Virtual power plant scheduling strategy based on consistency algorithm

Abstract

The embodiment of the invention provides a virtual power plant scheduling strategy based on a consistency algorithm. The method comprises the following steps: acquiring a plurality of power dispatching instructions sent by a power grid dispatching center; according to the plurality of power scheduling instructions, determining a total target allocation power of the plurality of distributed resource clusters and a target allocation power of a target distributed resource cluster, the target distributed resource cluster being any one of the plurality of distributed resource clusters; according to the target allocation power of the distributed resource cluster, the corresponding cooperative adjustment contribution parameter and the power error adjustment parameter, determining the target allocation power of other distributed resource clusters by adopting a consistency algorithm; and scheduling the distributed resources in the corresponding distributed resource cluster according to the plurality of target allocation powers. According to the method, through two-stage hierarchical distribution and adjustment of power, the dimensionality of scheduling instruction dynamic tracking optimization can be significantly reduced, the communication capacity is increased, and the economical efficiency and rapidity of virtual power plant power adjustment are ensured.

Description

Technical Field

[0001] The present application relates to the field of virtual power plants, and in particular to a virtual power plant scheduling strategy based on a consistency algorithm. Background Art

[0002] With the development of smart grids, information technology, energy storage technology, optimization algorithms and artificial intelligence, they have jointly promoted the formation and development of virtual power plants, making them an important part of modern power systems.

[0003] The regulation of distributed resources within the virtual power plant is mainly divided into centralized regulation and distributed regulation. Centralized regulation achieves the regulation of distributed resources through the control and coordination center to optimize the operation of the virtual power plant. Distributed regulation achieves the regulation of distributed resources in a modular way.

[0004] However, the scalability and compatibility of centralized control are greatly limited; distributed control uses consensus algorithms to achieve distributed scheduling, which requires trade-offs in multiple dimensions, so the fault tolerance rate needs to be improved. Summary of the invention

[0005] The embodiment of the present application provides a virtual power plant scheduling strategy based on a consistency algorithm, which uses a first-order consistency algorithm to solve the problem of power coordination allocation of each distributed resource cluster and each distributed resource.

[0006] In a first aspect, this embodiment provides a virtual power plant scheduling method based on a consistency algorithm, including:

[0007] Obtain multiple power dispatching instructions sent by the power grid dispatching center;

[0008] Determine, according to the plurality of power scheduling instructions, a total target allocated power of the plurality of distributed resource clusters and a target allocated power of the target distributed resource cluster;

[0009] According to the target allocation power of the distributed resource cluster, the corresponding collaborative adjustment contribution parameter and the power error adjustment parameter, a consensus algorithm is used to determine the target allocation power of other distributed resource clusters;

[0010] Power is allocated according to multiple objectives, and distributed resources in the corresponding distributed resource cluster are scheduled.

[0011] In a possible implementation, allocating power according to multiple targets and scheduling distributed resources in the corresponding distributed resource cluster include:

[0012] For any one of the multiple distributed resource clusters, determining adjustment speeds of multiple distributed resources in the distributed resource cluster;

[0013] According to the adjustment speed and the target allocation power corresponding to the distributed resource cluster, the target allocation sub-power of each distributed resource is determined based on the consistency algorithm;

[0014] The multiple target allocation sub-powers are sent to the corresponding distributed resources.

[0015] In a possible implementation, the target allocation power of other distributed resource clusters is determined by using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative adjustment contribution parameter, and the power error adjustment parameter, including:

[0016] Determine the association relationship between multiple distributed resource clusters, and based on the association relationship, construct a row random matrix corresponding to the multiple distributed resource clusters;

[0017] According to the row random matrix, the collaborative adjustment contribution parameters of each distributed resource cluster are determined;

[0018] Determine actual dispatching powers of the plurality of distributed resource clusters, and determine a power error adjustment parameter based on the actual dispatching powers of the plurality of distributed resource clusters and a total target allocated power;

[0019] According to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter, a consistency algorithm is used to determine the target allocation power of other distributed resource clusters.

[0020] In a possible implementation manner, before allocating power according to multiple targets and scheduling distributed resources in the corresponding distributed resource cluster, the method further includes:

[0021] Determine the power carrying range of multiple distributed resource clusters;

[0022] Determine the carrying results of multiple distributed resource clusters according to multiple target power allocations and corresponding power carrying ranges;

[0023] And according to the load bearing result, multiple target allocated powers are updated to obtain updated target allocated powers.

[0024] In a possible implementation manner, the above-mentioned determining the carrying results of multiple distributed resource clusters according to multiple target allocated powers and corresponding power carrying ranges includes:

[0025] For any one of the multiple distributed resource clusters, determining whether a target allocated power of the distributed resource cluster is within a corresponding power carrying range;

[0026] If yes, determining that the bearing result of the distributed resource cluster is the first bearing result, the first bearing result is used to indicate that the distributed resource cluster is not over-limited;

[0027] If not, it is determined that the bearing result of the distributed resource cluster is the second bearing result, and the second bearing result is used to indicate that the distributed resource cluster exceeds the limit.

[0028] In a possible implementation manner, the updating of multiple target allocated powers according to the load bearing result to obtain updated target allocated powers includes:

[0029] For any one of the multiple distributed resource clusters, when the bearing result of the distributed resource cluster is the second bearing result, the distributed resource cluster is determined as an inconsistency computing cluster, and based on the second bearing result, the row random matrix is ​​updated;

[0030] The upper limit or lower limit of the corresponding power carrying range is used as the target allocated power of the distributed resource cluster.

[0031] In a possible implementation, allocating power according to multiple targets and scheduling distributed resources in the corresponding distributed resource cluster include:

[0032] Allocate power according to multiple targets and determine total allocated power of multiple distributed resource clusters;

[0033] Determine whether the absolute difference between the total allocated power and the total target allocated power is less than a preset value;

[0034] When the absolute difference is less than a preset value, the actual power allocation instruction corresponding to each distributed resource cluster is generated according to multiple target power allocations, and the actual power allocation instruction is sent to the corresponding distributed resource cluster;

[0035] When the absolute difference is not less than the preset value, the target allocation power of other distributed resource clusters is re-determined based on the updated row random matrix, and the new total allocation power is determined based on multiple new target allocation powers until the absolute difference between the new total allocation power and the total target allocation power is less than the preset value.

[0036] In a second aspect, an embodiment of the present application provides a virtual power plant scheduling device based on a consistency algorithm, including:

[0037] An acquisition module, used to acquire multiple power dispatching instructions sent by a power grid dispatching center;

[0038] A processing module, configured to determine, according to a plurality of power scheduling instructions, a total target allocated power of a plurality of distributed resource clusters and a target allocated power of a target distributed resource cluster;

[0039] The processing module is further used to determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative adjustment contribution parameter and the power error adjustment parameter;

[0040] The scheduling module is used to allocate power according to multiple objectives and schedule the distributed resources in the corresponding distributed resource cluster.

[0041] In one possible implementation, the scheduling module is used to determine the adjustment speed of multiple distributed resources within a distributed resource cluster for any one of the multiple distributed resource clusters; determine the target allocation sub-power of each distributed resource based on a consistency algorithm according to the adjustment speed and the target allocation power corresponding to the distributed resource cluster; and send multiple target allocation sub-powers to the corresponding distributed resources.

[0042] In a possible implementation manner, the device further includes: a determination module;

[0043] A determination module is used to determine the association relationship between multiple distributed resource clusters, and based on the association relationship, construct a row random matrix corresponding to the multiple distributed resource clusters; determine the collaborative adjustment contribution parameter of each distributed resource cluster according to the row random matrix; determine the actual scheduling power of multiple distributed resource clusters, and determine the power error adjustment parameter based on the actual scheduling power of multiple distributed resource clusters and the total target allocation power;

[0044] The processing module is also used to determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter.

[0045] In one possible implementation, the processing module is used to determine the power carrying range of multiple distributed resource clusters; determine the carrying results of multiple distributed resource clusters based on multiple target allocated powers and corresponding power carrying ranges; and update the multiple target allocated powers based on the carrying results to obtain updated target allocated powers.

[0046] In a possible implementation manner, the device further includes: a judgment module;

[0047] The judgment module is used to determine the power carrying range of multiple distributed resource clusters; determine the carrying results of multiple distributed resource clusters according to multiple target allocated powers and corresponding power carrying ranges; and update the multiple target allocated powers according to the carrying results to obtain updated target allocated powers.

[0048] In one possible implementation, the processing module is used to determine, for any one of the multiple distributed resource clusters, a distributed resource cluster as an inconsistent computing cluster when the carrying result of the distributed resource cluster is the second carrying result, and to update the row random matrix based on the second carrying result; and to use the upper limit value or lower limit value of the corresponding power carrying range as the target allocated power of the distributed resource cluster.

[0049] In one possible implementation, the processing module is used to determine the total allocation power of multiple distributed resource clusters based on multiple target allocation powers; determine whether the absolute difference between the total allocation power and the total target allocation power is less than a preset value; if the absolute difference is less than the preset value, generate an actual allocation power instruction corresponding to each distributed resource cluster based on the multiple target allocation powers, and send the actual allocation power instruction to the corresponding distributed resource cluster; if the absolute difference is not less than the preset value, re-determine the target allocation power of other distributed resource clusters based on the updated row random matrix, and determine the new total allocation power based on the multiple new target allocation powers, until the absolute difference between the new total allocation power and the total target allocation power is less than the preset value.

[0050] In a third aspect, an embodiment of the present application provides a virtual power plant scheduling device based on a consistency algorithm, including: a memory and a processor;

[0051] Wherein, the memory stores computer-executable instructions;

[0052] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the virtual power plant scheduling method based on the consistency algorithm as described in the first aspect and various possible implementations of the first aspect above.

[0053] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the virtual power plant scheduling method based on the consistency algorithm as described in the first aspect and various possible implementation methods of the first aspect.

[0054] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the virtual power plant scheduling method based on the consistency algorithm as described in the first aspect and various possible implementation methods of the first aspect.

[0055] The present application provides a virtual power plant dispatching strategy based on a consistency algorithm. The power dispatching instructions are adjusted according to multiple power dispatching instructions sent by the power grid dispatching center and the power dispatching capabilities of the distributed resource clusters, and the target allocation power of other distributed resource clusters is determined by a consistency algorithm. According to the multiple target allocation powers, the distributed resources in the corresponding distributed resource clusters are dispatched and processed. This method can significantly reduce the dimension of dynamic tracking optimization of dispatching instructions and increase communication capacity through two-level hierarchical allocation and power adjustment, thereby ensuring the economy and speed of power regulation of virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0057] Figure 1 A scenario diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 1 ;

[0059] Figure 3 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 2 ;

[0060] Figure 4 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 3 ;

[0061] Figure 5 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 4 ;

[0062] Figure 6 A schematic diagram of the structure of a virtual power plant dispatching device based on a consistency algorithm provided in this application;

[0063] Figure 7 A structural schematic diagram of a virtual power plant scheduling device based on a consistency algorithm provided in this application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] "Multiple" means two or more, and other quantifiers are similar. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0066] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0067] The construction of smart grids, the advancement of information technology, the innovation of energy storage technology, the application of optimization algorithms and artificial intelligence have jointly promoted the formation and development of virtual power plants, making virtual power plants an important part of modern power systems.

[0068] The research on consistency algorithms aims to solve data consistency and synchronization problems in distributed environments, and its essence lies in decentralized autonomy.

[0069] Centralized control and distributed control are often used to control distributed resources in virtual power plants. Among them, centralized control is the easiest to achieve the optimal value, but its scalability and compatibility are relatively limited.

[0070] Using consensus algorithms to achieve distributed scheduling requires trade-offs in multiple dimensions, so the fault tolerance rate needs to be improved.

[0071] In response to the above problems, an embodiment of the present application provides a virtual power plant scheduling method based on a consistency algorithm. Figure 1 A scenario diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application, such as Figure 1 As shown, the virtual power plant 10 includes: a power grid dispatching center 101, a control center 102, and multiple distributed resource clusters 103.

[0072] The power grid dispatching center 101 can communicate with the control center to send power dispatching instructions to the control center.

[0073] The control center 102 obtains the power dispatching instruction formulated by the power grid dispatching center 101, and then sends the target allocated power to the multiple distributed resource clusters 103, so that the distributed resource clusters perform power adjustment based on the allocated target allocated power.

[0074] Multiple distributed resource clusters 103 are communicated and connected to achieve active power balance of the virtual power plant.

[0075] It is understandable that each distributed resource cluster 103 includes multiple distributed resources. The regulation capabilities of different distributed resources may be the same or different. The multiple distributed resources in each distributed resource cluster are connected in communication to achieve active power balance of the distributed resource cluster.

[0076] The embodiment of the present application provides a virtual power plant scheduling method based on a consistency algorithm. The method obtains power adjustment instructions, adjusts the consistency of distributed resource clusters, determines the target allocated power allocated to each distributed resource cluster, and then adjusts the distributed resources under each distributed resource cluster according to the target allocated power corresponding to each distributed resource cluster. It determines the adjusted power allocated to each distributed resource to achieve decentralized consistency control.

[0077] Figure 2 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 1 The execution subject of this embodiment may be, for example, Figure 1 The control center shown. Figure 2 As shown, the method includes:

[0078] S201. Acquire multiple power dispatching instructions sent by a power grid dispatching center.

[0079] The power grid dispatch center is used for coordination and management, and formulates corresponding adjustment power according to the adjustment capabilities of multiple distributed resources. The power dispatch instruction is obtained according to the adjustment power, and each distributed resource corresponds to a power dispatch instruction.

[0080] The control center obtains multiple power dispatching instructions sent by the power grid dispatching center, and each power dispatching instruction is related to its corresponding regulation capability.

[0081] S202: Determine, according to the multiple power scheduling instructions, a total target allocated power of multiple distributed resource clusters and a target allocated power of a target distributed resource cluster.

[0082] The total target allocated power is the sum of the adjusted powers of multiple distributed resources; and the sum of the adjusted powers of multiple distributed resources in the target distributed resource cluster is the target allocated power of the target distributed resource cluster.

[0083] According to the regulated powers corresponding to the multiple power scheduling instructions, the sum of the regulated powers corresponding to the multiple distributed resources is used as the total target allocated power. The target distributed resource cluster includes multiple distributed resources, and the sum of the regulated powers of the multiple distributed resources in the target distributed resource cluster is used as the target allocated power corresponding to the target distributed resource cluster.

[0084] For example, there are four distributed resources in the virtual power plant: a1, a2, b1, and b2. The corresponding adjustment powers of these four distributed resources are -30, 20, 15, and 30. Among them, a1 and a2 are distributed resources in the target distributed resource cluster A.

[0085] Therefore, the total target allocated power is: 45 and the target allocated power is -10.

[0086] S203: Determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter, and the power error adjustment parameter.

[0087] The collaborative adjustment contribution parameter represents the relationship between distributed resource clusters, and the power error adjustment parameter is the difference between the total target allocation power and the sum of the target allocation powers of multiple distributed resource clusters.

[0088] According to the collaborative adjustment contribution parameters and the total target allocation power, the target allocation power corresponding to the target distributed resource cluster is used as the benchmark, and the consistency algorithm is used to adjust the target allocation power of other distributed resource clusters, so that the adjustment costs of multiple distributed resource clusters finally reach a consistent state. The power error adjustment parameter is used to determine whether the power allocation is completed. If it is completed, it indicates that the target allocation power corresponding to multiple distributed resource clusters has been obtained. If it is not completed, the multiple distributed resource clusters will continue to be adjusted based on the consistency algorithm until the power allocation is completed.

[0089] S204: Allocate power according to multiple targets and schedule the distributed resources in the corresponding distributed resource cluster.

[0090] The process of allocating power according to the target and adjusting power to the distributed resources in the distributed resource cluster is called scheduling.

[0091] According to each target power allocation, the distributed resources in the distributed resource cluster may be scheduled in the following manner.

[0092] For example: using a consistency algorithm, the distributed resources are scheduled according to the target allocation power; according to the target allocation power, the distributed resources are allocated according to the adjustment capabilities of the distributed resources themselves; according to the target allocation power, the target allocation power is evenly distributed according to the number of distributed resources, and the distributed resources are scheduled.

[0093] The virtual power plant dispatching method based on the consistency algorithm provided in the embodiment of the present application obtains multiple power adjustment instructions sent by the power grid dispatching center, adopts the consistency algorithm, determines the target allocation power of the distributed resource cluster, makes the distributed resource cluster with low adjustment cost bear more adjustment power, and minimizes the total adjustment cost of each distributed resource cluster. According to multiple target allocation powers, the distributed resources in the corresponding distributed resource cluster are dispatched and processed, so that the distributed resources with fast adjustment speed bear more adjustment power, and minimize the total adjustment time of each distributed resource cluster, thereby ensuring the economy and rapidity of the power adjustment of the virtual power plant. By allocating the adjustment power in two levels, the dimension of the dynamic tracking optimization of the dispatching instruction can be significantly reduced, and the communication capacity can be increased.

[0094] Figure 3 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 2 This embodiment is in Figure 2 Based on the embodiment, a possible implementation method of using a consistency algorithm to determine the target allocation power of other distributed resource clusters according to the target allocation power of the distributed resource cluster, the corresponding collaborative adjustment contribution parameter and the power error adjustment parameter is described in detail. Figure 3 As shown, the method includes:

[0095] S301: Determine association relationships among the multiple distributed resource clusters, and based on the association relationships, construct row random matrices corresponding to the multiple distributed resource clusters.

[0096] The association relationship between the multiple distributed resource clusters is the communication direction and communication relationship between the multiple distributed resource clusters; and the row random matrix is ​​determined according to the multiple distributed resource clusters and the communication direction and communication relationship between the multiple distributed resource clusters.

[0097] The communication connection between distributed resource clusters is determined according to the connection directions and weights between multiple distributed resource clusters and multiple distributed resource clusters. The directed graph G represents the network topology structure of multiple distributed resource clusters. In the directed graph G, the nodes correspond to the distributed resource clusters, the connection directions correspond to the connection directions between the distributed resources, and the weights correspond to the connection relationships between the distributed resource clusters. According to the directed graph G, the weighted adjacency matrix A is determined as follows:

[0098] A=[a ij ]∈R n×n

[0099] Among them, R n×n It indicates that the weighted adjacency matrix A is a real matrix with n rows and n columns, where n is the number of nodes and a ij It is determined by the connection relationship between node i and node j. If node i is connected to node j, it is the weight; if node i is not connected to node j, it is 0.

[0100] According to the weighted adjacency matrix A, the Laplace matrix L is determined using the following formula:

[0101] L=[l ij ]∈R n×n

[0102]

[0103] Considering the communication delay problem of each distributed resource cluster during adjustment, the first-order consensus algorithm of discrete time system is adopted as follows:

[0104]

[0105] Among them, y i is the consistency state variable of the i-th distributed cluster; k is the discrete time index; d ij [k] represents the i-th row and j-th column element of the row random matrix D at the i-th iteration.

[0106] The row random matrix is ​​determined using the following formula:

[0107] D=[d ij ]∈R n×n

[0108]

[0109] S302: Determine the collaborative adjustment contribution parameter of each distributed resource cluster according to the row random matrix.

[0110] The collaborative regulation contribution parameters are determined based on the above row random matrix.

[0111] For example, during the kth consistency calculation, the coordinated adjustment contribution parameter of distributed resource cluster 3 to distributed resource cluster 4 is d 34 [k].

[0112] S303: Determine actual scheduling powers of multiple distributed resource clusters, and determine the power error adjustment parameter based on the actual scheduling powers of the multiple distributed resource clusters and the total target allocated power.

[0113] The actual scheduling power is the scheduling power of the current state of multiple distributed resource clusters when performing consistency calculation.

[0114] The expression of the power error adjustment parameter is as follows:

[0115]

[0116] Where ΔP error is the power error adjustment parameter, ΔP Order Allocate power for the total target, ΔP i It is the actual scheduling power of the distributed resource cluster.

[0117] It can be understood that when the distributed resource cluster allocation power is calculated based on the consistency algorithm, the actual scheduling of the distributed resource cluster is the target allocation power of the distributed resource cluster.

[0118] S304: Determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter, and the power error adjustment parameter.

[0119] Different distributed resource clusters have different adjustment costs. The adjustment cost is used as a consistent state variable of each distributed resource cluster, so that distributed resources with lower adjustment costs bear more adjustment power.

[0120] The following formula is used to calculate the adjustment cost of any distributed resource cluster i among multiple distributed resource clusters:

[0121] r i =C i |ΔP i |

[0122] Among them, r i To adjust the cost; C i is the average power adjustment cost coefficient of distributed resource cluster i, and the target allocated power of distributed resource cluster i.

[0123] According to the association relationship between the distributed resource cluster i and other distributed resource clusters, the distributed resource cluster j is determined, and the distributed resource cluster j is any distributed resource cluster that has an association relationship with the distributed resource cluster i.

[0124] The target allocation power of distributed resource cluster j is calculated using the following formula:

[0125]

[0126] Among them, d ij[k] is the contribution parameter of the coordinated adjustment between distributed resource cluster i and distributed resource cluster j obtained according to the row random matrix.

[0127] According to the above formula, the target allocation power of each distributed resource cluster is obtained, and the actual scheduling power is obtained. According to the actual scheduling power and the total target allocation power, the power error adjustment parameter is obtained. According to the relationship between the total target allocation power and the power error adjustment parameter, the adjustment cost of distributed resource cluster i is adjusted to achieve the adjustment of the target allocation power of other distributed resource clusters.

[0128] When ΔP Order >0, if ΔP error >0, indicating that the target allocation power of each distributed resource cluster does not reach the total target allocation power, so it is necessary to increase r i ;

[0129] When ΔP Order >0, if ΔP error <0, indicating that the target allocation power of each distributed resource exceeds the total target allocation power, so it is necessary to reduce r i ;

[0130] When ΔP Order <0, if ΔP error >0, indicating that the target allocation power of each distributed resource exceeds the total target allocation power, r i Need to reduce,

[0131] When ΔP Order <0, if ΔP error <0, indicating that the target allocation power of each distributed resource cluster does not reach the total target allocation power, r i Needs to be increased.

[0132] For example: When the total target power allocation ΔP Order =-20, ΔP error When it is 10, it indicates that the target allocation power of each distributed resource cluster exceeds the total target allocation power. In this case, the adjustment cost of distributed resource cluster i needs to be reduced to adjust the target allocation power of other distributed resource clusters.

[0133] In a possible implementation, the method of determining the target allocated power of other distributed resource clusters by using a consistency algorithm according to the target allocated power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter, and the power error adjustment parameter also includes:

[0134] According to the multiple target allocated powers, the total allocated powers of the multiple distributed resource clusters are determined, and it is determined whether the absolute difference between the total allocated power and the total target allocated power is less than a preset value.

[0135] When the absolute difference is less than a preset value, the actual power allocation instruction corresponding to each distributed resource cluster is generated according to the multiple target power allocations, and the actual power allocation instruction is sent to the corresponding distributed resource cluster.

[0136] When the absolute difference is not less than the preset value, the target allocation power of other distributed resource clusters is re-determined based on the updated row random matrix, and the new total allocation power is determined based on multiple new target allocation powers until the absolute difference between the new total allocation power and the total target allocation power is less than the preset value.

[0137] Among them, the preset value is the error accuracy.

[0138] When the absolute difference between the total allocated power and the total target allocated power is less than a preset value, it indicates that the consistency calculation has reached the error accuracy and the power allocation to the distributed resource cluster has been completed.

[0139] When the absolute difference between the total allocated power and the total target allocated power is not less than the preset value, it indicates that the consistency calculation has not reached the error accuracy. Then, according to the relationship between the total allocated power and the total target allocated power, the adjustment cost of the target distributed resource cluster is adjusted, and the consistency calculation of the distributed resource cluster is continued until the absolute difference between the total allocated power and the total target allocated power meets the error accuracy.

[0140] For example: When the total target power allocation ΔP Order =-20, ΔP error is 10, the error accuracy is 1, ΔP error is greater than 1, so it is necessary to reduce the adjustment cost of the target distributed resource cluster, perform consistency calculation, and obtain the target allocation power of other distributed resource clusters until ΔP error Less than the error precision.

[0141] The virtual power plant scheduling method based on the consistency algorithm provided in the embodiment of the present application determines the association relationship between multiple distributed resource clusters, constructs a row random matrix corresponding to multiple distributed resource clusters based on the association relationship, and determines the coordinated regulation relationship parameters of the distributed resource clusters according to the random matrix; determines the power error adjustment parameter by the actual scheduling power and the total target allocation power; and uses the consistency algorithm to determine the target allocation power of other distributed resource clusters according to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter, so that the distributed resource clusters with lower regulation cost undertake more regulation tasks, thereby ensuring the economy of the virtual power plant power regulation. Considering the problem of communication delay between distributed resource clusters, the first-order consistency algorithm of discrete time system is adopted to complete the effective information consistency calculation of adjacent individuals to reduce delay and channel noise, enhance the robustness of the algorithm, and can simultaneously deal with problems such as transmission delay and noise in actual communication networks.

[0142] Figure 4 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 3 , this embodiment is in Figure 3 Based on the embodiment, a possible implementation method for determining the target allocated power is supplemented. Figure 4 As shown, the method includes:

[0143] S401: Determine power carrying ranges of multiple distributed resource clusters.

[0144] The power carrying range is the power regulation range that the distributed resource cluster can bear.

[0145] The regulation capability of each distributed resource cluster is limited, and the regulation capability of distributed resources determines the power carrying range of distributed resources.

[0146] It is understandable that if ΔP Order The larger the absolute value of , the greater the adjustment power that each distributed resource cluster needs to bear, and the easier it is to exceed the power carrying range of the distributed resource cluster.

[0147] For example: Compared to ΔP Order =-10, ΔP Order The absolute value of -100 is larger, and the adjustment power that each distributed resource cluster needs to bear is correspondingly larger. Therefore, it is easier to exceed the power carrying range of the distributed resource cluster.

[0148] S402. For any one of the multiple distributed resource clusters, determine whether the target allocated power of the distributed resource cluster is within the corresponding power carrying range; if so, execute step S403; if not, execute step S404.

[0149] For any distributed resource cluster among the multiple distributed resource clusters, a relationship between a target allocated power of the distributed resource cluster and a power carrying range corresponding to the distributed resource cluster is determined.

[0150] If the target allocated power of the distributed resource cluster is within the corresponding power carrying range, it indicates that the distributed resource cluster can achieve the corresponding target allocated power. At this time, it can be determined that the carrying result of the distributed resource cluster is the first carrying result indicating that the distributed resource cluster is not over the limit.

[0151] If the target allocated power of the distributed resource cluster is not within the corresponding power carrying range, it indicates that the distributed resource cluster cannot achieve the corresponding target allocated power. At this time, it can be determined that the carrying result of the distributed resource cluster is the second carrying result.

[0152] S403: Determine that the bearing result of the distributed resource cluster is a first bearing result.

[0153] S404: Determine that the bearing result of the distributed resource cluster is a second bearing result.

[0154] S405: Update multiple target allocated powers according to the load bearing result to obtain updated target allocated powers.

[0155] The updated target allocated power may be, for example, updating the target allocated power according to a specified rule, and updating the target allocated power to the target allocated power calculated in the last consistency calculation.

[0156] In a possible implementation manner, the specific implementation manner of updating the multiple target allocated powers according to the load bearing result to obtain the updated target allocated powers is:

[0157] For any one of the multiple distributed resource clusters, when the carrying result of the distributed resource cluster is the second carrying result, the distributed resource cluster is determined as an inconsistent computing cluster, and based on the second carrying result, the row random matrix is ​​updated, and the upper limit value or lower limit value of the corresponding power carrying range is used as the target allocated power of the distributed resource cluster.

[0158] It is understandable that when the bearing result of a distributed resource cluster is the second bearing result, it indicates that the power adjustment task that the distributed resource cluster can undertake has reached the limit, and consistency calculation can no longer be performed to adjust the target allocated power.

[0159] Therefore, when the carrying result of a distributed resource cluster is the second carrying result, the distributed resource cluster is determined as an inconsistent computing cluster, that is, the distributed resource cluster exits the consistency calculation, the weight related to the distributed resource cluster becomes 0, the weighted adjacency matrix A changes accordingly, and the row random matrix also changes with the change of the weighted adjacency matrix A. Subsequent consistency calculations will determine the collaborative adjustment contribution parameters between distributed resource clusters based on the changed row random matrix.

[0160] It can be understood that for this distributed resource cluster, when ΔP Order >0, the upper limit of the corresponding power carrying range is used as the target allocation power of the distributed resource cluster; when ΔP Order <0, the lower limit of the corresponding power carrying range is used as the target allocated power of the distributed resource cluster.

[0161] For example: When ΔP Order >0, the target allocated power of distributed resource cluster i reaches the upper limit of the power carrying range corresponding to the distributed resource cluster, and the upper limit of the power carrying range is used as the target allocated power.

[0162] The virtual power plant scheduling method based on the consistency algorithm provided in the embodiment of the present application determines the power carrying range of multiple distributed resource clusters, and performs safety verification on the target allocated power according to the power carrying range and the target allocated power and the carrying results of multiple distributed resource clusters, so as to ensure that the target allocated power meets the power adjustment range of the distributed resource cluster and prevent the active power from exceeding the limit. By comparing the absolute difference between the total allocated power and the total target allocated power and the preset value, it is ensured that the result of the consistency calculation meets the consistency adjustment accuracy.

[0163] Figure 5 A schematic diagram of a virtual power plant scheduling method based on a consistency algorithm provided in an embodiment of the present application Figure 4 , this embodiment is in Figure 2 to Figure 4 Based on the embodiment, a possible implementation method for allocating power according to multiple targets and scheduling distributed resources in the corresponding distributed resource cluster is described in detail. Figure 5 As shown, the method includes:

[0164] S501. For any one of the multiple distributed resource clusters, determine adjustment speeds of multiple distributed resources in the distributed resource cluster.

[0165] Among them, the adjustment speed expresses how fast the distributed resources adjust power.

[0166] The state variable that is adjusted consistently based on the adjustment time allows distributed resources with fast adjustment speed to take on more adjustment tasks.

[0167] The adjustment time of the jth distributed resource in the xth distributed resource cluster is determined by the following formula:

[0168]

[0169] Among them, t xj is the adjustment time, ΔP xj is the adjustment power corresponding to the jth distributed resource power scheduling instruction in the xth distributed resource cluster, is the adjusted power corresponding to the j-th distributed resource power scheduling instruction in the x-th distributed resource cluster.

[0170] It can be understood that when the target allocation power of the distributed resource cluster x is ΔP x When , the relationship between the adjusted power corresponding to the j-th distributed resource power scheduling instruction in the x-th distributed resource cluster is:

[0171]

[0172] in, and are respectively the increasing rate and decreasing rate of the regulated power of the jth distributed resource in the xth distributed resource cluster.

[0173] S502: Determine a target allocation sub-power for each distributed resource based on a consistency algorithm according to the adjustment speed and the target allocation power corresponding to the distributed resource cluster.

[0174] The target allocated sub-power is the regulated power allocated to the distributed resource cluster based on the consistency algorithm.

[0175] It can be understood that multiple distributed resources in a distributed resource cluster are connected in communication. Therefore, similar to determining the directed graph G, weighted adjacency matrix A, Laplace matrix L and row random matrix D at the level of multiple distributed resource clusters, according to the connection relationship between multiple distributed resources, the second row random matrix D of the distributed resource cluster x can be determined. (x) as follows:

[0176]

[0177] Among them, M x is the number of adjustment instructions for the distributed resource cluster x.

[0178] Similarly, taking into account the communication delay, the adjustment time of each distributed resource can be calculated using the following formula:

[0179]

[0180] in, The collaborative adjustment contribution parameters between distributed resources j and distributed resources w are obtained according to the second row of the random matrix.

[0181] Similarly, the adjustment time of multiple distributed resources is adjusted to allocate the target allocation power through the relationship between the sum of the target allocation sub-powers of multiple distributed resources in a distributed resource cluster x, that is, the total target allocation sub-power, and the target allocation power.

[0182] When ΔP x >0, if ΔP error,x >0,t xj Need to increase if ΔP error,x <0,t xj Need to reduce;

[0183] When ΔP x <0, if ΔP error,x >0,t xj Need to be reduced if ΔP error,x <0,t xj Needs to be increased.

[0184] Similarly, the regulation capability of distributed resources is limited by the maximum regulation time and regulation power range, and safety inspection and correction are performed according to the following formula:

[0185]

[0186]

[0187] Similarly, when the target allocation sub-power of distributed resource j in distributed resource cluster x exceeds the limit, the distributed resource is treated as an inconsistent distributed resource, the consistency calculation is exited, and the row random matrix D is updated. (x) .

[0188] Similarly, according to the sum of the corresponding target allocation sub-powers of multiple distributed resources in the distributed resource cluster x, that is, the total target allocation sub-power, and the target allocation power of the distributed resource cluster, the corresponding second power error adjustment parameter ΔP is calculated using the following formula: error,x .

[0189]

[0190] Where: is the target allocated power of distributed resource cluster x, Allocate sub-powers for the overall target.

[0191] Similarly, according to the second power error adjustment parameter and the second error precision, it is determined whether the consistency calculation meets the calculation requirement. If the requirement is met, it indicates that the consistency calculation is completed. If not, the consistency calculation is continued until the calculation precision is met.

[0192] S503: Send multiple target allocation sub-powers to corresponding distributed resources.

[0193] Multiple target non-allocated sub-powers are sent to the corresponding distributed resources to achieve power scheduling of the virtual power plant.

[0194] The virtual power plant scheduling method based on the consistency algorithm provided in the embodiment of the present application uses a consistency algorithm to adjust the power of distributed resources in the distributed resource cluster according to the target allocation power corresponding to multiple distributed resource clusters, so that the distributed resources with faster adjustment speed undertake more adjustment tasks, thereby ensuring the rapidity of the power adjustment of the virtual power plant. According to the adjustment speed and the target allocation power corresponding to the distributed resource cluster, the target allocation sub-power of each distributed resource is determined based on the consistency algorithm, so that the distributed resources with fast adjustment speed undertake more adjustment power, and the total adjustment time of each distributed resource cluster is minimized, thereby ensuring the rapidity of the power adjustment of the virtual power plant. By allocating the adjustment power in a two-level hierarchical manner, the dimension of the dynamic tracking optimization of the scheduling instruction can be significantly reduced, and the communication capacity can be increased.

[0195] Figure 6 A schematic diagram of the structure of a virtual power plant dispatching device based on a consistency algorithm is provided for this application, such as Figure 6 As shown, a virtual power plant scheduling device 60 based on a consistency algorithm provided in this embodiment includes:

[0196] An acquisition module 601 is used to acquire multiple power dispatching instructions sent by a power grid dispatching center;

[0197] A processing module 602 is used to determine the total target allocated power of multiple distributed resource clusters and the target allocated power of a target distributed resource cluster according to multiple power scheduling instructions;

[0198] The processing module 602 is further used to determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative adjustment contribution parameter and the power error adjustment parameter;

[0199] The scheduling module 603 is used to allocate power according to multiple objectives and perform scheduling processing on the distributed resources in the corresponding distributed resource cluster.

[0200] In one possible implementation, the scheduling module 603 is used to determine the adjustment speed of multiple distributed resources within a distributed resource cluster for any one of the multiple distributed resource clusters; determine the target allocation sub-power of each distributed resource based on a consistency algorithm according to the adjustment speed and the target allocation power corresponding to the distributed resource cluster; and send multiple target allocation sub-powers to the corresponding distributed resources.

[0201] In a possible implementation, the device further includes: a determination module 604;

[0202] The determination module 604 is used to determine the association relationship between the multiple distributed resource clusters, and based on the association relationship, construct a row random matrix corresponding to the multiple distributed resource clusters; determine the collaborative adjustment contribution parameter of each distributed resource cluster according to the row random matrix; determine the actual scheduling power of the multiple distributed resource clusters, and determine the power error adjustment parameter based on the actual scheduling power of the multiple distributed resource clusters and the total target allocation power;

[0203] The processing module 602 is further configured to determine the target allocated powers of other distributed resource clusters using a consistency algorithm according to the target allocated powers of the distributed resource clusters, the corresponding collaborative regulation contribution parameters and the power error adjustment parameters.

[0204] In one possible implementation, the processing module 602 is used to determine the power carrying range of multiple distributed resource clusters; determine the carrying results of multiple distributed resource clusters based on multiple target allocated powers and corresponding power carrying ranges; and update the multiple target allocated powers based on the carrying results to obtain updated target allocated powers.

[0205] In a possible implementation manner, the device further includes: a determination module 605;

[0206] The judgment module 605 is used to determine the power carrying range of multiple distributed resource clusters; determine the carrying results of multiple distributed resource clusters according to multiple target allocated powers and corresponding power carrying ranges; and update the multiple target allocated powers according to the carrying results to obtain updated target allocated powers.

[0207] In one possible implementation, the processing module 602 is used to determine, for any one of the multiple distributed resource clusters, a distributed resource cluster as an inconsistent computing cluster when the carrying result of the distributed resource cluster is the second carrying result, and to update the row random matrix based on the second carrying result; and to use the upper limit value or lower limit value of the corresponding power carrying range as the target allocated power of the distributed resource cluster.

[0208] In one possible implementation, the processing module 602 is used to determine the total allocation power of multiple distributed resource clusters based on multiple target allocation powers; determine whether the absolute difference between the total allocation power and the total target allocation power is less than a preset value; if the absolute difference is less than the preset value, generate an actual allocation power instruction corresponding to each distributed resource cluster based on the multiple target allocation powers, and send the actual allocation power instruction to the corresponding distributed resource cluster; if the absolute difference is not less than the preset value, re-determine the target allocation power of other distributed resource clusters based on the updated row random matrix, and determine the new total allocation power based on the multiple new target allocation powers, until the absolute difference between the new total allocation power and the total target allocation power is less than the preset value.

[0209] Figure 7 A schematic diagram of the structure of a virtual power plant dispatching device based on a consistency algorithm provided in an embodiment of the present application. Figure 7 As shown, the virtual power plant scheduling device 70 based on the consistency algorithm in this embodiment includes: at least one processor 701 and a memory 702. Optionally, the device 70 also includes a communication component 703. The processor 701, the memory 702 and the communication component 703 are connected via a bus 704.

[0210] In the specific implementation process, at least one processor 701 executes the computer execution instructions stored in the memory 702, so that at least one processor 701 executes the virtual power plant scheduling method based on the consistency algorithm shown in the above embodiment.

[0211] The specific implementation process of the processor 701 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0212] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0213] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0214] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0215] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the virtual power plant scheduling method based on the consistency algorithm shown in the above embodiment.

[0216] An embodiment of the present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the virtual power plant scheduling method based on the consistency algorithm shown in the above embodiment is implemented.

[0217] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0218] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0219] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, 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 interface, device or unit, which can be electrical, mechanical or other forms.

[0220] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0221] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0222] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0223] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0224] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A virtual power plant scheduling method based on a consistency algorithm, characterized in that: include: Acquire multiple power dispatching instructions sent by a power grid dispatching center, where the power dispatching instructions are determined according to the regulation capabilities of multiple distributed resources; Determine, according to the multiple power scheduling instructions, a total target allocated power of the multiple distributed resource clusters and a target allocated power of a target distributed resource cluster, the target distributed resource cluster being any one of the multiple distributed resource clusters; According to the target allocated power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter, a consistency algorithm is used to determine the target allocated power of other distributed resource clusters, wherein the collaborative regulation contribution parameter is determined according to the association relationship between the distributed resource cluster and other distributed resource clusters, and the power error adjustment parameter is determined according to the total target allocated power and the actual scheduling power of multiple distributed resource clusters; Power is allocated according to multiple objectives, and distributed resources in the corresponding distributed resource cluster are scheduled.

2. The method according to claim 1, characterized in that The allocating power according to multiple targets and scheduling the distributed resources in the corresponding distributed resource cluster include: For any one of the multiple distributed resource clusters, determining adjustment speeds of multiple distributed resources in the distributed resource cluster; Determining a target allocation sub-power for each distributed resource based on a consistency algorithm according to the adjustment speed and the target allocation power corresponding to the distributed resource cluster; The multiple target allocation sub-powers are sent to the corresponding distributed resources.

3. The method according to claim 1, characterized in that The method of determining the target allocated power of other distributed resource clusters by using a consistency algorithm according to the target allocated power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter, and the power error adjustment parameter includes: Determine association relationships among the multiple distributed resource clusters, and based on the association relationships, construct row random matrices corresponding to the multiple distributed resource clusters; Determining a collaborative adjustment contribution parameter of each distributed resource cluster according to the row random matrix; Determining actual dispatch powers of the plurality of distributed resource clusters, and determining the power error adjustment parameter based on the actual dispatch powers of the plurality of distributed resource clusters and the total target allocated power; According to the target allocated power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter, a consistency algorithm is used to determine the target allocated power of other distributed resource clusters.

4. The method according to claim 3, characterized in that Before allocating power according to multiple targets and scheduling distributed resources in the corresponding distributed resource cluster, the method further includes: Determining power carrying ranges of the multiple distributed resource clusters; Determining the carrying results of the multiple distributed resource clusters according to the multiple target allocated powers and the corresponding power carrying ranges; And according to the load bearing result, the multiple target allocated powers are updated to obtain updated target allocated powers.

5. The method according to claim 4, characterized in that The determining, according to the multiple target allocated powers and the corresponding power carrying ranges, the carrying results of the multiple distributed resource clusters includes: For any one of the multiple distributed resource clusters, determining whether a target allocated power of the distributed resource cluster is within a corresponding power carrying range; If yes, determining that the bearing result of the distributed resource cluster is a first bearing result, where the first bearing result is used to indicate that the distributed resource cluster is not over-limited; If not, it is determined that the bearing result of the distributed resource cluster is a second bearing result, and the second bearing result is used to indicate that the distributed resource cluster exceeds the limit.

6. The method according to claim 5, characterized in that The updating of the multiple target allocated powers according to the load bearing result to obtain updated target allocated powers includes: For any one of the multiple distributed resource clusters, when a bearing result of the distributed resource cluster is a second bearing result, determining the distributed resource cluster as an inconsistency computing cluster, and updating the row random matrix based on the second bearing result; An upper limit value or a lower limit value of the corresponding power carrying range is used as the target allocated power of the distributed resource cluster.

7. The method according to claim 1, characterized in that The allocating power according to multiple targets and scheduling the distributed resources in the corresponding distributed resource cluster include: Determining the total allocated powers of the multiple distributed resource clusters according to the multiple target allocated powers; Determining whether an absolute difference between the total allocated power and the total target allocated power is less than a preset value; When the absolute difference is less than a preset value, generating an actual power allocation instruction corresponding to each distributed resource cluster according to the multiple target power allocations, and sending the actual power allocation instruction to the corresponding distributed resource cluster; When the absolute difference is not less than the preset value, the target allocation power of other distributed resource clusters is re-determined based on the updated row random matrix, and the new total allocation power is determined based on multiple new target allocation powers until the absolute difference between the new total allocation power and the total target allocation power is less than the preset value.

8. A virtual power plant dispatching device based on a consistency algorithm, characterized in that: include: An acquisition module, used to acquire multiple power dispatching instructions sent by a power grid dispatching center; A processing module, configured to determine, according to the plurality of power scheduling instructions, a total target allocated power of the plurality of distributed resource clusters and a target allocated power of the target distributed resource cluster; The processing module is further used to determine the target allocation power of other distributed resource clusters using a consistency algorithm according to the target allocation power of the distributed resource cluster, the corresponding collaborative regulation contribution parameter and the power error adjustment parameter; The scheduling module is used to allocate power according to multiple objectives and schedule the distributed resources in the corresponding distributed resource cluster.

9. A virtual power plant dispatching device based on a consistency algorithm, characterized in that: include: Memory and processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the virtual power plant scheduling method based on the consistency algorithm as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the virtual power plant scheduling method based on a consistency algorithm as described in any one of claims 1 to 7.

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