Virtual power plant provincial and regional collaborative security checking method based on secondary aggregation

Abstract

The invention discloses a virtual power plant provincial and regional collaborative security checking method based on secondary aggregation, and relates to the technical field of virtual power plant security checking, adjustable resource analysis and evaluation indexes are constructed according to the internal resource adjustment condition of a virtual power plant, and the adjustable monitoring capability of the virtual power plant is improved; the adjustable resources of the virtual power plant are aggregated and classified according to the principle of risk-free large aggregation and risk small aggregation, so that the adjustment resource blocking condition of the virtual power plant can be mastered conveniently; according to the adjustable resource condition of the virtual power plant, a corresponding power grid safety management and control optimization model is constructed, and a secondary aggregation-based provincial and regional scheduling collaborative virtual power plant safety checking method is further proposed, so that the safety of the adjustment of the virtual power plant to the power grid is ensured. The method can effectively improve the safety optimization scheduling of the virtual power plant, gives consideration to the economy and safety of the virtual power plant, perfects the regulation and control mechanism of the virtual power plant, and provides a powerful guarantee for the safe production and operation of electric power.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant safety verification, and specifically is a provincial and local collaborative safety verification method for virtual power plants based on secondary aggregation. Background Art

[0002] In recent years, with the advancement of the dual carbon target strategy, the scale of virtual power plant pilot projects has continued to expand. The government has issued a number of policy documents to encourage the further development of virtual power plants. The role of virtual power plants in the field of power safety dispatching has become increasingly important.

[0003] A virtual power plant is a subject or system that aggregates, optimizes and controls distributed power sources, energy storage and controllable loads, and its internal resources can be distributed in different regions. However, as an independent entity, a virtual power plant participates in grid regulation. For virtual power plants located in multiple areas and with unevenly distributed adjustable resources, the traditional safety verification model does not take into account their regulation differences, and some areas may be overloaded while others may be underloaded. If the main body is distributed across regions, in the event of congestion, the virtual power plant may not know that the grid structure can adjust itself, which may aggravate the grid congestion. Therefore, for virtual power plants, a new type of power generation entity, it is urgent to study their safe scheduling methods. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a virtual power plant provincial and local collaborative safety verification method based on secondary aggregation.

[0005] A virtual power plant provincial and local collaborative safety verification method based on secondary aggregation, the method specifically comprises the following steps:

[0006] The virtual power plant pushes the regulation capabilities of various resources to the local dispatch through the platform. The local dispatch conducts secondary aggregation of regulation resources based on the resources and flow conditions of the virtual power plant, and transmits the maximum regulation capabilities of various regulation resources of the virtual power plant to the provincial dispatch.

[0007] The provincial dispatching department determines the maximum adjustable capacity of various regulation resources of the virtual power plant transmitted by the local dispatching department, and combines it with the grid demand to give the total regulation value of the virtual power plant through market clearing or instruction issuance.

[0008] The provincial dispatching end issues the regulation instructions to each main grid node based on the total regulation value of the virtual power plant and meets the safety verification requirements;

[0009] The local regulator will decompose the instructions issued by each main network node to each substation under the condition of meeting the safety verification;

[0010] The virtual power plant platform clears the power in order from low to high according to the regulation instructions received by each area, and sends them to the corresponding execution unit;

[0011] After the virtual power plant is implemented, the execution status of the regulation instructions will be fed back to the local and provincial regulation departments.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The present invention constructs an adjustable resource analysis and evaluation index based on the internal adjustment resource situation of the virtual power plant, thereby improving the adjustable monitoring capability of the virtual power plant; the virtual power plant's adjustable resources are aggregated and classified according to the principle of large-scale aggregation without risk and small-scale aggregation with risk, so as to facilitate the mastery of the adjustment resource blocking situation of the virtual power plant; according to the adjustable resource situation of the virtual power plant, a corresponding power grid security management and control optimization model is constructed, and a virtual power plant safety verification method based on provincial and local dispatching coordination with secondary aggregation is further proposed, which ensures the safety of the virtual power plant's regulation on the power grid. This method can effectively improve the safe and optimized dispatching of virtual power plants, take into account the economy and safety of virtual power plants, improve the virtual power plant's regulation and management mechanism, and provide a strong guarantee for the safe production and operation of electric power. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the process of the present invention;

[0015] Figure 2 This is the flow chart of the Canopy clustering algorithm;

[0016] Figure 3 Flowchart of the coordinated safety verification mechanism for provincial and local dispatching of virtual power plants. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-Figure 3 The present application provides a virtual power plant provincial and local collaborative safety verification method based on secondary aggregation, which specifically includes the following steps:

[0019] In order to establish the analysis and evaluation indicators of the adjustable resources within the virtual power plant, analysis and evaluation can be carried out according to the characteristics of the adjustable resources. For distributed photovoltaics, the maximum upward and downward adjustment capabilities of the virtual power plant under the distribution transformer of the i station area are:

[0020]

[0021] Where, ΣP i,PV It is the sum of the real-time output of distributed photovoltaic power plants under the distribution transformer in area i; It is the sum of the maximum power generation output of distributed photovoltaic under the distribution transformer of the virtual power plant in the i-th area; It is the sum of the minimum technical output of all distributed photovoltaics under the distribution transformer of the virtual power plant in area i; The maximum output that can be reduced by distributed photovoltaic under the distribution transformer of area i for virtual power plant; The maximum output that can be reduced by distributed photovoltaic under the distribution transformer of area i for virtual power plant;

[0022] For energy storage equipment, the maximum upward and downward adjustment capabilities of the virtual power plant under the distribution transformer in area i are:

[0023]

[0024] Where, ΣP i,ES It is the sum of the real-time output of energy storage of the virtual power plant under the distribution transformer of area i, where discharge is positive and charge is negative; It is the sum of the maximum technical output of energy storage of the virtual power plant under the distribution transformer of area i, which is the maximum discharge power in theory; It is the sum of the minimum technical output of energy storage of the virtual power plant under the distribution transformer of area i, which is theoretically the negative value of the maximum charging power; It is the maximum upward regulation capacity of energy storage in the virtual power plant under the distribution transformer in the i-station area; It is the maximum downward regulation capacity of energy storage under the distribution transformer of the virtual power plant in the i-station area;

[0025] For the demand response load, the maximum upward and downward adjustment capabilities of the virtual power plant under the distribution transformer of the i-station area are:

[0026]

[0027] In the formula It is the maximum upward adjustment capacity of the virtual power plant in response to demand under the distribution transformer of area i; The maximum load that can be increased by the virtual power plant based on price demand response load under the distribution transformer of area i; The maximum load that can be increased by the virtual power plant based on the incentive-based demand response load under the distribution transformer of the i-station area; It is the maximum downward adjustment capability of the virtual power plant in response to demand under the distribution transformer of area i; The maximum load that can be reduced by the virtual power plant based on price demand response load under the distribution transformer of area i; The virtual power plant can reduce the maximum load by responding to the demand based on incentives under the distribution transformer of area i.

[0028] For electric vehicles, the maximum upward and downward adjustment capabilities of the virtual power plant under the distribution transformer in area i are:

[0029]

[0030] Where ΣP i,EV It is the sum of the real-time discharge power of electric vehicles to the grid through V2G technology under the distribution transformer of the virtual power plant in the i-station area. Discharging is a positive value, while charging is a negative value. It is the maximum sum of the power that electric vehicles can discharge to the grid through V2G technology under the distribution transformer of the virtual power plant in the i-th area; It is the minimum sum of the power that electric vehicles can discharge to the grid through V2G technology under the virtual power plant in the distribution transformer of area i. Theoretically, it is the negative value of the maximum charging power of electric vehicles. It is the maximum upward regulation capacity of electric vehicles in the virtual power plant under the distribution transformer in area i; It is the maximum downward adjustment capacity of electric vehicles of the virtual power plant under the distribution transformer in area i.

[0031] The virtual power plant is located in the substation area, and the adjustable capacity of its internal resources is:

[0032]

[0033] In the formula, The maximum output that can be reduced by distributed photovoltaic under the distribution transformer of area i for virtual power plant; The maximum output that can be reduced by distributed photovoltaic under the distribution transformer of area i for virtual power plant; It is the maximum upward regulation capacity of energy storage in the virtual power plant under the distribution transformer in the i-station area; It is the maximum downward regulation capacity of energy storage under the distribution transformer of the virtual power plant in the i-station area; It is the maximum upward adjustment capacity of the virtual power plant in response to demand under the distribution transformer of area i; It is the maximum downward adjustment capability of the virtual power plant in response to demand under the distribution transformer of area i; is the maximum upward regulation capacity of electric vehicles in the virtual power plant under the distribution transformer in area i; is the maximum downward adjustment capacity of electric vehicles in the virtual power plant under the distribution transformer in area i; is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; It is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformation of area i.

[0034] In order to meet the requirements of safe and reliable operation of the power grid, the adjustment range of the adjustable resources of the virtual power plant must meet the requirements of the distribution transformer load capacity; in the adjustment process of the virtual power plant, the increase of the output of the virtual power plant will intuitively lead to a decrease in the distribution transformer load, and the reduction of the output of the virtual power plant will intuitively lead to an increase in the distribution transformer load. Therefore, the adjustable capacity of the virtual power plant in the distribution transformer area is

[0035]

[0036] In the formula, is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in area i; is the maximum increase in the load of the distribution transformer in the i-station area, is the maximum reduction amount of the distribution transformer load in the i-station area; To ensure the maximum upward adjustment capability of internal resources in the virtual power plant under the condition of ensuring safety of distribution and transformation in area i; To ensure the maximum downward adjustment capability of internal resources of the virtual power plant under the condition of safety in the distribution and transformation of area i.

[0037] In aggregating and classifying adjustable resources according to the principles of large-scale risk-free aggregation and small-scale risk-based aggregation, large-scale risk-free aggregation means that the changes in the internal resource regulation of this type of virtual power plant will not cause problems of heavy overload of distribution transformers and transmission sections. The distribution transformer substation is used as the granularity, and in the topological relationship, it is connected to the main grid through node k. If the following conditions are met at the same time, the regulation resources of this type of virtual power plant can be regarded as "risk-free" regulation resources and clustered uniformly.

[0038]

[0039] S k,mn <ε

[0040] In the formula is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum reduction amount of the distribution transformer load in the i-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in area i; is the maximum increase in the load of the distribution transformer in the i-station area; S k,mn

[0041] is the sensitivity of the internal resource power of the virtual power plant to the section mn at the main grid node k; ε is the sensitivity judgment threshold, which is an extremely small number and can be set to 0.001.

[0042] For the “risk-free” adjustment resources that meet the above conditions, they can be clustered into

[0043]

[0044] In the formula A pool of resources for “risk-free” regulation of virtual power plants; A collection of distributed PV in a “risk-free” regulating resource for a virtual power plant; the aggregation of energy storage in “risk-free” regulating resources for virtual power plants; Aggregation of demand response loads in “risk-free” regulation resources for virtual power plants; A collection of electric vehicles in a “risk-free” regulating resource for a virtual power plant; The ability to adjust resources upwards for virtual power plants “risk-free”; The upward regulation capability of distributed photovoltaics in the “risk-free” regulation resources for virtual power plants; The upward regulation capability of energy storage in the resource for “risk-free” regulation of virtual power plants; Downward regulation capability of demand response loads in the virtual power plant “risk-free” regulation resources; The upward regulation capability of EV V2G in virtual power plants’ “risk-free” regulation resources; Downward regulation capability for all “risk-free” regulation resources of the virtual power plant; The downward regulation capability of distributed photovoltaics in the “risk-free” regulation resources for virtual power plants; Downward regulation capability of energy storage in the resource for “risk-free” regulation of virtual power plants; Provides the virtual power plant with the ability to adjust the demand response load upwards in the resource “risk-free”, Provides “risk-free” downward regulation capability for electric vehicles (V2G) in virtual power plants.

[0045] Risky small aggregation refers to the fact that changes in internal resource regulation of this type of virtual power plant will bring about problems of heavy overload of distribution transformers and transmission sections.

[0046] If the change of virtual power plant regulation resources may cause overload in the distribution transformer of the substation, if the following situations occur, it is necessary to conduct a separate cluster analysis on the regulation resources of this type of virtual power plant

[0047]

[0048] In the formula is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the j-station area; is the maximum reduction amount of the distribution transformer load in the j-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformation of the j-station area; is the maximum amount of increase in the distribution transformer load in area j.

[0049] In this case, the resource regulation capacity of the virtual power plant will be limited by the load capacity of the distribution transformer. At this time, the internal resource regulation capacity of the virtual power plant under the distribution transformer is

[0050]

[0051] In the formula is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in area i; is the maximum increase in the load of the distribution transformer in the i-station area, is the maximum reduction amount of the distribution transformer load in the i-station area; To ensure the maximum upward adjustment capability of internal resources in the virtual power plant under the condition of ensuring safety of distribution and transformation in area i; To ensure the maximum downward adjustment capability of internal resources of the virtual power plant under the condition of safety in the distribution and transformation of area i.

[0052] Therefore, the risk resources of overloaded distribution transformers in the substation area can be aggregated into two categories:

[0053] (1) Virtual power plants adjust risk resources upward due to heavy loads on distribution transformers in the distribution area

[0054]

[0055] In the formula, is the maximum upward adjustment capacity of the internal resources of the virtual power plant to ensure safe operation under the distribution transformer of the j-station area. This type of resource can be recorded as

[0056] (2) Virtual power plants adjust risk resources downward due to heavy loads on distribution transformers in the substation area

[0057]

[0058] In the formula, The maximum downward adjustment capacity of internal resources to ensure safe operation of the virtual power plant under the distribution transformer of the j-station area can be recorded as

[0059] In the case where the internal resources of the virtual power plant are connected to the main grid through a single or multiple nodes and are sensitive to the heavily overloaded sections of the main grid, it is necessary to consider the impact of the internal resource regulation of the virtual power plant on the safe operation of the power grid transmission section.

[0060] Let S k,mn is the sensitivity of the internal resource power of the virtual power plant to the section mn at the main grid node k. If

[0061] S k,mn >ε

[0062] Where ε is the sensitivity judgment threshold, which is a very small number and can be set to 0.001.

[0063] If there are s main grid nodes that meet the requirements, the virtual power plant can be simply divided into s categories due to the risk of transmission section overload regulation. For one of the w categories, there is

[0064] ΣP w,VPP =∑P w,PV +∑P w,ES +∑P w,DR +∑P w,EV

[0065]

[0066] In the formula, ∑P w,VPP is the set of regulation resources belonging to class w in the virtual power plant; ∑P w,PV is the set of distributed photovoltaics in the regulation resources of type w in the virtual power plant; ∑P w,ES is the set of energy storage in the regulation resources of type w in the virtual power plant; ΣP w,DR is the set of demand response loads in the regulation resources of type w in the virtual power plant; ∑P w,EV is the set of electric vehicles in the regulation resources of type w in the virtual power plant; is the upward regulation capability of the regulation resources of the virtual power plant belonging to category w; is the upward regulation capability of distributed photovoltaics among the regulation resources of category w in the virtual power plant; is the upward regulation capability of energy storage in the regulation resource of category w in the virtual power plant; is the downward regulation capability of the demand response load in the regulation resource of category w belonging to the virtual power plant; is the upward regulation capability of electric vehicles V2G in the regulation resources of category w in the virtual power plant; is the downward regulation capacity of all regulation resources belonging to class w in the virtual power plant; The downward regulation capability of distributed photovoltaics in the regulation resources of category w of the virtual power plant; is the downward regulation capability of energy storage in the regulation resource of category w of the virtual power plant; is the upward regulation capability of the demand response load in the regulation resource of type w of the virtual power plant, It is the downward regulation capability of electric vehicles V2G among the regulation resources of category w in the virtual power plant.

[0067] In order to solve the problem that there may be too many s categories, clustering algorithms can be used to classify and aggregate them again. In data mining, the Canopy clustering algorithm is simple and convenient, and can quickly and automatically classify according to the characteristics of the original data. Its principle is: by setting two distance thresholds D1 and D2, and then taking values ​​from the set to calculate the Canopy distance to a point, and comparing it with the set distance threshold, continuously deleting points with closer distances until the set is empty, thereby obtaining multiple Canopies and achieving the effect of clustering. The specific steps of the algorithm are as follows:

[0068] (1) For a given set of sample points S, it contains N s samples, randomly select one point x i As the first Canopy class;

[0069] (2) According to the dispersion degree of the sample set S, set the distance thresholds D1 and D2, and D1>D2. The values ​​can be determined based on the average Euclidean distance of the samples.

[0070] (3) Continue to take point xj from the set and calculate the distance E between the point and all Canopy centers ij If a Canopy with a distance less than D1 can be found, then point x j Add to the Canopy; if all distances are greater than D1, define x j is a new Canopy; if x j If the distance to a Canopy is less than D2, delete the point from the set S;

[0071] (4) Repeat step 3 until the set S does not contain any elements, that is,

[0072] The calculation process of the above Canopy clustering method is as follows Figure 3 shown.

[0073] By using the Canopy clustering algorithm, the number of s main network nodes can be effectively classified, so that the overload risk adjustment resources of the virtual power plant transmission section can be aggregated again, and after aggregation, they can be divided into v categories.

[0074] Combined with the above analysis of the virtual power plant regulation resources, it can be divided into s sub-virtual power plants according to the number of nodes connected to the main grid. Taking the lowest regulation cost of the virtual power plant as the objective function, an optimization scheduling model is constructed.

[0075]

[0076] Where: C PV , C ES , C DR , C EV They are the virtual power plant distributed photovoltaic regulation cost, energy storage regulation cost, demand response load regulation cost, and electric vehicle load regulation cost; They are the upward adjustment cost and downward adjustment cost of the corresponding adjustable resources respectively; They are respectively the upward adjustment cost and the downward adjustment power of the corresponding adjustable resources; They are respectively the upward adjustment flag and the downward adjustment flag of the corresponding adjustable resources, which are 0-1 variables; the adjustable resources are λ=PV, ES, DR, EV.

[0077] The regulation of virtual power plants needs to meet the power balance and section safety constraints. When other power sources and loads remain unchanged, for the power balance constraints, there are

[0078] ΔP VPP =∑(ΔPi,PV +ΔP i,ES +ΔP i,DR +ΔP i,ES )

[0079]

[0080] Where ΔP VPP and ΔP i,PV , ΔP i,ES , ΔP i,DR , ΔP i,ES They are the total regulation value sent to the virtual power plant and the regulation deviation of distributed photovoltaic, energy storage, demand response load, and electric vehicle load in each substation compared with the typical forecast value;

[0081] According to the grid operation constraints, for each main grid node k, its net output power is

[0082] ζ k =∑(P k,Gen -P k,LD +P k,VPP )

[0083] In the formula, ζ k is the net output power of node k; P k,Gen is the output of the conventional unit at node k; P k,LD is the normal load at node k; P k,VPP It is the adjustable resource output of the virtual power plant under node k.

[0084] Therefore, the DC power flow constraint of the line is

[0085]

[0086] Where: N is the number of all nodes in the system; T l,k is the value of the 1st row and the kth column of the power transmission distribution coefficient PTDF matrix; F l max is the maximum active transmission power of line l.

[0087] In solving the model, this problem is a mixed integer programming problem, which can be solved directly by calling solvers such as Gurobi or Cplex.

[0088] In order to further improve the safety verification efficiency of virtual power plants, based on the above research, a provincial and local collaborative safety verification mechanism is proposed, which mainly includes the following links: Figure 3 shown.

[0089] The virtual power plant pushes various resource regulation capabilities to local dispatch through the platform;

[0090] The local dispatching department conducts "secondary aggregation" of regulation resources according to the virtual power plant resources and power flow conditions, and transmits the maximum regulation capacity of various regulation resources of the virtual power plant to the provincial dispatching department;

[0091] The provincial dispatching department determines the maximum adjustable capacity of various regulation resources of the virtual power plant transmitted by the local dispatching department, and combines it with the grid demand to give the total regulation value of the virtual power plant through market clearing or instruction issuance.

[0092] The provincial dispatching end issues the regulation instructions to each main grid node based on the total regulation value of the virtual power plant and meets the safety verification requirements;

[0093] The local regulator will decompose the instructions issued by each main network node to each substation under the condition of meeting the safety verification;

[0094] The virtual power plant platform clears the power in order from low to high according to the regulation instructions received by each area, and sends them to the corresponding execution unit;

[0095] After the virtual power plant is implemented, the execution status of the regulation instructions will be fed back to the local and provincial regulation departments.

[0096] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A virtual power plant provincial and local collaborative safety verification method based on secondary aggregation, characterized in that: The method specifically comprises the following steps: The virtual power plant pushes the regulation capabilities of various resources to the local dispatch through the platform. The local dispatch conducts secondary aggregation of regulation resources based on the resources and flow conditions of the virtual power plant, and transmits the maximum regulation capabilities of various regulation resources of the virtual power plant to the provincial dispatch. The provincial dispatching department determines the maximum adjustable capacity of various regulation resources of the virtual power plant transmitted by the local dispatching department, and combines it with the grid demand to give the total regulation value of the virtual power plant through market clearing or instruction issuance. The provincial dispatching end issues the regulation instructions to each main grid node based on the total regulation value of the virtual power plant and meets the safety verification requirements; The local regulator will decompose the instructions issued by each main network node to each substation under the condition of meeting the safety verification; The virtual power plant platform clears the power in order from low to high according to the regulation instructions received by each area, and sends them to the corresponding execution unit; After the virtual power plant is implemented, the execution status of the regulation instructions will be fed back to the local and provincial regulation departments.

2. According to the method of claim 1, the method is characterized by: The capabilities of pushing various resource adjustment capabilities to the ground adjustment include: The maximum upward and downward adjustment capability of the virtual power plant for associated elements under the distribution transformer of area i; Related elements include distributed photovoltaics, energy storage equipment, demand response loads, and electric vehicles.

3. According to the secondary aggregation-based virtual power plant provincial and local collaborative safety verification method of claim 1, it is characterized in that: The specific manifestations of the secondary aggregation of regulating resources carried out by the local dispatching according to the resources and power flow of the virtual power plant are as follows: The adjustable capacity of the virtual power plant in the distribution transformer area is limited to: In the formula, is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum increase in the load of the distribution transformer in the i-station area, is the maximum reduction amount of the distribution transformer load in the i-station area; To ensure the maximum upward adjustment capability of internal resources in the virtual power plant under the condition of ensuring safety of distribution and transformation in area i; To ensure the maximum downward adjustment capability of internal resources in the virtual power plant under the condition of ensuring safety of distribution and transformation in area i; The adjustable resources are aggregated and classified according to the principle of large aggregation without risk and small aggregation with risk. That is, virtual power plant resources with no blocking risk are simply aggregated, and virtual power plant resources with blocking risk are more finely aggregated.

4. According to the method of claim 3, the method is characterized by: Risk-free large aggregation means that changes in internal resource regulation of this type of virtual power plant will not cause problems of heavy overload of distribution transformers and transmission sections. The distribution transformer area is used as the granularity, and in terms of topological relationship, it is connected to the main grid through node k.

5. According to the method of claim 4, the method is characterized by: Risk-free means that the following conditions are met at the same time: S k,mn <e; In the formula is the maximum upward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum reduction amount of the distribution transformer load in the i-station area; is the maximum downward adjustment capacity of the internal resources of the virtual power plant under the distribution transformer in the i-station area; is the maximum increase in the load of the distribution transformer in the i-station area; S k,mn is the sensitivity of the internal resource power of the virtual power plant to the section mn at the main grid node k; ε is the sensitivity judgment threshold, which is an extremely small number and is set to 0.

001.

6. According to the secondary aggregation-based virtual power plant provincial and local collaborative safety verification method of claim 3, it is characterized in that: Risk means that the changes in the internal resource regulation of such virtual power plants will lead to the problem of heavy overload of distribution transformers and transmission sections; The risk resources of overloaded distribution transformers in the substation area are grouped into two categories: (1) Virtual power plants adjust risk resources upward due to heavy loads on distribution transformers in the distribution area In the formula, is the maximum upward adjustment capacity of the internal resources of the virtual power plant to ensure safe operation under the distribution transformer of the j-station area. This type of resource can be recorded as (2) Virtual power plants adjust risk resources downward due to heavy loads on distribution transformers in the substation area In the formula, The maximum downward adjustment capacity of internal resources to ensure safe operation of the virtual power plant under the distribution transformer of the j-station area can be recorded as 7. According to the method of claim 6, a virtual power plant provincial and local collaborative safety verification method based on secondary aggregation is characterized in that: When the internal resources of the virtual power plant are connected to the main grid through a single or multiple nodes and there is sensitivity to the heavily overloaded section of the main grid, the impact of the internal resource regulation of the virtual power plant on the safe operation of the power transmission section of the power grid is analyzed; Let S k,mn is the sensitivity of the internal resource power of the virtual power plant to the section mn at the main grid node k. If S k,mn >ε; In the formula, ε is the sensitivity judgment threshold, which is a very small number and is set to 0.001; If there are s main grid nodes that meet the requirements, the virtual power plant can be divided into s categories due to the risk of transmission section overload regulation. For one of the w categories, there is ∑P w,VPP =∑P w,PV +∑P w,ES +∑P w,DR +∑P w,EV In the formula, ∑P w,VPP is the set of regulation resources belonging to class w in the virtual power plant; ∑P w,PV is the set of distributed photovoltaics in the regulation resources of type w in the virtual power plant; ∑P w,ES is the set of energy storage in the regulation resources of type w in the virtual power plant; ΣP w,DR is the set of demand response loads in the regulation resources of type w in the virtual power plant; ∑P w,EV is the set of electric vehicles in the regulation resources of type w in the virtual power plant; is the upward regulation capability of the regulation resources of the virtual power plant belonging to category w; is the upward regulation capability of distributed photovoltaics among the regulation resources of category w in the virtual power plant; is the upward regulation capability of energy storage in the regulation resource of category w in the virtual power plant; is the downward regulation capability of the demand response load in the regulation resource of category w belonging to the virtual power plant; is the upward regulation capability of electric vehicles V2G in the regulation resources of category w in the virtual power plant; is the downward regulation capacity of all regulation resources belonging to class w in the virtual power plant; The downward regulation capability of distributed photovoltaics in the regulation resources of category w of the virtual power plant; is the downward regulation capability of energy storage in the regulation resource of category w of the virtual power plant; is the upward regulation capability of the demand response load in the regulation resource of type w of the virtual power plant, is the downward regulation capability of electric vehicles V2G in the regulation resources of category w in the virtual power plant; By using the Canopy clustering algorithm, the number of s main network nodes can be effectively classified, so that the overload risk adjustment resources of the virtual power plant transmission section can be aggregated again, and after aggregation, they can be divided into v categories.

8. The method for verifying the safety of a virtual power plant based on secondary aggregation according to claim 7 is characterized in that: Taking the lowest regulation cost of virtual power plant as the objective function, the optimal dispatch model is constructed as follows: Where: C PV , C ES , C DR , C EV They are the virtual power plant distributed photovoltaic regulation cost, energy storage regulation cost, demand response load regulation cost, and electric vehicle load regulation cost; They are the upward adjustment cost and downward adjustment cost of the corresponding adjustable resources respectively; They are respectively the upward adjustment cost and the downward adjustment power of the corresponding adjustable resources; They are respectively the upward adjustment flag and the downward adjustment flag of the corresponding adjustable resources, which are 0-1 variables; the adjustable resources are λ=PV, ES, DR, EV.

9. The method for verifying the safety of a virtual power plant based on secondary aggregation according to claim 8 is characterized in that: The regulation of virtual power plants needs to meet the power balance and section safety constraints. When other power sources and loads remain unchanged, for the power balance constraints, there are ΔP VPP =∑(ΔP i,PV +ΔP i,ES +ΔP i,DR +ΔP i,ES ) Where ΔP VPP and ΔP i,PV , ΔP i,ES , ΔP i,DR , ΔP i,ES They are the total regulation value sent to the virtual power plant and the regulation deviation of distributed photovoltaic, energy storage, demand response load and electric vehicle load in each substation compared with the typical forecast value.

10. The method for verifying the safety of a virtual power plant based on secondary aggregation according to claim 9 is characterized in that: According to the grid operation constraints, for each main grid node k, its net output power is g k =Σ(P k,Gen -P k,LD +P k,VPP ) In the formula, ζ k is the net output power of node k; P k,Gen is the output of the conventional unit at node k; P k,LD is the normal load at node k; P k,VPP Provide the adjustable resources of the virtual power plant under node k; Therefore, the DC power flow constraint of the line is Where: N is the number of all nodes in the system; T l,k is the value of the 1st row and the kth column of the power transmission distribution coefficient PTDF matrix; F l max is the maximum active transmission power of line l.

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