A smart grid economic dispatch control method based on fixed-time event-triggered consensus algorithm

Abstract

The application belongs to the field of smart grid, and particularly relates to a smart grid economic dispatching control method based on a fixed time event triggering consistency algorithm; the method comprises the following steps: obtaining interaction information of a generator in a power grid system, determining a topological structure and a convergence parameter of the system according to the interaction information; calculating a fixed time upper bound according to the convergence parameter; sending self state information of the generator to neighbor generators; judging whether an update event triggering condition is met according to the self state information of the generator, the state information of the neighbor generators and an update event triggering protocol; if the triggering condition is met, the generator updates the self state information according to a control protocol; judging whether the system converges according to the self state information, and if the system converges, recording a convergence time; judging whether the convergence time is less than the fixed time upper bound, and if the convergence time is less than the fixed time upper bound, the generator saves the current state information to realize the economic dispatching control of the power grid; the application can reduce the cost of the smart grid and the continuous communication probability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of smart grids, and in particular relates to a smart grid economic dispatch control method based on a fixed-time event-triggered consistency algorithm. Background Art

[0002] In reality, as power systems become more complex, collecting detailed information is often expensive in terms of data processing and communication. Centralized algorithms cannot meet the plug-and-play requirements of next-generation smart grid systems. Distributed optimization techniques are often used to solve large-scale and complex optimization problems that require information transmission over communication networks. In smart grid operations, distributed optimization techniques are often used to solve the economic dispatch problem (EDP) with equality constraints. Specifically, it deals with the distribution of active power between generators.

[0003] For a flexible distributed optimization scheme, each generator only needs to communicate with adjacent generators, and no centralized data processing unit is required, which can effectively avoid single point of failure. Most of the existing research on distributed economic scheduling is based on consensus methods.

[0004] However, for large-scale networks, information transmission may be limited by bandwidth resources. Therefore, event-triggered communication strategies seem more advantageous and have been used in existing work to design distributed algorithms to solve EDP problems.

[0005] Most distributed algorithms can asymptotically or exponentially approach the optimal solution, meaning they can obtain the exact optimal solution within an infinite timeframe. However, in practical applications, only suboptimal solutions are available. When communication topologies are complex, under constantly changing unknown loads, and in plug-and-play renewable scenarios, asymptotic convergence may not be sufficient to adapt to rapidly changing conditions. Therefore, obtaining the exact optimal solution within a finite timeframe is crucial.

[0006] However, the estimation of the finite-time settling time depends on the initial conditions of the generator, which are difficult to obtain, limiting the practical application of finite-time theory. Therefore, we can use the fixed-time stability theory to design a distributed fixed-time optimization algorithm to solve this type of convex optimization problem, thus overcoming the disadvantage of relying on initial conditions.

[0007] However, as research on EDP problems progresses, the solutions for obtaining precise optimal solutions within a finite timeframe continue to improve and become increasingly complex. In the field of economic dispatch, most research has been limited to undirected topologies. Leveraging the symmetry of the Laplacian matrix obtained from undirected topologies, where both row and column sums are zero, relevant protocols and proofs can be easily derived. Recent research in this area has applied the latest fixed-time stability theory to directed graphs, but has not yet addressed the issue of continuous communication. Summary of the Invention

[0008] In response to the shortcomings of the existing technology, the present invention proposes a smart grid economic dispatch control method based on a fixed-time event-triggered consensus algorithm. The method is implemented based on the fixed-time event-triggered consensus algorithm designed by the present invention. The method includes:

[0009] S1: Obtain the interaction information of generators in the power grid system and determine the system topology and convergence parameters based on the interaction information;

[0010] S2: Calculate the fixed time upper bound based on the convergence parameter;

[0011] S3: Determine whether the fixed time upper bound meets the cost condition. If so, the generator sends its own status information to the neighboring generators. Otherwise, reconfirm the convergence parameters and return to step S2.

[0012] S4: Determine whether the event triggering conditions are met based on the generator's own status information, neighboring generator status information, and the event triggering protocol;

[0013] S5: If the trigger condition is met, the generator updates its own status information according to the control protocol; otherwise, no status information update is performed;

[0014] S6: Determine whether the system has converged based on its own state information. If converged, the generator saves the current state information to achieve economic dispatch control of the power grid. Otherwise, the generator sends its own state information to neighboring generators and returns to step S4.

[0015] Preferably, the process of determining the convergence parameter according to the interactive information includes: selecting the convergence parameter, setting a value range of the convergence parameter, and randomly confirming the value of the convergence parameter within the corresponding value range.

[0016] Preferably, the formula for calculating the fixed time upper bound is:

[0017]

[0018] Among them, T maxrepresents the fixed time upper bound, k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable parameters of the class A event trigger protocol, ∈0, ∈1, ∈2, ∈3 represent the first, second, third and fourth adjustable parameters of the class B event trigger protocol, θ0, θ1, θ2 represent the first, second and third adjustable parameters of the class C event trigger protocol, σ represents the adjustable parameter of the class D event trigger protocol; λ2, λ n represents the second smallest eigenvalue and the largest eigenvalue; n represents the number of generators.

[0019] Preferably, the event triggering protocol is expressed as:

[0020]

[0021] Among them, g i (t) represents the event triggering function of the i-th generator, e i (t) represents the difference between the state variables of the i-th node, k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable parameters of the class A event trigger protocol, ∈0, ∈1, ∈2, ∈3, ∈4 represent the first, second, third, fourth and fifth adjustable parameters of the class B event trigger protocol, θ0, θ1, θ2 represent the first, second and third adjustable parameters of the class C event trigger protocol, σ represents the adjustable parameter of the class D event trigger protocol; λ2, λ n Indicates the second smallest eigenvalue and the largest eigenvalue, h i (t) represents the first auxiliary variable introduced in the event trigger function of the i-th generator, M i (t) represents the second auxiliary variable introduced in the event trigger function of the i-th generator, and n represents the number of generators.

[0022] Preferably, the process of determining whether the update event trigger condition is met includes: determining whether the update event trigger threshold in the event trigger protocol is greater than 0; if so, it indicates that the event trigger condition is met; otherwise, it indicates that the event trigger condition is not met.

[0023] Preferably, the control protocol is expressed as:

[0024]

[0025] Among them, P i (t) represents the generator power of the i-th generator at time t, N i represents the set of generators adjacent to the i-th generator, a ij Indicates whether the connection from the i-th generator to the j-th generator is established, ζ j (t) represents the first auxiliary variable introduced by the control protocol of the jth generator, ζ i(t) represents the auxiliary variable introduced by the control protocol of the i-th generator, P i (0) represents the generator power set manually at the initial moment of the i-th generator, k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable Class A event trigger protocol parameters respectively, Φ i (t) represents the second auxiliary variable introduced by the control protocol of the i-th generator, sig(·) p represents a composite function, p represents the exponent of the composite function, represents the incremental cost of the j-th generator, represents the incremental cost of the i-th generator.

[0026] Preferably, the process of determining whether the system has converged includes: determining whether the first condition and the second condition are simultaneously satisfied; if both are satisfied, it indicates that all generators in the system have reached consistency, i.e., the system has converged; otherwise, it indicates that the system has not converged; wherein the first condition is: whether the incremental cost in the generator's own state information remains unchanged; and the second condition is: whether the incremental cost in the generator's own state information is the same as that of its neighboring generators.

[0027] Furthermore, all generators reach consistency as follows:

[0028]

[0029] in, Indicates that the i-th generator is at t k+1 The incremental cost of time, Indicates that the i-th generator is at t k The incremental cost of time, represents the incremental cost of the i-th generator at time t, represents the incremental cost of the jth generator at time t, N represents the set of generators, N i Represents the set of neighbor generators of a generator.

[0030] The beneficial effects of the present invention are as follows: the protocol designed by the present invention can be applied to directed topological graphs, and can converge in the direction of cost minimization, and can obtain the optimal power generation value of the generator under the condition of satisfying the supply and demand balance; the upper limit of the convergence time can be estimated in advance to ensure that the protocol can converge within a limited time, and the parameters can also be adjusted to control the protocol to converge within the upper limit of a set fixed time; the latest fixed-time stability theory is adopted, and the upper limit of the convergence time can be estimated more accurately; an event-triggered control mechanism is introduced to reduce the number of communications and calculations, reduce the performance requirements of the communication network and the calculation performance requirements of the generator, and thus reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1This is a flow chart of the economic dispatch control method of the smart economic power grid in the present invention;

[0032] Figure 2 is a topological diagram of a generator set in one embodiment of the present invention;

[0033] Figure 3 A diagram showing the evolution of Lagrange multipliers in a generator system protocol according to an embodiment of the present invention;

[0034] Figure 4 This is a diagram of the generator power convergence process in one embodiment of the present invention;

[0035] Figure 5 This is a triggering timing diagram of an embodiment of the present invention;

[0036] Figure 6 This is a cost optimization diagram for a generator according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The present invention proposes a smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm, such as Figure 1 As shown, the method includes the following contents:

[0039] S1: Obtain the interaction information of generators in the power grid system and determine the system topology and convergence parameters based on the interaction information.

[0040] Obtain the interaction information between all generators in the power grid system, and determine the system topology and convergence parameters based on the interaction information; the determined convergence parameters include but are not limited to some constant parameters, such as: k, r, s, ω, ξ, ∈0, ∈1, ∈2, ∈3, ∈4, θ0, θ1, θ2; after determining the topology of the generator set, the corresponding Laplace matrix L can be obtained, thereby obtaining L T The eigenvalues ​​of the L matrix are λ i , and 0=λ1<λ2<...<λ n ; Among them, the Laplace matrix of the system topology is L = DA, D represents the in-degree matrix of the directed graph, and A represents the adjacency matrix; the topology of the system is a directed communication topology that is strongly connected and weight-balanced, and LL is a generalized positive semidefinite matrix, that is:

[0041]

[0042] The communication channel is secure, reliable and has no delay during the communication process.

[0043] The above convergence parameters need to satisfy the following inequalities:

[0044] k>0, r>0, s>0, 0<ω<1, ξ>1

[0045] ∈0>0,∈1>0,∈2>0,∈3>0,∈4>0

[0046] 0<θ0<1, 0<θ1<1, 0<θ2<1

[0047] LL+L T L T ≥∈0L T L

[0048]

[0049]

[0050] Where k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable type A event trigger protocol parameters, respectively; ∈0, ∈1, ∈2, ∈3, ∈4 represent the first, second, third and fourth adjustable type B event trigger protocol parameters, respectively; θ0, θ1, θ2 represent the first, second and third adjustable type C event trigger protocol parameters, respectively; σ represents the adjustable type D event trigger protocol parameter, which is defined as min{2α}, i.e., twice the generator parameter α. represents the incremental cost, i.e. the derivative of the cost consumed by the generator set with respect to time, sig(·) p represents a composite function, defined as sign(·)|·| p , where sign(·) is the sign function, p represents the exponent of the composite function, and e(t) represents the difference between the node state variables.

[0051] S2: Calculate the fixed time upper bound based on the convergence parameters.

[0052] Substituting the convergence parameter obtained in step S1 into the previous function of fixed time, we can get the upper bound of fixed time. The previous function of fixed time is:

[0053]

[0054] Where n represents the number of generators, λ2, λ n Represents the parameters related to the generator set topology diagram, defined as L T LThe second smallest eigenvalue and the largest eigenvalue.

[0055] S3: Determine whether the fixed time upper bound meets the cost condition. If so, the generator sends its own status information to the neighboring generators. Otherwise, reconfirm the convergence parameters and return to step S2.

[0056] The upper bound of firmware time is T max , the system's convergence time must fall within this upper bound, and various parameters can be adjusted according to the upper bound function. This is because the fixed time upper bound is not positively correlated with the event triggering effect. The smaller the upper bound, the smaller the event triggering effect. The larger the upper bound, the delayed convergence of the system will occur. Therefore, a relatively intermediate value can be selected according to the actual convergence cost required to make the effect of event triggering more uniform.

[0057] Each generator sends its own status information to its neighbors. After receiving the information sent by the neighboring agents, the generator divides and records the received information.

[0058] S4: Determine whether the event triggering conditions are met based on the generator's own status information, neighboring generator status information, and the event triggering protocol.

[0059] The generator calculates the state error based on the state information at the previous moment and the state information at the current moment, and calculates the threshold based on its own state information and neighbor state information at the previous moment and the current moment. If the error is greater than the threshold, it triggers an update event. The entire process can be expressed as whether the event triggering protocol is satisfied. The triggering protocol is expressed as:

[0060]

[0061] Among them, g i (t) represents the event triggering function of the i-th generator, that is, the event triggering threshold of the i-th generator at time t. If g i (t)>0, it means the error is greater than the threshold, and the event triggering condition is met. The threshold is Otherwise, it means that the event triggering condition is not met; h i (t) represents the first auxiliary variable introduced in the event trigger function of the i-th generator, M i (t) represents the second auxiliary variable introduced in the event trigger function of the i-th generator, n represents the number of generators, e i (t) represents the difference between the state variables of the i-th node, Φ i (t) represents the auxiliary variable introduced by the control protocol of the i-th generator.

[0062] S5: If the triggering condition is met, the generator updates its own status information according to the control protocol; otherwise, the status information is not updated.

[0063] The control protocol is expressed as:

[0064]

[0065] Among them, P i (t) represents the generator power of the i-th generator at time t, N i represents the set of generators adjacent to the i-th generator; a ij Indicates whether the connection from the i-th generator to the j-th generator is established. If so, a ij =1, otherwise, a ij =0;ζ j (t) represents the first auxiliary variable introduced by the control protocol of the jth generator, ζ i (t) represents the auxiliary variable introduced by the control protocol of the i-th generator, P i (0) represents the generator power set manually at the initial moment of the i-th generator, Φ i (t) represents the second auxiliary variable introduced by the control protocol of the i-th generator, represents the incremental cost of the j-th generator, i.e., the derivative of the cost consumed by the j-th generator with respect to power (also the Lagrange multiplier), represents the incremental cost of the i-th generator, that is, the derivative of the cost consumed by the i-th generator with respect to power (also the Lagrange multiplier).

[0066] S6: Determine whether the system has converged based on its own state information. If converged, the generator saves the current state information to achieve economic dispatch control of the power grid. Otherwise, the generator sends its own state information to neighboring generators and returns to step S4.

[0067] The process of determining whether the system has converged includes determining whether the first and second conditions are met simultaneously. If so, the system has converged; otherwise, the system has not converged. The first condition is whether the incremental cost in the generator's own state information remains unchanged. The second condition is whether the incremental cost in the generator's own state information is the same as that of its neighboring generators, that is, determining whether the incremental costs of all generators are the same. When both the first and second conditions are met, it indicates that the system agents have reached a consensus on the pinning grouping, that is, the system has converged.

[0068] The expression for the agents to achieve consistent pinning grouping is as follows:

[0069]

[0070] in, Indicates that the i-th generator is at t k+1 The incremental cost at time t is the cost of the i-th generator at time t k+1 The derivative of the cost consumed with respect to time, Indicates that the i-th generator is at t k The incremental cost at time t is the cost of the i-th generator at time t k The derivative of the cost consumed at a moment with respect to time, represents the incremental cost of the i-th generator at time t, that is, the derivative of the cost consumed by the i-th generator at time t with respect to time, represents the incremental cost of the jth generator at time t, that is, the derivative of the cost consumed by the jth generator at time t with respect to time, N represents the generator set, N i Represents the set of neighbor generators of a generator.

[0071] It is proved that the present invention can optimize the cost of power generation by the generator:

[0072] The relationship between the power and cost of generator sets in smart grids is a quadratic function, which is accumulated to form the total cost. A single quadratic function is a convex function, and the concavity and convexity of the original function after accumulation is the concavity and convexity of any convex function, which is also a convex function. Therefore, the Lagrange multiplier method, which was originally used to solve the extreme values ​​of multivariate functions with constraints, can directly find the maximum value here.

[0073] The following is the convex optimization problem:

[0074]

[0075]

[0076]

[0077] Through the Lagrange multiplier method, the relationship between the Lagrange multiplier and power is obtained:

[0078]

[0079] Since the original function is a convex function, the maximum value is unique, so when (P1, P2, P3, ..., P i , ...) is the maximum value point, Only unique values ​​are available, and the calculations for any point are equal. On the contrary, through the unique The corresponding power can be obtained

[0080] At the same time, the power derivative of the cost function C(P) can also be obtained:

[0081] C(P)'=2α i P i +β i

[0082] so That is, the derivative of cost with respect to power, called incremental cost, which is also the Lagrange multiplier.

[0083] By considering the mathematical merging and simplification of the consistency control protocol, we can finally get:

[0084]

[0085] Therefore, it can be seen that the control protocol of the present invention satisfies the relationship between the optimal Lagrange multiplier and the power. If it converges, then the parameters must be the optimal parameters if they meet the conditions.

[0086] It is proved that the present invention can meet the generator set consistency conditions:

[0087] Define the agent position error: Among them i (t) is the state information of the i-th generator, and the Lagrange multiplier λ i (t) is the convergence value it will eventually confirm. If e i (t) approaches 0, which means that the Lagrange multiplier λ i (t) can converge to consistency.

[0088] Define the error matrix: e i (t)=(e1(t) T , e2(t) T ,…,e m+n (t) T ) T

[0089] The relationship between the error and the control protocol is obtained:

[0090] ζ′ i (t)=-(e i (t)+kΦ i (t)+rsig(Φ i (t)) ω +ssig(Φ i (t)) ξ )

[0091] Consider a Lyapunov function:

[0092]

[0093] Among them * , is the optimal value, that is, C(ζ * )≤C(ζ(t)).

[0094] Let ∈4|e T (t)||Φ(t)|-|φ T (t)Le(t)|>0, take the derivative of V(t), and then replace ζ′ i (t) brought in:

[0095]

[0096] By amplifying V'(t) through various mathematical formulas and related lemmas, we can finally obtain:

[0097]

[0098] Therefore, through the latest fixed-time stability theory, we can get the upper bound of the convergence time:

[0099]

[0100] For the system convergence time, T≤T max .

[0101] It is proved that the present invention can make the generator generate electricity to meet the supply and demand balance:

[0102] In the control protocol, the update protocol of the generator power is accumulated, which is equal to ∑P i (0), that is, the sum of the initial artificially set values, which is a constant. In other words, during the convergence process, the sum of the powers is guaranteed to remain unchanged. If the sum is designated as the required power, the supply and demand balance is satisfied.

[0103] Evaluation of the present invention:

[0104] In order to verify the effect of the fixed time event triggering consistency algorithm proposed in this invention, Matlab is used for simulation verification. Six generator nodes are selected to form a power grid system, and the topology diagram is as follows: Figure 2 As shown, the parameter values ​​in the protocol are selected as k=1, r=36, s=5, ω=2, ξ=3.3413, ∈0=82.2460, ∈1=0.06129, ∈2=0.2096, ∈3=69.4954, ∈4=2.0150, θ0=0.8647, θ1=0.9503, θ2=0.9175.

[0105] Initial power P(0) = [90, 115, 85, 100, 120, 90], ζ(0) = [10, 10, 10, 10, 10, 10]; some parameters of the six generators are shown in Table 1:

[0106] Table 1 Generator fixed parameters

[0107] Node 1 2 3 4 5 6 i ]]> ​ 0.096 0.072 0.105 0.082 0.078 0.090 <![CDATA[β i ]]> 1.22 3.41 2.53 4.02 2.90 2.72 <![CDATA[γ i ]]> 51 31 78 42 57 49

[0108] Among them, these three parameters are the relationship between generator cost and power The fixed parameter in i represents the first fixed parameter of the i-th generator, β irepresents the second fixed parameter of the i-th generator, γ i represents the third fixed parameter of the i-th generator.

[0109] Substitute the above parameters into the formula

[0110]

[0111] Predictable T≤15.9773(s).

[0112] according to Figure 2 The topological graph of , we can get the adjacency matrix as:

[0113]

[0114] From the simulation results, it can be concluded that Figure 3 As shown in Figure 1, it shows the evolution of the Lagrange multipliers in all generator system protocols, indicating that as the convergence progresses, the power of each generator has found the optimal value. And the convergence time is T real =1.01(s)≤T max =15.9773(s).

[0115] After verification by simulation experiments, the fixed time event trigger control protocol under the directed graph of the present invention can be used Figure 3 It can be seen that the states (Lagrange multipliers) in the generator group tend to be consistent, such as Figure 4 As shown, each generator has found the optimal power value ( Figure 4 The value of the generator stability in the Figure 6 The cost in the overall is reduced to the minimum; Figure 5 As shown, while reducing the cost of smart grid, Figure 5 It can be seen that continuous communication of the system can be effectively prevented under the directed graph (column coordinates represent the sequence number of the generator, and each cross represents an update).

[0116] It should be noted that those skilled in the art will appreciate that all or part of the processes in the above method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0117] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm, characterized in that: include: S1: Obtain the interaction information of generators in the power grid system and determine the system topology and convergence parameters based on the interaction information; S2: Calculate the fixed time upper bound based on the convergence parameter; the formula for calculating the fixed time upper bound is: Among them, T max represents the fixed time upper bound, k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable parameters of the class A event trigger protocol, ∈0, ∈1, ∈2, ∈3 represent the first, second, third and fourth adjustable parameters of the class B event trigger protocol, θ0, θ1, θ2 represent the first, second and third adjustable parameters of the class C event trigger protocol, σ represents the adjustable parameter of the class D event trigger protocol; λ2, λ n represents the second smallest eigenvalue and the largest eigenvalue; n represents the number of generators; S3: Determine whether the fixed time upper bound meets the cost condition. If so, the generator sends its own status information to the neighboring generators. Otherwise, reconfirm the convergence parameters and return to step S2. S4: Determine whether the event triggering condition is met based on the generator's own status information, neighboring generator status information, and the event triggering protocol; the event triggering protocol is expressed as: Among them, g i (t) represents the event triggering function of the i-th generator, e i (t) represents the difference between the state variables of the i-th node, ∈4 represents the parameters of the fifth adjustable type B event triggering protocol, h i (t) represents the first auxiliary variable introduced in the event trigger function of the i-th generator, M i (t) represents the second auxiliary variable introduced in the event triggering function of the i-th generator; S5: If the trigger condition is met, the generator updates its own status information according to the control protocol; otherwise, no status information update is performed; S6: Determine whether the system has converged based on its own state information. If converged, the generator saves the current state information to achieve economic dispatch control of the power grid. Otherwise, the generator sends its own state information to neighboring generators and returns to step S4.

2. The smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm according to claim 1 is characterized in that: The process of determining the convergence parameter according to the interactive information includes: selecting the convergence parameter, setting a value range of the convergence parameter, and randomly confirming the value of the convergence parameter within the corresponding value range.

3. The smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm according to claim 1 is characterized in that: The process of determining whether the update event trigger condition is met includes: determining whether the update event trigger threshold in the event trigger protocol is greater than 0; if so, it indicates that the event trigger condition is met; otherwise, it indicates that the event trigger condition is not met.

4. The smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm according to claim 1 is characterized in that: The control protocol is expressed as: Among them, P i (t) represents the generator power of the i-th generator at time t, N i represents the set of generators adjacent to the i-th generator, a ij Indicates whether the connection from the i-th generator to the j-th generator is established, ζ j (t) represents the first auxiliary variable introduced by the control protocol of the jth generator, ζ i (t) represents the auxiliary variable introduced by the control protocol of the i-th generator, P i (0) represents the generator power set manually at the initial moment of the i-th generator, k, s, r, ω, ξ represent the first, second, third, fourth and fifth adjustable Class A event trigger protocol parameters respectively, Φ i (t) represents the second auxiliary variable introduced by the control protocol of the i-th generator, sig(·) p represents a composite function, p represents the exponent of the composite function, represents the incremental cost of the j-th generator, represents the incremental cost of the i-th generator.

5. The smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm according to claim 1 is characterized in that: The process of determining whether the system has converged includes determining whether the first and second conditions are met simultaneously. If both conditions are met, it means that all generators in the system have reached consistency, that is, the system has converged. Otherwise, it means that the system has not converged. Among them, the first condition is: whether the incremental cost in the generator's own state information remains unchanged; the second condition is: whether the incremental cost in the generator's own state information is the same as that of its neighboring generators.

6. The smart grid economic dispatch control method based on a fixed time event triggering consensus algorithm according to claim 5 is characterized in that: All generators reach consensus as: in, Indicates that the i-th generator is at t k+1 The incremental cost of time, Indicates that the i-th generator is at t k The incremental cost of time, represents the incremental cost of the i-th generator at time t, represents the incremental cost of the jth generator at time t, N represents the set of generators, N i Represents the set of neighbor generators of a generator.

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