A novel multi-time scale scheduling method for power systems

Abstract

The application discloses a novel multi-time scale scheduling method of a power system, and relates to the technical field of power system scheduling control, and comprises the following steps: S1, constructing a space-time state atlas; S2, using the state atlas to perform regional constructability mapping analysis; S3, using a network construction capability matrix to construct a resource allocation time sequence structure under a multi-time scale; S4, using the network construction capability matrix and a resource regulation atlas to perform regulation link constraint integration; S5, using a regulation capability structure to perform scheduling hierarchical regulation structure fusion; and S6, using response mapping to generate a scheduling output of constraint fusion. Through the setting of a regional response behavior evaluation mechanism based on a residual mapping and a response reconstruction model, response trajectory deviation information of regional resources under a multi-time scale can be dynamically extracted, and a residual dominant partition dynamic adjustment basis is established, so that resource scheduling of different regions under a future time scale is optimized and adjusted according to response trend evolution.

Description

Technical Field

[0001] This invention relates to the field of power system dispatch and control technology, specifically to a novel multi-timescale dispatch method for power systems. Background Technology

[0002] With the large-scale integration of distributed renewable energy, mobile energy storage devices, and flexible loads into new power systems, the coupling relationship between sources, grids, and loads exhibits strong nonlinearity, strong uncertainty, and strong time-varying characteristics. Against this backdrop, dispatch and control systems need to possess dynamic response capabilities across multiple time scales to match real-time fluctuations in power supply and demand and the differences in regional regulation capabilities.

[0003] In existing technologies, simplified processing methods such as historical mean modeling, static weight allocation, or fixed regional response factors are commonly used to address the response characteristics of mobile energy storage or mobile load resources within a region. These methods lack dynamic modeling mechanisms for regional response behavior and temporal evolution characterization of behavior-driven features. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a novel multi-timescale scheduling method for power systems, thereby resolving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a novel multi-timescale scheduling method for power systems, comprising the following steps:

[0007] S1. Construct a movable resource space-time state graph;

[0008] S2. Use state maps to perform regional network-building capability mapping analysis to obtain the network-building capability matrix;

[0009] S3. Use the network construction capability matrix to construct the resource allocation time-series structure at multiple time scales to obtain a resource controllable map.

[0010] S4. Use the network capability matrix and resource regulation map to integrate the constraints of the regulation links and obtain the schedulable area regulation capability structure.

[0011] S5. Use the regulation capability structure to fuse the scheduling hierarchical regulation structure to obtain the regulation capability response mapping across multiple scales;

[0012] S6. Use response mapping to generate scheduling output based on revenue-equilibrium constraint fusion to obtain the park-level control and scheduling results.

[0013] To further optimize this technical solution, step S2 first involves dividing and numbering the target park into M control area units according to a preset physical layout and power grid planning logic.

[0014]

[0015] Each regional unit A i For a spatial boundary area, connect mobile resources to construct the power grid.

[0016] To further optimize this technical solution, step S2 then establishes a network construction capability determination function to determine resource R. j Is it possible to access region A at the current time t? i This includes the following two judgment conditions:

[0017] Distance conditions:

[0018] ||p j (t)-cent(A i )||≤δ j (t);

[0019] Where, p j (t) represents resource R j The physical coordinate vector at time t;

[0020] cent(A i ) represents region A i The coordinates of the center point;

[0021] δ j (t) Resource R j The maximum communication / access radius at time t.

[0022] Power requirements:

[0023] l j (t)≥γ i ;

[0024] Among them, l j (t) represents resource R j The instantaneous remaining available power at time t;

[0025] γ i For region A i The minimum access power threshold is set in advance by the system;

[0026] The final conclusion is:

[0027]

[0028] To further optimize this technical solution, step S2 then constructs a network construction capability matrix, calculating Φ for all resources j=1,…,N and all regions i=1,…,M. ij (t), generating the space mapping matrix:

[0029] Φ(t)=[Φ ij (t)] M×N ;

[0030] This matrix is ​​a two-dimensional Boolean matrix, with each row corresponding to a region A. i Each column corresponds to a resource R j Element value Φ ij (t) represents resource R j Is it available for region A? i The construction of a temporary power grid.

[0031] To further optimize this technical solution, step S2 concludes with a statistical attribute analysis of the network construction capability matrix, yielding the following results:

[0032] Number of available resources per region:

[0033] Number of regions that can be accessed per resource:

[0034] To further optimize this technical solution, step S3 first performs a regional scarcity assessment and constructs region A. i Resource scarcity weighting function:

[0035]

[0036] Where, n i (t): Region A i The amount of available resources;

[0037] ε: A positive decimal constant to prevent division by zero; usually taken as 10. -5 ;

[0038] The larger the value, the scarcer the resource, and the higher the priority should be.

[0039] To further optimize this technical solution, step S3 then involves a resource dilution assessment to construct resource R. j Purpose of the penalty weight function:

[0040]

[0041] Where, m j (t): Resource R j Number of areas currently available for service.

[0042] To further optimize this technical solution, step S3 then involves constructing a joint scoring function. Combining network construction capability and the weights on both sides, the unit of the joint scoring matrix S(t) is as follows:

[0043]

[0044] S ij (t)∈[0,1] is the subsequent scheduling selection criterion.

[0045] To further optimize this technical solution, the joint scoring matrix S(t) constructed in step S3 includes the following judgment content:

[0046] If Φ ij (t) = 0, indicating that resource R j Unable to construct a network to region A i S ij (t) = 0;

[0047] If Φ ij (t)=1, then S ij The value of (t) is determined by both regional scarcity and resource specificity;

[0048] The calculated S ij The higher the (t) value, the higher the score, indicating a higher priority for the "resource-region" matching.

[0049] To further optimize this technical solution, step S3 finally generates an allocation strategy matrix. To ensure scheduling sparsity and controllability, a scheduling score threshold θ(t) is introduced to filter high-scoring pairs for recommendation.

[0050]

[0051] Ψ ij (t) indicates whether it is recommended to allocate resource R. j Assigned to region A i ;

[0052] The threshold θ(t)∈(0,1) can be dynamically adjusted according to the system load status;

[0053] The output is a Boolean matrix Ψ(t) = [Ψ ij (t)] M×N .

[0054] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program instructions, when executed by the processor, implement the steps of a novel multi-timescale scheduling method for a power system as described in the first aspect of the present invention.

[0055] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a novel multi-timescale scheduling method for a power system as described in the first aspect of the present invention.

[0056] Compared with existing technologies, this invention provides a novel multi-time-scale scheduling method for power systems, which has the following advantages:

[0057] This novel multi-timescale scheduling method for power systems, by setting up a regional response behavior evaluation mechanism based on residual mapping and response reconstruction models, can dynamically extract the deviation information of regional resource response trajectories at multiple timescales, and establish a residual-driven dynamic adjustment basis for regional divisions. This enables resource scheduling in different regions at future timescales to be optimized and adjusted according to the evolution of their response trends, thereby achieving adaptive fusion of regional behavior under unified objective constraints and improving the linkage, matching, and rapid network construction capabilities of the scheduling model at multiple timescales. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating a novel multi-time-scale scheduling method for power systems proposed in this invention.

[0060] Figure 2 This is a schematic diagram of the regional networkability mapping analysis process for a novel multi-timescale scheduling method for power systems proposed in this invention.

[0061] Figure 3 This is a schematic diagram illustrating the process of constructing the resource allocation sequence structure for a novel multi-timescale scheduling method for power systems proposed in this invention. Detailed Implementation

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0065] Example 1:

[0066] Reference Figures 1-3 This is the first embodiment of the present invention, which provides a novel multi-time-scale scheduling method for power systems, comprising the following steps:

[0067] S1. Construct a movable resource space-time state graph;

[0068] Step S1 first collects resource status data, using edge computing node-based data synchronization technology to deploy collection terminals on each mobile resource. Within each collection period t, the following data is collected and recorded:

[0069] Location data: Resource location information is acquired in real time based on RTK (Real-Time Kinematic) or UWB (Ultra-Wideband) positioning modules. i (t);

[0070] State of charge c i (t) and power information l i (t): Real-time reporting via built-in battery management system (BMS);

[0071] Adjustable distance: The maximum movable distance is calculated based on the current SoC of the resources and the electric drive parameters (energy consumption curve, remaining power), and is achieved by using the energy consumption estimation technology in the electric vehicle mobility model;

[0072] Response time τ i (t): The estimated time required for resources to complete the scheduling path and reach the target point is calculated using the shortest path graph algorithm combined with the current congestion factor and the unit displacement delay;

[0073] Organize the above parameters into a standard vector form:

[0074] S i (t)=[p i (t),c i (t),l i (t),δ i (t),τ i (t)];

[0075] Step S1 then proceeds to construct the graph structure: joint state set By using graph database construction methods, a unified organization of all resource state vectors is achieved, resulting in a unified resource state graph:

[0076]

[0077] Step S1 concludes with a valid map verification, using a spatial-temporal consistency verification method to check the map's integrity.

[0078] S2. Use state maps to perform regional network-building capability mapping analysis to obtain the network-building capability matrix;

[0079] Step S2 first involves dividing and numbering the target area, dividing it into M control zone units according to the preset physical layout and power grid planning logic:

[0080]

[0081] Each regional unit A i For a spatial boundary area, connect mobile resources to construct the power grid.

[0082] Step S2 then establishes a network construction capability determination function to determine resource R. j Is it possible to access region A at the current time t? i This includes the following two judgment conditions:

[0083] Distance conditions:

[0084] ||p j (t)-cent(A i )||≤δ j (t);

[0085] Where, p j (t) represents resource R j The physical coordinate vector at time t;

[0086] cent(A i ) represents region A i The coordinates of the center point;

[0087] δ j (t) Resource R j The maximum communication / access radius at time t.

[0088] Power requirements:

[0089] l j (t)≥γ i ;

[0090] Among them, l j (t) represents resource R jThe instantaneous remaining available power at time t;

[0091] γ i For region A i The minimum access power threshold is set in advance by the system;

[0092] The final conclusion is:

[0093]

[0094] Step S2: Then construct the network construction capability matrix, and calculate Φ for all resources j=1,…,N and all regions i=1,…,M. ij (t), generating the space mapping matrix:

[0095]

[0096] This matrix is ​​a two-dimensional Boolean matrix, with each row corresponding to a region A. i Each column corresponds to a resource R j Element value Φ ij (t) represents resource R j Is it available for region A? i The construction of a temporary power grid.

[0097] Step S2 concludes with a statistical attribute analysis of the network construction capability matrix, yielding the following results:

[0098] Number of available resources per region:

[0099] Number of regions that can be accessed per resource:

[0100] The network capability matrix analysis logic constructed in this step differs from the static topology constraints or traditional resource coverage models in existing mature scheduling technologies. The main differences are reflected in the following three aspects:

[0101] State dynamism: This step introduces a real-time state graph. Establish time-sensitive regional access relationships;

[0102] Spatial physical constraint embedding: not only considering geographical distribution (physical distance δ) j (t)), and also incorporates power support constraints;

[0103] The network construction capability is expressed differently: traditional methods often use continuous numerical scoring or distance penalty functions, while this step transforms it into a binary Boolean matrix of structural reachability, which is convenient for use in subsequent boundary control and combinatorial screening.

[0104] S3. Use the network construction capability matrix to construct the resource allocation time-series structure at multiple time scales to obtain a resource controllable map.

[0105] Step S3 is based on the network construction capability matrix Φ(t) output from step S2 = [Φ ij (t)] M×N Combining resource scarcity and versatility, a scheduling priority scoring matrix S between resources and regions is constructed. ij (t), and generate a preliminary resource scheduling suggestion matrix Ψ(t) based on the scoring results.

[0106] Step S3 first performs a regional scarcity assessment to construct region A. i Resource scarcity weighting function:

[0107]

[0108] Where, n i (t): Region A i The amount of available resources;

[0109] ε: A positive decimal constant to prevent division by zero; usually taken as 10. -5 ;

[0110] The larger the value, the scarcer the resource, and the higher the priority should be.

[0111] Step S3 then performs a resource dilution assessment and constructs resource R. j Purpose of the penalty weight function:

[0112]

[0113] Where, m j (t): Resource R j Number of areas currently available for service.

[0114] Step S3 then proceeds to construct the joint scoring function. Combining the network construction capability and the weights on both sides, the unit of the joint scoring matrix S(t) is constructed as follows:

[0115]

[0116] S ij (t)∈[0,1], which is the subsequent scheduling selection criterion;

[0117] If Φ ij (t) = 0, indicating that resource R j Unable to construct a network to region A i S ij (t) = 0;

[0118] If Φ ij (t)=1, then S ij The value of (t) is determined by both regional scarcity and resource specificity;

[0119] The calculated S ij The higher the (t) value, the higher the score, indicating a higher priority for the "resource-region" matching.

[0120] Step S3 concludes with the generation of the allocation strategy matrix. To ensure scheduling sparsity and controllability, a scheduling score threshold θ(t) is introduced to select high-scoring pairs for recommendation.

[0121]

[0122] Ψ ij (t) indicates whether it is recommended to allocate resource R. j Assigned to region A i ;

[0123] The threshold θ(t)∈(0,1) can be dynamically adjusted according to the system load status;

[0124] The output is a Boolean matrix Ψ(t) = [Ψ ij (t)] M×N .

[0125] The analytical logic employed in this step differs significantly from existing mature scheduling techniques: it is based on a network capability matrix, combined with dynamically changing regional scarcity and resource-specific weights, to construct a bidirectional weighted scoring function, thereby achieving quantitative evaluation of priority between resources and regions, and outputting a time-varying Boolean suggestion matrix Ψ(t) to provide constraint input for subsequent optimization models; compared with traditional mechanisms that rely on static reachability matrices, fixed weight ranking, and non-adjustable strategies, this method has significant advantages in terms of input dynamism, scoring mechanism complexity, structural hierarchicality, and scheduling adaptability.

[0126] S4. Use the network capability matrix and resource regulation map to integrate the constraints of the regulation links and obtain the schedulable area regulation capability structure.

[0127] Step S4 performs Boolean structure fusion of the network construction capability matrix Φ(t) obtained in step S2 and the resource regulation map Ψ(t) obtained in step S3 to obtain the schedulable area regulation capability structure. The formula for the intersection of the Boolean structures is as follows:

[0128] Λ ij (t)=Φ ij (t)∧Ψ ij (t);

[0129] Its matrix representation is as follows:

[0130]

[0131] The symbol ° represents the logical AND operation between corresponding elements.

[0132] Step S4 is performed during the calculation process:

[0133] If Φ ij (t) = 0: Resource R j No physical connectivity to Area A i Even if there are rating suggestions, they cannot be reassigned;

[0134] If Ψ ij (t) = 0: This connection is not recommended and cannot be scheduled even if physical capabilities exist;

[0135] Only when Φ ij (t)=Ψ ij When (t) = 1, Λ ij (t) = 1, indicating a schedulable state where "physical connectivity + policy proposal feasibility" is achieved.

[0136] S5. Use the regulation capability structure to fuse the scheduling hierarchical regulation structure to obtain the regulation capability response mapping across multiple scales;

[0137] Step S5 uses the Λ(t) output from step S4 to perform structural decomposition, identify scheduling nodes and connection relationships with multi-level features, and uses a mature graph hierarchical clustering method to hierarchically divide the graph structure represented by matrix Λ(t) to identify the multi-level nested structure in the resource-region connection relationship.

[0138] For each effective control pair (A) with a value of 1 in Λ(t) i ,R j Based on its hierarchical position in the hierarchical structure and the number of its adjacent connections, its regulatory capacity is measured and uniformly converted into a standard response factor. This conversion process uses mature normalized regulation intensity assessment technology to count the number of incoming resources of each regional node, which is defined as its "controllability". According to the level of the regional node, the "controllability" is standardized by z-score, and the standardized result is multiplied by the regulation sensitivity coefficient of the region to form a response factor vector.

[0139] Based on the standard response factor vector, a multi-scale response matrix is ​​further formed according to the hierarchical structure and mapped to the time series. In this process, a mature multi-scale nested tensor modeling method is adopted. The standard response factors are embedded into three-dimensional tensors according to the regional level (e.g., city-district-unit level) and the time dimension is introduced to form a four-dimensional tensor structure. Tensor expansion technology is used to realize the mapping visualization of the adjustment capabilities between each scale to support dynamic schedulable modeling.

[0140] The final expression for the multi-scale regulation capability response mapping tensor is:

[0141]

[0142] in, : Represents the regulation capability response factor of region node a and resource node r at time t under the l-th region level. This tensor is used to characterize the regulation intensity between region and resource at different scales.

[0143] : A flag variable indicating whether region node a belongs to level l. It takes the value 1 when region a belongs to this level, and 0 otherwise. This variable is used to indicate the hierarchical structure of regions under multiple levels.

[0144] The connectivity regulation factor between region a and resource r is determined by the Boolean regulation structure Λ in step S4. a,r (t) is obtained by weighting the physical performance parameters of the region and resources, representing the actual adjustment intensity between the two;

[0145] The standard response correction coefficient for region a at time t is derived from the results of its controllability and hierarchical classification statistics after Z-score standardization, and is combined with the linear mapping of the sensitivity factor of the region. It is used to adjust the sensitivity differences of different regions in the response mapping.

[0146] S6. Use response mapping to generate scheduling output based on revenue-equilibrium constraint fusion to obtain the park-level control and scheduling results.

[0147] Step S6 first constructs the fusion objective function expression, whose formula model is as follows:

[0148]

[0149] Among them, the regional equilibrium center term is:

[0150] J: Final fusion optimization objective function;

[0151] λ∈(0,1): Target fusion weight coefficient, reflecting whether benefit priority or equilibrium priority is given;

[0152] A: Represents the number of regions participating in the scheduling, that is, the variable a∈{1,2,…,A} corresponds to the a-th region;

[0153] R: Represents the number of selectable response units in each region, i.e., the variable r∈{1,2,…,R} corresponds to the r-th response unit.

[0154] T: Represents the total length of the discrete time series of the schedule, that is, the variable t∈{1,2,…,T} corresponds to the t-th time step or scheduling period.

[0155] Step S6 then introduces two constraints, including the response capability boundary constraint and the regional equilibrium deviation tolerance. The formula model for the response capability boundary constraint is as follows:

[0156]

[0157] This formula guarantees that the actual scheduling output must not exceed the maximum controllability boundary given in the response mapping.

[0158] The formula model for the regional equilibrium deviation tolerance is as follows:

[0159]

[0160] in, : Average scheduling output of all regions at time t;

[0161] θ a : Maximum control deviation tolerance for region a;

[0162] This formula is used to control the distribution differences of scheduling load between regions and avoid excessive concentration of resource allocation.

[0163] In step S6, the optimal scheduling output is then solved. Using constrained optimization methods, we obtain the optimal scheduling scheme that maximizes the objective function J while satisfying the above constraints:

[0164]

[0165] in, The scheduling output decision variable represents the amount of resource r allocated to region a at time t.

[0166] γ a,r The regional resource regulation benefit coefficient reflects the unit regulation benefit brought about by allocating resource r to region a.

[0167] Step S6 will finally yield the solution result. Output to the scheduling and execution unit.

[0168] Traditional scheduling optimization models often employ static linear programming or heuristic algorithms to focus on maximizing profits, without incorporating the dynamic responsiveness coupling relationships between multiple levels, regions, and resources. This approach, however, is based on a "response mapping tensor" structure, combining the complex coupling relationships between multi-scale adjustment capabilities, profit functions, and equilibrium control. Essentially, it achieves control optimization starting from the "feasibility of response capabilities," differing from existing strategies that are solely oriented towards optimal goals.

[0169] Example 2:

[0170] This embodiment also provides a computer device applicable to a novel multi-timescale scheduling method for a power system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the novel multi-timescale scheduling method for a power system as proposed in the above embodiment.

[0171] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a novel multi-timescale scheduling method for a power system as proposed in the above embodiments.

[0172] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0173] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0175] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0176] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0177] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A novel multi-time-scale scheduling method for power systems, characterized in that, Includes the following steps: S1. Construct a movable resource space-time state graph; S2. Use state maps to perform regional network-building capability mapping analysis to obtain the network-building capability matrix; For all resources With all regions calculate Generate a network construction capability matrix: ； This matrix is ​​a two-dimensional Boolean matrix, with each row corresponding to a region. Each column corresponds to one resource. Element value Representing resources Is it available for the region? The construction of temporary power grids; S3. Use the network construction capability matrix to construct the resource allocation time-series structure at multiple time scales to obtain the resource regulation map; Based on the network construction capability matrix, and combined with dynamically changing regional scarcity and resource specificity weights, a two-way weighted scoring function is constructed to achieve quantitative evaluation of resource-region priority and output a time-varying Boolean proposal matrix. ; S4. Using the network capability matrix and resource regulation map, constraints of the regulation links are integrated to obtain the schedulable region regulation capability structure. The Boolean intersection formula of the schedulable region regulation capability structure is as follows: ； Its matrix representation is as follows: ； Among the symbols This represents a logical AND operation between corresponding elements; S5. Use the regulation capability structure to fuse the scheduling hierarchical regulation structure to obtain the regulation capability response mapping across multiple scales; The expression for the multi-scale regulation capability response mapping tensor is: ； in, : indicates the first At each regional level, regional nodes With resource nodes At any moment The regulatory capacity response factor, which is used to characterize the regulatory intensity between regions and resources at different scales; : regional nodes Does it belong to a hierarchy? The flag variable, when the region If it belongs to this level, the value is 1; otherwise, the value is 0. This variable is used to implement the hierarchical indication of the region under multiple levels. :area With resources The inter-connection regulation capability factor is determined by the schedulable region regulation capability structure in step S4. It is obtained by weighting the physical performance parameters of the region and resources, representing the actual adjustment intensity between the two; :area At any moment The standard response correction coefficient is derived from the results of Z-score standardization of its controllability and hierarchical classification statistics, combined with the linear mapping of the sensitivity factors of the region, and is used to adjust the sensitivity differences of different regions in the response mapping. S6. Use response mapping to generate scheduling output based on revenue-equilibrium constraint fusion to obtain the park-level control and scheduling results.

2. The novel multi-time-scale scheduling method for power systems according to claim 1, characterized in that, Step S2 first involves dividing and numbering the target area according to a pre-defined physical layout and power grid planning logic. One regulatory region unit: ； Each regional unit For a spatial boundary area, connect mobile resources to construct the power grid.

3. The novel multi-timescale scheduling method for power systems according to claim 2, characterized in that, Step S2 then establishes a network construction capability determination function to judge resources. Is it at the current moment? Accessible area This includes the following two judgment conditions: Distance conditions: ； in, Representing resources At any moment The physical coordinate vector; Indicates the area The coordinates of the center point; :resource At any moment Maximum communication / access radius; Power requirements: ； in, For resources At any moment The instantaneous remaining available power; For the region The minimum access power threshold is set in advance by the system; The final conclusion is: 。 4. A novel multi-time-scale scheduling method for power systems according to claim 3, characterized in that, The final step in step S2 involves statistical attribute analysis of the network construction capability matrix, yielding the following results: Number of available resources per region: ; Number of regions that can be accessed per resource: .

5. A novel multi-time-scale scheduling method for power systems according to claim 1, characterized in that, Step S3 first performs a regional scarcity assessment and constructs a regional... Resource scarcity weighting function: ； in, :area The amount of available resources; Positive decimal constant, to prevent division by zero, take... ; The larger the value, the scarcer the resource and the higher the priority.

6. A novel multi-time-scale scheduling method for power systems according to claim 5, characterized in that, Step S3 then performs a resource dilution assessment and constructs resources. Purpose of the penalty weight function: ； in, :resource The number of areas currently available for service.

7. A novel multi-time-scale scheduling method for power systems according to claim 6, characterized in that, Step S3 then involves constructing a joint scoring function, combining network construction capability with the weights on both sides to build a joint scoring matrix. The unit is: ； This is the criterion for subsequent scheduling and selection.

8. A novel multi-time-scale scheduling method for power systems according to claim 7, characterized in that, The joint scoring matrix constructed in step S3 Includes the following judgment content: like Explanation of resources Unable to build a network to the region ; like ,but The value is determined by both regional scarcity and resource specificity. Calculated The higher the score, the higher the priority of the "resource-region" matching.

9. A novel multi-time-scale scheduling method for a power system according to claim 8, characterized in that, Step S3 concludes with the generation of the allocation strategy matrix. To ensure scheduling sparsity and controllability, a scheduling scoring threshold is introduced. Filter and recommend high-rated pairs: ； Indicate whether it is recommended to allocate resources Assigned to region ; threshold It can be dynamically adjusted according to the system load status; The output is a Boolean matrix. .

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