An emergency demand response load side flexibility resource scheduling optimization method and system

Abstract

The application provides an emergency demand response load side flexibility resource scheduling optimization method and system, and the method comprises the following steps: constructing a resource scheduling control architecture based on regional flexibility resource adjustment capacity; the resource scheduling control architecture comprises a cloud server, a control terminal and a regional control unit (RCU) which is respectively in communication connection with the cloud server and the control terminal; an emergency demand response scheduling model is established by comprehensively considering timeliness, economic benefits and user satisfaction; and an emergency demand response scheduling decision under the resource scheduling control architecture is calculated by using a RAFT distributed algorithm based on the emergency demand response scheduling model. Through the application, an emergency demand response architecture and a scheduling decision optimization method based on a distributed algorithm are provided, rapid decision and optimal benefits are considered, the dynamic change of flexible resources is adapted, various complex power grid operation environments can be coped with, and dynamic evaluation and optimal distribution of flexible resource adjustment potential are realized.

Description

Technical Field

[0001] This invention belongs to the field of flexible resource scheduling technology on the load side, and particularly relates to an optimization method and system for flexible resource scheduling on the load side in response to emergency demands. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the expansion of the power grid and the diversification of load types, the application of flexible load-side resources is becoming increasingly important in power grid operation. Currently, the application of flexible load-side resources mainly focuses on routine demand response operations directly related to peak shaving and valley filling. From the perspective of overall grid safety and power supply security, when facing regional supply imbalances, emergency demand response needs to be activated. The most direct method is load shedding, which brings direct economic impact and welfare losses. Therefore, it is necessary to start from a control perspective, fully utilize the flexibility of the distribution network area, and connect it with the business characteristics of emergency demand response. Under the premise of ensuring overall grid safety, this will enable rapid decision-making for emergency demand response requirements and the stability of corresponding communication and control processes.

[0004] Existing scheduling technologies and communication architectures for emergency response have the following drawbacks:

[0005] (1) Traditional architectures suffer from communication delays and low reliability. For example, centralized control relies on communication between the scheduling center and the load-side equipment. Communication delays and failures may lead to untimely responses. Distributed control lacks global information and is difficult to achieve optimal coordination.

[0006] (2) It is difficult to fully tap the adjustment potential of flexible resources on the load side, and there is a lack of dynamic assessment and optimized allocation of the adjustment potential of flexible resources;

[0007] (3) The communication architecture is not scientifically integrated with the distribution network. The existing hierarchical partitioning method of the control system is usually based on a fixed power grid topology in terms of regional division, which is difficult to adapt to the dynamic changes of flexible resources. Furthermore, the coordination mechanism between regions lacks efficiency and robustness, making it difficult to cope with complex power grid operating environments. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, the present invention provides a method and system for optimizing load-side flexibility resource scheduling in emergency demand response, aiming to solve the technical problems of inability to make rapid decisions, local overload, and resource waste in the prior art for emergency demand response.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] The first aspect of this invention provides a method for optimizing load-side flexible resource scheduling in emergency demand response;

[0011] An optimization method for load-side flexibility resource scheduling in emergency demand response includes:

[0012] A resource scheduling and control architecture based on regional flexible resource adjustment capabilities is constructed; the resource scheduling and control architecture includes a cloud server, a control terminal, and a regional control unit (RCU) that is communicatively connected to the cloud server and the control terminal respectively;

[0013] Establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits, and user satisfaction;

[0014] Based on the aforementioned emergency demand response scheduling model, the RAFT distributed algorithm is used to calculate the emergency demand response scheduling decision under the resource scheduling control architecture.

[0015] As a further technical solution, the cloud server is communicatively connected to the regional control unit (RCU) for global monitoring, command issuance, and / or algorithm deployment.

[0016] The Region Control Unit (RCU) corresponds to a target region and is used to coordinate and control the load-side flexibility resources within the target region, receive instructions from the cloud server and distribute them to the control terminal, and / or periodically evaluate the resource status within the target region.

[0017] The control terminal is communicatively connected to the area control unit (RCU) and is used to receive and execute instructions distributed by the RCU, and / or calculate and call resource data within the control area.

[0018] The communication connection adopts a low-latency communication protocol or an ultra-reliable low-latency communication protocol.

[0019] As a further technical solution, the objective function of the emergency demand response scheduling model is expressed by the following formula:

[0020]

[0021] in, This represents the objective function related to timeliness; This represents the objective function for economic benefits; This represents the objective function for user satisfaction. , , They represent , , The weights;

[0022] The objective function for timeliness is expressed as follows:

[0023]

[0024] in, This represents the response time within time period t for the k-th area control unit (RCU) to complete the instruction distribution to load reduction. This indicates the maximum response time set according to the specifications and scheduling objectives; This represents the penalty coefficient for response delay within time period t; T represents the total number of time periods involved in the scheduling.

[0025] The objective function for economic benefits is expressed as follows:

[0026]

[0027] in, This represents the cost factor for load reduction; This represents the growth factor of costs as load reduction increases; This represents the amount of load reduced by the k-th area control unit (RCU) during time period t.

[0028] The objective function for user satisfaction is expressed as follows:

[0029]

[0030] in, This represents the maximum user satisfaction within the target area corresponding to the k-th Regional Control Unit (RCU) during time period t, before load reduction. This represents the coefficient indicating the impact of load reduction on satisfaction. This represents the impact coefficient of satisfaction as load reduction decreases.

[0031] As a further technical solution, the constraints of the emergency demand response scheduling model are expressed by the following formula:

[0032]

[0033] in, Constraints of the emergency demand response scheduling model; This represents the amount of load reduced by the k-th area control unit (RCU) during time period t. This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the maximum load that can be reduced in the target area corresponding to the k-th area control unit (RCU) during time period t; This represents the total power that line l can carry during time period t; This represents the power transfer distribution factor of all line nodes corresponding to line l for the k-th area control unit (RCU). This represents the initial load level of the target area corresponding to the k-th regional control unit (RCU) before power reduction in time period t.

[0034] As a further technical solution, the step of calculating the emergency demand response scheduling decision under the resource scheduling control architecture based on the emergency demand response scheduling model using the RAFT distributed algorithm includes:

[0035] When a demand response event occurs, the cloud server issues instructions to each of the Regional Control Units (RCUs) based on the total power deficit.

[0036] Each of the aforementioned Region Control Units (RCUs) is used as a computing node, and the computing nodes are divided into Leader nodes and ordinary nodes; wherein, the Leader node is responsible for coordinating communication and computing between target regions, copying instructions and status information to other computing nodes, and ensuring that the status of all Region Control Units (RCUs) is consistent.

[0037] A distributed algorithm based on communication interaction is used to determine the flexible resources that each Regional Control Unit (RCU) should call, and the final scheduling result is obtained as the emergency demand response scheduling decision.

[0038] As a further technical solution, the election rules for the Leader node include:

[0039] Each of the area control units (RCUs) is treated as a communication node, and the communication relationships between all RCUs are transformed into a directed graph, represented as follows: Where V represents the set of all Area Control Units (RCUs), This indicates the connection relationship between every two Region Control Units (RCUs).

[0040] Define adjacency matrix , of which elements The formula is expressed as follows:

[0041]

[0042] For the i-th region control unit The formulas for in-degree and out-degree are as follows:

[0043]

[0044] Where N represents the total number of rows in the adjacency matrix; M represents the total number of columns in the adjacency matrix;

[0045] The region control unit (RCU) with the largest out-degree is selected as the Leader node; when multiple RCUs have the same out-degree, the RCU with the largest in-degree is selected as the Leader node; if a Leader node still cannot be selected, a random RCU is selected as the Leader node.

[0046] As a further technical solution, the distributed algorithm employing communication interaction to determine the flexibility resources that each of the Regional Control Units (RCUs) should invoke, and to obtain the final scheduling result, includes:

[0047] Consensus variables are constructed using the objective function of the aforementioned emergency demand response scheduling model, as expressed in the following formula:

[0048]

[0049] in, Represents the consensus variable of the k-th Regional Control Unit (RCU); This represents the objective function of the emergency demand response scheduling model; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t;

[0050] Each Region Control Unit (RCU) sends its consensus variables to its adjacent computing nodes, and the changes in each computing node must meet the principle of equal rates of change, as expressed by the following formula:

[0051]

[0052] For the k-th region control unit Define the Lagrange multipliers, and under initialization conditions, the formula is expressed as:

[0053]

[0054] in, This represents the initial value of the Lagrange multiplier; after an emergency demand response command is issued, the Area Control Unit (RCU) determines the load reduction amount based on the current Lagrange multiplier, as shown in the following formula:

[0055]

[0056] Where s represents the iteration step; This represents the area control unit at step s. The multiplier;

[0057] After the Leader node obtains the Lagrange multipliers of all the computing nodes, it broadcasts the information across the entire network. Each computing node then updates its multipliers, as shown in the following formula:

[0058]

[0059] in, Represents a directed graph The element in the k-th row and l-th column of the generated row random matrix; Here, represents the learning rate parameter for the RAFT distributed algorithm; L represents the total number of columns in the row random matrix.

[0060] Let W be the row random matrix, and its formula is as follows:

[0061]

[0062] in, Represents the identity matrix with the same number of rows and columns as W; for A definite Laplace matrix; The step size parameter must satisfy the following conditions:

[0063]

[0064] in, Let represent the i-th element on the diagonal of the degree matrix D. The degree matrix D is a diagonal matrix whose elements satisfy the following condition:

[0065]

[0066] Set a scheduling deviation threshold. The algorithm converges and outputs the final scheduling result when the following conditions are met:

[0067]

[0068] in, This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t; Indicates the maximum number of iterations; This represents the scheduling deviation threshold set according to specifications and scheduling requirements; This indicates that the constraints of the emergency demand response scheduling model should be followed.

[0069] A second aspect of the present invention provides an emergency demand response load-side flexible resource scheduling optimization system.

[0070] An emergency demand response load-side flexibility resource scheduling optimization system includes:

[0071] The resource scheduling and control architecture construction module is configured to: construct a resource scheduling and control architecture based on regional flexible resource adjustment capabilities; the resource scheduling and control architecture includes a cloud server, a control terminal, and a regional control unit (RCU) that is communicatively connected to the cloud server and the control terminal respectively;

[0072] The scheduling model construction module is configured to: establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits, and user satisfaction;

[0073] The scheduling decision calculation module is configured to: calculate the emergency demand response scheduling decision under the resource scheduling control architecture based on the emergency demand response scheduling model and using the RAFT distributed algorithm.

[0074] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of an emergency demand response load-side flexibility resource scheduling optimization method as described in the first aspect of the present invention.

[0075] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of an emergency demand response load-side flexibility resource scheduling optimization method as described in the first aspect of the present invention.

[0076] The above one or more technical solutions have the following beneficial effects:

[0077] (1) This invention significantly reduces the latency of command transmission and execution through a hierarchical control architecture (cloud server, regional control unit, control terminal) and a low-latency communication protocol (such as 5G URLLC), ensuring a second-level response in emergency power grid situations and meeting timeliness requirements. The RAFT distributed algorithm is used to dynamically elect the Leader node, optimize communication paths, reduce redundant calculations, and further improve decision-making speed.

[0078] (2) This invention dynamically divides target areas using a clustering algorithm to ensure balanced flexibility resource adjustment capabilities in each area, avoiding local overload or resource waste. Global optimal scheduling is achieved through a multi-objective optimization model (timeliness, economic benefits, user satisfaction), while distributed algorithms ensure rapid coordination among areas, balancing overall benefits and local flexibility. The distributed architecture also avoids single-point-of-failure risks; even if some nodes fail, the system can quickly recover and continue operating through the RAFT consensus mechanism. The dynamic adjustment mechanism can adapt to changes in power grid topology and dynamic fluctuations in flexibility resources, ensuring the stability and adaptability of the scheduling scheme.

[0079] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0080] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0081] Figure 1 A flowchart illustrating an embodiment of the emergency demand response load-side flexibility resource scheduling optimization method provided by the present invention is shown.

[0082] Figure 2 A schematic diagram of the resource scheduling and control architecture provided in an embodiment of the present invention is shown;

[0083] Figure 3 A schematic diagram of the structure of an emergency demand response load-side flexibility resource scheduling optimization system provided in an embodiment of the present invention is shown. Detailed Implementation

[0084] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0085] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0086] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0087] Example 1

[0088] This embodiment discloses a method for optimizing load-side flexible resource scheduling in emergency demand response;

[0089] like Figure 1 As shown, an optimization method for load-side flexibility resource scheduling in emergency demand response includes:

[0090] Step S101: Construct a resource scheduling and control architecture based on regional flexible resource adjustment capabilities.

[0091] At the communication and control level, this embodiment of the invention considers the need to allocate flexible load-side resources to emergency demand responses, which places high demands on the real-time performance and granularity of data. Unlike traditional cloud-edge collaborative architectures, which require significant communication resources to handle node data processing and specific resource allocation, this embodiment of the invention adopts a hierarchical approach to communication and control, dividing the architecture into three layers, such as... Figure 2 As shown, it specifically includes a cloud server, multiple control terminals, and multiple area control units (RCUs) that are respectively connected to the cloud server and the control terminals.

[0092] The cloud server communicates with the Regional Control Unit (RCU), corresponding to the global control layer, and is used for global monitoring, command issuance, and algorithm deployment. Each RCU is responsible for a target area and is used to coordinate and control the load-side flexibility resources within the target area. It receives commands from the cloud server and distributes them to the control terminal, and can also periodically evaluate the resource status within the target area. The control terminal communicates with the RCU and is used to receive and execute commands distributed by the RCU. It can also calculate and call resource data within the control area.

[0093] It should be noted that the communication connection between the cloud server, the Regional Control Unit (RCU), and the control terminal can adopt a low-latency communication protocol (such as DDS data distribution service) or an ultra-reliable low-latency communication protocol (such as 5G URLLC).

[0094] Based on the above description, each area control unit (RCU) corresponds to a target area it is responsible for. In practical applications, the boundaries of the area control unit (RCU) and its specific corresponding distribution network area can be defined through the following steps S1011-S1015:

[0095] Step S1011: Assume there are a total The load-side flexible resource adjustment capability of each distribution network area can be represented as a multi-dimensional vector, as shown in the following formula:

[0096]

[0097] in, This represents the evaluation vector for the flexibility resource adjustment capability of the i-th distribution network area; M represents the adjustment and assessment capability of the j-th type of flexibility resource in distribution network area i; M represents the total number of categories of flexibility resources.

[0098] Step S1012: Assess the flexibility resource adjustment capability vector The formula is standardized as follows:

[0099]

[0100] in, This represents a standardized assessment vector for the regulatory capacity of active resources. This represents the average adjustment capability of the j-th type of flexible resource in distribution network area i; The standard deviation represents the standard deviation of the flexibility resource adjustment capability of distribution network area i.

[0101] Step S1013: Based on the standardized evaluation vector of the regulatory capacity of active resources, construct a clustering objective function, expressed by the following formula:

[0102]

[0103] Where K represents the total number of cluster partitions; Represents the set of clusters in the partition; Represents the square of the distance in two-dimensional Euclidean space; Let represent the cluster center of the k-th cluster, expressed by the following formula:

[0104]

[0105] Step S1014: Initially generate a cluster center sequence The cluster regions are allocated using the following formula:

[0106]

[0107] Step S1015: Calculate the new cluster center using the above formula (4), and repeat the above steps S1013-S1014 until the cluster center no longer changes, to obtain the cluster optimization result, which is used as the target area divided based on the flexibility resource adjustment capability.

[0108] Furthermore, to take into account the coordination of adjustment resources in the target region, an objective function is added as a constraint to further optimize the clustering results based on the above clustering. The formula is expressed as follows:

[0109]

[0110] in, The overall regulating capacity of the k-th cluster is expressed by the following formula:

[0111]

[0112] in, This represents the evaluation vector for the flexibility resource adjustment capability of the i-th allocation cluster region.

[0113] The embodiments of the present invention can calculate the clustering optimization results of the distribution network based on the assessment of the flexibility resource adjustment capabilities of each region by combining equations (1)-(7). and Clustering optimization results RCUs (Regional Control Units) Target area under responsibility Division

[0114] Step S102: Establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits, and user satisfaction.

[0115] To maintain the security of the entire network operation, the implementation of emergency demand response must ensure the timeliness of scheduling. Based on this, the embodiments of the present invention consider the economic benefits of business operations and user satisfaction, and construct an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits and user satisfaction, so as to improve social welfare.

[0116] Specifically, the objective function of the emergency demand response scheduling model is expressed by the following formula:

[0117]

[0118] in, This represents the objective function related to timeliness; This represents the objective function for economic benefits; This represents the objective function for user satisfaction. , , They represent , , The weight.

[0119] From the perspective of timeliness, this invention establishes an objective function for timeliness, which can be expressed as:

[0120]

[0121] in, This represents the response time within time period t for the k-th area control unit (RCU) to complete the instruction distribution to load reduction. This indicates the maximum response time set according to the specifications and scheduling objectives; This represents the penalty coefficient for response delay within time period t; T represents the total number of time periods involved in the scheduling.

[0122] From the perspective of economic benefits, this invention establishes an objective function for economic benefits, which can be expressed as:

[0123]

[0124] in, This represents the cost factor for load reduction; This represents the growth factor of costs as load reduction increases; This represents the amount of load reduction by the k-th area control unit (RCU) during time period t.

[0125] The objective function for user satisfaction is expressed as follows:

[0126]

[0127] in, This represents the maximum user satisfaction within the target area corresponding to the k-th Regional Control Unit (RCU) during time period t, before load reduction. This represents the coefficient indicating the impact of load reduction on satisfaction. This represents the impact coefficient of satisfaction as load reduction decreases.

[0128] Furthermore, the objective function of the emergency demand response scheduling model must also satisfy the following constraints:

[0129]

[0130] in, Constraints of the emergency demand response scheduling model; This represents the amount of load reduced by the k-th area control unit (RCU) during time period t. This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the maximum load that can be reduced in the target area corresponding to the k-th area control unit (RCU) during time period t; This represents the total power that line l can carry during time period t; This represents the power transfer distribution factor of all line nodes corresponding to line l for the k-th area control unit (RCU). This represents the initial load level of the target area corresponding to the k-th regional control unit (RCU) before power reduction in time period t.

[0131] Step S103: Based on the emergency demand response scheduling model, the RAFT distributed algorithm is used to calculate the emergency demand response scheduling decision under the resource scheduling control architecture.

[0132] When an emergency demand occurs in the regional power distribution network, the dispatch center quickly determines the total power deficit, and the cloud server issues instructions to each regional control unit (RCU) based on the total power deficit. This then requires determining the flexible resources that each RCU should utilize, i.e., determining the corresponding load reduction amount for each RCU while ensuring timeliness.

[0133] To fully utilize communication resources and ensure the rapid and robust execution of processes, this embodiment of the invention employs a distributed algorithm based on communication interaction to calculate the flexible resources that each regional control unit (RCU) should utilize.

[0134] First, each Region Control Unit (RCU) is used as a computing node, and these nodes are divided into Leader nodes and ordinary nodes. The Leader node is responsible for coordinating communication and computation between target regions, replicating instructions and status information to other computing nodes, and ensuring consistency in the status of all RCUs. Preferably, the communication protocol between computing nodes can be MQTT.

[0135] It should be noted that the Leader node is elected, and the specific election rules are as follows:

[0136] Treating each Area Control Unit (RCU) as a communication node, the communication relationships between all RCUs are transformed into a directed graph, represented as follows: Where V represents the set of all Area Control Units (RCUs), This indicates the connection relationship between every two Region Control Units (RCUs).

[0137] Define adjacency matrix , of which elements The formula is expressed as follows:

[0138]

[0139] For the i-th region control unit The formulas for in-degree and out-degree are as follows:

[0140]

[0141] Where N represents the total number of rows in the adjacency matrix; M represents the total number of columns in the adjacency matrix;

[0142] In this embodiment of the invention, the Region Control Unit (RCU) with the largest out-degree is selected as the Leader node; when multiple Region Control Units (RCUs) have the same out-degree, the Region Control Unit (RCU) with the largest in-degree is selected as the Leader node; if a Leader node still cannot be selected, a Region Control Unit (RCU) is randomly selected as the Leader node.

[0143] Furthermore, after the Leader node election is completed, an emergency demand response scheduling decision based on the RAFT distributed consensus algorithm is executed, specifically including steps S1031-S1035:

[0144] Step S1031: Construct consensus variables using the objective function of the emergency demand response scheduling model, as shown in the following formula:

[0145]

[0146] in, Represents the consensus variable of the k-th Regional Control Unit (RCU); This represents the objective function of the emergency demand response scheduling model; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t.

[0147] Step S1032: Each Region Control Unit (RCU) sends its consensus variables to its neighboring computing nodes, and the changes in each computing node must meet the principle of equal rates of change for each infinitesimal variable, as expressed in the following formula:

[0148]

[0149] Step S1033: For the k-th region control unit Define the Lagrange multipliers, and under initialization conditions, the formula is expressed as:

[0150]

[0151] in, This represents the initial value of the Lagrange multiplier. After an emergency demand response command is issued, the Area Control Unit (RCU) determines the load reduction amount based on the current Lagrange multiplier, as shown in the following formula:

[0152]

[0153] Where s represents the iteration step; This represents the area control unit at step s. The multiplier.

[0154] Step S1034: After the Leader node obtains the Lagrange multipliers of all computing nodes, it broadcasts the information across the entire network. Each computing node then updates its multipliers, as shown in the following formula:

[0155]

[0156] in, Represents a directed graph The element in the k-th row and l-th column of the generated row random matrix; Here, L represents the learning rate parameter of the RAFT distributed algorithm; L represents the total number of columns in the row random matrix, and W is the row random matrix, expressed by the formula:

[0157]

[0158] in, This represents the identity matrix with the same number of rows and columns as W (i.e., all elements in the matrix are 1). for A definite Laplace matrix; The step size parameter must satisfy the following conditions:

[0159]

[0160] in, Let represent the i-th element on the diagonal of the degree matrix D. The degree matrix D is a diagonal matrix whose elements satisfy the following condition:

[0161]

[0162] Step S1035: Set the scheduling deviation threshold. The algorithm converges and outputs the final scheduling result when the following conditions are met, which serves as the scheduling decision for emergency demand response:

[0163]

[0164] in, This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t; Indicates the maximum number of iterations; This represents the scheduling deviation threshold set according to specifications and scheduling requirements; This indicates that the constraints of the emergency demand response scheduling model should be followed. .

[0165] It should be noted that in practical applications, the scheduling deviation threshold can be set according to actual needs, and this invention does not limit this. After the above steps S1031-S1035, the optimal flexible resource allocation amount for each distribution network area is calculated and used as the emergency demand response scheduling decision. The resource scheduling control architecture can then execute emergency demand response scheduling based on the emergency demand response scheduling decision.

[0166] This invention provides an optimization method for load-side flexibility resource scheduling in emergency demand response, including constructing a resource scheduling control architecture based on regional flexibility resource adjustment capabilities. The resource scheduling control architecture includes a cloud server, a control terminal, and Regional Control Units (RCUs) communicatively connected to both the cloud server and the control terminal. An emergency demand response scheduling model is established, comprehensively considering timeliness, economic benefits, and user satisfaction. Based on the emergency demand response scheduling model, the RAFT distributed algorithm is used to calculate emergency demand response scheduling decisions under the resource scheduling control architecture. This invention proposes an emergency demand response method based on dynamic partitioning, distributed collaborative computing, and flexible resource optimization. It combines the line characteristics of the distribution network with the specificities of emergency demand response, based on the adjustment capabilities of flexible resources and the power grid topology, achieving a combination of global optimization and rapid local response. The RAFT distributed algorithm enables efficient collaboration between regions, with each region's adjustment behavior following a consensus mechanism to ensure the robustness of the scheduling process. A dynamic evaluation model for the adjustment potential of flexible resources is established, and the optimal load reduction for each region is calculated using an optimization algorithm, maximizing resource utilization efficiency.

[0167] Example 2

[0168] This embodiment discloses an emergency demand response load-side flexible resource scheduling optimization system;

[0169] like Figure 3 As shown, an emergency demand response load-side flexibility resource scheduling optimization system includes:

[0170] The resource scheduling and control architecture construction module 301 is configured to: construct a resource scheduling and control architecture based on regional flexible resource adjustment capabilities; the resource scheduling and control architecture includes a cloud server, a control terminal, and a regional control unit (RCU) that is communicatively connected to the cloud server and the control terminal respectively;

[0171] In the resource scheduling and control architecture construction module 301, the cloud server communicates with the regional control unit (RCU), corresponding to the global control layer, and is used for global monitoring, instruction issuance, and algorithm deployment. Each RCU is responsible for a target area and is used to coordinate and control the load-side flexibility resources within the target area. It receives instructions from the cloud server and distributes them to the control terminal, and can also periodically evaluate the resource status within the target area. The control terminal communicates with the RCU and is used to receive and execute instructions distributed by the RCU. It can also calculate and call resource data within the control area.

[0172] The scheduling model construction module 302 is configured to: establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits and user satisfaction; wherein, in the scheduling model construction module 302, objective functions for timeliness, economic benefits and user satisfaction are constructed respectively, and the emergency demand response scheduling model is constructed by setting the constraints satisfied by the above objective functions.

[0173] The scheduling decision calculation module 303 is configured to: calculate the emergency demand response scheduling decision under the resource scheduling control architecture based on the emergency demand response scheduling model and using the RAFT distributed algorithm.

[0174] In the scheduling decision calculation module 303, based on the emergency demand response scheduling model, a distributed algorithm with communication interaction is used to calculate the flexibility resources that each Regional Control Unit (RCU) should call. First, each RCU is used as a computing node, and a Leader node is elected. Based on the elected Leader node, an emergency demand response scheduling decision based on the RAFT distributed consensus algorithm is executed. By setting a scheduling deviation threshold, the final scheduling result is output as the emergency demand response scheduling decision.

[0175] It should be noted that other corresponding descriptions of the functional modules involved in the emergency demand response load-side flexibility resource scheduling optimization system provided in this embodiment can be found in the corresponding descriptions of the method shown in Embodiment 1, and will not be repeated here.

[0176] Example 3

[0177] The purpose of this embodiment is to provide a computer-readable storage medium.

[0178] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an emergency demand response load-side flexibility resource scheduling optimization method as described in Example 1.

[0179] Example 4

[0180] The purpose of this embodiment is to provide an electronic device.

[0181] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in an emergency demand response load-side flexibility resource scheduling optimization method as described in Embodiment 1.

[0182] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0183] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0184] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for optimizing load-side flexible resource scheduling in emergency demand response, characterized in that, include: Construct a resource scheduling and control architecture based on regional flexibility and resource adjustment capabilities; The resource scheduling and control architecture includes a cloud server, a control terminal, and a regional control unit (RCU) that is communicatively connected to the cloud server and the control terminal, respectively. Establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits, and user satisfaction; When a demand response event occurs, the cloud server issues instructions to each of the Regional Control Units (RCUs) based on the total power deficit; each of the RCUs is used as a computing node, and the computing nodes are divided into Leader nodes and ordinary nodes; The election rules for the Leader node include: Each of the area control units (RCUs) is treated as a communication node, and the communication relationships between all RCUs are transformed into a directed graph, represented as follows: Where V represents the set of all Area Control Units (RCUs), This indicates the connection relationship between any two Region Control Units (RCUs); the RCU with the largest out-degree is selected as the Leader node; when multiple RCUs have the same out-degree, the RCU with the largest in-degree is selected as the Leader node; if a Leader node still cannot be selected, a random RCU is selected as the Leader node. Based on the aforementioned emergency demand response scheduling model, the RAFT distributed algorithm is used to calculate the emergency demand response scheduling decision under the resource scheduling control architecture.

2. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 1, characterized in that, The cloud server is communicatively connected to the regional control unit (RCU) for global monitoring, command issuance, and / or algorithm deployment. The Region Control Unit (RCU) corresponds to a target region and is used to coordinate and control the load-side flexibility resources within the target region, receive instructions from the cloud server and distribute them to the control terminal, and / or periodically evaluate the resource status within the target region. The control terminal is communicatively connected to the area control unit (RCU) and is used to receive and execute instructions distributed by the RCU, and / or calculate and call resource data within the control area. The communication connection adopts a low-latency communication protocol or an ultra-reliable low-latency communication protocol.

3. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 1, characterized in that, The objective function of the emergency demand response scheduling model is expressed by the following formula: in, This represents the objective function related to timeliness; This represents the objective function for economic benefits; This represents the objective function for user satisfaction. , , They represent , , The weights; The objective function for timeliness is expressed as follows: in, This represents the response time within time period t for the k-th area control unit (RCU) to complete the instruction distribution to load reduction. This indicates the maximum response time set according to the specifications and scheduling objectives; This represents the penalty coefficient for response delay within time period t; T represents the total number of time periods involved in the scheduling. The objective function for economic benefits is expressed as follows: in, This represents the cost factor for load reduction; This represents the growth factor of costs as load reduction increases; This represents the amount of load reduced by the k-th area control unit (RCU) during time period t. The objective function for user satisfaction is expressed as follows: in, This represents the maximum user satisfaction within the target area corresponding to the k-th Regional Control Unit (RCU) during time period t, before load reduction. This represents the coefficient indicating the impact of load reduction on satisfaction. This represents the impact coefficient of satisfaction as load reduction decreases.

4. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 1, characterized in that, The constraints of the emergency demand response scheduling model are expressed by the following formula: in, Constraints of the emergency demand response scheduling model; This represents the amount of load reduced by the k-th area control unit (RCU) during time period t. This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the maximum load that can be reduced in the target area corresponding to the k-th area control unit (RCU) during time period t; This represents the total power that line l can carry during time period t; This represents the power transfer distribution factor of all line nodes corresponding to line l for the k-th area control unit (RCU). This represents the initial load level of the target area corresponding to the k-th regional control unit (RCU) before power reduction in time period t.

5. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 1, characterized in that, Based on the aforementioned emergency demand response scheduling model, the RAFT distributed algorithm is used to calculate emergency demand response scheduling decisions under the resource scheduling control architecture, including: When a demand response event occurs, the cloud server issues instructions to each of the Regional Control Units (RCUs) based on the total power deficit. Each of the aforementioned Region Control Units (RCUs) is used as a computing node, and the computing nodes are divided into Leader nodes and ordinary nodes; wherein, the Leader node is responsible for coordinating communication and computing between target regions, copying instructions and status information to other computing nodes, and ensuring that the status of all Region Control Units (RCUs) is consistent. A distributed algorithm based on communication interaction is used to determine the flexible resources that each Regional Control Unit (RCU) should call, and the final scheduling result is obtained as the emergency demand response scheduling decision.

6. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 1, characterized in that, The adjacency matrix , of which elements The formula is expressed as follows: For the i-th region control unit The formulas for in-degree and out-degree are as follows: Where N represents the total number of rows in the adjacency matrix; M represents the total number of columns in the adjacency matrix.

7. The method for optimizing load-side flexibility resource scheduling in emergency demand response as described in claim 5, characterized in that, The distributed algorithm employing communication interaction determines the flexibility resources that each Regional Control Unit (RCU) should invoke, obtaining the final scheduling result, including: Consensus variables are constructed using the objective function of the aforementioned emergency demand response scheduling model, as expressed in the following formula: in, Represents the consensus variable of the k-th Regional Control Unit (RCU); This represents the objective function of the emergency demand response scheduling model; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t; Each Region Control Unit (RCU) sends its consensus variables to its adjacent computing nodes, and the changes in each computing node must meet the principle of equal rates of change, as expressed by the following formula: For the k-th region control unit Define the Lagrange multipliers, and under initialization conditions, the formula is expressed as: in, This represents the initial value of the Lagrange multiplier; after an emergency demand response command is issued, the Area Control Unit (RCU) determines the load reduction amount based on the current Lagrange multiplier, as shown in the following formula: Where s represents the iteration step; This represents the area control unit at step s. The multiplier; After the Leader node obtains the Lagrange multipliers of all the computing nodes, it broadcasts the information across the entire network. Each computing node then updates its multipliers, as shown in the following formula: in, Represents a directed graph The element in the k-th row and l-th column of the generated row random matrix; Here, represents the learning rate parameter for the RAFT distributed algorithm; L represents the total number of columns in the row random matrix. Let W be the row random matrix, and its formula is as follows: in, Represents the identity matrix with the same number of rows and columns as W; for A definite Laplace matrix; The step size parameter must satisfy the following conditions: in, Let represent the i-th element on the diagonal of the degree matrix D. The degree matrix D is a diagonal matrix whose elements satisfy the following condition: Set a scheduling deviation threshold. The algorithm converges and outputs the final scheduling result when the following conditions are met: in, This indicates the total power shortage faced by the regional power distribution network when an emergency demand response event is triggered; This represents the load reduction amount of the target area corresponding to the k-th regional control unit (RCU) during time period t; Indicates the maximum number of iterations; This represents the scheduling deviation threshold set according to specifications and scheduling requirements; This indicates that the constraints of the emergency demand response scheduling model should be followed.

8. An emergency demand response load-side flexible resource scheduling optimization system, characterized in that, The emergency demand response load-side flexibility resource scheduling optimization method according to any one of claims 1-7 includes: The resource scheduling and control architecture construction module is configured to: construct a resource scheduling and control architecture based on regional flexible resource adjustment capabilities; the resource scheduling and control architecture includes a cloud server, a control terminal, and a regional control unit (RCU) that is communicatively connected to the cloud server and the control terminal respectively; The scheduling model construction module is configured to: establish an emergency demand response scheduling model that comprehensively considers timeliness, economic benefits, and user satisfaction; The scheduling decision calculation module is configured to: calculate the emergency demand response scheduling decision under the resource scheduling control architecture based on the emergency demand response scheduling model and using the RAFT distributed algorithm.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the emergency demand response load-side flexibility resource scheduling optimization method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the emergency demand response load-side flexibility resource scheduling optimization method as described in any one of claims 1-7.

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