the complexity of energy attributes in the virtual power plant, step S1 and its sub-steps are intended to extract energy characteristics through careful analysis, and then form energy blocks, so as to allocate resources more effectively. The specific implementation steps are as follows: First, according to the historical energy supply and demand data, the energy radiation area corresponding to each energy unit is determined. The energy type corresponding to the energy unit can be solar energy, wind energy, energy storage, flexible energy (such as charging and feeding of new energy vehicles), etc., and then a clustering algorithm is used to conduct an in-depth analysis of these areas to reveal their inherent energy distribution pattern, thereby obtaining an energy clustering space. In cluster analysis, the load entities in each energy radiation area are first extracted and their location information is determined. Subsequently, the energy transmission amount and transmission direction obtained by each load entity within a unit time scale are collected as energy flow information, and then the minimum rectangle method (the minimum rectangle method is a geometric algorithm that determines the boundary of the rectangle by calculating the maximum and minimum coordinate values ​​of the load entity position) is used, combined with the position information of the load entity, to calculate the center position of the energy radiation area. At the same time, according to the mean value of the energy transmission amount, the target energy flow information of the area can be determined. Finally, the K-means algorithm is used to comprehensively consider the center location information and target energy flow information to perform cluster analysis on the energy radiation area, thereby determining the energy clustering space.

[0075] It can be understood that, assuming that there are n load entities in the energy block, their position information is (x1, y1), (x2, y2), ..., (xn, yn), the energy transmission amount per unit time scale is E1, E2, ..., En, and the energy transmission direction is θ1, θ2, ..., θn. The following formula is used to calculate the center position (Xc, Yc) of the energy radiation area and the target energy flow information Etarget, θtarget:

[0076]

[0077] Subsequently, the K-means algorithm is used to perform cluster analysis on the energy radiation area with (Xc, Yc, Etarget, θtarget) as the feature vector to obtain the energy clustering space.

[0078] Furthermore, after the energy cluster space is determined, the geographic information system (GIS) technology is further used to combine the location information and shape characteristics of the energy radiation area to determine the geographical outline of each energy cluster space. The discrete load entity locations are connected into continuous areas, and the boundaries of the areas are determined; the extracted energy characteristics and the determined regional outlines are combined to construct an energy block for each energy cluster space. The energy block is an abstract entity that combines energy characteristics and geographic information, and is used to represent an area with specific energy attributes and geographical distribution in the virtual power plant. A unique identifier is assigned to each energy block, and its energy characteristics and regional outline information are recorded for subsequent resource allocation and optimization. Through the above technical means, the accurate extraction and spatial representation of energy attributes in the virtual power plant are realized, laying a solid foundation for subsequent resource allocation optimization.

[0079] S2. Obtain an energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain an energy elasticity space portrait; specifically, the steps include:

[0080] S21. According to the long-time scale prediction algorithm, the energy supply of each energy unit in the energy block on the time scale is obtained and an energy supply sequence is constructed, and the energy demand of each load entity on the time scale is obtained and an energy demand sequence is constructed; S22. According to the energy supply and demand relationship of the corresponding sequence positions of the energy supply sequence and the energy demand sequence, the energy adjustable space is determined, and the energy adjustable space sequence on the time scale is constructed according to the energy adjustable space;

[0081] S23. Obtain the energy type and corresponding energy flow information corresponding to the sequence position of the energy adjustable spatial sequence; and construct an energy elasticity spatial portrait based on the energy flow information and energy type.

[0082] In this embodiment, it is necessary to use a long-time scale prediction algorithm (for example, a time series analysis model (ARIMA, SARIMA, etc.)) to predict the energy supply of each energy unit in the energy block on a time scale. The time scale can be season, month, or week, and construct an energy supply sequence. The energy demand of each load entity on a time scale is predicted through historical data, and an energy demand sequence is constructed.

[0083] As a further elaboration of this embodiment, so that those skilled in the art can understand the concept of the present invention, the following examples cannot be used as a specific limitation of the concept of the present invention; assuming that there are m energy units and n load entities in the energy block, and the time scale is T. For each energy unit i, the energy supply sequence on the time scale T can be expressed as: ESi = {ESi (1), ESi (2), ..., ESi (T)}; for each load entity j, the energy demand sequence on the time scale T can be expressed as: EDj = {EDj (1), EDj (2), ..., EDj (T)}.

[0084] Furthermore, it is necessary to determine the energy adjustable space according to the energy supply and demand relationship of the energy supply sequence and the energy demand sequence, and construct the energy adjustable space sequence on the time scale according to the energy adjustable space. For each sequence position t on the time scale T, the total supply of the energy block can be calculated: And total demand: The energy adjustable space AS(t) can be defined as the difference between supply and demand: AS(t) = ES(t) - ED(t); therefore, the energy adjustable space sequence on the time scale can be expressed as: AS = {AS(1), AS(2), ..., AS(T)}.

[0085] Furthermore, it is necessary to obtain the energy type and corresponding energy flow information corresponding to the sequence position of the energy adjustable space sequence, and construct an energy elasticity space portrait based on this information. For each sequence position t, the main energy type (such as solar energy, wind energy, etc.) can be determined, and the corresponding energy flow information (such as energy transmission amount, transmission direction, etc.) can be obtained. The energy elasticity space portrait can be represented as a multidimensional vector, which contains the energy adjustable space, energy type and energy flow information on the time scale. For example, for sequence position t, its energy elasticity space portrait can be expressed as: ESP(t) = (AS(t), ET(t), EF(t)), where ET(t) represents the main energy type and EF(t) represents the corresponding energy flow information.

[0086] S3. Obtain the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimize the energy elasticity space portrait within the energy block by combining the regulation potential factor and the demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block; specifically, the following steps are included:

[0087] S31. According to the energy supply and demand relationship, the energy adjustable space sequence is cut to obtain an energy positive adjustable space sequence and an energy negative adjustable space sequence;

[0088] S32, obtaining the first supply price and the first adjustable energy amount of the unit energy corresponding to each sequence position in the energy forward adjustable space sequence; and obtaining the first adjustment potential factor of the corresponding sequence position according to the first electricity selling price, the first adjustable energy amount and the first electricity selling price elasticity factor, and thereby obtaining the first adjustment potential sequence of the corresponding energy block;

[0089] S33, obtaining the first electricity purchase price and the first energy demand of each unit energy corresponding to each sequence position in the negative adjustable energy space sequence; and obtaining the first demand catering factor of the corresponding sequence position according to the first electricity purchase price, the first energy demand and the first electricity purchase price elasticity factor, and thereby obtaining the first demand catering sequence of the corresponding energy block;

[0090] S34, according to the supply priority principle, sequentially configure the first demand catering factor corresponding to the first regulation potential sequence to obtain the first in-plant energy matching chain;

[0091] Simultaneously, according to the principle of demand priority, the first regulation potential factor corresponding to the first demand catering sequence is configured in turn to obtain the second in-plant energy matching chain;

[0092] S35. Based on the first in-plant energy matching chain and the second in-plant energy matching chain, the energy type and energy flow information in the energy elasticity space portrait corresponding to the energy block are updated to obtain the in-plant energy dynamic configuration strategy.

[0093] Furthermore, the energy adjustable space sequence is cut according to the energy supply and demand relationship to obtain an energy positive adjustable space sequence and an energy negative adjustable space sequence; the steps include:

[0094] S311, removing the sequence position of the energy adjustable space sequence whose energy supply corresponding to the energy block is equal to the energy demand, to obtain the target energy adjustable space sequence;

[0095] S312, sequentially extracting the sequence positions of the target energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the energy positive adjustable space sequence;

[0096] S313 , sequentially extracting the sequence positions of the target energy adjustable space sequence where the energy supply is less than the energy demand to obtain the negative energy adjustable space sequence.

[0097] In this embodiment, the energy configuration within the energy block is dynamically adjusted according to the energy supply and demand relationship and the price elasticity factor to optimize energy utilization efficiency and economic benefits. Through the construction of the internal bidding mechanism and the matching chain, it is possible to quickly respond to changes in energy supply and demand and improve the flexibility and stability of the energy system. The energy configuration strategy based on the price elasticity factor helps to promote competition and development in the energy market and promote the rational allocation and utilization of energy resources.

[0098] In combination with the specific implementation, the implementation process of the above embodiment is described. It is assumed that there is an energy block, which contains multiple energy types (such as electricity, gas, etc.), and the following parameters are set: energy supply Si; energy demand Di; the first supply price per unit energy Ps1,i; the first electricity purchase price per unit energy Pb1,i; the first adjustable energy Er1,i; the first energy demand Dr1,i; the first electricity sales price elasticity factor αs1; the first electricity purchase price elasticity factor αb1; according to the description of steps S311-S313, the sequence position target energy adjustable space sequence with supply equal to demand is eliminated = {(Si,Di)|Si≠Di}; the sequence position energy positive adjustable space sequence with supply greater than demand is extracted = {(Si,Di)|Si>Di}; the sequence position energy negative adjustable space sequence with supply less than demand is extracted = {(Si,Di)|Si <Di}。

[0099] Further, the first adjustment potential sequence is calculated, as follows: for each sequence position in the energy positive adjustable space sequence: The first regulation potential sequence = {first regulation potential factor i}.

[0100] Further, the first demand-compliance sequence is calculated: for each sequence position in the energy negative adjustable space sequence: The first demand catering sequence = {first demand catering factor i}.

[0101] Further, an in-plant energy matching chain is constructed, specifically, a first in-plant energy matching chain is constructed: based on the supply priority principle, the first regulation potential sequence is matched with the first demand catering sequence. For example, step 1, sort the first regulation potential sequence in descending order according to the size of the regulation potential factor; sort the first demand catering sequence in descending order according to the size of the demand catering factor; step 2, take out the sequence position with the largest regulation potential factor from the sorted first regulation potential sequence; take out the sequence position with the largest demand catering factor from the sorted first demand catering sequence; match these two sequence positions to form a matching pair. Step 3, remove the matched sequence positions from the first regulation potential sequence and the first demand catering sequence respectively. Add the matching pair to the first in-plant energy matching chain; step 4, repeat steps 2 and 3 until the first regulation potential sequence or the first demand catering sequence is empty.

[0102] Construct the second in-plant energy matching chain: Based on the principle of demand priority, match the first demand catering sequence with the first regulation potential sequence. Similar to the construction method of the first in-plant energy matching chain, step 1, sort the first demand catering sequence in descending order according to the size of the demand catering factor. Sort the first regulation potential sequence in descending order according to the size of the regulation potential factor. Step 2, take out the sequence position with the largest demand catering factor from the sorted first demand catering sequence. Take out the sequence position with the largest regulation potential factor from the sorted first regulation potential sequence. Match these two sequence positions to form a matching pair. Step 3, remove the matched sequence positions from the first demand catering sequence and the first regulation potential sequence respectively; add the matching pair to the second in-plant energy matching chain. Step 4, repeat steps 2 and 3 until the first demand catering sequence or the first regulation potential sequence is empty.

[0103] Through the above steps, we can obtain the first in-plant energy matching chain based on the supply priority principle and the second in-plant energy matching chain based on the demand priority principle. These two matching chains reflect how to optimize the energy allocation within the energy block under different priorities.

[0104] Further, the energy elasticity spatial portrait is updated: according to the first in-plant energy matching chain and the second in-plant energy matching chain, the energy elasticity spatial portrait of the energy block is updated. It can be understood that the energy elasticity spatial portrait contains the initial distribution and energy flow information of all energy types in the energy block. The results of the first in-plant energy matching chain and the second in-plant energy matching chain are integrated together. Each matching pair represents a specific energy transaction or configuration decision in the energy block, including the matched energy type, quantity, price and information of the two parties to the transaction. According to the information in the matching chain, the distribution of energy types in the energy elasticity spatial portrait is updated. For each matched energy type, its representation in the portrait is increased or decreased to reflect the actual energy configuration. According to the energy transaction and configuration decision in the matching chain, the energy flow information in the energy elasticity spatial portrait is adjusted. The data of energy supply, demand and adjustable energy are updated to reflect the impact of the matching chain on energy flow.

[0105] S4. Determine the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy; specifically, the steps include:

[0106] S41, determining the energy supply and demand matching situation in the factory according to the dynamic energy configuration strategy in the factory to obtain the positive adjustable space sequence of the remaining energy and the negative adjustable space sequence of the remaining energy;

[0107] S42, obtaining the second supply price and the second adjustable energy quantity of the unit energy corresponding to each sequence position in the positive adjustable space sequence of the remaining energy; and obtaining the second adjustment potential factor of the corresponding sequence position according to the second electricity selling price, the second adjustable energy quantity and the second electricity selling price elasticity factor, and thereby obtaining the second adjustment potential sequence of the corresponding virtual power plant;

[0108] S43, obtaining the second electricity purchase price and the second energy demand of the unit energy corresponding to each sequence position in the negative adjustable space sequence of the remaining energy; and obtaining the second demand catering factor of the corresponding sequence position according to the second electricity purchase price, the second energy demand and the second electricity purchase price elasticity factor, and thereby obtaining the second demand catering sequence of the corresponding energy block;

[0109] S44, according to the supply priority principle, sequentially configure the corresponding second demand catering factors for the second regulation potential sequence to obtain the first off-site energy matching chain;

[0110] Simultaneously, according to the demand priority principle, the corresponding second regulation potential factors are sequentially configured for the second demand catering sequence to obtain the second off-site energy matching chain;

[0111] S45. Based on the first off-site energy matching chain and the second off-site energy matching chain, the energy type and energy flow information in the energy elastic space portraits in the virtual power plant are updated for the second time to obtain the off-site energy dynamic configuration strategy.

[0112] Preferably, the method of determining the matching of energy supply and demand in the plant according to the dynamic configuration strategy of energy in the plant to obtain a positive adjustable spatial sequence of residual energy and a negative adjustable spatial sequence of residual energy comprises the following steps:

[0113] S411, determining the adjustable spatial sequence of remaining energy corresponding to each virtual power plant according to the energy supply and demand relationship and the dynamic energy configuration strategy within the plant;

[0114] S412, eliminating the sequence positions of the remaining energy adjustable space sequence whose energy supply corresponding to the virtual power plant is equal to the energy demand, to obtain the target remaining energy adjustable space sequence;

[0115] S413, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the remaining energy positive adjustable space sequence;

[0116] S414, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence where the energy supply is less than the energy demand to obtain the remaining energy negative adjustable space sequence.

[0117] In this embodiment, by determining the positive adjustable spatial sequence and the negative adjustable spatial sequence of the remaining energy, the surplus or gap of each virtual power plant in energy supply and demand can be accurately identified, so that energy can flow effectively from the surplus power plant to the demand power plant, thereby reducing energy waste and improving the overall energy utilization efficiency; this embodiment takes into account the second supply price, the second electricity purchase price and the corresponding price elasticity factor, so that energy transactions are more in line with market laws; by constructing the first off-site energy matching chain and the second off-site energy matching chain, energy optimization configuration under two different strategies based on supply priority and demand priority is realized, which provides more flexibility and choices for transactions in the energy market, helps to optimize the market structure and improve economic benefits. By dynamically adjusting the energy configuration between virtual power plants, it helps to balance the supply and demand relationship of the entire energy system, reduce energy shortages or surpluses, and thus enhance the stability and reliability of the energy system; in virtual power plants that include renewable energy, this embodiment takes into account the intermittent and uncertain nature of renewable energy, and by dynamically adjusting the energy configuration, it can better integrate renewable energy, improve its utilization rate, and reduce dependence on traditional energy; this application shows in detail the energy supply and demand matching and energy flow information; it provides strong data support for energy managers and policymakers, helping them make more informed decisions to optimize the allocation and management of energy resources.

[0118] In combination with the specific implementation, the implementation process of the above embodiment is described. It is assumed that there are three virtual power plants (VEP1, VEP2, VEP3), each of which has its specific energy supply, demand, adjustable energy and corresponding prices. For example: VEP1: supply: 100MWh; demand: 80MWh; adjustable energy: 20MWh; second supply price: $50 / MWh; second purchase price: $45 / MWh; VEP2: supply: 120MWh; demand: 100MWh; adjustable energy: 20MWh; second supply price: $48 / MWh; second purchase price: $47 / MWh; VEP3: supply: 80MWh; demand: 100MWh; adjustable energy: 20MWh; second supply price: $52 / MWh; second purchase price: $49 / MWh.

[0119] Further determine the sequence of the remaining energy adjustable space; specifically: S411: According to the energy supply and demand relationship, the remaining energy adjustable space of each virtual power plant can be calculated. For example, VEP1: remaining energy = supply - demand = 100-80 = 20MWh (positively adjustable); VEP2: remaining energy = supply - demand = 120-100 = 20MWh (positively adjustable); VEP3: remaining energy = demand - supply = 100-80 = 20MWh (negatively adjustable); S412-S414: remove the sequence position of supply and demand balance, and extract the positive and negative adjustable space sequences respectively; then the remaining energy positive adjustable space sequence: [(VEP1, 20MWh), (VEP2, 20MWh)]; the remaining energy negative adjustable space sequence: [(VEP3, 20MWh)].

[0120] Further calculate the second regulation potential sequence; specifically, for each positive adjustable space sequence position, calculate the second regulation potential factor. For example: VEP1: second regulation potential factor = second electricity price elasticity factor * second adjustable energy amount = $50 / MWh*20MWh=1000; VEP2: second regulation potential factor = $48 / MWh*20MWh=960; second regulation potential sequence: [1000,960].

[0121] Further calculate the second demand catering sequence; specifically: for each negative adjustable space sequence position, calculate the second demand catering factor. For example: VEP3: second demand catering factor = second electricity purchase price elasticity factor * second energy demand = $49 / MWh * 20MWh = 980; second demand catering sequence:

[980] .

[0122] Further build the off-site energy matching chain; the first off-site energy matching chain (supply priority): VEP1—>VEP3: 20MWh; the second off-site energy matching chain (demand priority): VEP3—>VEP1: 20MWh.

[0123] Further update the energy elasticity space portrait; specifically, obtain the information of the first off-site energy matching chain and the second off-site energy matching chain, including the matched virtual power plant pairs, energy trading volume and trading direction. Traverse each matching chain, and for each matching pair in the chain (for example, VEP1—>VEP3), perform the following operations: For the supplier (such as VEP1), reduce the corresponding energy amount in the positive adjustable space sequence of its remaining energy, and update its energy type to "traded" or "to be traded". For the demander (such as VEP3), reduce the corresponding energy amount in the negative adjustable space sequence of its remaining energy, and also update its energy type to "traded" or "to be traded". Add a record to the energy flow information of the supplier, indicating that a specific amount of energy will flow to the demander. Also add a record to the energy flow information of the demander, indicating that a specific amount of energy will be received from the supplier. If a virtual power plant participates in multiple matching pairs in the matching chain, it is necessary to accumulate its energy trading volume and update the corresponding energy type and energy flow information. The updated energy type and energy flow information is stored back in the energy elasticity space portrait of each virtual power plant. Verify whether the updated energy elasticity space portrait accurately reflects the energy transactions in the matching chain. After confirmation, the updated portrait is used as a new benchmark for subsequent energy configuration and management decisions. Through the above steps, the energy type and energy flow information of each virtual power plant is updated according to the off-site energy matching chain, thereby maintaining the accuracy and real-time nature of the energy elasticity space portrait; it helps virtual power plants to better manage and make decisions on energy to adapt to the ever-changing energy market and environmental conditions.

[0124] S5. Integrate the energy of the virtual power plant according to the dynamic configuration strategy of the energy within the plant and the dynamic configuration strategy of the energy outside the plant to obtain a reconstructed energy elasticity space portrait.

[0125] As a preferred method, the virtual power plant energy is integrated according to the in-plant energy dynamic configuration strategy and the out-plant energy dynamic configuration strategy, including the following steps:

[0126] S51, obtaining first energy types and first energy flow information of a first in-plant energy matching chain and a second in-plant energy matching chain corresponding to each energy block in the in-plant energy dynamic configuration strategy;

[0127] S52, obtaining the second energy type and the second energy flow information in the first off-site energy matching chain and the second off-site energy matching chain corresponding to each virtual power plant in the off-site energy dynamic configuration strategy;

[0128] S53, modifying the energy type and energy flow information in the energy elasticity spatial portrait corresponding to each energy block according to the first energy type, the first energy flow information, the second energy type and the second energy flow information to obtain a reconstructed energy elasticity spatial portrait.

[0129] In this embodiment, the energy allocation strategies inside and outside the plant are combined to ensure that energy is reasonably distributed between virtual power plants and within the power plant, which helps to reduce energy waste and improve overall energy utilization efficiency, thereby achieving global energy optimization configuration; by correcting the energy elasticity space portrait, the energy status and flow of the virtual power plant are reflected in real time; accurate data support is provided for energy managers, enabling them to monitor and manage energy in real time and make timely adjustments to cope with changes in energy supply and demand; the reconstructed energy elasticity space portrait contains rich energy information and data, providing energy managers and policy makers with a comprehensive decision-making basis, helping them to formulate more scientific and reasonable energy management strategies and policies; through clear energy type and energy flow information, energy market transactions between virtual power plants are promoted, which helps to optimize the market structure, improve market efficiency, and further promote the prosperity and development of the energy market; the real-time updated energy elasticity space portrait helps to balance the relationship between energy supply and demand, reduce energy shortages or surpluses, and help to improve the stability and reliability of the energy system and ensure the continuity and security of energy supply.

[0130] In combination with the specific implementation, the implementation process of the above embodiment is described. It is assumed that there are three virtual power plants (VEP1, VEP2, VEP3), each of which has its specific energy supply, demand, adjustable energy and corresponding price, and the off-site energy dynamic configuration strategy has been obtained through step S4. Among them, VEP1: supply: 100MWh; demand: 80MWh; adjustable energy: 20MWh; off-site matching chain: supply 20MWh to VEP3; VEP2: supply: 120MWh; demand: 100MWh; adjustable energy: 20MWh; off-site matching chain: none; VEP3: supply: 80MWh; demand: 100MWh; adjustable energy: 20MWh; off-site matching chain: receive 20MWh from VEP1; further obtain the in-plant energy dynamic configuration strategy information; specifically, for each virtual power plant, obtain the first energy type and first energy flow information of the first in-plant energy matching chain and the second in-plant energy matching chain in its in-plant energy dynamic configuration strategy. For example: VEP1: First energy type: adjustable energy; first energy flow information: no in-plant transactions, all for out-plant transactions; VEP2: First energy type: adjustable energy; first energy flow information: no in-plant and out-plant transactions; VEP3: First energy type: energy demand; first energy flow information: no in-plant transactions, all from out-plant transactions.

[0131] Further obtain the off-site energy dynamic configuration strategy information; specifically: for each virtual power plant, obtain the second energy type and second energy flow information of the first off-site energy matching chain and the second off-site energy matching chain in its off-site energy dynamic configuration strategy. For example: VEP1: second energy type: supply energy; second energy flow information: supply 20MWh to VEP3; VEP2: second energy type: none; second energy flow information: none; VEP3: second energy type: demand energy; second energy flow information: receive 20MWh from VEP1.

[0132] Further revise the energy elasticity spatial portrait; specifically, according to the acquired energy type and energy flow information, revise the energy elasticity spatial portrait of each virtual power plant. VEP1: Revised portrait: Remaining energy 0MWh (all supplied to VEP3), energy flow information updated to supply 20MWh to VEP3. VEP2: Revised portrait: Remaining energy 20MWh (no transaction), energy flow information unchanged. VEP3: Revised portrait: Remaining energy 0MWh (all from VEP1), energy flow information updated to receive 20MWh from VEP1.

[0133] Embodiment 2

[0134] A technical solution also provided in an embodiment of the present invention is a virtual power plant energy integration system, which is applicable to a virtual power plant energy integration method based on a bidding mechanism, including:

[0135] Analysis module: Analyzes the energy attributes in the virtual power plant, extracts energy characteristics and spatializes them to obtain energy blocks;

[0136] Construction module: Obtain the energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain the energy elasticity space portrait;

[0137] The first configuration module: obtains the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimizes the energy elasticity spatial portrait within the energy block by combining the regulation potential factor and demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block;

[0138] The second configuration module: determines the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy;

[0139] Reconstruction module: Integrate the energy of the virtual power plant according to the dynamic configuration strategy of in-plant energy and the dynamic configuration strategy of out-plant energy to obtain a reconstructed energy elasticity space portrait.

[0140] The present invention has at least the following technical effects: through the analysis module and the construction module, the energy characteristics in the virtual power plant are extracted in real time and the energy elasticity space portrait is constructed to provide basic data support for the subsequent optimization configuration. The first configuration module and the second configuration module dynamically adjust the energy configuration based on the bidding mechanism to ensure the efficient use of energy. Through the combination of internal and external bidding mechanisms, the virtual power plant can quickly adjust the energy configuration strategy according to market price fluctuations and better participate in market competition; the dynamically optimized energy configuration strategy helps to balance the energy supply and demand relationship within the virtual power plant and reduce energy shortages or surpluses caused by supply and demand mismatches. By optimizing energy configuration through a bidding mechanism, the virtual power plant can obtain the required energy at a lower cost and sell excess energy at a higher price, thereby improving the overall economic benefits.

[0141] Embodiment three:

[0142] An optional embodiment also provided in the embodiments of the present invention is: an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the virtual power plant energy integration method based on the bidding mechanism are implemented.

[0143] Embodiment 4:

[0144] An optional embodiment also provided in the embodiments of the present invention is: a storage medium, which stores computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the steps of the virtual power plant energy integration method based on the bidding mechanism are implemented.

[0145] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0146] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0148] In addition, each functional unit in the embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0150] The specific implementation described above is a preferred implementation of the virtual power plant energy integration method and system based on the bidding mechanism of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A virtual power plant energy integration method based on a bidding mechanism, characterized by: The steps include: S1. Analyze the energy attributes in the virtual power plant, extract energy characteristics and spatialize them to obtain energy blocks; S2. Obtain the energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain the energy elasticity space portrait; S3. Obtain the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimize the energy elasticity spatial portrait within the energy block by combining the regulation potential factor and demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block; S4. Determine the off-site energy dynamic configuration strategy between virtual power plants based on the external bidding mechanism and the on-site energy dynamic configuration strategy; S5. Integrate the energy of the virtual power plant according to the dynamic configuration strategy of the energy inside the plant and the dynamic configuration strategy of the energy outside the plant to obtain a reconstructed energy elasticity space portrait; The S2 comprises the following steps: S21. According to the long time scale prediction algorithm, the energy supply of each energy unit in the energy block on the time scale is obtained and an energy supply sequence is constructed, and the energy demand of each load entity on the time scale is obtained and an energy demand sequence is constructed; S22, determining the energy adjustable space according to the energy supply and demand relationship of the energy supply sequence and the energy demand sequence corresponding to the sequence position, and constructing the energy adjustable space sequence on the time scale according to the energy adjustable space; S23, obtaining the energy type and the corresponding energy flow information corresponding to the sequence position of the energy adjustable spatial sequence; constructing an energy elasticity spatial portrait according to the energy flow information and the energy type; The S3 comprises the following steps: S31. According to the energy supply and demand relationship, the energy adjustable space sequence is cut to obtain an energy positive adjustable space sequence and an energy negative adjustable space sequence; S32, obtaining the first supply price and the first adjustable energy amount of the unit energy corresponding to each sequence position in the energy forward adjustable space sequence; and obtaining the first adjustment potential factor of the corresponding sequence position according to the first electricity selling price, the first adjustable energy amount and the first electricity selling price elasticity factor, and thereby obtaining the first adjustment potential sequence of the corresponding energy block; S33, obtaining the first electricity purchase price and the first energy demand of each unit energy corresponding to each sequence position in the negative adjustable energy space sequence; and obtaining the first demand catering factor of the corresponding sequence position according to the first electricity purchase price, the first energy demand and the first electricity purchase price elasticity factor, and thereby obtaining the first demand catering sequence of the corresponding energy block; S34, according to the supply priority principle, sequentially configure the first demand catering factor corresponding to the first regulation potential sequence to obtain the first in-plant energy matching chain; Simultaneously, according to the principle of demand priority, the first regulation potential factor corresponding to the first demand catering sequence is configured in turn to obtain the second in-plant energy matching chain; S35. Based on the first in-plant energy matching chain and the second in-plant energy matching chain, the energy type and energy flow information in the energy elasticity space portrait corresponding to the energy block are updated to obtain the in-plant energy dynamic configuration strategy.

2. The virtual power plant energy integration method based on the bidding mechanism according to claim 1 is characterized by: The energy attributes in the virtual power plant are analyzed, energy characteristics are extracted and spatialized to obtain energy blocks, including the following steps: S11. Obtain the energy radiation area corresponding to each energy unit according to the historical energy supply and demand relationship; analyze the energy radiation area through a clustering algorithm to obtain an energy clustering space; S12. Obtain the energy characteristics and regional outlines of various energy sources in the energy clustering space to construct energy blocks.

3. The virtual power plant energy integration method based on the bidding mechanism according to claim 2 is characterized by: The method of performing cluster analysis on energy radiation areas by using a clustering algorithm to obtain an energy clustering space includes the following steps: S111, extracting the load entity corresponding to each energy radiation area, determining the location information of the load entity, and taking the energy transmission amount and energy transmission direction obtained by each load entity within a unit time scale as energy flow information; S112, determining the center position information of the energy radiation area according to the minimum rectangle method in combination with the position information corresponding to the load entity, and determining the target energy flow information of the energy radiation area according to the mean value of the energy transmission amount; S113. Use K-means algorithm to combine center location information and target energy flow information to perform cluster analysis on energy radiation areas to determine energy clustering space.

4. The virtual power plant energy integration method based on the bidding mechanism according to claim 1 is characterized by: The method of cutting the energy adjustable space sequence according to the energy supply and demand relationship to obtain the energy positive adjustable space sequence and the energy negative adjustable space sequence comprises the following steps: S311, removing the sequence position of the energy adjustable space sequence whose energy supply corresponding to the energy block is equal to the energy demand, to obtain the target energy adjustable space sequence; S312, sequentially extracting the sequence positions of the target energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the energy positive adjustable space sequence; S313 , sequentially extracting the sequence positions of the target energy adjustable space sequence where the energy supply is less than the energy demand to obtain the negative energy adjustable space sequence.

5. The virtual power plant energy integration method based on bidding mechanism according to claim 1 is characterized by: The method of determining the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy comprises the following steps: S41, determining the energy supply and demand matching situation in the factory according to the dynamic energy configuration strategy in the factory to obtain the positive adjustable space sequence of the remaining energy and the negative adjustable space sequence of the remaining energy; S42, obtaining the second supply price and the second adjustable energy quantity of the unit energy corresponding to each sequence position in the positive adjustable space sequence of the remaining energy; and obtaining the second adjustment potential factor of the corresponding sequence position according to the second electricity selling price, the second adjustable energy quantity and the second electricity selling price elasticity factor, and thereby obtaining the second adjustment potential sequence of the corresponding virtual power plant; S43, obtaining the second electricity purchase price and the second energy demand of the unit energy corresponding to each sequence position in the negative adjustable space sequence of the remaining energy; and obtaining the second demand catering factor of the corresponding sequence position according to the second electricity purchase price, the second energy demand and the second electricity purchase price elasticity factor, and thereby obtaining the second demand catering sequence of the corresponding energy block; S44, according to the supply priority principle, sequentially configure the corresponding second demand catering factors for the second regulation potential sequence to obtain the first off-site energy matching chain; Simultaneously, according to the demand priority principle, the corresponding second regulation potential factors are sequentially configured for the second demand catering sequence to obtain the second off-site energy matching chain; S45. Based on the first off-site energy matching chain and the second off-site energy matching chain, the energy type and energy flow information in the energy elastic space portraits in the virtual power plant are updated for the second time to obtain the off-site energy dynamic configuration strategy.

6. The virtual power plant energy integration method based on the bidding mechanism according to claim 5 is characterized by: The method of determining the matching of energy supply and demand in the plant according to the dynamic configuration strategy of energy in the plant to obtain a positive adjustable spatial sequence of residual energy and a negative adjustable spatial sequence of residual energy comprises the following steps: S411, determining the adjustable spatial sequence of remaining energy corresponding to each virtual power plant according to the energy supply and demand relationship and the dynamic energy configuration strategy within the plant; S412, eliminating the sequence positions of the remaining energy adjustable space sequence whose energy supply corresponding to the virtual power plant is equal to the energy demand, to obtain the target remaining energy adjustable space sequence; S413, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence whose energy supply is greater than the energy demand to obtain the remaining energy positive adjustable space sequence; S414, sequentially extracting the sequence positions of the target remaining energy adjustable space sequence where the energy supply is less than the energy demand to obtain the remaining energy negative adjustable space sequence.

7. The virtual power plant energy integration method based on bidding mechanism according to claim 1 is characterized by: The virtual power plant energy is integrated according to the dynamic configuration strategy of the energy inside the plant and the dynamic configuration strategy of the energy outside the plant, including the following steps: S51, obtaining first energy types and first energy flow information of a first in-plant energy matching chain and a second in-plant energy matching chain corresponding to each energy block in the in-plant energy dynamic configuration strategy; S52, obtaining the second energy type and the second energy flow information in the first off-site energy matching chain and the second off-site energy matching chain corresponding to each virtual power plant in the off-site energy dynamic configuration strategy; S53, modifying the energy type and energy flow information in the energy elasticity spatial portrait corresponding to each energy block according to the first energy type, the first energy flow information, the second energy type and the second energy flow information to obtain a reconstructed energy elasticity spatial portrait.

8. A virtual power plant energy integration system, applicable to the virtual power plant energy integration method based on the bidding mechanism as claimed in any one of claims 1 to 7, characterized in that: include: Analysis module: Analyzes the energy attributes in the virtual power plant, extracts energy characteristics and spatializes them to obtain energy blocks; Construction module: Obtain the energy adjustable space sequence according to the energy supply and demand relationship of each energy block, and associate the energy characteristics with the energy adjustable space sequence to obtain the energy elasticity space portrait; The first configuration module: obtains the regulation potential sequence and demand catering sequence of each energy block according to the internal bidding mechanism, and dynamically optimizes the energy elasticity spatial portrait within the energy block by combining the regulation potential factor and demand catering factor to obtain the in-plant energy dynamic configuration strategy belonging to each energy block; The second configuration module: determines the off-site energy dynamic configuration strategy between virtual power plants according to the external bidding mechanism and the on-site energy dynamic configuration strategy; Reconstruction module: Integrate the energy of the virtual power plant according to the dynamic configuration strategy of in-plant energy and the dynamic configuration strategy of out-plant energy to obtain a reconstructed energy elasticity space portrait.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the virtual power plant energy integration method based on the bidding mechanism as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the steps of a virtual power plant energy integration method based on a bidding mechanism as described in any one of claims 1 to 7.

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