Finite time load control active power acquisition and verification method and system under equality constraint

Abstract

The invention discloses a finite time load control active power acquisition method under equality constraint, which comprises the following steps of: constructing equality constraint conditions according to the total conservation of load distribution, constructing a communication topological structure of load nodes in a smart power grid by adopting an undirected graph, and obtaining active power of the load nodes in the smart power grid according to the equality constraint conditions and the communication topological structure of the load nodes in the smart power grid. And constructing a finite time distributed optimization algorithm framework based on projection gradient descent to obtain load control active power. The method has the advantages of being high in convergence speed, low in calculation complexity, good in real-time performance, capable of meeting equality constraint conditions and the like, and a new effective solution is provided for power system load control.

Description

Technical Field

[0001] The patent of this invention relates to the field of industrial control technology, and relates to a method and system for acquiring and verifying active power of finite-time load control under equality constraints. Background Art

[0002] As an important part of power system operation and management, power load control is of great significance for ensuring safe and stable operation of power grids, improving power supply efficiency and optimizing resource allocation. With the continuous growth of power demand and the rapid development of smart grid technology, traditional load control methods can no longer meet the needs of modern power systems.

[0003] Traditional load control methods mainly include direct control, indirect control, decentralized control and centralized control. Direct control balances the supply and demand of the power grid by directly removing or adjusting the load, but this method often lacks flexibility and easily causes inconvenience to users. Indirect control guides users to actively adjust their electricity consumption behavior by adjusting electricity prices and other means, but the response speed and effect are difficult to guarantee. Decentralized control distributes load control tasks to various power users or load aggregators. Although it improves the flexibility of the system, it lacks global optimization capabilities. Centralized control relies on a central controller to uniformly dispatch and optimize the load of the entire network, but faces challenges such as high computational complexity, large communication overhead and poor real-time performance.

[0004] In recent years, with the development of distributed systems and multi-agent technology, distributed optimization algorithms have gradually become an effective means to solve power system load control problems. Distributed optimization algorithms decompose complex optimization problems into multiple sub-problems, which are solved separately by multiple agents and work together to finally obtain the global optimal solution. This method not only reduces the computational complexity, but also improves the scalability and flexibility of the system.

[0005] However, the application of existing distributed optimization algorithms under equality constraints still faces many challenges. Equality constraints usually represent physical limitations or operational requirements in power systems, such as power balance, voltage stability, etc. These constraints make the solution of optimization problems more complicated, and traditional distributed optimization algorithms often find it difficult to meet equality constraints while ensuring convergence. In addition, the convergence of existing distributed optimization algorithms cannot be guaranteed within a finite time, which limits their application in real-time load control. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for acquiring and verifying active power of finite-time load control under equality constraints, by introducing a distributed optimization strategy with finite-time convergence, to ensure that the algorithm can converge to the optimal solution within the specified time, thereby meeting the needs of real-time load control; under equality constraints, an effective distributed optimization algorithm is designed to ensure that the algorithm can stably converge to the global optimal solution, avoiding falling into the local optimum or unable to converge; using the advantages of distributed systems and multi-agent technology, complex optimization problems are decomposed into multiple sub-problems, which are solved in parallel by multiple agents, thereby reducing the computational complexity and improving the execution efficiency of the algorithm. .

[0007] To achieve this purpose, the present invention provides a method for acquiring active power under finite time load control under equality constraints, which comprises the following steps:

[0008] Construct equality constraints based on the total conservation of load distribution;

[0009] An undirected graph is used to describe the connection relationship and communication path between load nodes, and the communication topology structure of load nodes in smart grid is constructed;

[0010] According to the equality constraints and the communication topology of load nodes in smart grid, a finite-time distributed optimization algorithm framework based on projected gradient descent is constructed to obtain the load controlled active power.

[0011] A method for verifying the correctness of finite-time distributed optimization based on projected gradient descent, comprising the following steps:

[0012] Verify that the designed algorithm satisfies the equality constraints;

[0013] Verify that the designed finite-time distributed optimization algorithm converges to the global optimal solution within a finite time.

[0014] A finite-time load-controlled active power acquisition system under equality constraints includes an equality constraint condition building module, an undirected graph building module and an active power acquisition module:

[0015] The equality constraint condition building module is used to build equality constraint conditions according to the total amount conservation of load distribution;

[0016] The undirected graph construction module is used to use an undirected graph to describe the connection relationship and communication path between load nodes, and to construct a communication topology structure of load nodes in the smart grid;

[0017] The active power acquisition module is used to construct a finite-time distributed optimization algorithm framework based on projected gradient descent according to the equality constraints and the communication topology of the load nodes in the smart grid to obtain the load controlled active power.

[0018] Beneficial effects of the present invention:

[0019] The present invention proposes a method for acquiring active power under finite-time load control under equality constraints. The algorithm ensures that the physical limitations and operating requirements of the power system in the load control process are met by introducing equality constraints. At the same time, the algorithm adopts a distributed optimization strategy with finite-time convergence to improve the real-time and accuracy of load control. In addition, the algorithm also makes full use of the advantages of distributed systems and multi-agent technology, realizes distributed computing and collaborative work of load control, and reduces computational complexity and communication overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The load network topology diagram of the present invention;

[0021] Figure 2 is the load curve of the present invention;

[0022] Figure 3 is the load summation curve of the present invention;

[0023] Figure 4 is the global cost function curve of the present invention;

[0024] Figure 5 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0025] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0026] Example 1

[0027] A method for acquiring active power under finite time load control under equality constraints comprises the following steps;

[0028] Construct equality constraints based on the total conservation of load distribution;

[0029] An undirected graph is used to describe the connection relationship and communication path between load nodes, and the communication topology structure of load nodes in smart grid is constructed;

[0030] According to the equality constraints and the communication topology of load nodes in smart grid, a finite-time distributed optimization algorithm framework based on projected gradient descent is constructed to obtain the load control active power.

[0031] A method for obtaining active power under finite-time load control under equality constraints, the specific method of constructing equality constraints according to the total amount conservation of load distribution is as follows;

[0032] The total amount of load distribution is conserved, that is, the sum of the loads of all load nodes is equal to the total load of the power supply system composed of all load nodes.

[0033] The equality constraints constructed are:

[0034]

[0035] Among them, p i represents the active power required by the i-th load, c i represents the initial value of active power required by the i-th load, C represents the total load, and n is the number of load nodes.

[0036] A method for acquiring active power under finite-time load control under equality constraints is proposed. An undirected graph is used to describe the connection relationship and communication path between load nodes. The specific method for constructing the communication topology structure of load nodes in a smart grid is as follows:

[0037] 1. Determine the load node:

[0038] First, it is necessary to identify all load nodes in the smart grid, which can be actual power load points or intermediate nodes used for data transmission and communication.

[0039] 2. Create a node collection:

[0040] All load nodes are grouped into a set V, where each node has a unique identifier.

[0041] 3. Determine the connection relationship:

[0042] According to the communication mode and network structure of the smart grid, the connection relationship between each load node and other nodes is determined. These connection relationships can be direct physical connections or virtual connections established through the communication network.

[0043] 4. Build edge set:

[0044] According to the determined connection relationship, an edge set E is constructed. Each edge represents the connection relationship between two load nodes. Since it is an undirected graph, the edge has no direction.

[0045] 5. Form an undirected graph:

[0046] Combining the node set V and the edge set E forms an undirected graph G = (V, E). This undirected graph describes the connection relationship and communication path between load nodes in the smart grid.

[0047] A method for acquiring active power under finite-time load control under equality constraints is proposed. An undirected graph is used to describe the connection relationship and communication path between load nodes. The following factors should be considered when constructing the communication topology of load nodes in smart grids:

[0048] 1. The number and scale of load nodes, as well as the scalability and flexibility of the system. The number and scale of nodes determine the complexity and resource requirements of the system. As the number of nodes increases, the communication topology may become more complex. The scalability and flexibility of the system are important criteria for evaluating whether the algorithm can adapt to future changes;

[0049] 2. The geographical location and distribution of load nodes, and the coverage of the communication network. The geographical location and distribution directly affect the physical distance between nodes, which further affects the feasibility and efficiency of communication. The coverage of the communication network determines which nodes can communicate directly with each other, which is the basis for building an undirected graph.

[0050] A method for acquiring active power under finite-time load control under equality constraints is proposed. According to the equality constraints and the communication topology of load nodes in smart grids, a specific method for constructing a finite-time distributed optimization algorithm framework based on projected gradient descent is as follows:

[0051] Finite-time distributed optimization load control algorithm under equality constraints constructed based on projected gradient descent algorithm:

[0052]

[0053] Among them, p i Represents the active power required by the i-th load, N i represents the number of neighbor nodes of the ith load, c i represents the initial value of active power required by the i-th load, f i (p i ) represents the cost function of the i-th load node (satisfying ▽ 2 f(p)≥σ, where ▽ 2 f(p) represents the Hessian matrix of f(p), σ is a first positive constant, and the present invention takes σ=0.0096), is the first auxiliary variable of load node i, ξ i is the second auxiliary variable of load node i ( and j Respectively represent and i neighbor nodes), yes The derivative of ij represents the weight of the edge connecting load node i to load node j, k is a second normal number (k=20 in the present invention), h is a third normal number and satisfies 0<h<1 (k=20 in the present invention), ).

[0054] Example 2

[0055] A method for verifying the correctness of finite-time distributed optimization based on projected gradient descent, comprising the following steps:

[0056] Verify that the designed algorithm satisfies the equality constraints;

[0057] Verify that the designed finite-time distributed optimization algorithm converges to the global optimal solution within a finite time.

[0058] A method to verify the correctness of finite-time distributed optimization based on projected gradient descent. The specific method to verify that the designed algorithm satisfies the equality constraints is:

[0059]

[0060] A method to verify the correctness of finite-time distributed optimization based on projected gradient descent. The specific method to verify that the designed finite-time distributed optimization algorithm converges to the global optimal solution within a finite time is:

[0061] The global cost function is:

[0062]

[0063] Substituting the designed algorithm into the global cost function of the auxiliary variable:

[0064]

[0065] The partial derivative of the load with respect to the auxiliary variable is:

[0066]

[0067] The partial derivative of the local cost function with respect to the auxiliary variable can be obtained:

[0068]

[0069] Then we can get:

[0070]

[0071] Where L represents the Laplace matrix of the smart grid system communication network.

[0072] According to Taylor expansion, we can get:

[0073]

[0074] in, Represents the optimal solution of the global cost function, δ is the fourth constant, and θ is the fifth constant, where δ and θ are only used in the proof process and range from 0 to 1.

[0075] Since the topological graph is undirected, the eigenvalue of the system Laplacian matrix and the eigenvector corresponding to the eigenvalue can be expressed as 0 = λ 1 <λ 2 ≤…≤λ n and 1,ψ 2 ,...,ψ n ,in Moreover, ||ψ i ||=1. Therefore, the vector It can be expressed as:

[0076]

[0077] Among them, ρ i (i=1,2,...,n) are some constants. Let ψ=ρ 2 ψ 2 +…+ρ n ψ n ,So

[0078] According to the Taylor expansion of the optimal solution of the global cost function and graph theory knowledge, we can get:

[0079]

[0080] Right now

[0081] Since ||ψ||>0, we can get

[0082] The Lyapunov function is designed as follows:

[0083]

[0084] Substituting the designed finite time distributed optimal load control algorithm under equality constraints, the derivative of the Lyapunov function with respect to time t can be obtained as follows:

[0085]

[0086] Among them, and inequality Substituting the derivative of the Lyapunov function with respect to time t, we can obtain:

[0087]

[0088] That is, the designed algorithm can converge to the global optimal solution in a limited time, where the convergence time is:

[0089]

[0090] in, Represents the initial value of the auxiliary variable. The present invention takes

[0091] The above algorithm is simulated and verified. Assuming that the system has 4 load nodes, this simulation experiment uses 4 load nodes to establish a topology diagram according to the above method. Figure 1 As shown, Figure 1 1, 2, 3, and 4 represent the nodes respectively. The initial value of the active power of the load node is c 1 =c 2 =c 3 =c 4 =160MW, the local cost function of each load node is:

[0092]

[0093] The relevant parameters of the algorithm are taken as k = 20, Figure 2-4 It is a series of curves obtained from simulation experiments.

[0094] Example 3

[0095] A finite-time load-controlled active power acquisition system under equality constraints includes an equality constraint condition building module, an undirected graph building module and an active power acquisition module:

[0096] The equality constraint condition building module is used to build equality constraint conditions according to the total amount conservation of load distribution;

[0097] The undirected graph construction module is used to use an undirected graph to describe the connection relationship and communication path between load nodes, and to construct a communication topology structure of load nodes in the smart grid;

[0098] The active power acquisition module is used to construct a finite time distributed optimization algorithm framework based on projected gradient descent according to the equality constraint conditions and the communication topology structure of the load nodes in the smart grid to obtain the load control active power;

[0099] A method for acquiring active power under finite-time load control under equality constraints further includes the following steps:

[0100] 1) During the algorithm iteration process, the status information and communication status of the load nodes and the satisfaction of the equality constraints are monitored in real time;

[0101] 2) If the state information or communication state of the load node is detected to be abnormal, reinitialize; if the equality constraint is detected to be unsatisfied, adjust the iteration step size or modify the equality constraint to ensure the normal operation and convergence of the algorithm;

[0102] 3) After the algorithm converges, the optimized load distribution results are simulated and verified (such as Figure 2-Figure 4 ), ensuring its practicality and reliability (converging to the optimal solution within a finite time T and the total load satisfies the equality constraint);

[0103] 4) According to the verification results, if the equality constraint in point 3) is not met, the parameters k, h, Further optimization and improvement are carried out, specifically by increasing parameters k and h and reducing parameters To improve its performance and adaptability.

[0104] The load control active power obtained by a method for obtaining active power of load control in finite time under equality constraint can be applied to different types of smart grid scenarios, including but not limited to:

[0105] 1) Economic dispatch of power system

[0106] Economic dispatch of power systems refers to a dispatching method that rationally utilizes energy and equipment to ensure reliable power supply to users at the lowest power generation cost or fuel cost while meeting the safety and power quality requirements. Its core goal is to optimize resource allocation and reduce power generation costs while ensuring stable operation of the power grid. In economic dispatch of power systems, equality constraints usually indicate that the total output power of the generator set must be equal to the total load demand of the system. The load-controlled active power obtained by a finite-time load-controlled active power acquisition method under an equality constraint can achieve the following goals:

[0107] ⅰBalance supply and demand: Ensure that the output power of the generator set is balanced with the load demand to avoid energy waste or insufficient power supply;

[0108] ⅱ Optimize costs: Through distributed optimization algorithms, the operating costs, efficiency and other factors of each generator set can be comprehensively considered to minimize costs;

[0109] ⅲ Finite time convergence: Within a limited time, the algorithm can quickly converge to the optimal solution to ensure the real-time and stability of the power system.

[0110] 2) Electric vehicle charging station load management

[0111] The load management of electric vehicle charging stations involves the coordinated work of multiple charging stations to meet the charging needs of electric vehicles. The load control active power obtained by a finite time load control active power acquisition method under an equality constraint can optimize the load distribution of charging stations and achieve the following goals:

[0112] i. Load balancing: Through distributed optimization algorithms, the charging load is evenly distributed to each charging station to avoid the situation where one charging station is overloaded while other charging stations are idle;

[0113] ⅱ Optimize charging strategy: formulate the optimal charging strategy, including charging time, charging power, etc., based on the charging needs of electric vehicles and the actual situation of charging stations;

[0114] ⅲ Improve charging efficiency: Through the algorithm with finite time convergence, it can ensure that the allocation of charging load and the formulation of charging strategy can be completed in a shorter time, thereby improving charging efficiency.

[0115] 3) Distributed energy system optimization

[0116] Distributed energy systems include renewable energy generation systems such as solar energy and wind energy, as well as energy storage systems. The load-controlled active power obtained by applying a finite-time load-controlled active power acquisition method under an equality constraint can optimize the operation of distributed energy systems and achieve the following goals:

[0117] ⅰEnergy balance: Ensure that the total output power of the distributed energy system is balanced with the load demand to avoid energy waste or insufficient power supply;

[0118] ⅱ Improve energy utilization: Through distributed optimization algorithms, the power generation efficiency, cost and other factors of each energy system can be comprehensively considered to maximize energy utilization;

[0119] ⅲ Cooperative control: Within a limited time, the algorithm can quickly converge to the optimal solution, realize the coordinated control of various energy systems, and ensure the stability and reliability of the distributed energy system.

[0120] The specific application methods are as follows:

[0121] 1) Model building: Establish a mathematical model for economic dispatch of the power system, including the cost function of the generator set, supply and demand balance constraints, etc. Transform the model into a distributed optimization problem, and clarify the local and global goals of each generator set (or node).

[0122] 2) The algorithm of the present invention is applied in combination with the climbing constraints of the generator set, the transmission capacity of the transmission interface and other constraints, and a load control strategy is formulated according to the results of the finite time distributed optimization algorithm to adjust the power generation of each generator set.

[0123] 3) According to the results of the finite time distributed optimization algorithm, a load control strategy is formulated to adjust the power generation of each generator set. The load control strategy should ensure that the power generation cost is minimized while satisfying the equality constraints.

[0124] The active power acquisition method for finite-time load control under equality constraints can also be combined with other smart grid control algorithms to achieve more complex control objectives and functions, such as combining with voltage control, frequency control, power flow control and other algorithms to jointly achieve the optimized operation and stable control of the smart grid.

[0125] A method for acquiring active power under finite-time load control under equality constraints utilizes the advantages of distributed systems and multi-agent technology to decompose complex optimization problems into multiple sub-problems, which are solved in parallel by multiple agents, thereby reducing computational complexity and improving algorithm execution efficiency. The distributed system includes multiple intelligent terminals and a central processor, and the multiple intelligent terminals process results to the central processor respectively, and the central processor fuses multiple results to obtain a fused result.

[0126] Example 4

[0127] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program performs steps of a method for acquiring active power by finite-time load control under equality constraints.

[0128] Example 5

[0129] A computer program product includes a computer program, characterized in that when the computer program is executed by a processor, the steps of a method for obtaining active power by finite-time load control under equality constraints are implemented.

[0130] The contents not described in detail in this specification belong to the prior art known to those skilled in the art. It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

Claims

1. A method for acquiring active power under finite time load control under equality constraints, characterized in that: It includes the following steps; Construct equality constraints based on the total conservation of load distribution; An undirected graph is used to describe the connection relationship and communication path between load nodes, and the communication topology structure of load nodes in smart grid is constructed; According to the equality constraints and the communication topology of load nodes in smart grid, a finite-time distributed optimization algorithm framework based on projected gradient descent is constructed to obtain the load controlled active power.

2. The method for acquiring active power under finite time load control under equality constraint according to claim 1, characterized in that: The specific method of constructing the equality constraint condition according to the total conservation of load distribution is: The total amount of load distribution is conserved, that is, the sum of the loads of all load nodes is equal to the total load of the power supply system composed of all load nodes; The equality constraints constructed are: Among them, p i represents the active power required by the i-th load, c i represents the initial value of active power required by the i-th load, C represents the total load, and n is the number of load nodes.

3. The method for acquiring active power under finite time load control under equality constraint according to claim 1, characterized in that: An undirected graph is used to describe the connection relationship and communication path between load nodes. The specific method of constructing the communication topology structure of load nodes in the smart grid is as follows: First, identify all load nodes in the smart grid; All load nodes are grouped into a set V, where each node has a unique identifier; Determine the connection relationship between each load node and other nodes according to the communication mode and network structure of the smart grid; According to the determined connection relationship, an edge set E is constructed; each edge represents the connection relationship between two load nodes; Combine the node set V and the edge set E to form an undirected graph G = (V, E); The undirected graph G describes the connection relationship and communication path between the load nodes in the smart grid, and constructs the communication topology structure of the load nodes in the smart grid.

4. The method for acquiring active power under finite time load control under equality constraint according to claim 1, characterized in that: According to the equality constraints and the communication topology of the load nodes in the smart grid, a finite-time distributed optimization algorithm framework based on projected gradient descent is constructed, and the specific method for obtaining the active power of load control is as follows: Finite-time distributed optimization load control algorithm under equality constraints constructed based on projected gradient descent algorithm: Among them, p i Represents the active power required by the i-th load, N i represents the number of neighbor nodes of the ith load, c i represents the initial value of active power required by the i-th load, f i (p i ) represents the cost function of the i-th load node, satisfying in represents the Hessian matrix of f(p), σ is the first positive constant, is the first auxiliary variable of load node i, ξ i is the second auxiliary variable of load node i, is the first auxiliary variable of load node j, ξ j is the second auxiliary variable of load node j. Load node i and load node j are neighboring nodes. yes The derivative of ij represents the weight of the edge from load node i to load node j, k is the second normal constant, h is the third normal constant and satisfies 0<h<1, and n is the number of load nodes; Requested i That is, the load control active power of the i-th load node.

5. A method for verifying the correctness of the finite-time distributed optimization based on projected gradient descent in claim 4, characterized in that: It includes the following steps: Verify that the designed finite-time distributed optimization algorithm framework based on projected gradient descent satisfies the equality constraints; It is verified that the designed finite-time distributed optimization algorithm framework based on projected gradient descent converges to the global optimal solution within a finite time.

6. The method for correctness of finite-time distributed optimization based on projected gradient descent according to claim 5, characterized in that: The specific method to verify that the designed algorithm satisfies the equality constraints is:

7. The method for correctness of a finite-time distributed optimization algorithm framework based on projected gradient descent according to claim 5, characterized in that: The specific method to verify that the designed finite-time distributed optimization algorithm converges to the global optimal solution within a finite time is: The global cost function is: Substituting the designed algorithm into the global cost function of the auxiliary variable: The partial derivative of the load with respect to the auxiliary variable is: Get the partial derivative of the local cost function with respect to the auxiliary variable: Then we get: Where L represents the Laplace matrix of the smart grid system communication network; According to Taylor expansion, we can get: in represents the optimal solution of the global cost function, δ is the fourth constant, and θ is the fifth constant; Since the topological graph is undirected, the eigenvalue of the system Laplacian matrix and the eigenvector corresponding to the eigenvalue can be expressed as 0 = λ1 < λ2 ≤ ... ≤ λ n and 1,ψ2,...,ψ n ,in Moreover |ψ i ||=1; therefore, the vector It can be expressed as: where ρ i (i=1,2,...,n) are some constants; let ψ=ρ2ψ2+…+ρ n ψ n ,So According to the Taylor expansion of the optimal solution of the global cost function and graph theory knowledge, we can get: Right now Since |ψ||>0, we can get The Lyapunov function is designed as follows: Substituting the designed finite time distributed optimal load control algorithm under equality constraints, the derivative of the Lyapunov function with respect to time t can be obtained as follows: Among them, and inequality Substituting the derivative of the Lyapunov function with respect to time t, we obtain: That is, the designed algorithm can converge to the global optimal solution in a limited time, where the convergence time is: in, Represents the initial value of the auxiliary variable. The present invention takes 8. A finite time load control active power acquisition system under equality constraint, characterized in that: It includes an equality constraint building block, an undirected graph building block, and an active power acquisition module: The equality constraint condition building module is used to build equality constraint conditions according to the total amount conservation of load distribution; The undirected graph construction module is used to use an undirected graph to describe the connection relationship and communication path between load nodes, and to construct a communication topology structure of load nodes in the smart grid; The active power acquisition module is used to construct a finite-time distributed optimization algorithm framework based on projected gradient descent according to the equality constraints and the communication topology of the load nodes in the smart grid to obtain the load controlled active power.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in claim 1 are implemented.

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