Power system dispatching method and apparatus, computer device, and storage medium

Abstract

A power system dispatching method, applied to a virtual power plant. The virtual power plant comprises a plurality of resource devices. The method comprises: acquiring an operating model of a single resource device (102); acquiring a first feasible region space on the basis of the operating model, the first feasible region space being a feasible region space for operating parameters of the single resource device (104); acquiring a second feasible region space on the basis of the first feasible region space and the operating parameters of the single resource device, the second feasible region space being a feasible region space for operating parameters of total resource devices (106); and establishing a power system dispatching model on the basis of the operating parameters of the total resource devices and the second feasible region space, so as to dispatch power resources (108).

Description

Power system dispatching method, device, computer equipment and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on November 2, 2023, with application number 202311456146.2 and invention name “Power System Dispatching Method, Device, Computer Equipment and Storage Medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the technical field of virtual power plants, and in particular to a power system scheduling method, apparatus, computer equipment, and storage medium. Background Art

[0004] The statements herein merely provide background information related to the present application and do not necessarily constitute exemplary techniques.

[0005] The development of virtual power plants (VPPs) has made significant progress. The refinement and widespread application of these technologies are gradually transforming the operating model of traditional electricity markets. Virtual power plants integrate multiple distributed energy resources, such as wind power, photovoltaics, and energy storage, and leverage information technology to coordinate and control these resources, thereby achieving efficient, clean, economical, and stable electricity supply. At the market level, the operational model of virtual power plants offers a new perspective for the development of electricity markets. Traditional electricity markets are dominated by large power plants. The rise of virtual power plants has enabled the integration of distributed energy resources into the electricity market, expanding its scale while also providing more diversified energy supply options and enhancing market competitiveness. Virtual power plants' participation in diverse electricity markets, including energy consumption and ancillary services, has significant implications for improving the utilization of distributed resources, facilitating grid supply and demand balance, reducing power system operating costs, and promoting the development of renewable energy.

[0006] However, participants in traditional electricity markets are generally centralized entities. How to model the operating characteristics of virtual power plants that contain massive heterogeneous resources into entities similar to single entities on the power generation side and enable them to operate collaboratively in a diversified electricity market is a key technical challenge faced by virtual power plants in participating in the electricity market.

[0007] Summary of the Invention

[0008] According to various embodiments of the present application, a power system scheduling method, apparatus, computer equipment, and storage medium are provided.

[0009] In a first aspect, the present application provides a power system scheduling method, which is applied to a virtual power plant, wherein the virtual power plant includes multiple resource devices; the method includes:

[0010] Get the operating model of a single resource device;

[0011] Acquire a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of operation parameters of a single resource device;

[0012] Acquire a second feasible domain space based on the first feasible domain space and the single resource device operating parameter, wherein the second feasible domain space is the feasible domain space of the total resource device operating parameter;

[0013] A power system scheduling model is established based on the total resource equipment operating parameters and the second feasible domain space to schedule power resources.

[0014] In one embodiment, obtaining the operating model of a single resource device includes:

[0015] respectively obtaining the operating power constraint, energy consumption constraint, ramp constraint, adjustment number and time constraint, and predicted load of the single resource device;

[0016] The operation model is acquired based on the operation power constraint, the energy usage constraint, the ramp constraint, the adjustment number and time constraint, and the predicted load of the single resource device.

[0017] In one embodiment, obtaining a first feasible domain space based on the operation model includes:

[0018] Acquiring corresponding operating parameters based on the application scenario of the resource device;

[0019] The first feasible domain space is established based on the operation model and the operation parameters.

[0020] In one embodiment,

[0021] When the application scenario includes an electric energy system and an auxiliary service system, the corresponding operating parameters include device operating power, device up-scaling capability, and device down-scaling capability; the first feasible domain space is established by the following formula: P DER,i,t -R Down,i,t ≥P DER,i ; R Up,i,t -slope Up,i ·τ i s i,t-1 ≤slope Up,i ·(Δt-τ i ); R Down,i,t -slope Down,i ·τ i s i,t-1 ≤slopeDown,i ·(Δt-τ i );

[0022] When the running variable x of resource device i DER,i = <P DER,i ,R Up,i ,R Down,i >, the feasible domain space of the operating parameters is:

[0023] Among them, P DER,i,t R is the operating power of resource device i; Up,i,t The capacity of resource device i can be increased; is the upper bound of the operating boundary of resource device i; R Down,i,t P is the capacity of resource device i that can be adjusted downward; DER,i is the lower bound of the operating boundary of resource device i; is the target adjustable range of resource device i; slope Up,i is the upward climbing rate of resource equipment i; slope Down,i is the downward climbing rate of resource device i; τ i is the response time of resource device i; s i,t-1 is the regulation state of resource device i in time period t-1; Ω DER,i is the feasible domain space; M i x DER,i +L i s i ≤N i is a compact form of linear constraints based on the running model composition.

[0024] In one embodiment, acquiring the second feasible domain space based on the first feasible domain space and the operating parameters of the single resource device includes:

[0025] Obtaining the aggregate resource device operating parameters based on the individual resource device operating parameters;

[0026] An inscribed polyhedron approximation method is adopted, and the second feasible domain space is obtained based on the first feasible domain space.

[0027] In one embodiment, the total resource equipment operating parameter aggregation includes resource equipment operating power aggregation, resource equipment upward adjustment capability aggregation, and resource equipment downward adjustment capability aggregation; and the power system scheduling model is established by the following formula:

[0028] Among them, P VPP,t Run power aggregation for the resource device, R VPP,up,t R is the aggregation of the resource device's adjustable capacity. VPP,down,tis the aggregate of the resource equipment's down-scaling capabilities, π e,t is the target replacement of the virtual power plant at time t, μ up,t The target upward displacement for the virtual power plant, μ down,t The target downward displacement for the virtual power plant, y VPP The total resource equipment operating parameters; is the second feasible domain space.

[0029] In a second aspect, the present application further provides a power system dispatching device, the device comprising:

[0030] Model acquisition module, used to obtain the operation model of a single resource device;

[0031] A first data processing module is configured to obtain a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of operation parameters of a single resource device;

[0032] A second data processing module is configured to obtain a second feasible domain space based on the first feasible domain space and the single resource device operating parameter, wherein the second feasible domain space is the feasible domain space of the total resource device operating parameter;

[0033] A model building module is used to build a power system scheduling model based on the total resource equipment operating parameters and the second feasible domain space to schedule power resources.

[0034] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0036] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0037] The above-mentioned power system scheduling method, device, computer equipment and storage medium are applied to the electronic resource system of the virtual power plant. Since the operation model of a single resource device is obtained, the operation model includes the operation constraints of the resource device. Therefore, after obtaining the first feasible domain space based on the operation model, the second feasible domain space is obtained according to the first feasible domain space and the operation parameters of the single resource device. The power system scheduling model established according to the total resource device operation parameters and the second feasible domain space includes the coupling constraint relationship between the various constraints of the resource equipment, and can accurately characterize the feasible domain space of the total resource equipment operation parameters of the virtual power plant. Therefore, based on the power system scheduling model finally obtained, multiple resource devices of the virtual power plant can be efficiently coordinated.

[0038] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments or exemplary technologies of the present application, the following briefly introduces the drawings required for use in the description of the embodiments or exemplary technologies. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0040] FIG1 is a flow chart of a method for dispatching a power system according to an embodiment of the present invention;

[0041] FIG2 is a schematic diagram of a process for obtaining an operating model of a single resource device in one embodiment;

[0042] FIG3 is a schematic diagram of a process of executing a model to obtain a first feasible domain space in one embodiment;

[0043] FIG4 is a schematic diagram of a process for obtaining a second feasible domain space based on a first feasible domain space and operating parameters of a single resource device in one embodiment;

[0044] FIG5 is a second flow chart of a power system dispatching method in one embodiment;

[0045] FIG6 is a schematic block diagram of the structure of a power system dispatching device in one embodiment;

[0046] FIG7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] Understandably, in the context of a multi-faceted electricity market, the primary goal of modeling the external characteristics of virtual power plants (VPPs), which encompass a vast array of heterogeneous resources, is to integrate various types of distributed resources with varying parameters, such as electric vehicles, electric heating, and central air conditioning, to form the VPP's external characteristics, including adjustable capacity, adjustable capacity, frequency regulation capacity, and reserve capacity. These characteristics serve as the boundary conditions for application decisions for participation in the electricity energy market, frequency regulation, reserve capacity, and other ancillary service markets, thereby enabling coordinated resource exchange across various electricity markets. Different external characteristic parameters often exhibit coupling constraints, which can be represented by their feasible domain space. Therefore, it is necessary to solve for the feasible domain space of the VPP's external characteristic parameters.

[0049] However, virtual power plants often require the dispatch of large-scale distributed resource devices, each with different model parameters. Calculating the feasible domain space for all resources individually would lead to the challenges of large-scale computation due to high dimensionality. Even if the aggregated feasible domain space is obtained by taking the Minkowski sum of the feasible domain space, calculating the Minkowski space of any two feasible domains is an NP-hard problem—it cannot be solved within a polynomial time scale. Therefore, employing a low-complexity feasible domain space aggregation approximation algorithm is key to achieving efficient and coordinated operation of the various resource devices in a virtual power plant.

[0050] In one embodiment, as one of the flow charts of the power system dispatching method shown in Figure 1, a power system dispatching method is provided, which is illustrated by taking the application of the power system dispatching method to a virtual power plant as an example, wherein the virtual power plant includes multiple resource devices; the power system dispatching method includes the following steps 102 to 108.

[0051] Step 102: Obtain an operation model of a single resource device.

[0052] The resource devices mentioned above are distributed resource devices in a virtual power plant, such as electric vehicles, electric heating, central air conditioning, etc. The operation model of the above-mentioned single resource device includes the operation constraints of the resource device.

[0053] Step 104: Acquire a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of the operation parameters of a single resource device.

[0054] The feasible domain space refers to the range of design points within the design space that satisfy all constraints. In this embodiment, obtaining the feasible domain space is equivalent to obtaining the collaborative operation capabilities of each resource device. Therefore, obtaining the feasible domain space of the operating parameters of each resource device can achieve collaborative operation in resource swapping across different power markets.

[0055] Step 106: Acquire a second feasible domain space based on the first feasible domain space and the operating parameters of a single resource device. The second feasible domain space is the feasible domain space of the total resource device operating parameters.

[0056] It's understandable that since a virtual power plant includes multiple heterogeneous resources, the primary goal of modeling its external characteristics is to integrate distributed resources of varying types and parameters. In other words, the operating parameters of the total resource equipment in a virtual power plant constitute its external characteristics. Therefore, the feasible domain of these parameters can also be understood as the feasible domain of the external characteristics of the virtual power plant.

[0057] Step 108: Establish a power system dispatching model based on the total resource equipment operating parameters and the second feasible domain space to dispatch power resources.

[0058] In the above-mentioned power system dispatching method, since the operation model of a single resource device is obtained, and the operation model includes the operation constraints of the resource device, after obtaining the first feasible domain space based on the operation model, and after obtaining the second feasible domain space according to the first feasible domain space and the operation parameters of the single resource device, the power system dispatching model established according to the total resource device operation parameters and the second feasible domain space includes the coupling constraint relationship between the various constraints of the resource equipment, and can accurately characterize the feasible domain space of the total resource device operation parameters of the virtual power plant. Therefore, based on the power system dispatching model finally obtained, multiple resource devices of the virtual power plant can be efficiently coordinated.

[0059] In one embodiment, as shown in FIG. 2 , a flowchart of obtaining an operating model of a single resource device includes the following steps 202 to 204 .

[0060] Step 202 : Obtain the operating power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of a single resource device respectively.

[0061] Specifically, obtaining the operating power constraint of a single resource device is also called obtaining the operating boundary constraint of a single resource device, including obtaining the minimum power constraint and maximum power constraint of a single resource device. The formula is:

[0062] Among them, P DER,i is the minimum power constraint of a single resource device i, is the maximum power constraint of a single resource device i.

[0063] Specifically, since the total energy consumption of a single resource device i remains consistent before and after adjustment, the energy consumption constraint of a single resource device i can be obtained by the following formula:

[0064] Where Δt is the time interval, E DER,i It is the total energy consumption of resource device i within a preset time (such as 24 hours).

[0065] Specifically, assuming that the response time of resource device i is τ i (min), that is, the time interval for the power of resource device i to start adjusting after receiving the adjustment instruction, s i,t is the regulation state of resource device i in period t. If resource device i is in the regulation state in period t, then s i,t =1, otherwise, s i,t =0; and assuming that the rate at which resource device i climbs upward is slope Up,i (kW / min), the rate at which resource device i climbs downward is slope Down,i (kW / min), the ramp constraint of resource device i (also known as the cross-time domain constraint of power) is:

[0066] Specifically, assume that resource device i participates in regulation, and the maximum continuous regulation time limit is h i , and the maximum number of adjustments per day is M i , then the adjustment number constraint is:

[0067] Among them, T is the set of all time periods, and the adjustment time μ i,t The constraints are:

[0068] Specifically, for some electrical equipment, assuming that users have inherent energy usage habits, the load when users participate in regulation can be predicted to be Then the power of resource device i that is not regulated is:

[0069] Therefore, the predicted load of a single resource device can be obtained according to formula (8).

[0070] Step 204 : Acquire an operation model based on the operation power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of a single resource device.

[0071] The operation model of a single resource device obtained in this embodiment includes the operation power constraints, energy consumption constraints, ramp constraints, adjustment times and time constraints of the single resource device. Therefore, the power system scheduling model finally obtained based on the operation model includes the coupling constraint relationship of the above-mentioned constraints, and can accurately solve the aggregated feasible domain space of the total resource equipment operation parameters. Therefore, based on the power system scheduling model, multiple resource devices of the virtual power plant can be efficiently coordinated.

[0072] In one embodiment, as shown in FIG3 , a flow chart of obtaining a first feasible domain space by running a model includes the following steps 302 to 304 .

[0073] Step 302: Obtain corresponding operating parameters based on the application scenario of the resource device.

[0074] For example, when the application scenario includes an electric energy system and an ancillary service system (i.e., distributed resources participate in the electric energy market and the ancillary service market), the corresponding operating parameters include equipment operating power, equipment up-scaling capability, and equipment down-scaling capability.

[0075] Step 304: Establish a first feasible domain space based on the operation model and the operation parameters.

[0076] For example, when the application scenario includes an electric energy system and an auxiliary service system, the corresponding operating parameters include device operating power, device up-scaling capability, and device down-scaling capability. The first feasible domain space is established by the following formula: P DER,i,t -R Down,i,t ≥P DER,i ; (10) R Up,i,t -slope Up,i ·τ i s i,t-1 ≤slope Up,i ·(Δt-τ i ); (13) R Down,i,t -slope Down,i ·τ i s i,t-1 ≤slope Down,i ·(Δt-τ i ); (14)

[0077] When the running variable x of resource device i DER,i = <P DER,i ,R Up,i ,R Down,i >, the feasible domain space of the operating parameters is:

[0078] Among them, P DER,i,t R is the operating power of resource device i; Up,i,t The capacity of resource device i can be increased; is the upper bound of the operating boundary of resource device i; R Down,i,t P is the capacity of resource device i that can be adjusted downward; DER,i is the lower bound of the operating boundary of resource device i; is the target adjustable range of resource device i; slope Up,i is the upward climbing rate of resource equipment i; slope Down,i is the downward climbing rate of resource device i; τ i is the response time of resource device i; s i,t-1 is the regulation state of resource device i in time period t-1; Ω DER,i is the feasible domain space; M i x DER,i +L i s i ≤N i It is a compact form of linear constraints based on the operating model (i.e., formula (1) to formula (14)).

[0079] In one embodiment, as shown in FIG4 , a flow chart of obtaining a second feasible domain space based on a first feasible domain space and operating parameters of a single resource device, obtaining a second feasible domain space based on the first feasible domain space and operating parameters of a single resource device includes the following steps 402 to 404 .

[0080] Step 402: Obtain an aggregate of total resource device operating parameters based on individual resource device operating parameters.

[0081] The aggregated total resource equipment operating parameters can also be understood as the aggregated external characteristic parameters of the virtual power plant. For example, the aggregated total resource equipment operating parameters include the aggregated total resource equipment operating power, the aggregated total resource equipment adjustable capacity, and the aggregated total resource equipment adjustable capacity.

[0082] Step 404 : adopt an inscribed polyhedron approximation method and obtain a second feasible domain space based on the first feasible domain space.

[0083] Specifically, the resource equipment set in the virtual power plant is N, and the calculation method of the aggregation of various operating parameters is as follows: VPP,t =∑ i∈N P DER,i,t ; (16) R VPP,up,t =∑ i∈N R Up,i,t ; (17) RVPP,down,t =∑ i∈N R Down,i,t ; (18)

[0084] Among them, P VPP,t Run power aggregation for resource devices; R VPP,up,t Aggregation of resource equipment's adjustable capabilities; R VPP,down,t The resource equipment can be aggregated downwards to adjust the capacity.

[0085] The feasible domain space of the external characteristic parameters of the virtual power plant is determined by the feasible domain space of the operating parameters of each resource equipment. Let the total resource equipment operating parameters aggregate y VPP = <P VPP ,R VPP,Up ,R VPP,Down >, then according to the aggregation of the operating parameters in formula (16) to formula (18), we can obtain: y VPP =∑ i∈N x DER,i ; (19)

[0086] Furthermore, VPP The feasible domain space can be expressed as follows:

[0087] To solve equation (20), we use the inscribed polyhedron approximation method and assume is Ω VPP If it is an inscribed polyhedron, then the solution of equation (20) is transformed into the solution of the following optimization problem:

[0088] and

[0089] in, Represents the volume of hyperspace enclosed by a polyhedron.

[0090] Furthermore, based on the variable radius random search approximation method in the inscribed polyhedron approximation method, equation (21) is solved. First, a center point y is generated. VPP,0 , the center point y VPP,0 Represents a feasible operating point, usually the predicted power under no response is taken as the operating power And assume that R VPP,up,t =0, R VPP,down,t = 0. Then set a search radius r, randomly generate a set of new operating points within the radius and satisfy ‖y VPP,n -y VPP,0 ‖=r. For any y VPP,n , confirm whether the vertex is in the feasible region by solving the anti-aggregation problem in equation (22).

[0091] Due to s i The existence of , the anti-aggregation problem of formula (22) is a mixed integer linear programming problem, which can be solved by the solver. If (22) has a feasible solution, it means that the operating point is in the feasible region, and y VPP,n Add it to the operating point set S. Otherwise, reduce r according to the preset ratio, and regenerate and search for a new operating point. Repeat the above steps for each operating point in the set S until the search times reach the maximum or no new operating point can be found. Generate a polyhedron in the form of a convex hull with all the operating points in the set S as vertices As y VPP The feasible domain of . The methods for generating the convex hull include Graham's scan search algorithm and other algorithms.

[0092] Furthermore, assuming that the virtual power plant is used in the electric energy market and the reserve market, a joint power system dispatch model for the virtual power plant electric energy market and the reserve market is established. The goal of this power system dispatch model is to maximize the replacement resources while satisfying the operation constraints of the virtual power plant. For example, the power system dispatch model is established by the following formula:

[0093] Among them, P VPP,t For devices running power aggregation, R VPP,up,t R is the aggregate of the device's adjustable capabilities. VPP,down,t is the aggregation of the device's downgradable capabilities, π e,t is the target replacement of the virtual power plant at time t, μ up,t To increase the replacement for the virtual power plant target, μ down,t The target of virtual power plant is to reduce the replacement, y VPP It is the total resource equipment operating parameters; is the second feasible domain space.

[0094] It can be understood that, according to actual application, the replacement resources may refer to the expected benefits, the target replacement may refer to the expected price of the electricity energy market, the target increase replacement may refer to the expected price of the reserve market, and the target decrease replacement may refer to the expected price of the reserve market.

[0095] In this embodiment, by adopting the approximation method of the inscribed polyhedron and obtaining the second feasible domain space based on the first feasible domain space, the technical problem of the difficulty in obtaining the feasible domain space is solved. Since obtaining the feasible domain space of the external characteristic variables of the virtual power plant is essentially to find the Minkowski sum of the feasible domain space of the operating parameters of each resource equipment, and there is a lack of a fast method to obtain the Minkowski sum in space, this embodiment proposes an approximation method based on variable radius random search in the approximation method of the inscribed polyhedron within the feasible domain to obtain the operable vertices, thereby obtaining the inscribed polyhedron feasible domain in the form of a convex hull, which can obtain an approximate feasible domain space within a limited time. Therefore, based on this power system scheduling model, multiple resource equipment of the virtual power plant can be efficiently coordinated.

[0096] In one embodiment, as shown in the second flowchart of the power system dispatching method in FIG5 , the power system dispatching method further includes:

[0097] Step 502 : Obtain the operating power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of a single resource device respectively.

[0098] Step 504 : Acquire an operation model based on the operation power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of a single resource device.

[0099] Step 506: Obtain corresponding operating parameters based on the application scenario of the resource device.

[0100] Step 508: Establish a first feasible domain space based on the operation model and the operation parameters.

[0101] Step 510: Obtain an aggregate of total resource device operating parameters based on individual resource device operating parameters.

[0102] Step 512: adopt an inscribed polyhedron approximation method and obtain a second feasible domain space based on the first feasible domain space.

[0103] Step 514: Establish a power system dispatch model based on the total resource equipment operating parameters and the second feasible domain space to dispatch power resources.

[0104] In this embodiment, since the operation model of a single resource device is obtained, and the operation model includes the operation constraints of the resource device, after obtaining the first feasible domain space based on the operation model, and after obtaining the second feasible domain space based on the first feasible domain space and the operation parameters of the single resource device, the power system scheduling model established based on the total resource device operation parameters and the second feasible domain space includes the coupling constraint relationship between the various constraints of the resource equipment, and can accurately characterize the feasible domain space of the total resource device operation parameters of the virtual power plant. Therefore, based on the power system scheduling model finally obtained, multiple resource devices of the virtual power plant can be efficiently coordinated.

[0105] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0106] Based on the same inventive concept, embodiments of the present application also provide a power system dispatching device for implementing the power system dispatching method involved above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more power system dispatching device embodiments provided below can be found in the above-mentioned limitations of the power system dispatching method and will not be repeated here.

[0107] In one embodiment, as shown in the structural schematic block diagram of the power system dispatching device in Figure 6, a power system dispatching device 600 is provided, and the power system dispatching device 600 includes: a model acquisition module 610, a first data processing module 620, a second data processing module 630 and a model establishment module 640.

[0108] The model acquisition module 610 is used to acquire the operation model of a single resource device.

[0109] The first data processing module 620 is configured to obtain a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of the operation parameters of a single resource device.

[0110] The second data processing module 630 is configured to obtain a second feasible domain space based on the first feasible domain space and the operating parameters of a single resource device, wherein the second feasible domain space is the feasible domain space of the total resource device operating parameters.

[0111] The model building module 640 is used to build a power system scheduling model based on the total resource equipment operating parameters and the second feasible domain space to schedule power resources.

[0112] In one embodiment, the model acquisition module is also used to respectively obtain the operating power constraints, energy consumption constraints, climbing constraints, adjustment times and time constraints, and predicted load of a single resource device; and obtain the operating model based on the operating power constraints, energy consumption constraints, climbing constraints, adjustment times and time constraints, and predicted load of a single resource device.

[0113] In one embodiment, the first data processing module is further configured to obtain corresponding operating parameters based on an application scenario of the resource device; and establish a first feasible domain space based on the operating model and the operating parameters.

[0114] In one embodiment, the first data processing module is further configured to establish a first feasible domain space using the following formula when the application scenario includes an electric energy system and an auxiliary service system, and the corresponding operating parameters include device operating power, device up-scaling capability, and device down-scaling capability: P DER,i,t -R Down,i,t ≥P DER,i ; R Up,i,t -slope Up,i ·τ i s i,t-1 ≤slope Up,i ·(Δt-τ i ); R Down,i,t -slope Down,i ·τ i s i,t-1 ≤slope Down,i ·(Δt-τ i );

[0115] When the running variable x of resource device i DER,i = <P DER,i ,R Up,i ,R Down,i >, the feasible domain space of the operating parameters is:

[0116] Among them, P DER,i,t R is the operating power of resource device i; Up,i,t The capacity of resource device i can be increased; is the upper bound of the operating boundary of resource device i; RDown,i,t P is the capacity of resource device i that can be adjusted downward; DER,i is the lower bound of the operating boundary of resource device i; is the target adjustable range of resource device i; slope Up,i is the upward climbing rate of resource equipment i; slope Down,i is the downward climbing rate of resource device i; τ i is the response time of resource device i; s i,t-1 is the regulation state of resource device i in time period t-1; Ω DER,i is the feasible domain space; M i x DER,i +L i s i ≤N i It is a compact form of linear constraints based on the composition of the running model.

[0117] In one embodiment, the second data processing module is further configured to obtain an aggregate of total resource device operating parameters based on individual resource device operating parameters; and to obtain a second feasible domain space based on the first feasible domain space using an inscribed polyhedron approximation method.

[0118] In one embodiment, the total resource device operating parameter aggregation includes resource device operating power aggregation, resource device upward adjustment capability aggregation, and resource device downward adjustment capability aggregation; the model building module is further used to establish a power system scheduling model using the following formula:

[0119] Among them, P VPP,t Run power aggregation for resource devices, R VPP,up,t The resource equipment can be increased to aggregate capacity, R VPP,down,t The aggregation of resource equipment's downwardly adjustable capabilities, π e,t is the target replacement of the virtual power plant at time t, μ up,t To increase the replacement for the virtual power plant target, μ down,t The target of virtual power plant is to reduce the replacement, y VPP It is the total resource equipment operating parameters; is the second feasible domain space.

[0120] Each module in the above-mentioned power system dispatching device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0121] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG7 . The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all data involved in the power system dispatching method. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a power system dispatching method is implemented.

[0122] Those skilled in the art will understand that the structure shown in FIG7 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0125] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0127] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0128] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for dispatching a power system, wherein: Applied to a virtual power plant, the virtual power plant includes a plurality of resource devices; the method includes: Get the operation model of a single resource device; Acquire a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of operation parameters of a single resource device; Acquire a second feasible domain space based on the first feasible domain space and the single resource device operating parameter, wherein the second feasible domain space is the feasible domain space of the total resource device operating parameter; A power system dispatching model is established based on the total resource equipment operating parameters and the second feasible domain space to dispatch power resources.

2. The method according to claim 1, wherein: Obtaining the operating model of a single resource device includes: Respectively obtain the operating power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of the single resource equipment; The operation model is obtained based on the operation power constraint, the energy usage constraint, the ramp constraint, the adjustment number and time constraint, and the predicted load of the single resource device.

3. The method according to claim 1, wherein: Acquiring a first feasible domain space based on the operation model includes: Acquire corresponding operating parameters based on the application scenario of the resource device; The first feasible domain space is established based on the operation model and the operation parameters.

4. The method according to claim 3, wherein: When the application scenario includes an electric energy system and an auxiliary service system, the corresponding operating parameters include equipment operating power, equipment up-adjustability, and equipment down-adjustability; the first feasible domain space is established by the following formula: P DER,i,t -R Down,i,t ≥P DER,i ; R Up,i,t -slope Up,i ·t i s i,t-1 ≤slope Up,i ·(Δt-τ i ); R Down,i,t -slope Down,i ·t i s i,t-1 ≤slope Down,i ·(Δt-τ i ); When the running variable x of resource device i DER,i = <P DER,i ,R Up,i ,R Down,i >, the feasible domain space of the operating parameters is: Among them, P DER,i,t R is the operating power of resource device i; Up,i,t The capacity of resource device i can be increased; is the upper bound of the operating boundary of resource device i; R Down,i,t P is the capacity of resource device i that can be adjusted downward; DER,i is the lower bound of the running boundary of resource device i; is the target adjustable range of resource device i; slope Up,i is the upward climbing rate of resource device i; slope Down,i is the downward climbing rate of resource device i; τ i is the response time of resource device i; s i,t-1 For time period t-1 The regulation status of resource device i; Ω DER,i is the feasible domain space; M i x DER,i +L i s i ≤N i is a compact form of linear constraints composed based on the running model.

5. The method according to claim 1, wherein: The acquiring of the second feasible domain space based on the first feasible domain space and the single resource device operating parameter comprises: Obtaining the total resource device operating parameter aggregation based on the single resource device operating parameter; An inscribed polyhedron approximation method is adopted, and the second feasible domain space is acquired based on the first feasible domain space.

6. The method according to claim 1, wherein: The total resource equipment operation parameter aggregation includes resource equipment operation power aggregation, resource equipment upward adjustable capacity aggregation and resource equipment downward adjustable capacity aggregation; The power system dispatch model is established by the following formula: Among them, P VPP,t Run power aggregation for the resource device, R VPP,up,t R is the aggregate of the resource device’s up-scaling capabilities. VPP,down,t is the aggregation of the down-adjustable capabilities of the resource device, π e,t is the target replacement of the virtual power plant at time t, μ up,t The target displacement for the virtual power plant is adjusted upward, μ down,t The target downward displacement for the virtual power plant, y VPP The operating parameters of the total resource equipment; It is the second feasible domain space.

7. A power system dispatching device, wherein: The device comprises: A model acquisition module is used to obtain the operation model of a single resource device; A first data processing module, configured to obtain a first feasible domain space based on the operation model; the first feasible domain space is a feasible domain space of operation parameters of a single resource device; A second data processing module is used to obtain a second feasible domain space based on the first feasible domain space and the single resource device operating parameters, wherein the second feasible domain space is the feasible domain space of the total resource device operating parameters; A model building module is used to build a power system dispatching model based on the total resource equipment operating parameters and the second feasible domain space to dispatch power resources.

8. The power system dispatching device according to claim 7, wherein: The model acquisition module is also used for: Respectively obtain the operating power constraint, energy consumption constraint, ramp constraint, adjustment times and time constraint, and predicted load of the single resource equipment; The operation model is obtained based on the operation power constraint, the energy usage constraint, the ramp constraint, the adjustment number and time constraint, and the predicted load of the single resource device.

9. The power system dispatching device according to claim 7, wherein: The first data processing module is also used for: Acquire corresponding operating parameters based on the application scenario of the resource device; The first feasible domain space is established based on the operation model and the operation parameters.

10. The power system dispatching device according to claim 9, wherein: The first data processing module is also used for: When the application scenario includes an electric energy system and an auxiliary service system, the corresponding operating parameters include equipment operating power, equipment up-adjustability, and equipment down-adjustability; the first feasible domain space is established by the following formula: P DER,i,t -R Down,i,t ≥P DER,i ; R Up,i,t -slope Up,i ·t i s i,t-1 ≤slope Up,i ·(Δt-τ i ); R Down,i,t -slope Down,i ·t i s i,t-1 ≤slope Down,i ·(Δt-τ i ); When the running variable x of resource device i DER,i = <P DER,i ,R Up,i ,R Down,i >, the feasible domain space of the operating parameters is: Among them, P DER,i,t R is the operating power of resource device i; Up,i,t The capacity of resource device i can be increased; is the upper bound of the operating boundary of resource device i; R Down,i,t P is the capacity of resource device i that can be adjusted downward; DER,i is the lower bound of the running boundary of resource device i; is the target adjustable range of resource device i; slope Up,i is the upward climbing rate of resource device i; slope Down,i is the downward climbing rate of resource device i; τ i is the response time of resource device i; s i,t-1 is the regulation state of resource device i in time period t-1; Ω DER,i is the feasible domain space; M i x DER,i +L i s i ≤N i is a compact form of linear constraints composed based on the running model.

11. The power system dispatching device according to claim 7, wherein: The second data processing module is also used for: Obtaining the total resource device operating parameter aggregation based on the single resource device operating parameter; An inscribed polyhedron approximation method is adopted, and the second feasible domain space is acquired based on the first feasible domain space.

12. The power system dispatching device according to claim 7, wherein: The total resource equipment operation parameter aggregation includes resource equipment operation power aggregation, resource equipment adjustable capacity aggregation and resource equipment adjustable capacity aggregation; the model building module is also used to establish a power system scheduling model through the following formula: Among them, P VPP,t Run power aggregation for the resource device, R VPP,up,t R is the aggregate of the resource device’s up-scaling capabilities. VPP,down,t is the aggregation of the down-adjustable capabilities of the resource device, π e,t is the target replacement of the virtual power plant at time t, μ up,t The target displacement for the virtual power plant is adjusted upward, μ down,t The target downward displacement for the virtual power plant, y VPP The operating parameters of the total resource equipment; It is the second feasible domain space.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

15. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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