Resource partitioning based balanced aggregation, external characteristic representation and operation regulation method and platform

Abstract

The present disclosure relates to the technical field of electric energy dispatching, and provides a resource partition balancing aggregation, external characteristic representation and operation regulation method and platform. The aggregation method considers resource spatial distribution distance, aggregation cost, partition plan power deviation, and minimization of partition loss caused by factors such as partition balance between local consumption and cross-zone transaction power, determines an aggregation node and a partition corresponding to the aggregation node and a corresponding virtual machine group. The external characteristic representation method predicts the future output condition of flexible resources based on a data-driven prediction method to construct a resource feasible region and its aggregation feasible region considering the foresight. The operation regulation method considers resource output and climbing power to obtain an operation regulation decision that minimizes the maximum uncertainty loss through a two-stage combination of the maximum uncertainty loss model and the minimum maximum expected loss model. The partition aggregation optimization, external characteristic depiction accuracy, and regulation decision accuracy are realized respectively.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of electric energy deployment, and particularly relates to a resource partition balance-based aggregation, external characteristic representation and operation regulation method and platform. BACKGROUND

[0002] A virtual power plant is a medium for connecting flexible resources that can provide electric energy with a superior power grid, and various different types of flexible resources are aggregated to form a scale effect to participate in power grid operation, etc. In order to improve the operation management capability and market competitiveness, the virtual power plant needs to develop an effective distributed flexible resource integration management strategy.

[0003] However, the current construction of the virtual power plant still faces many problems to be solved. For example, on the one hand, the flexible resources called by the virtual power plant have the characteristics of small capacity, large quantity, complex relationship between the distribution network and the flexible resource access point, etc., which greatly increases the difficulty of aggregation, and the scale aggregation technology needs to be broken through. On the other hand, due to the rich types of flexible resources and different technical characteristics, the identification of the external characteristic parameters of the aggregation body after aggregation is difficult, and the accuracy of the current aggregation technology for the characterization of the characteristics of the aggregation body is insufficient, and needs to be broken through. On the other hand, there are many uncertain factors of new energy output, which increases the operation fluctuation of the virtual power plant, and the stable operation of the virtual power plant faces great challenges, and the operation management technology of the virtual power plant needs to be broken through to improve the operation stability of the virtual power plant. SUMMARY

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present disclosure is to provide a resource partition balance-based aggregation, external characteristic representation and operation regulation method and platform to solve the problems in the related art.

[0005] The first aspect of the present disclosure provides a resource partitioning-based balancing method for aggregating a virtual power plant, which is used for partitioning flexible resources accessed by nodes of a power distribution network; the method comprises: determining an aggregation node in each node and a partition corresponding to each aggregation node, respectively, according to minimization of a partition objective function; wherein the partition objective function comprises: a first term based on a global total distance of aggregation nodes, a second term based on a global aggregation cost, a third term based on a global partition planned power deviation amount, and a fourth term not satisfying a partition balancing principle; wherein the global total distance of aggregation nodes is defined as a sum of distances between each current aggregation node and each non-aggregation node adjacent to the current aggregation node; the global aggregation cost is defined as a sum of aggregation costs of coordination controllers of each aggregation node; the global partition planned power deviation amount is defined as a sum of partition deviation amounts of real-time power and day-ahead planned power of each partition; and the partition balancing principle refers to a tendency of increasing a total amount of local consumption power of each partition and a tendency of decreasing a total amount of cross-zone transaction input power between partitions; and flexible resources in each partition are aggregated to obtain a corresponding virtual power plant.

[0006] In an embodiment of the first aspect, the second term comprises a binary decision variable used to indicate whether a node is an aggregation node, and used as a coefficient of an aggregation node-related cost to exclude non-aggregation nodes from the calculation of the aggregation cost; and the first term comprises an auxiliary variable constructed based on a product of mutual exclusion variables of binary decision variables of a current node and other nodes, and used as a coefficient of the distance to exclude distances between two non-aggregation nodes and distances from an aggregation node to a node through another aggregation node from the sum of distances.

[0007] In an embodiment of the first aspect, the partition objective function has a set of partition constraints, which comprises: an upper limit of a total number of virtual power plants; a maximum distance limit of an aggregation node to a boundary node of a corresponding partition; a first preset ratio of local consumption power to power generation within a partition; and a total amount of cross-zone transactions between each partition and other regions does not exceed a transaction capacity upper limit determined based on a second preset ratio of a total power generation of each partition.

[0008] The second aspect of the present disclosure provides a resource external characteristic representation method, comprising: obtaining historical operation data of each flexible resource; preprocessing the historical operation data to obtain input data; using a data-driven output time sequence prediction model arranged for each flexible resource, predicting a predicted output sequence of each flexible resource at each future time according to the input data; constructing a feasible region of each flexible resource according to at least one operation characteristic constraint determined by the predicted output sequence; aggregating the feasible regions of the flexible resources belonging to the same virtual machine group to obtain a virtual machine group feasible region of each virtual machine group; wherein the virtual machine group is determined based on the resource partition balance-based aggregation method of any one of the first aspect; and the aggregation method of the feasible region comprises calculating the Minkowski sum of the feasible regions.

[0009] In an embodiment of the second aspect, the construction of the feasible region of each flexible resource according to at least one set of operation characteristic constraints determined by the predicted output sequence comprises: determining at least one operation characteristic constraint corresponding to each flexible resource according to the operation characteristic of the flexible resource; the operation characteristic constraint comprises: an output constraint, a ramping constraint and an energy state constraint; the at least one operation characteristic constraint is applicable to all time points or each periodic time point of the flexible resource; and the feasible region in a unified form is obtained by combining the at least one operation characteristic constraint and the elements of the predicted output sequence.

[0010] In an embodiment of the second aspect, the aggregation of the virtual machine group feasible region of each virtual machine group by aggregating the feasible regions of the flexible resources belonging to the same virtual machine group comprises: representing the feasible regions of the flexible resources belonging to the same virtual machine group into the form of original convex polyhedrons by calculating the intersection of half-planes; selecting a same polyhedron based on each original convex polyhedron; and maximizing the approximation of the original convex polyhedron of each flexible resource by different scaling and translation of the same polyhedron to obtain the maximum approximation feasible region of each flexible resource based on the same polyhedron, which is used for the aggregation of the virtual machine group.

[0011] In an embodiment of the second aspect, the maximum inner approximation of each flexible resource to its original convex polyhedron by the same homogenous polyhedron through different scaling and translation, to obtain a maximum inner approximation feasible region of each flexible resource based on the same homogenous polyhedron, for aggregation of the virtual machine groups, comprises: defining the feasible region in the form of an approximation polyhedron as the feasible region in the form of a homogenous polyhedron multiplied by a scaling coefficient and added to a translation coefficient; solving to obtain a maximum inner approximation scaling coefficient and a maximum inner approximation translation coefficient for each group, based on the maximum inner approximation scaling coefficient, the maximum inner approximation translation coefficient and the feasible region in the form of a homogenous polyhedron for each group, to obtain a maximum inner approximation feasible region of each flexible resource based on the same homogenous polyhedron; and the aggregation of the feasible regions of the flexible resources belonging to the same virtual machine group to obtain a virtual machine group feasible region for each virtual machine group further comprises: aggregating the maximum inner approximation feasible regions of the flexible resources belonging to the same virtual machine group to obtain the virtual machine group feasible region based on the homogenous polyhedron with a first aggregation scaling coefficient and a first aggregation translation coefficient; wherein the scaling coefficient and the translation coefficient of the virtual machine group feasible region are the sum of the scaling coefficients and the sum of the translation coefficients of the maximum inner approximation feasible regions of the flexible resources belonging to it, respectively.

[0012] In an embodiment of the second aspect, the resource external characteristic representation method further comprises: aggregating each virtual machine group feasible region to obtain a virtual power plant feasible region.

[0013] The third aspect of the present disclosure provides a running control method, comprising: obtaining a maximum uncertainty loss model, for maximizing the uncertainty loss between the maximum benefit that can be achieved under the running control decision of the flexible resource output uncertainty scenario of the uncontrollable flexible resource and the current benefit that can be achieved under the current running control decision under the constraint of the first running constraint set of the power supply unit in the virtual power plant or virtual machine group, and determining conditions, to obtain the maximum uncertainty loss; the power supply unit is implemented as a virtual power plant or a virtual machine group; the running control decision includes a set of actions on the controllable flexible resource in the power supply unit; the first running constraint set includes an external characteristic constraint for the power supply unit, which is formed based on the feasible region of the virtual power plant or virtual machine group obtained by the resource external characteristic representation method in any one of the second aspects; obtaining each set of maximum uncertainty losses of each candidate running control decision under a set of the climbing power scenarios through the maximum uncertainty loss model; obtaining each expected loss based on each set of maximum uncertainty losses through minimizing the maximum expected loss model, and determining the corresponding candidate running control decision as the target running control decision according to the minimum value in each expected loss.

[0014] In an embodiment of the third aspect, the controllable flexible resources comprise one or more of energy storage devices, gas / oil-fired power generation devices, transferable loads, and curable loads; the set of actions comprise one or more of gas / oil-fired power generation device actions, charging, discharging, power transfer, and power curtailment; and / or the uncontrollable flexible resources comprise new energy power generation devices.

[0015] In an embodiment of the third aspect, the first set of operational constraints comprises a power balance constraint, which means that the sum of actual output power of the power supply units and output deviation amount is equal to the sum of day-ahead power and ramping power; the output deviation amount means the deviation amount of actual output power of the power supply units from predetermined output power, and the output deviation penalty cost is related to the output deviation amount; the operational regulation decision comprises a set of actions on controllable flexible resources; the maximum benefit is obtained based on searching for optimal operational regulation decisions and output deviation amounts of the virtual machine groups under operational regulation decisions in the output uncertainty scenarios to maximize a first benefit; the current benefit is obtained based on searching for output deviation amounts under the current operational regulation decision to minimize power supply deviation cost to maximize a second benefit; and the first benefit and the second benefit are represented by the difference between the sum of day-ahead power revenue and ramping power revenue and the sum of total flexible resource scheduling cost and output deviation penalty cost.

[0016] In an embodiment of the third aspect, the output deviation penalty cost is related to the output deviation amount; the output deviation amount means the deviation amount of actual output power of the virtual machine groups from predetermined output power; and the output deviation amount is calculated in the following manner: when the output deviation amount is greater than zero, the output deviation penalty cost is a positive value positively related to the output deviation amount; and when the output deviation amount is not greater than zero, the output deviation penalty cost is a negative value negatively related to the output deviation amount.

[0017] In an embodiment of the third aspect, the maximum uncertainty loss model is expressed as:

[0018] ;

[0019] wherein, represents the current operational regulation decision and the maximum uncertainty loss under the determined ramping capacity scenario; represents various output uncertainty scenarios; the current operational regulation decision , p ope represents a set of actions on controllable flexible resources; P m and P m,u respectively represent day-ahead power under the current operational regulation decision and the operational regulation decision under the output uncertainty scenario; represents the power supply units in a total cost of flexible resource scheduling for implementing the operation regulation decision under the scenario, a total cost of flexible resource scheduling for implementing the current operation regulation decision by the power supply unit; and C B respectively represent the output deviation penalty cost to be borne under the operation regulation decision under the output uncertainty scenario and under the current operation regulation decision; R m and respectively represent the day-ahead power income under the current operation regulation decision and under the operation regulation decision under the output uncertainty scenario; R R represent the ramping power income; R m and The calculation method of is as shown in the following formula: , is a day-ahead power value coefficient; R R The calculation method of is as shown in the following formula: , is a ramping power value coefficient; and C B The calculation method of is as shown in the following formula:

[0020] ;

[0021] wherein, and are deviation power cost coefficients.

[0022] In an embodiment of the third aspect, the first set of operation constraints comprises at least one of an external characteristic constraint of the power supply unit, an output power characteristic constraint of each controllable flexible resource of the power supply unit, and an adjustable power constraint; wherein the external characteristic constraint is used to describe the characteristics of the power supply unit; and the output power characteristic constraint comprises a set of action constraints on the set of actions.

[0023] In an embodiment of the third aspect, the minimization maximum expected loss model is represented as:

[0024] ;

[0025] wherein p VPP represents the current operation regulation decision, represents the ramping power under the Kth ramping power scenario, represents the probability of the Kth ramping power scenario.

[0026] In an embodiment of the third aspect, each of the expected losses is calculated as a weighted sum of a corresponding set of losses with each of the weight values being a probability of occurrence of a respective one of the ramping power scenarios; and the minimax expected loss model comprises a second set of operational constraints, the second set of operational constraints comprising: the first set of operational constraints, a dispatchable power constraint under each ramping power scenario, and a sum of the weight values being 1.

[0027] In a fourth aspect, the present disclosure provides an operation regulation platform, comprising: a processor and a memory; the memory stores program instructions; the processor is configured to execute the program instructions to perform at least one of: the resource partitioning balancing-based aggregation method according to any one of the first aspect; the resource external characteristic representation method according to any one of the second aspect; and the operation regulation method according to any one of the third aspect.

[0028] As described above, the present disclosure relates to the technical field of electric energy dispatching, and provides a resource partitioning balancing-based aggregation method, an external characteristic representation method, and an operation regulation method and platform. The aggregation method considers the minimization of partitioning losses caused by factors such as resource spatial distribution distance, aggregation cost, partition plan power deviation, and partition balancing between local consumption and cross-zone transaction power, to determine aggregation nodes, and partitions and corresponding virtual machine groups respectively divided by the aggregation nodes. The external characteristic representation method predicts the future output condition of flexible resources based on a data-driven prediction method to construct a resource feasible region and its aggregation feasible region. The operation regulation method considers resource output and ramping power conditions to obtain operation regulation decisions that minimize the maximum uncertain loss through a two-stage combination of the maximum uncertain loss model and the minimax expected loss model. The partition aggregation optimization, accurate external characteristic description to support more dispersed user-side heterogeneous flexible resource control, and improved regulation decision accuracy are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A schematic diagram of a virtual power plant application scenario in an embodiment of the present disclosure is shown.

[0030] Figure 2 A flowchart of the resource partitioning balancing-based aggregation method in an embodiment of the present disclosure is shown.

[0031] Figure 3 A flowchart of the resource external characteristic representation method in an embodiment of the present disclosure is shown.

[0032] Figure 4 A structural diagram of an LSTM network is shown.

[0033] Figure 5 A specific flowchart of step S304 in an embodiment of the present disclosure is shown.

[0034] Figure 6 A schematic diagram showing Minkowski and computational geometry principles.

[0035] Figure 7 A schematic diagram showing the specific flow of step S305 in an embodiment of the present disclosure.

[0036] Figure 8 A schematic diagram showing the specific implementation steps of step S703 in an embodiment of the present disclosure.

[0037] Figure 9 A schematic diagram showing the flow of running the regulation method in an embodiment of the present disclosure.

[0038] Figure 10 A schematic diagram showing the principle of the combined application of Embodiment 1 to Embodiment 3 of the present disclosure.

[0039] Figure 11 A schematic diagram showing the effect of the flexible resource partitioning in the network in an embodiment of the present disclosure using the resource partitioning balancing-based aggregation method.

[0040] Figure 12 A schematic diagram showing the structure of the computer device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] The embodiments of the present disclosure will be described in detail by specific, concrete examples. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the messages disclosed in the present disclosure. The present disclosure can also be implemented or applied in different specific embodiments or modules, and the details in the present disclosure can be modified or changed in different views and application modules without departing from the spirit of the present disclosure. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0042] The embodiments of the present disclosure will be described in detail by specific, concrete examples. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the messages disclosed in the present disclosure. The present disclosure can also be implemented or applied in different specific embodiments or modules, and the details in the present disclosure can be modified or changed in different views and application modules without departing from the spirit of the present disclosure. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0043] In the description of the present disclosure, the expressions of "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like mean that the specific features, structures, materials or characteristics represented in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials or characteristics represented can be combined in any one or at least one embodiment or example in a suitable manner. In addition, the skilled in the art can combine and combine the different embodiments or examples represented in the present disclosure and the features of the different embodiments or examples without contradiction.

[0044] Furthermore, the terms "first", "second", etc. are used herein only to distinguish one element from another and do not necessarily indicate relative importance or a number of the indicated technical features. Thus, a feature defined with "first", "second", etc. can include at least one of the features explicitly or implicitly. In the expression of the present disclosure, the meaning of "at least one" is two or more, unless specifically limited otherwise.

[0045] In order to clearly explain the present disclosure, devices irrelevant to the explanation are omitted, and the same reference numerals are assigned to the same or similar constituent elements throughout the specification.

[0046] Throughout the specification, when it is said that a certain device is "connected" to another device, this includes not only the case of "direct connection" but also the case of "indirect connection" in which other elements are interposed therebetween. In addition, when it is said that a certain device "includes" a certain constituent element, other constituent elements are not excluded unless specifically stated to the contrary, but it means that other constituent elements can also be included.

[0047] Although the terms first, second, etc. are used herein in some examples to refer to various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first interface and a second interface, etc. are indicated. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises", "comprising", "includes" and / or "including" means that the mentioned features, steps, operations, elements, modules, items, species, and / or groups are present, but do not preclude the presence or addition of one or at least one other feature, step, operation, element, module, item, species, and / or group. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean either one or any combination. Thus, "A, B, or C" or "A, B, and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B, and C". This exception occurs only when a combination of elements, functions, steps, or operations is inherently mutually exclusive based on their nature.

[0048] The professional terms used herein are used only to refer to specific embodiments and are not intended to limit the present disclosure. The singular form used herein, unless the context clearly indicates otherwise, also includes the plural form. The meaning of "include" used in the specification is to specify a certain feature, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and / or components.

[0049] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with the relevant technical literature and the message of the present disclosure, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0050] Virtual power plants can integrate and regulate dispersed energy resources such as sources, grids, loads, and storage, effectively becoming a controllable power source with strong flexibility. They can be used as a power generator to supply power to the power system, or as a load absorber to the system, and have the ability to regulate loads both upwards and downwards.

[0051] However, precisely because virtual power plants are essentially a technology for the intelligent allocation / aggregation of existing flexible resources, they face challenges arising from the inherent characteristics of these resources. For example, flexible resources of new energy types, such as photovoltaic and wind farms, provide energy with significant randomness, i.e., uncertainty. Furthermore, the addition of other uncertainties (such as energy market uncertainty) leads to increased fluctuations in the operation of virtual power plants, posing a significant challenge to their stable operation and necessitating improvements in operational stability. Additionally, the diverse types of flexible resources—including "source, grid, load, and storage"—make the identification of the external characteristic parameters of the aggregated entity complex, and current methods for accurately characterizing the aggregated entity's properties are still insufficient. Moreover, the flexible resources utilized by virtual power plants are characterized by small capacity, large quantity, and complex relationships between the distribution network and the access points of these flexible resources, significantly increasing the difficulty of aggregation and necessitating breakthroughs in large-scale aggregation technology.

[0052] In view of this, the present disclosure provides a method and platform based on resource partitioning balanced aggregation, external characteristic representation and operation control to solve the problems in related technologies.

[0053] like Figure 1 The diagram shown illustrates a virtual power plant application scenario in one embodiment of this disclosure.

[0054] exist Figure 1 The diagram illustrates the connections between various flexible resources that form energy flows in a real physical space. In some embodiments, these flexible resources may include, for example, gas / oil-fired power generation equipment, renewable energy power generation equipment, energy storage, transferable loads, and load shedding. Specifically, gas / oil-fired power generation equipment includes, for example, gas turbines. Renewable energy power generation equipment includes, for example, photovoltaic power plants and wind farms. Energy storage may include, for example, batteries. Transferable loads and renewable energy correspond to different types of flexible resources, and the connection carriers for energy flows can be implemented in different ways.

[0055] Specifically, the flexible resources enter the power distribution network 101 by connecting nodes in the power distribution network 101, and the operation information of the flexible resources can be collected (for example, by sensors arranged on the corresponding flexible resources) and transmitted to the operation and control platform 102 through a communication connection. The operation and control platform 102 can analyze the information flow and control the flexible resources through the power distribution network 101 to aggregate the dispersed flexible resources into, for example, a “virtual generator group” to be called like a real physical generator group, achieving the function of a “virtual power plant”.

[0056] In some embodiments, the operation and control platform 102 can analyze the resource information of the above flexible resources and perform means for regulating the capacity / energy storage of the flexible resources. In some embodiments, the operation and control platform 102 can include terminals with processing capabilities, such as a server group, a server, etc., and the number thereof can be one or more. In some embodiments, the operation and control platform 102 can be implemented as a power grid regulation and control center, or as a virtual power plant management platform integrated with or in communication with the power grid regulation and control center. The virtual power plant management platform can be used to analyze the information flow and send dispatch control instructions to form flexible resource aggregation control, achieving the function of a “virtual power plant”. In some embodiments, the implementation of the virtual power plant management platform is related to the operation mode of the virtual power plant. For example, for the operation scenario of separation of power transmission and distribution, the virtual power plant management platform can include, for example, a distribution system operator (DSO) and a transformer system operator (TSO). In some embodiments, the virtual power plant management platform can only include a distribution system operator (DSO), and the number of operators corresponding to the operation mode is determined.

[0057] The "power supply unit" in the virtual power plant referred to herein is defined here and can be taken as an object to be regulated. As an example, the "power supply unit" can be implemented as a flexible resource aggregation body for the virtual power plant as a whole or in part. That is, the operation regulation platform 102 can generate an operation regulation decision for the aggregation of all flexible resources of the virtual power plant to be regulated, i.e., an operation regulation decision for the virtual power plant as a whole; or generate an operation regulation decision for the day-ahead operation optimization of the aggregation of local flexible resources, i.e., an operation regulation decision for the virtual power plant in part. Thus, operation regulation optimization for different ranges of the virtual power plant can be achieved, which is more flexible. In some embodiments, the aggregation includes at least one of the following: a virtual power plant, a virtual unit, a flexible resource cluster corresponding to a flexible resource type, a flexible resource cluster in a geographical area, etc. As an example, a flexible resource cluster corresponding to a flexible resource type, such as a cluster of energy storage devices, etc. Of course, the above local flexible resource cluster is only an example and can be changed according to actual needs and is not limited.

[0058] Specifically, the operation regulation platform 102 obtains relevant information of each flexible resource, including but not limited to historical and current operation data of the flexible resource, including, for example, day-ahead planned output, actual output, day-ahead planned power, actual power, historical operation power, state of charge, etc. Some resource-related information can also be included, including but not limited to meteorological parameters, electricity price data, resource aggregation cost, electrical distance of the resource in the power distribution network, etc. The resource aggregation cost includes but is not limited to installation and operation cost of the aggregation node, setting and operation cost of the communication infrastructure, etc.

[0059] Embodiment 1

[0060] Embodiment 1 relates to a specific implementation of the aggregation method based on resource partition balance. The aggregation method based on resource partition balance is used to determine the partition of resources and can further aggregate the resources in the determined partition into a virtual unit. Thus, efficient regulation of decentralized heterogeneous flexible resources can be effectively supported.

[0061] As shown in Figure 2 , a flowchart of the aggregation method based on resource partition balance in an embodiment of the present disclosure is shown. The aggregation method based on resource partition balance can be applied to the operation regulation platform 102 described in the previous embodiments.

[0062] In a specific scenario, the power distribution network includes a plurality of nodes, which can be locations where various electrical devices access the power distribution network. These devices include transformers, switching devices, loads, etc., which are connected to other parts of the power distribution network through the nodes. The nodes can also be places where a plurality of feeder lines, branch lines, or electrical devices are connected to each other in the power distribution network. At these connection points, the current can be split or combined. A coordination controller can be configured at the node to coordinate and control flexible resources in the corresponding partition, so as to control the corresponding resource aggregation, i.e., the virtual machine group. Each coordination controller can be communicatively connected to the central aggregation controller to perform regulation and control on the flexible resources in the corresponding partition according to the instructions issued by the central aggregation controller. In some embodiments, the coordination controller can be arranged only at the node, and the node where the coordination controller is arranged is the "aggregation node" in the corresponding "partition".

[0063] The resource partitioning-based aggregation method of the virtual power plant includes:

[0064] Step S201: Determine the aggregation nodes in each node and the partitions corresponding to each aggregation node, according to the minimization of the partition objective function.

[0065] The partitioning of resources needs to consider multiple optimization objectives of various factors (such as maximizing the benefit or minimizing the loss). For example, how to set the aggregation nodes of each partition to minimize the total distance between the aggregation nodes and other non-aggregation nodes, so as to speed up the current transmission as much as possible and reduce the loss. For another example, how to set the aggregation nodes of each partition to minimize the aggregation cost. The aggregation cost can include, but is not limited to, the installation and operation cost of the coordination controller, the communication facility cost, etc. Thus, based on the above multiple optimization objectives of various factors, a partition objective function can be constructed, and the partition result, i.e., the determination of the aggregation nodes and non-aggregation nodes belonging to each partition, can be solved by minimizing the function value.

[0066] Thus, in some embodiments, the partition objective function can include a first term based on the total distance between global aggregation nodes, a second term based on the global aggregation cost, a third term based on the global partition plan power deviation amount, and a fourth term based on the non-satisfaction of the partition balance principle. The total distance between global aggregation nodes can be defined as the sum of the distances between each current aggregation node and the non-aggregation nodes between adjacent aggregation nodes. The global aggregation cost can be defined as the sum of the aggregation costs of the coordination controllers of each aggregation node. The global partition plan power deviation amount can be defined as the sum of the partition deviation amounts of the day-ahead plan power and the real-time power of each partition. The partition balance principle refers to the tendency of the sum of the local consumption power of each partition to be large, and the tendency of the sum of the cross-zone transaction input power between partitions to be small.

[0067] It should be noted that in the fourth point, by introducing the principle of regional balancing, the local consumption of new energy within a region can be maximized, thereby reducing the load impact on the upper-level dispatching system. By utilizing the differences in the flexibility and resource endowment of each region, cross-regional dispatching can achieve complementarity, thereby improving the overall system stability and economy.

[0068] The above principles will be explained in detail. First, the implementation of the partitioning objective function will be simply explained using the following equation (1):

[0069] (1);

[0070] in, l ij Let b be the distance from the j-th node to the i-th aggregation node; i N is a binary decision variable describing whether the i-th node has an aggregation node; n The total number of nodes in a given network; c op,i and c ins,i These represent the operating and installation costs of the coordination controller associated with the aggregation node on node i, respectively; N z Total number of partitions; This represents the planned electricity volume of partition i in the day-ahead market (t=d indicates day-ahead, t=r indicates real-time). This indicates the planned electricity consumption of partition i in the real-time market; To facilitate the local consumption of electricity within zone k; Let k be the cross-regional input electricity; β be the cross-regional transaction penalty factor. It can be understood that the terms added on the right side of the equation are, in order, the first, second, third, and fourth terms mentioned above.

[0071] In addition, B ij As an auxiliary variable, use a binary decision variable b. i Represented as:

[0072] ;

[0073] It is understandable that in the above formula, the binary decision variable b i The coefficient used for the aggregation cost when the node is an aggregation node, to exclude non-aggregation nodes from the calculation of aggregation cost. And, the auxiliary variable B... ij The distance coefficient is constructed based on the product of the binary decision variable of the current node and the mutually exclusive variables of the binary decision variables of all other nodes. Specifically, when the binary decision variable of node i is 0 or the binary decision variable b of any other node is 0, the distance coefficient is calculated as follows: jWhen B ij is zero. So the auxiliary variable B ij is not used to consider the distance of the simple aggregation node, but is combined with l ij to calculate the nearest distance from the ith aggregation node to the jth aggregation node.

[0074] By the auxiliary variable B ij , the distance between two non-aggregation nodes, and the distance of an aggregation node across one aggregation node to another node can be excluded (that is, other aggregation nodes are excluded from the range of the partition in which the current aggregation node is located). Thus, the distance between two non-aggregation nodes, and the distance of an aggregation node across another aggregation node to a node can be excluded from the sum of the distances.

[0075] Since in the process of accumulating the distance of each aggregation node in the first term, some distances of non-aggregation nodes that are not expected to be counted are excluded by the binary decision variable and the auxiliary variable, it is mainly calculated from the direction of the distance of the aggregation node, thereby effectively reducing the amount of calculation compared with the scheme of considering the distance between all nodes.

[0076] Since l ij depends on the spatial distribution of nodes and consumers in the power distribution network, and since the aggregation node is the only setting position of the aggregation coordination controller, it is assumed that l ij is a constant in a given network.

[0077] For n nodes, l ij is expressed in matrix form as follows:

[0078] ;

[0079] The above formula (1) is a conceptual expression, and the values of the four terms of the function need to be normalized to the same interval in actual calculation, thereby obtaining the normalized partition objective function, which is expressed as:

[0080] (2);

[0081] wherein, , , and corresponding to the four terms of the original objective function, w1, w2, w3 and w4 are weights assigned to the four terms, related to the preferences or requirements of the virtual power plant on the overall aggregation. D max the maximum possible distance for a given system, such as the total distance from the central aggregator directly to each distributed resource without carrying out aggregation; or, D max It can also be the maximum value in the total distance from each aggregation node to other nodes, of course, the calculation amount is greater than the former, the former only needs to be calculated once according to the central aggregator. N max The maximum number of aggregation nodes that can exist in a given network. Since each node can appear as an aggregation node, N max It can be equal to the total number of nodes in the given network. E max The maximum value of the total power of the day-ahead plan of the whole network; T max The upper limit of the total cross-zone transaction capacity.

[0082] In addition, considering that the virtual power plant has restrictions on the total number of internal virtual machine groups and the maximum possible distance of each virtual machine group, the partition objective function has a set of partition constraints.

[0083] The set of partition constraints includes:

[0084] 1) The upper limit of the total number of virtual machine groups;

[0085] ;

[0086] 2) The maximum distance limit of the aggregation node to other non-aggregation nodes:

[0087] .

[0088] Wherein, The maximum possible distance between aggregation node i and node j within the partition, i.e. the maximum possible distance between the flexible resources connected to j. Describes the geographical and communication facility restrictions, which limit the number of virtual machine groups.

[0089] Furthermore, considering the restrictions of the partition balance principle, the partition aggregation is allowed to share flexible resources through market transactions or coordinated scheduling, but the proportion of local consumption of electricity within the partition to the total power generation cannot be lower than the threshold value, and the cross-zone transaction volume between the partition and other regions cannot exceed the capacity upper limit, so the set of partition constraints includes the constraint:

[0090] 3) The first preset proportion of local consumption of electricity within the partition to the power generation within the partition:

[0091] ;

[0092] Wherein, The total power generation of the distributed energy in the partition k; γ is a first preset proportion, i.e., a local consumption threshold, which ensures the regional self-balancing ability;

[0093] 4) The total amount of cross-zone transactions of each partition and other regions does not exceed the upper limit of the transaction capacity, which is determined based on a second preset proportion of the total power generation of each partition, and is expressed as:

[0094]

[0095] wherein δ is a second preset proportion, representing a cross-zone transaction capacity upper limit rate, which avoids excessive dependence of each partition on external transactions for power purchase to adjust itself.

[0096] Back to Figure 2 It also includes:

[0097] Step S202: Obtain a virtual machine group corresponding to each partition by aggregating flexible resources in the partition.

[0098] Through the resource partitioning balancing-based aggregation method, the partition balancing problem in distributed resource aggregation is innovatively considered in the partition objective function, and the partition balancing is finally realized by optimizing resource allocation. Firstly, the spatial distribution of user resources (the first term) and the aggregation cost (the second term) are considered, and secondly, the deviation problem between the day-ahead plan and the actual power is considered to reduce the deviation to reduce the penalty for not meeting the plan. Finally, the power flow between different regions is combined to obtain a multi-objective optimization resource partitioning result for aggregation, which effectively improves the self-balancing ability and operation resilience of the partition aggregation body. Thus, each partition can be used as a "micro balancing unit" to independently manage local resources and reduce real-time dependence on the power grid; the cross-partition transaction mechanism allows power to flow between partitions to achieve mutual transaction and improve the overall flexibility of the system.

[0099] Embodiment 2:

[0100] As shown in Figure 3 , a flowchart of a resource external characteristic representation method in an embodiment of the present disclosure is shown. Figure 3 The resource external characteristic representation method in the present disclosure is used to accurately identify the feasible region of the flexible resource and even the aggregation body (such as a virtual machine group or a virtual power plant) formed by the aggregation of the flexible resource according to the characteristics (such as the type, output, and ramp rate limit) of the flexible resource, and the obtained feasible region can be applied as an external characteristic constraint of the virtual machine group, so that more accurate operation control decisions can be obtained.

[0101] The feasible region constructed directly using historical real data can only reflect the past operation behavior and cannot cover the potential changes under different future scenarios. Historical data can provide the "experience response domain" of resources, but it cannot predict the response capability under different external conditions in the future (such as load fluctuation, weather change, price fluctuation, etc.). At the same time, the feasible region constructed directly based on historical data may be distorted due to abnormal points in the data, resulting in inaccurate modeling results. Therefore, directly using historical data may lead to overly conservative decisions and cannot effectively cope with future dynamic challenges.

[0102] In order to improve the accuracy and flexibility of the virtual power plant external characteristic identification, a data-driven prediction method is adopted in the embodiment, and the time series prediction of resource output is performed based on historical operation data, and then the predicted feasible region in the future is constructed. Unlike the static feasible region constructed directly using historical data, the predicted feasible region can consider the possible response changes of resources under different external conditions in the future, and has stronger time sequence adaptability and foresight.

[0103] In Figure 3 , the resource external characteristic representation method comprises:

[0104] Step S301: Obtain historical operation data of each flexible resource.

[0105] In some embodiments, the historical operation data can include data related to output such as historical operation power, state of charge, weather parameters, and price curve.

[0106] Step S302: Preprocess the historical operation data to obtain input data.

[0107] In some embodiments, the preprocessing can include outlier rejection, normalization, etc. As an example, the collected data can be normalized by the min-max method, as shown in the following formula:

[0108] ;

[0109] wherein x norm is the normalized value of the data, x is the data before normalization, x max is the maximum value of the data before normalization, and x min is the minimum value of the data before normalization.

[0110] Step S303: Use the data-driven output time series prediction model corresponding to the arrangement of each flexible resource to predict the predicted output sequence of each flexible resource at each future time according to the input data.

[0111] In some embodiments, the output timing prediction model can be trained based on, for example, a recurrent neural network model, such as an RNN, an LSTM, or a variant thereof.

[0112] For example, as shown in FIG. 1, a structure diagram of an LSTM network is shown. Figure 4

[0113] The unit of the LSTM network is composed of a cell, an input gate, an output gate, and a forget gate. The cell remembers the value at any time interval and the three gates regulate the information flow in and out of the cell.

[0114] (1) Forget gate

[0115] The forget gate decides which information to discard from the previous state by assigning a value between 0 and 1 to the previous state. A value of 1 means keeping the information, and a value of 0 means discarding it, as shown in the following equation:

[0116] ;

[0117] In the equation, W f is the weight matrix of the forget gate, represents concatenating two vectors into a longer vector, b f is the bias term of the forget gate, is the Sigmoid activation function.

[0118] (2) Input gate

[0119] The input gate decides which new information to store in the current state, using the same system as the forget gate. Considering the output value of the previous state and the input value of the current state, the output gate controls the output in the current state by assigning a value between 0 and 1 to the information. Selectively outputting relevant information from the current state allows the LSTM network to maintain useful long-term dependencies for prediction in the current and future time steps. As shown in the following equation:

[0120] ;

[0121] ;

[0122] (3) Output gate

[0123] The output gate outputs the calculation of the cell state c t at the current time, which is the element-wise multiplication of the last cell state c t-1 , the forget gate f t , the element-wise multiplication of the current input cell state , the input gate i t , and the sum of the two products. In this way, the current memory c t ​and long-term memory c t-1 are combined together to form a new unit state c t . As shown in the following formula:

[0124] ;

[0125] ;

[0126] .

[0127] In some embodiments, each flexible resource can be arranged with a respective output time sequence prediction model, which is trained according to historical operation data of each flexible resource. The output time sequence prediction model of each flexible resource can be sent to the flexible resource for use after being trained externally. In other embodiments, the output time sequence prediction model corresponding to each flexible resource can also be arranged outside the flexible resource, such as being stored remotely, and can be trained by the remote end, and the implementation manner is not limited.

[0128] Step S304: constructing a feasible region of each flexible resource according to at least one operation characteristic constraint determined according to the predicted output sequence.

[0129] Specifically, as shown in Figure 5 , step S304 can include:

[0130] Step S501: determining at least one operation characteristic constraint corresponding to each flexible resource according to an operation characteristic of the flexible resource.

[0131] The operation characteristic constraint includes an output constraint, a ramping constraint, and an energy state constraint. The operation characteristic constraint can include a physical characteristic constraint inherent to the resource and not changing with time, such as a known upper and lower limit of output, an upper and lower limit of ramping, an upper and lower limit of corresponding energy state, and a conversion efficiency of output to energy state change of the electrical equipment corresponding to the flexible resource, which can be obtained from specification information and measured information of the electrical equipment.

[0132] Step S502: obtaining a feasible region in a unified form by combining the at least one operation characteristic constraint and elements of the predicted output sequence.

[0133] For example, the obtained operation characteristic constraint can be expressed as:

[0134] ;

[0135] Wherein, the output power of the flexible resource i at time t is represented as P i,t , the corresponding energy state is , and the upper and lower limit values of the ramping rate are R i,min and Ri,max , the upper and lower limit values of the output power are P i,min and P i,max , the upper and lower limit values of the energy state are and , and the efficiency coefficient of the output power affecting the energy state conversion is .

[0136] It should be particularly noted that the meaning of the formula is that although the output power P i,t is between P i,min and P i,max , the actual feasible region does not cover all the value ranges between P i,min and P i,max , but is based on the range that P i,t can reach in the entire range.

[0137] In some embodiments, the at least one operating characteristic constraint can also include multiple constraints applicable to each periodic time of the flexible resource, such as an upper and lower limit constraint of the output power existing from 2 o'clock to 3 o'clock every Monday, another upper and lower limit constraint of the output power existing from 3 o'clock to 4 o'clock; or an upper and lower limit constraint of the output power existing from 2 o'clock to 3 o'clock every day, another upper and lower limit constraint of the output power existing from 3 o'clock to 4 o'clock, and so on.

[0138] In a linear programming problem, the set of decision variables that satisfy all the constraints is called a feasible region. The feasible region is a polyhedron, and its definition form is the same as that of a polyhedron, that is, {x∈Rn|Ax≤b}. According to the power and energy constraints of the flexible resource, the feasible region of the flexible resource i in the form of a convex polyhedron can be obtained by intersecting the corresponding half-planes according to various constraints, and is expressed as:

[0139] (3)

[0140] where M i is the feasible region of the i-th flexible resource, P i is the output power vector of the i-th flexible resource in the scheduling period, A i and b i are the inequality coefficient matrix and constraint vector, respectively, representing the original feasible region of the i-th flexible resource; E is a unit matrix, is an energy dissipation coefficient, and represent the power characteristic constraint and the energy constraint, respectively.

[0141] After the feasible region of the flexible resource is accurately represented, the feasible region of the aggregation of the aggregated flexible resource can be obtained by aggregating the feasible regions of the flexible resources. For example, the virtual machine group feasible region of the virtual machine group aggregation of the flexible resource can be obtained; in addition, the virtual power plant response feasible region can be obtained by aggregating the feasible regions of various flexible resources under the virtual power plant, that is, the characteristics of the virtual power plant are accurately described.

[0142] Step S305: The virtual machine group feasible region of each virtual machine group is obtained by aggregating the feasible regions of the flexible resources belonging to the same virtual machine group.

[0143] The virtual machine group can be determined based on the resource partition balance-based aggregation method in Embodiment 1. In some embodiments, the calculation method of the aggregation can be the Minkowski sum method.

[0144] As shown in Figure 6 , the Minkowski sum is the sum of the point sets of two Euclidean spaces, also known as the expansion set of the two spaces. It can be popularly understood as the union of the areas passed after a convex hull B is rotated along the boundary of a convex hull A, that is, C.

[0145] The calculation of the general Minkowski sum requires the calculation of the sum of each pair of vertices of the two convex hulls. Preferably, to reduce the calculation complexity, an approximate calculation method can be used when aggregating the flexible resources of the virtual machine group. Specifically, the original feasible region of a single flexible resource is transformed into the maximum inner approximation of the approximate polyhedron shape obtained by different scaling and translation of the same isomorphic polyhedron through the translation and scaling of the isomorphic polyhedron, and the Minkowski sum calculation of the original convex polyhedron form of the feasible region of each flexible resource is transformed into the Minkowski sum between the maximum inner approximation feasible regions constructed based on the same isomorphic polyhedron, so that the accurate acquisition of the feasible region of the aggregation is realized, and the calculation amount is effectively reduced.

[0146] As shown in Figure 7 , the specific implementation process of step S305 is shown:

[0147] Step S701: The feasible regions of the flexible resources belonging to the same virtual machine group are represented in the form of original convex polyhedrons by the half-plane intersection set method.

[0148] That is, as shown in formula (3).

[0149] Step S702: An isomorphic polyhedron is selected based on each original convex polyhedron.

[0150] As an example, similar to the above-mentioned example form of the feasible region in the form of the original convex polyhedron, the isomorphic polyhedron can be represented as:

[0151] ;

[0152] Optionally, the isomorphic polytope can select part of the running characteristic constraints to construct.

[0153] Step S703: Maximal inner approximation to each flexible resource's original convex polytope by the isomorphic polytope respectively via different scaling and translation, to obtain each flexible resource's maximal inner approximation feasible region based on the same isomorphic polytope, for the aggregation of the virtual machine group.

[0154] In particular, Figure 6 Two different convex hulls are shown in FIG. 7, and if the shapes of the two convex hulls are the same, only different in size or position, then the union region obtained by moving one along the boundary of the other is still a convex hull of such shape, only different in size and position. Therefore, by constructing the maximal inner approximation feasible region of each flexible resource's original convex polytope form via the same isomorphic polytope, the Minkowski sum of each original convex polytope form feasible region is approximated by the Minkowski sum of the maximal inner approximation feasible region. In calculating the Minkowski sum of the maximal inner approximation feasible region, since it is all replaced based on the same isomorphic polytope, the sum result will still be based on the isomorphic polytope, so as to avoid the calculation of adding two vertices between different convex polytopes, and instead to the superposition between the scaling amount and the translation amount of the isomorphic polytope.

[0155] As Figure 8 shown, the specific implementation of step S703 is shown.

[0156] In Figure 8 , step S703 can specifically include:

[0157] Step S801: Define the approximation polytope form feasible region as the isomorphic polytope form feasible region multiplied by a scaling coefficient and then added by a translation coefficient.

[0158] As an example, the definition of the isomorphic polytope's approximation polytope form feasible region is represented as:

[0159] ;

[0160] wherein, is a scaling coefficient, is a translation coefficient;

[0161] Step S802: solving the maximum inner approximation scaling factor and the maximum inner approximation translation factor of each group, based on the maximum inner approximation scaling factor, the maximum inner approximation translation factor and the feasible region of the isomorphic polyhedron form, to obtain the maximum inner approximation feasible region of each flexible resource based on the same isomorphic polyhedron.

[0162] In continuation of the previous example, the maximum inner approximation of the original feasible region of the flexible resource i in the form of the isomorphic polyhedron is determined as the target, and to obtain the maximum inner approximation feasible region of the flexible resource i. Exemplarily, the scaling factor can be solved first to reach the maximum value max of the original convex polyhedron form feasible region of the flexible resource i without exceeding .

[0163] In Figure 8 , step S305 can further include:

[0164] Step S803: aggregating the maximum inner approximation feasible regions of the flexible resources belonging to the same virtual machine group to obtain the virtual machine group feasible region based on the isomorphic polyhedron applying the first aggregated scaling factor and the first aggregated translation factor; wherein the scaling factor and the translation factor of the virtual machine group feasible region are the sum of the scaling factors and the sum of the translation factors of the maximum inner approximation feasible regions of each flexible resource belonging to it, respectively.

[0165] As an example, the virtual machine group feasible region after aggregation of the flexible resources is defined as:

[0166] ;

[0167] In the formula, is the set of flexible resources in the virtual machine group j, is the output power of the virtual machine group, and is the Minkowski sum symbol;

[0168] The maximum inner approximation feasible regions of the same virtual machine group are accumulated to obtain the virtual machine group response feasible region, which is expressed as:

[0169] ;

[0170] In the formula,

[0171] ; is the output power of the virtual machine group j; is the set of flexible resources in the virtual machine group j; A VU is the inequality coefficient matrix of the virtual machine group.

[0172] Thus, after the feasible region of the flexible resource is accurately represented, the accurate feasible region of the aggregation of the flexible resource aggregation is obtained by aggregating the feasible region of the flexible resource. For example, the virtual machine group feasible region of the flexible resource aggregation of the virtual machine group is obtained in the above embodiment; further, the virtual power plant response feasible region can be obtained by aggregating the feasible region of various flexible resources under the virtual power plant, that is, the characteristics of the virtual power plant are accurately described.

[0173] Since the virtual machine group feasible region of each virtual machine group has been obtained, the virtual machine group feasible region can be aggregated to obtain the virtual power plant feasible region.

[0174] Returning to Figure 3 Optionally, the method further comprises a step S306 of aggregating each virtual machine group feasible region to obtain a virtual power plant feasible region.

[0175] In some embodiments, the calculation method of the simplified Minkowski sum of the original polyhedron by the isomorphic polyhedron approximation can also be used in step S306 to obtain the virtual power plant feasible region.

[0176] By using the resource external characteristic representation method, the resource endowment difference can be identified at the partition level, and the feasible region of the regional virtual machine group / virtual power plant can be accurately constructed, so that the virtual power plant external characteristic identification and modeling with regional coordination capability is realized, and accurate model support is provided for subsequent virtual power plant operation scheduling optimization, transaction decision, etc.

[0177] Embodiment 3:

[0178] As shown in Figure 9 , a flowchart of a running control method in an embodiment of the present disclosure is shown.

[0179] In Figure 9 , the flowchart comprises:

[0180] Step S901: Obtain a maximum uncertainty loss model.

[0181] The maximum uncertainty loss model is used to maximize the uncertainty loss between the maximum benefit that can be achieved under the running control decision of the flexible resource output uncertainty scene of the uncontrollable flexible resource and the current benefit that can be achieved under the current running control decision, under the constraint of the first running constraint set of the power supply unit in the virtual power plant and in the case of the climbing power scenario determination, to obtain the maximum uncertainty loss.

[0182] First, the output power P VPP of the power supply unit (such as a virtual power plant or a virtual machine group) is defined as opeThe uncertain scenario corresponds to the case of uncertain (i.e., incomplete flexible resource output information) of the uncontrollable flexible resource output power. For example, new energy power generation devices, such as photovoltaic power stations, wind farms, etc., belong to uncontrollable flexible resources. Other devices, such as gas / oil power generation devices, energy storage, transferable loads, and cuttable loads, belong to controllable flexible resources. In the deterministic scenario, for example, if the output power of the photovoltaic power station and the wind farm can be relatively accurately predicted. Of course, even in the case of predicting the output power of the uncontrollable flexible resource, it cannot be guaranteed that the output of the uncontrollable flexible resource will not change as predicted, so the uncertainty needs to be considered.

[0183] The maximum uncertain loss can be defined as the difference between the optimal solution achieved by different decisions in the case of information uncertainty (i.e., output uncertainty) and the optimal solution achieved by the current decision.

[0184] The following is a specific description. The operation control decision includes a set of actions on the controllable flexible resources in the power supply unit, and can also include the day-ahead power at the same time. As an example, the operation control decision can be represented as , represents the set of actions on the controllable flexible resources at time t; represents the day-ahead power at time t.

[0185] As an example, for controllable flexible resources such as gas / oil power generation devices, energy storage, transferable loads, and cuttable loads, can be represented as wherein the superscripts g, dis, ch, tran, and cut represent the gas / oil power generation device action, charging, discharging, power transfer, and power cut, respectively. It can be understood that corresponding to the change in the type of controllable flexible resource, the corresponding action will change. In some embodiments, the "action" can be the output power of the corresponding flexible resource controlled. In some embodiments, since the power of each controllable flexible resource should be limited, the set of actions should also have a set of action constraints corresponding to the constraints, denoted as M ope .

[0186] The supplyable power of the power supply unit includes day-ahead power and ramping power. The day-ahead power refers to predicted power of output power of the next day. The ramping power refers to power that can be rapidly increased by the flexible resource to meet sudden increase of power demand of the system or compensation. In some embodiments, the first set of operation constraints includes a power balance constraint, which refers to a sum of actual output power related to the action set and an output deviation amount being equal to a sum of the day-ahead power and the ramping power. The output deviation amount refers to a deviation amount of actual output power of the power supply unit compared to predetermined output power, and the output deviation penalty cost is related to the output deviation amount.

[0187] As an example, after removing t, assume that the output deviation amounts under the operation regulation and control decision of the flexible resource output uncertainty scenario and under the current operation regulation and control decision are P B,u and P B , the day-ahead power is P m,u and P m , the ramping power is determined as P R , and the actual output power is P VPP,u and P VPP , respectively. The power balance constraint can be expressed as:

[0188] ;

[0189] .

[0190] Through the power balance constraint, the maximum uncertainty loss model can associate the actual output power and the ramping power to obtain the maximum uncertainty loss obtained by implementing the current operation regulation and control decision under each determined ramping capacity scenario, expressed as .

[0191] As an example, to simplify the expression, the time symbol t is uniformly removed, and each variable is the value at time t. The maximum uncertainty loss model is expressed as:

[0192] (4);

[0193] wherein, represents the maximum uncertainty loss under the current operation regulation and control decision and the determined ramping capacity scenario P R ; represents various output uncertainty scenarios; the current operation regulation and control decision ; represents the total flexible resource scheduling cost of the power supply unit under the scenario executing the operation regulation and control decision, represents the total cost of flexible resource scheduling performed by the power supply unit for the current operation regulation decision, and the action set p ope are related; and C B respectively represent the output deviation penalty cost to be borne under the operation regulation decision of the output uncertainty scenario and under the current operation regulation decision; R m and respectively represent the day-ahead power revenue under the current operation regulation decision and under the operation regulation decision of the output uncertainty scenario; R R represents the ramping power revenue.

[0194] R m and is calculated in the following formula: , is a day-ahead power value coefficient; R R is calculated in the following formula: , is a ramping power value coefficient. In some embodiments, may be the clearing price of the day-ahead market, may be the price of the ramping service market, and thus can be used to measure the value of the day-ahead power and the ramping power, which corresponds to the revenue.

[0195] In some embodiments, the calculation of the output deviation amount comprises: when the output deviation amount is greater than zero, the output deviation penalty cost is a positive value positively related to the output deviation amount; and when the output deviation amount is not greater than zero, the output deviation penalty cost is a negative value negatively related to the output deviation amount.

[0196] As an example, and C B is calculated in the following formula: ; wherein, and is a deviation power cost coefficient. In some embodiments, the predetermined output power can be implemented as a pre-agreed output power, and if the actual output power deviates from the agreed output power, a penalty cost can be generated. If the actual output power is lower than the predetermined output power, the penalty cost is positive; if the actual output power is not lower than the predetermined output power, the penalty cost is 0 (i.e. no penalty) or negative (the power of over-output also constitutes a revenue), and may be obtained from an agreement or other reference.

[0197] In some embodiments, the first set of operation constraints can include, in addition to the power balance constraint, an external characteristic constraint of the power supply unit. As an example, the external characteristic constraint can be related to an output of the power supply unit, such as one or more of an upper and lower limit of the output, a ramp rate, a response, etc. By way of example, the external characteristic constraint M VPP , can be obtained as , where the external characteristic constraint M VPP can be obtained based on the resource external characteristic representation method in Embodiment 2.

[0198] In some embodiments, the first set of operation constraints further includes a set of actions that should be satisfied before the set of action constraints M ope , i.e., .

[0199] In some embodiments, the first set of operation constraints further includes an adjustable power constraint. For example, the adjustable power constraint is implemented as a market access power limit, such as an adjustable power no less than a megawatt, an adjustable time no less than b hours, etc.

[0200] As an example, the corresponding constraint of the power limit, denoted as

[0201] ;

[0202] where P m- , P m+ are upper and lower limit values of the bid amount required by market access.

[0203] Based on the maximum uncertainty loss model, the maximum uncertainty loss under the given current operation regulation decision and P R .

[0204] A simple analysis of the model principle is as follows:

[0205] According to equation (4), the maximum benefit is obtained based on searching for an optimal operation regulation decision and an output deviation amount of the power supply unit under the operation regulation decision in the output uncertainty scenario, with the goal of maximizing the first benefit obtained; the current benefit is obtained based on searching for the output deviation amount under the current operation regulation decision, with the goal of minimizing the power deviation cost to maximize the second benefit obtained. The first benefit and the second benefit are represented by the difference between the sum of the day-ahead power revenue and the ramp power revenue and the sum of the total cost of flexible resource scheduling and the cost of output deviation penalty.

[0206] It is further known that the maximum benefit is obtained based on searching the optimal operation regulation decision and the output deviation amount of the power supply unit under the operation regulation decision of the output uncertain scenario to maximize the first benefit, i.e., the first minimization problem in formula (4):

[0207]

[0208] In the minimization problem, based on trying each operation regulation decision under the output uncertain scenario, i.e. , the combination of p ope and the output deviation amount of the power supply unit is searched to minimize the cost, in other words, to maximize the first benefit. The optimal operation regulation decision is intended to be found, so that the first benefit reaches the maximum under the operation regulation decision of the output uncertain scenario.

[0209] The current benefit can be obtained by maximizing the second benefit based on searching the output deviation amount under the current operation regulation decision to minimize the power supply deviation cost, i.e., the second minimization problem in formula (4):

[0210]

[0211] Among them, under the output uncertain scenario and given the current operation regulation decision, the output deviation amount P B is searched, aiming to find the minimum output deviation penalty cost under the given operation regulation decision.

[0212] The outermost maximization problem in formula (4) aims to find the maximum profit difference between the optimal solution (maximum benefit, i.e., maximum first benefit) under the output uncertain scenario and the optimal solution (minimum deviation penalty cost, i.e., maximum second benefit) that can be reached by the current operation regulation decision, i.e., the maximum opportunity loss.

[0213] Step S902: Obtain each set of maximum uncertain losses of each candidate operation regulation decision under a set of the climbing power scenarios through the maximum uncertain loss model.

[0214] Step S903: Obtain each expected loss based on each set of maximum uncertain losses through the minimum maximum expected loss model, and determine the target operation regulation decision as the candidate operation regulation decision corresponding to the minimum value in each expected loss.

[0215] ​​It can be understood that the maximum uncertain loss model considers the uncertainty of flexible resource output, and in steps S902-S903, by changing the ramping power scenario (i.e., different ramping powers) of the maximum uncertain loss model, the consideration of the uncertainty of the ramping capacity is further increased, the multiple uncertainties of the virtual power plant are considered, a more accurate and efficient target operation control decision is obtained, the output deviation generated during operation is reduced, the operation stability is improved, and the income is improved.

[0216] In some embodiments, each of the expected losses is calculated as a weighted sum of a corresponding set of losses with the occurrence probability of each of the ramping power scenarios as a weight value.

[0217] As an example, the minimum maximum expected loss model is represented as:

[0218] (5) ;

[0219] where p VPP represents an alternative operation control decision, represents the ramping power under the Kth ramping power scenario, represents the probability of the Kth ramping power scenario.

[0220] Using the minimum maximum expected loss model, K maximum uncertain losses can be obtained by sequentially solving the problems under K ramping power scenarios. Since each ramping capacity condition corresponds to a certain probability Therefore, by taking it as a weight, adding the product of each maximum uncertain loss and the probability of the occurrence of the corresponding scenario, i.e., calculating the weighted sum, the "maximum uncertain loss expected loss" of an alternative operation control decision p VPP can be obtained. Thus, the minimum maximum uncertain loss expected loss of each alternative operation control decision is obtained, which is the target operation control decision.

[0221] Specifically, the maximum uncertain loss expected loss represents the operation control decision under the worst case of uncertainty (deviating from the maximum benefit the most), and the minimization thereof is a kind of target operation control decision that can reduce the opportunity loss under the worst case as much as possible.

[0222] In some embodiments, various ramping capacity conditions and their probabilities can be obtained by distribution statistics according to historical ramping capacity data.

[0223] In some embodiments, each expected loss is obtained by calculating a weighted sum of a set of losses using the probability of occurrence of each of the ramp power scenarios as weight values; the minimization of the maximum expected loss model includes a second set of operating constraints, which includes: the first set of operating constraints, adjustable power constraints under each ramp power scenario, and a sum of weight values ​​equal to 1 (i.e., ...). As an example, the adjustable power constraints under each ramp power scenario are, for example, the upper and lower limits that can be met under market access power restrictions under the ramp power scenario.

[0224] As an example, the adjustable power constraint can be expressed as:

[0225] ;

[0226] Among them, the To adjust the parameters, P is set between [0, 1]. RE.f This indicates the range of output fluctuations of uncontrollable flexible resources. This indicates the upper limit of output of uncontrollable flexible resources, ensuring their security.

[0227] It is understood that Examples 1 to 3 are provided to intuitively illustrate the linkage process between Examples 1 to 3, and can be referred to for further information. Figure 10 The diagram shown illustrates the principle of the combined application of Embodiments 1 to 3 of this disclosure.

[0228] exist Figure 10 In this embodiment, the resource aggregation method in Example 1 can be used to divide the flexible resources in the power grid into aggregation regions, forming flexible resource partitions for each virtual machine group. Further optionally, the feasible region of the virtual machine group and the feasible region of the virtual power plant response corresponding to each aggregation region can be obtained using the resource characteristic representation method in Example 2, and external characteristic constraints can be applied to the first set of operating constraints for the maximum uncertain loss model 1001 and the second set of operating constraints for minimizing the maximum expected loss model 1002 in Example 3.

[0229] The maximum uncertainty loss model 1001 calculates the maximum uncertainty loss of each candidate operation regulation and control decision under each climbing power scenario condition according to a given current operation regulation and control decision and a set of climbing power scenarios under the constraint of a first set of operation constraints, thereby obtaining a set of maximum uncertainty losses corresponding to each current operation regulation and control decision. The minimum maximum expected loss model 1002 calculates the maximum uncertainty expected loss of each candidate operation regulation and control decision according to each set of losses under the constraint of a second set of operation constraints, and can select the candidate operation regulation and control decision corresponding to the minimum expected loss as the target operation regulation and control decision. Thus, the target operation regulation and control decision determined based on the minimized maximum uncertainty expected loss can be used by the virtual power plant to regulate the corresponding flexible resource, which can effectively reduce the risk of output deviation caused by the uncertainty of the output of the new energy type flexible resource and the uncertainty of the climbing capacity.

[0230] By combining the methods of Embodiments 1-3, the advantages of these methods can be stacked, and more accurate and reliable virtual power plant operation regulation and control results can be obtained.

[0231] As shown in FIG. 6, a schematic diagram showing the effect of dividing the flexible resources in the network into zones by using the resource zoning balance-based aggregation method in an embodiment of the present disclosure is shown. Figure 11

[0232] The original power grid is optimally divided into four sub-networks (i.e., zones), which are represented by VU1, VU2, VU3, and VU4 in the figure. Each sub-network has its own regional control center, which corresponds to the node where the coordination controller of each virtual generator group is located, and is marked as C1, C2, C3, and C4, respectively. From the division result, it can be directly seen that the positions of the virtual generator groups in the network basically meet the convenience of geographical location, and have high feasibility in actual engineering.

[0233] It should be particularly noted that each functional module (such as the operation regulation and control platform, each model, method, etc.) in the above embodiments can be realized by software, hardware, firmware, or any combination thereof, in whole or in part. When implemented by software, it can be realized in the form of a computer program or instruction product in whole or in part. The computer program or instruction product includes one or at least one computer program or instruction. When the computer program or instruction is loaded and executed on a computer, it produces a process or function according to the present disclosure in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program or instruction can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0234] ​Moreover, the method disclosed in the foregoing embodiments can be implemented by other module division manners. The embodiments shown above are merely illustrative. For example, the division of the modules is merely logical function division, and actual implementation can have another division manner, for example, at least one module or modules can be combined or can be dynamically added to another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the modules shown or discussed can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical or other forms.

[0235] As shown in Figure 12 , a structural schematic diagram of a computer device in an embodiment of the present disclosure is shown.

[0236] The computer device 1200 can be used to implement, for example, the running regulation platform 102 in Figure 1 .

[0237] The computer device 1200 includes a bus 1201, a processor 1202 and a memory 1203. The processor 1202 and the memory 1203 can communicate through the bus 1201. The memory 1203 can store computer programs or instructions. The processor 1202 implements the method in any one of the foregoing embodiments 1-3 by running the computer programs or instructions in the memory 1203.

[0238] The bus 1201 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, although only one thick line is shown in the figure, it does not mean that there is only one bus or only one type of bus.

[0239] In some embodiments, the processor 1202 can be implemented as a Central Processing Unit (CPU), a Micro Control Unit (MCU), a System On Chip (SOC), or a Field Programmable Gate Array (FPGA), etc. The memory 1203 can include a volatile memory for data temporary storage during program running, such as a Random Access Memory (RAM).

[0240] The memory 1203 can also include non-volatile memory for data storage, for example, Read-Only Memory (ROM), flash memory, Hard Disk Drive (HDD) or Solid-State Disk (SSD).

[0241] In some embodiments, the computer device 1200 can further include a communicator 1204. The communicator 1204 is configured to communicate with the outside. In a specific example, the communicator 1204 can include one or at least one wired and / or wireless communication circuit module. For example, the communicator 1204 can include one or more of, for example, a wired network card, a USB module, a serial interface module, and the like. The wireless communication module can comply with one or more of, for example, Near Field Communication (NFC) technology, Infared (IR) technology, Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Blue Tooth (BT), Global Navigation Satellite System (GNSS), and the like.

[0242] In summary, the present disclosure relates to the technical field of electric energy dispatching, and provides a resource partition balancing aggregation-based, external characteristic representation and operation regulation method and platform. The aggregation method considers resource spatial distribution distance, aggregation cost, partition plan power deviation, and minimization of partition loss caused by factors such as in-situ consumption and cross-zone transaction power balancing, determines an aggregation node and a partition corresponding to the aggregation node and a corresponding virtual machine group respectively. The external characteristic representation method predicts the future output condition of flexible resources based on a data-driven prediction method to construct a resource feasible region and its aggregation feasible region considering the foresight. The operation regulation method considers resource output and climbing power conditions to obtain an operation regulation decision that minimizes the maximum uncertainty loss through a two-stage combination of a maximum uncertainty loss model and a minimum maximum expected loss model. The partition aggregation optimization, accurate external characteristic description to support more dispersed user-side heterogeneous flexible resource control, and improved regulation decision accuracy are realized respectively.

[0243] The above embodiments only exemplarily illustrate the principles and effects of the present disclosure, and are not intended to limit the present disclosure. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present disclosure. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas disclosed by the present disclosure shall still be covered by the protection scope of the present disclosure.

Claims

1. A resource partitioning balancing based aggregation method, characterized in that, The application is applied to a virtual power plant, and is used for partitioning flexible resources accessed by nodes of a power distribution network; the method comprises: According to minimization of a partition target function, an aggregated node in each node is determined, and a partition corresponding to each aggregated node is determined; wherein the partition target function comprises: a first term based on a global aggregated node total distance, a second term based on a global aggregated cost, a third term based on a global partition planned power deviation amount, and a fourth term not satisfying a partition balance principle; wherein the global aggregated node total distance is defined as a sum of distances between each current aggregated node and adjacent aggregated nodes; the global aggregated cost is defined as a sum of aggregated costs of coordination controllers of each aggregated node; the global partition planned power deviation amount is defined as a sum of partition deviation amounts of real-time power and day-ahead planned power of each partition; the fourth term is constructed based on a sum of local consumption power of each partition and a sum of cross-zone transaction input power between partitions; the partition balance principle refers to a tendency of the sum of local consumption power of each partition to be large, and a tendency of the sum of cross-zone transaction input power between partitions to be small; Flexible resources in the aggregated partition are aggregated to obtain a virtual machine group corresponding to each partition.

2. The resource partitioning balance based aggregation method of claim 1, wherein, The second term comprises a binary decision variable used for indicating whether a node is an aggregated node, and used as a coefficient of an aggregated node related cost to exclude non-aggregated nodes from calculation of the aggregated cost; the first term comprises an auxiliary variable constructed based on a product of a binary decision variable of a current node and a mutual exclusion variable of binary decision variables of other nodes, and used as a coefficient of the distance to exclude distances between two non-aggregated nodes and a distance from an aggregated node to a node through another aggregated node.

3. The resource partitioning balance based aggregation method of claim 1, wherein, The partition target function has a set of partition constraints, which comprises: an upper limit of a total number of virtual machine groups; a maximum distance limit of an aggregated node to a boundary node of a corresponding partition; a first preset proportion of local consumption power in power generation within a partition; and a total cross-zone transaction amount between each partition and other zones does not exceed an upper limit of transaction capacity, which is determined based on a second preset proportion of total power generation of each partition.

4. A method of representing a resource-external property, characterized by, The method comprises: obtaining historical operation data of each flexible resource; preprocessing the historical operation data to obtain input data; using a data-driven output data time series prediction model arranged corresponding to each flexible resource, predicting a predicted output sequence of each flexible resource at each future time according to the input data; constructing a feasible region of each flexible resource according to at least one operation characteristic constraint determined based on the predicted output sequence; aggregating feasible regions of flexible resources belonging to a same virtual machine group to obtain a virtual machine group feasible region of each virtual machine group; wherein the virtual machine group is determined based on the resource partition balance based aggregation method in any one of claims 1 to 3; and the aggregation manner of the feasible regions comprises calculating a Minkowski sum of the feasible regions.

5. The method of claim 4, wherein, The at least one set of operation characteristic constraints determined according to the predicted output sequence is used to construct a feasible region of each flexible resource, including: According to the operation characteristics of the flexible resource, at least one operation characteristic constraint corresponding to each flexible resource is determined; the operation characteristic constraint includes an output constraint, a ramp constraint, and an energy state constraint; the at least one operation characteristic constraint is applicable to all time points or each periodic time point of the flexible resource; In combination with the at least one operation characteristic constraint and an element of the predicted output sequence, a feasible region in a unified form is obtained.

6. The method of claim 4, wherein, The feasible regions of the flexible resources belonging to the same virtual machine group are aggregated to obtain a virtual machine group feasible region of each virtual machine group, including: The feasible regions of the flexible resources belonging to the same virtual machine group are represented in the form of original convex polyhedrons by means of semi-plane intersection set; A same polyhedron is selected based on the original convex polyhedrons; The original convex polyhedrons of each flexible resource are maximally internally approximated by the same polyhedron through different scaling and translation of the polyhedron, to obtain a maximally internally approximated feasible region of each flexible resource based on the same polyhedron, for aggregation of the virtual machine group.

7. The method of claim 6, wherein, The original convex polyhedrons of each flexible resource are maximally internally approximated by the same polyhedron through different scaling and translation of the polyhedron, to obtain a maximally internally approximated feasible region of each flexible resource based on the same polyhedron, for aggregation of the virtual machine group, including: The feasible region in the form of an approximate polyhedron is defined as the feasible region in the form of a same polyhedron multiplied by a scaling coefficient and added by a translation coefficient; Each set of maximally internally approximated scaling coefficients and maximally internally approximated translation coefficients is obtained by solving the target of maximally internally approximating the original polyhedrons of each flexible resource by an approximate polyhedron, and a maximally internally approximated feasible region of each flexible resource based on the same polyhedron is obtained based on each set of maximally internally approximated scaling coefficients, maximally internally approximated translation coefficients, and the feasible region in the form of a same polyhedron; The feasible regions of the flexible resources belonging to the same virtual machine group are aggregated to obtain a virtual machine group feasible region of each virtual machine group, including: The maximally internally approximated feasible regions of the flexible resources belonging to the same virtual machine group are aggregated to obtain the virtual machine group feasible region obtained by applying a first aggregation scaling coefficient and a first aggregation translation coefficient to the same polyhedron; wherein the scaling coefficient and the translation coefficient of the virtual machine group feasible region are the sum of the scaling coefficients and the sum of the translation coefficients of the maximally internally approximated feasible regions of the flexible resources belonging to the virtual machine group.

8. The method of claim 4, wherein, Further including: The virtual power plant feasible region is obtained by aggregating the virtual machine group feasible regions.

9. A method of operating a regulatory system, characterized by, Including: obtaining a maximum uncertainty loss model, for maximizing an uncertainty loss between a maximum benefit achievable under an operation regulation decision of a flexible resource output uncertainty scenario of an uncontrollable flexible resource and a current benefit achievable under a current operation regulation decision, with a constraint of a first operation constraint set of a power supply unit in a virtual power plant, and under a condition of a ramping power scenario determination, the power supply unit being implemented as a virtual power plant or a virtual power group, the operation regulation decision including a set of actions on a controllable flexible resource in the power supply unit, the first operation constraint set including an external characteristic constraint on the power supply unit, the external characteristic constraint being formed based on a feasible region of the virtual power plant or the virtual power group obtained by a resource external characteristic representation method as claimed in any one of claims 4 to 8; obtaining, by the maximum uncertainty loss model, each set of maximum uncertainty losses of each alternative operation regulation decision under a set of the ramping power scenarios; obtaining, by minimizing a maximum expected loss model based on each set of maximum uncertainty losses, each expected loss, and determining a corresponding alternative operation regulation decision as a target operation regulation decision according to a minimum value in the respective expected losses.

10. The operational regulation method according to claim 9, wherein, The controllable flexible resource includes one or more of an energy storage device, a gas / oil power generation device, a transferable load, and a reducible load; the set of actions includes one or more of a gas / oil power generation device action, charging, discharging, power transfer, and power reduction; and / or, the uncontrollable flexible resource includes a new energy power generation device.

11. The operational regulation method according to claim 9, wherein The first operation constraint set includes a power balance constraint, indicating that a sum of an actual output power of the power supply unit and an output deviation amount is equal to a sum of a day-ahead power and a ramping power; the output deviation amount indicates a deviation amount of the actual output power of the power supply unit compared to a predetermined output power, and the output deviation penalty cost is related to the output deviation amount; the operation regulation decision includes the set of actions on the controllable flexible resource; the maximum benefit is obtained based on searching for an optimal operation regulation decision and the output deviation amount of the virtual power group under the operation regulation decision of the output uncertainty scenario to maximize a first benefit obtained as a target; The current benefit is obtained based on searching for the output deviation amount under the current operation regulation decision to minimize a power supply deviation cost to maximize a second benefit as a target; The first benefit and the second benefit are represented by a difference between a sum of a day-ahead power benefit and a ramping power benefit and a sum of a flexible resource scheduling total cost and an output deviation penalty cost.

12. The operational regulation method according to claim 11, wherein, The output deviation penalty cost is related to the output deviation amount; the output deviation amount indicates a deviation amount of the actual output power of the virtual power group compared to a predetermined output power; and the output deviation amount is calculated in a manner including: When the output deviation amount is greater than zero, the output deviation penalty cost is a positive value positively related to the output deviation amount; When the output deviation amount is not greater than zero, the output deviation penalty cost is a negative value negatively related to the output deviation amount.

13. The operational regulation method of claim 9, wherein, The maximum uncertainty loss model is represented as: ； wherein, represents the current operational regulation decision and the determined ramping capacity scenario P R the maximum uncertainty loss under the current operational regulation decision; represents various output uncertainty scenarios; the current operational regulation decision , p ope represents the action set on the controllable flexible resources; P m and P m,u respectively represent the day-ahead power under the current operational regulation decision and the operational regulation decision under the output uncertainty scenario; represents the output deviation amount under various output uncertainty scenarios; represents the total flexible resource scheduling cost of the power supply unit in the scenario under the operational regulation decision, represents the total flexible resource scheduling cost of the power supply unit under the current operational regulation decision; and C B respectively represent the output deviation penalty cost to be borne under the operational regulation decision under the output uncertainty scenario and under the current operational regulation decision; R m and respectively represent the day-ahead power revenue under the current operational regulation decision and the operational regulation decision under the output uncertainty scenario; R R represents the ramping power revenue; R m and are calculated in the following formula: , is a day-ahead power value coefficient; R R is calculated in the following formula: , is a ramping power value coefficient; and C B are calculated in the following formula: ； wherein, and is a bias power cost coefficient; P B is the output deviation amount under the current operation control decision.

14. The operational regulatory method of claim 9, wherein, The first operation constraint set comprises at least one of an external characteristic constraint of the power supply unit, an output power characteristic constraint of each controllable flexible resource of the power supply unit, and an adjustable power constraint; wherein the external characteristic constraint is used to describe the characteristics of the power supply unit; and the output power characteristic constraint comprises an action constraint set of the action set.

15. The operational regulatory method of claim 9, wherein, The minimization maximum expected loss model is expressed as: ； wherein, p VPP represents a current operating regulatory decision, represents a ramp-up power in the Kth ramp-up power scenario, represents a probability of the Kth ramp-up power scenario.

16. The operational regulatory method of claim 9, wherein, Each of the expected losses is calculated by weighting a weighted sum of a corresponding set of losses with a probability of occurrence of a respective ramping power scenario; and the minimization maximum expected loss model comprises a second operation constraint set, the second operation constraint set comprising the first operation constraint set, an adjustable power constraint under each ramping power scenario, and a sum of the respective weights being 1.

17. A run governance platform, characterized by, Comprise: A processor and a memory; The memory stores program instructions; The processor is configured to execute the program instructions to perform at least one of the following: The resource partitioning balancing-based aggregation method according to any one of claims 1 to 3; The resource external characteristic representation method according to any one of claims 4 to 8; The operation regulation method according to any one of claims 9 to 16.

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