A smart grid economic dispatch method based on fixed-time dynamic event triggering

Abstract

The present invention belongs to the technical field of economic dispatch of power systems, and specifically relates to a smart grid economic dispatch method based on fixed-time dynamic event triggering; in this method, after a generator meets the dynamic event triggering conditions, a fixed-time consistency algorithm is used to update its own incremental cost; the generated power is calculated using the updated incremental cost; if the generated power of all generators is not within the power limit range, an auxiliary variable is introduced to update the incremental cost of the generator again; the fixed-time consistency algorithm proposed by the present invention can ensure that the upper limit of the stabilization time is not affected by the initial state of the generator, and extends the static event triggering to the field of dynamic event triggering, further reducing the number of interactions between the generator and its neighbors and the frequency of updating the incremental cost, greatly reducing the traffic in the network, and at the same time facilitating secure communication between generators in the smart grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system economic dispatch, and in particular relates to a smart grid economic dispatch method based on fixed-time dynamic event triggering. Background Art

[0002] In recent years, smart grids have gradually become a new hotspot in the development and research of future power grids. As one of the classic problems in the smart grid field, the economic dispatch problem has also received widespread attention and research from scholars. The main goal of the economic dispatch problem is to meet the balance between the power generation and consumption sides and the capacity constraints of the generators, while minimizing the total power generation cost. Most cost functions in the economic dispatch problem are assumed to be convex functions, so the economic dispatch problem is essentially a convex optimization problem. Traditional economic dispatch algorithms, such as lambda iteration, dynamic programming, particle swarm optimization, and evolutionary algorithms, are all implemented in a centralized manner. Centralized control structures require a central controller to collect global information, calculate optimal decisions, and send control commands. This is prone to single point failures and requires high communication and computational burdens, especially for smart grids with a large number of distributed generation units.

[0003] To overcome the shortcomings of traditional centralized control methods, distributed economic dispatch algorithms based on multi-agent systems have attracted extensive research attention in recent years. Distributed algorithms compute optimal decisions solely through interactions with neighboring nodes in the network, offering advantages such as low communication and computational complexity, robustness, and scalability. Current research on distributed economic dispatch is largely based on consensus methods. All of these distributed algorithms can asymptotically or exponentially obtain optimal solutions—that is, exact optimal solutions within infinite time. In practical applications, only suboptimal solutions are available, which is unsuitable when high convergence speed is required. In the future, with frequent and drastic changes in operating conditions, it will be necessary to improve the responsiveness and economic efficiency of existing economic dispatch algorithms. Asymptotic convergence may not be sufficient to adapt to these rapid changes. Therefore, obtaining exact optimal solutions within infinite time is of great significance. Several existing works have proposed finite-time stability theories. Based on this, a distributed finite-time algorithm is proposed for solving economic dispatch problems with and without generation constraints. However, estimating the finite-time stability time depends on the initial state of the generator, which is difficult when the initial state of the generator is unavailable. To solve this problem, scholars later proposed the concept of fixed-time stability, which makes the stability time independent of the initial state of the system.

[0004] It is generally assumed that local information about each generator can be continuously transmitted via a communication network. Most of the aforementioned work is based on undirected graphs or directed communication topologies and Lyapunov stability theory. However, for large and complex communication networks, information transmission may be limited by bandwidth resources or other constraints. Therefore, to conserve communication resources and generator update frequency, event-triggered mechanisms have been introduced in the economic dispatch problem, where generators are updated only when a trigger condition is met. Event-triggered mechanisms need to avoid Zeno behavior, which is naturally avoided by discrete-time event-triggered mechanisms. It should be noted that the trigger conditions in the aforementioned work depend on the state; an event is triggered when the measurement error equals or exceeds a threshold, which can be considered a static trigger condition. Initially, static trigger conditions effectively reduce communication costs because they are not easily met. However, over time, as the threshold becomes smaller, they trigger more frequently, potentially leading to unnecessary triggering moments. There is an urgent need to develop more flexible event-triggered conditions to further reduce communication costs and control update frequency. To meet this demand, some scholars proposed a dynamic event triggering mechanism, which introduced a distributed event triggering scheme with dynamic parameters, where the update rule of each dynamic parameter depends on the measurement error at the triggering moment and the relative error between the adjacent state and its own state, and demonstrated the superiority of dynamic event triggering over static event triggering. Summary of the Invention

[0005] To solve the above problems, the present invention provides a smart grid economic dispatch method based on fixed-time dynamic event triggering, which is characterized by comprising the following steps:

[0006] S1. Mathematically model the economic dispatch problem in smart grids;

[0007] S2. Design a fixed-time consistent economic dispatch algorithm and dynamic event triggering conditions;

[0008] S3. At the initial time t0, each generator sends its own incremental cost to the adjacent generators through the communication network and sets the trigger time to

[0009] S4. Assume that the last event triggering time of the i-th generator is k represents the total number of dynamic event triggers that have occurred in the generator;

[0010] S5. Determine at the current moment Does the i-th generator meet the dynamic event triggering conditions? If not, it will not update its own incremental cost; if it does, then The i-th generator uses a fixed-time consistency algorithm to update its own incremental cost and sends its updated incremental cost to adjacent generators through the communication network. The adjacent generators record the updated incremental cost of the i-th generator.

[0011] S6. All generators calculate the generated power based on their own incremental costs and their own cost parameters;

[0012] S7. Determine whether the power generation of all generators is within the power limit range. If so, complete the economic dispatch; if not, introduce auxiliary variables to update the incremental costs of all generators again, and return to step S6.

[0013] Furthermore, with the goal of minimizing the total power generation cost of the smart grid and the supply and demand balance of the smart grid and the output power limit of the generator as constraints, a mathematical model of the economic dispatch problem of the smart grid is established, which is expressed as:

[0014]

[0015]

[0016] P i min <P i <P i max

[0017] Among them, P i represents the output power of the i-th generator, P i min represents the minimum output power of the i-th generator, P i max represents the maximum output power of the i-th generator, P D represents the total power required by the smart grid, This is the supply and demand balance constraint of the smart grid, N represents the total number of generators, C i (P i ) represents the cost function of the i-th generator, and the specific formula is:

[0018] C i (P i )=α i P i 2 +β i P i +γ i

[0019] Among them, α i , β i and γ i is the cost parameter of the i-th generator.

[0020] Furthermore, the dynamic event triggering condition is expressed as:

[0021]

[0022] Among them, F(e i (t),y i (t),η i (t)) represents the dynamic event triggering condition of the i-th generator, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent the gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), e i (t) represents the number of generators i The error between the incremental cost at the moment and the incremental cost at the last triggering moment, y i (t) represents the number of generators i The interaction result of incremental cost with all neighbor nodes at the moment, η i (t) represents the number of generators i The dynamic parameters at the moment, θ i represents a constant greater than 0 set for the i-th generator, and ρ represents a constant greater than 0;

[0023] The i-th generator meets the dynamic event triggering condition F(e i (t),y i (t),η i When (t))>0, the generator uses the fixed time consistency algorithm to update the cost increment.

[0024] Further,

[0025]

[0026]

[0027] Among them, λ i (t) indicates when The incremental cost of the i-th generator when λ j (t) indicates when The incremental cost of the j-th generator when ; Indicates that the i-th generator is The interaction result of incremental cost with all neighbor nodes at the moment, sig i (·) means |·| i ·sign(·)., where sign(·) is the sign function.

[0028] Furthermore, each generator uses a fixed-time consistency algorithm to update its own incremental cost. The fixed-time consistency algorithm is expressed as:

[0029]

[0030] Among them, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), represents the time when the i-th generator meets the dynamic event triggering condition for the kth time, λ i (t) indicates when The incremental cost of the i-th generator when ; sig(·) represents express express where sign(·) is the sign function, a ij represents the communication weight between the i-th generator and the j-th generator, Indicates the i-th generator at the time of dynamic event triggering The incremental cost of It represents the jth generator at the time of dynamic event triggering The incremental cost when N i represents the set of adjacent generators of the i-th generator.

[0031] Furthermore, in step S6, when the output power of the i-th generator is not within the power limit range, an auxiliary variable is introduced to update the incremental cost again, which is expressed as:

[0032]

[0033]

[0034]

[0035] in, Indicates when When , the incremental cost of updating the i-th generator after introducing the auxiliary variable; λ i (t) indicates when When , the incremental cost of the i-th generator; α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), Indicates that the i-th generator is Auxiliary variables at time ψ i (t) represents the number of generators i Auxiliary variables at time Represents the auxiliary variable of the i-th generator when it is triggered for the kth time a ij represents the communication weight between the i-th generator and the j-th generator, Represents the auxiliary variable of the i-th generator when it is triggered for the kth time

[0036] Furthermore, at the initial time t0, each generator calculates its generated power based on its own incremental cost and its own cost parameter; it is determined whether the generated power of all generators is within the power limit. If so, economic dispatch is completed; if not, the unsatisfied generators are placed in the power unsatisfied set, and the satisfied generators are placed in the power satisfied set, and the following operations are performed at the same time:

[0037] If the i∈Ω p If the power generation power of the ith generator is greater than the upper limit of the power limit range, the power generation power of the ith generator is set to the upper limit of the power limit range; if the power generation power of the ith generator is less than the lower limit of the power limit range, the power generation power of the ith generator is set to the lower limit of the power limit range; where Ω p Indicates that the power does not satisfy the set;

[0038] At the initial moment, different auxiliary variables are introduced to update the incremental costs of all generators, and then the process returns to step S6. The auxiliary variables at the initial moment are expressed as:

[0039]

[0040]

[0041] Among them, Ω p Indicates that the power at the initial moment does not satisfy the set, represents the auxiliary variable of the i-th generator at the initial moment ψ i (t) represents the auxiliary variable of the i-th generator at the initial moment λ i (0) represents the incremental cost of the i-th generator at the initial moment, α i , β i represents the cost parameter of the i-th generator, P i represents the power generated by the i-th generator.

[0042] Beneficial effects of the present invention:

[0043] This invention provides a fixed-time consensus algorithm. In the generator economic dispatch problem, the expression for the stabilization time is determined by the control parameters in the control protocol, the number of generators, the second smallest eigenvalue of the Laplace matrix, and other factors. Therefore, an upper limit for the stabilization time can be set artificially. While the stabilization time in current finite-time consensus algorithms is strictly dependent on the initial state of the generators, the fixed-time consensus algorithm proposed in this invention ensures that the upper limit of the stabilization time is not affected by the initial state of the generators, making it more flexible and practical than current finite-time consensus algorithms.

[0044] In the research on economic dispatch of generators, static event triggering is currently basically considered. The present invention improves on the static event triggering strategy and extends the static event triggering to the field of dynamic event triggering, further reducing the number of interactions between generators and neighbors and the frequency of updating incremental costs, greatly reducing the traffic in the network, and also facilitating secure communication between generators in smart grids.

[0045] Most current research only considers equality constraints, where total power generation equals total demand. However, in reality, each generator's power generation capacity is limited, typically within a certain range. The fixed-time consistency algorithm proposed in this paper considers inequality constraints. When the power generation capacity of each generator exceeds the specified range, the power system will be re-dispatched economically. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0047] Figure 2 A topological diagram of a communication network for a generator according to an embodiment of the present invention;

[0048] Figure 3 The incremental cost of the generator according to the embodiment of the present invention is

[0049] Figure 4 This is a diagram showing changes in power generation according to an embodiment of the present invention;

[0050] Figure 5 This is a graph showing changes in incremental costs when a generator exceeds power generation constraints according to an embodiment of the present invention;

[0051] Figure 6 This is a triggering time diagram of dynamic event triggering of each generator in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] In one embodiment, the present invention provides a smart grid economic dispatch method based on fixed-time dynamic event triggering, such as Figure 1 As shown, the following steps are included:

[0054] S1. Mathematically model the economic dispatch problem in smart grids;

[0055] S2. Design a fixed-time consistent economic dispatch algorithm and dynamic event triggering conditions;

[0056] S3. At the initial time t0, each generator sends its own incremental cost to the adjacent generators through the communication network and sets the trigger time to

[0057] S4. Assume that the last event triggering time of the i-th generator is k represents the total number of dynamic event triggers that have occurred in the generator;

[0058] S5. Determine at the current moment Does the i-th generator meet the dynamic event triggering conditions? If not, its incremental cost will not be updated. If it does, the total number of dynamic event triggering times of the i-th generator will be increased by one, and the current time t is the triggering time of the k+1th dynamic event triggering of the i-th generator, that is, The i-th generator uses a fixed-time consistency algorithm to update its own incremental cost and sends its updated incremental cost to adjacent generators through the communication network. The adjacent generators record the updated incremental cost of the i-th generator.

[0059] Specifically, at each moment, each generator needs to determine whether it meets the dynamic event triggering conditions. The dynamic event triggering conditions of each generator are constantly changing, and the dynamic event triggering conditions are related to the incremental costs of adjacent generators. Therefore, each generator records the incremental costs of adjacent generators after updates to determine whether it needs to be updated.

[0060] S6. All generators calculate the generated power based on their own incremental costs and their own cost parameters;

[0061] S7. Determine whether the power generation of all generators is within the power limit range. If so, complete the economic dispatch; if not, introduce auxiliary variables to update the incremental costs of all generators again, and return to step S6.

[0062] Specifically, with the goal of minimizing the total power generation cost of the smart grid and the supply and demand balance of the smart grid and the output power limit of the generator as constraints, a mathematical model of the economic dispatch problem of the smart grid is established, which can be expressed as:

[0063]

[0064]

[0065] P i min <P i <P i max

[0066] Among them, P i represents the output power of the i-th generator, P i min represents the minimum output power of the i-th generator, P i max represents the maximum output power of the i-th generator, P D represents the total power required by the smart grid, This is the supply and demand balance constraint of the smart grid, N represents the total number of generators, C i (P i ) represents the cost function of the i-th generator, and the specific formula is:

[0067] C i (P i )=α i P i 2 +β i P i +γ i

[0068] Among them, α i , β i and γ i is the cost parameter of the i-th generator.

[0069] Based on the above mathematical model, in this embodiment, the Lagrange multiplier method is used to calculate the mathematical model of the economic dispatch problem, and the obtained Lagrange function is expressed as:

[0070]

[0071] For the parameter P in the Lagrangian function i, λ are differentiated separately to obtain the equation group:

[0072]

[0073] The consistency variables are calculated according to the equation group:

[0074]

[0075] This gives the general formula for calculating the power generated by each generator:

[0076]

[0077] in, represents the power generation of the i-th generator; λ is a consistency variable. In the specific application of generator economic dispatch, the consistency variable is replaced by the incremental cost of each generator to calculate its own power generation.

[0078] In one embodiment, the present invention designs a fixed-time consistency algorithm. Each generator calculates the incremental cost of updating itself through the fixed-time consistency algorithm. The calculation formula of the fixed-time consistency algorithm is expressed as:

[0079]

[0080] Among them, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), represents the time when the i-th generator meets the dynamic event triggering condition for the kth time, λ i (t) indicates when The incremental cost of the i-th generator when ; sig(·) represents |·|·sign(·), express express where sign(·) is the sign function, a ij represents the communication weight between the i-th generator and the j-th generator, Indicates the i-th generator at the time of dynamic event triggering The incremental cost of It represents the jth generator at the time of dynamic event triggering The incremental cost when N i represents the set of adjacent generators of the i-th generator.

[0081] Specifically, before the generator uses the fixed-time consistency algorithm to update the incremental cost, the generator needs to determine whether it meets the dynamic event triggering conditions. Only when the conditions are met can the fixed-time consistency algorithm be started to update its own incremental cost. The dynamic event triggering conditions designed in this embodiment are expressed as follows:

[0082]

[0083]

[0084]

[0085] Among them, F(e i (t),y i (t),η i (t)) represents the dynamic event triggering condition of the i-th generator, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent the gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), e i (t) represents the number of generators i The error between the incremental cost at the moment and the incremental cost at the last triggering moment, y i (t) represents the number of generators i The interaction result of incremental cost with all neighbor nodes at the moment, η i (t) represents the number of generators i The dynamic parameters at the moment, θ i represents a constant greater than 0 set for the i-th generator, and ρ represents a constant greater than 0; Indicates that the i-th generator is The interaction results of incremental cost with all neighbor nodes at any moment.

[0086] The i-th generator meets the dynamic event triggering condition F(e i (t),y i (t),η i When (t))>0, the generator uses the fixed time consistency algorithm to update the cost increment.

[0087] In this embodiment, by proving that the Lyapunov function The derivative of satisfies this formula: This allows the incremental cost to converge within a fixed time and reach the optimal incremental cost. represents the derivative of the Lyapunov function of the system at time t, λ(t) represents the vector of incremental costs of all generators at time t, λ T(t) represents the transpose of the vector that constitutes the incremental cost of all generators at time t, x(t) is equivalent to λ(t), a and b represent constants and are both positive numbers, p represents a constant in the interval (0, 1), and q represents a constant in the interval (1, ∞).

[0088] In one embodiment, after calculating and updating the incremental cost of the generators and outputting the generated power at time t, it is necessary to determine whether the generated power of all generators is within the power limit. If so, economic dispatch is completed; if not, auxiliary variables need to be introduced to update the incremental costs of all generators.

[0089] Specifically, an auxiliary variable is introduced to recalculate and update its own incremental cost, which is expressed as:

[0090]

[0091]

[0092]

[0093] in, Indicates when When , the incremental cost of updating the i-th generator after introducing the auxiliary variable; λ i (t) indicates when When , the incremental cost of the i-th generator; α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), Indicates that the i-th generator is Auxiliary variables at time ψ i (t) represents the number of generators i Auxiliary variables at time Represents the auxiliary variable of the i-th generator when it is triggered for the kth time a ij represents the communication weight between the i-th generator and the j-th generator, Represents the auxiliary variable of the i-th generator when it is triggered for the kth time

[0094] Specifically, when introducing auxiliary variables for updating, the operations at the initial moment are slightly different from those at subsequent moments. At the initial moment t0=0, each generator calculates the generated power based on its own incremental cost and its own cost parameter; it is determined whether the generated power of all generators is within the power limit. If so, economic dispatch is completed; if not, the unsatisfied generators are placed in the power unsatisfied set, and the satisfied generators are placed in the power satisfied set. At the same time, the following operations are performed:

[0095]

[0096] If the i∈Ω p If the generated power of the ith generator is greater than the upper limit of the power limit range, the generated power of the ith generator is set to the upper limit of the power limit range; if the generated power of the ith generator is less than the lower limit of the power limit range, the generated power of the ith generator is set to the lower limit of the power limit range; if the generated power of the ith generator is within the power limit range, the generated power of the ith generator remains unchanged.

[0097] Then, at the initial moment, different auxiliary variables are introduced according to the different sets in which the generators are located to update the incremental cost of each generator. The auxiliary variables at the initial moment are expressed as:

[0098] Among them, Ω p represents the set of generators whose power generation at the initial moment does not meet the power limit (power not meeting the set), represents the auxiliary variable of the i-th generator at the initial moment ψ i (t) represents the auxiliary variable of the i-th generator at the initial moment λ i (0) represents the incremental cost of the i-th generator at the initial moment, α i , β i represents the cost parameter of the i-th generator, P i Represents the power generated by the i-th generator.

[0099] In one embodiment, the communication topology of all generators in the smart grid is constructed and represented by an undirected graph. in is the node set of the undirected graph G, N is the total number of nodes; ε is the edge set of the undirected graph G, and e ij =(v i ,v j ) represents node v i and node v jThe edge, that is, node v i and node v j They are adjacent nodes and can exchange information with each other; Is the adjacency matrix of the undirected graph G, if (v i ,v j )∈ε, then a ij =1, otherwise a ij =0. Represents node v i The set of neighbor nodes.

[0100] Specifically, in this embodiment, five generators are used to construct a smart grid for illustration. The communication topology of these five generators is as follows: Figure 2 The undirected graph shown is represented by Table 1, and the cost parameters of each generator are shown in Table 1:

[0101] Table 1 Cost parameters

[0102]

[0103] Table 2 Comparison of trigger times between static event triggering and dynamic event triggering

[0104]

[0105] The effectiveness of the present invention is verified through simulation experiments. Figure 3 and Figure 4 It can be seen that when the power of the generators does not exceed their respective power limits, the optimal incremental cost after final convergence is λ = 19.92, and the optimal power generation power is P1 * =97.40,P2 * =114.66,P3 * =82.81,P4 * =96.96,P5 * =109.13. Figure 6 The figure shows the change in incremental cost when the generator power exceeds the limit. At approximately t = 0.6s, the output power of generators 2 and 3 exceeds the power limit. Under the control of the algorithm, the output power of all generators is adjusted. As a result, the convergence trend of the incremental cost in the figure changes, and then gradually converges to a consistent value under the control of the algorithm. The final converged incremental cost is λ = 20.06.

[0106] Figure 5 The triggering moments during the entire convergence process are shown. Each point in the figure represents a triggering moment of the generator. Table 2 shows the comparison of the triggering times of dynamic event triggering and static event triggering. Dynamic event triggering further reduces the triggering times by about 64% compared to static event triggering, which shows the advantages of dynamic event triggering.

[0107] In the present invention, unless otherwise clearly stipulated and limited, the terms "installation", "setting", "connection", "fixation", "rotation" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0108] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart grid economic dispatch method based on fixed-time dynamic event triggering, characterized in that: The following steps are involved: S1. Mathematically model the economic dispatch problem in smart grids; With the goal of minimizing the total power generation cost of the smart grid and the supply and demand balance of the smart grid and the output power limit of the generator as constraints, a mathematical model of the economic dispatch problem of the smart grid is established, which can be expressed as follows: P i min <P i <P i max Among them, P i represents the output power of the i-th generator, P i min represents the minimum output power of the i-th generator, P i max represents the maximum output power of the i-th generator, P D represents the total power required by the smart grid, This is the supply and demand balance constraint of the smart grid, N represents the total number of generators, C i (P i ) represents the cost function of the i-th generator, and the specific formula is: C i (P i )=a i P i 2 +b i P i +g i Among them, α i , β i and γ i is the cost parameter of the i-th generator; S2. Design a fixed-time consistency algorithm and dynamic event triggering conditions; Dynamic event triggering conditions are expressed as: Among them, F(e i (t),y i (t),η i (t)) represents the dynamic event triggering condition of the i-th generator, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent the gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), e i (t) represents the number of generators i The error between the incremental cost at the moment and the incremental cost at the last triggering moment, y i (t) represents the number of generators i The interaction result of incremental cost with all neighbor nodes at the moment, η i (t) represents the number of generators i The dynamic parameters at the moment, θ i represents a constant greater than 0 set for the i-th generator, and ρ represents a constant greater than 0; The i-th generator meets the dynamic event triggering condition F(e i (t),y i (t),η i When (t))>0, the generator uses the fixed-time consistency algorithm to update the cost increment; Each generator uses a fixed-time consistency algorithm to update its own incremental cost. The fixed-time consistency algorithm is expressed as: Among them, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), represents the time when the i-th generator meets the dynamic event triggering condition for the kth time, λ i (t) means when The incremental cost of the i-th generator when ; sig(·) represents |·|·sign(·), express express where sign(·) is the sign function; a ij represents the communication weight between the i-th generator and the j-th generator, Indicates the i-th generator at the time of dynamic event triggering The incremental cost of It represents the jth generator at the time of dynamic event triggering The incremental cost when N i represents the set of adjacent generators of the i-th generator; S3. At the initial time t0, each generator sends its own incremental cost to the adjacent generators through the communication network and sets the trigger time to S4. Assume that the last event triggering time of the i-th generator is k represents the total number of dynamic event triggers that have occurred in the generator; S5. Determine at the current moment Does the i-th generator meet the dynamic event triggering conditions? If not, it will not update its own incremental cost; if it does, then The i-th generator uses a fixed-time consistency algorithm to update its own incremental cost and sends its updated incremental cost to adjacent generators through the communication network. The adjacent generators record the updated incremental cost of the i-th generator. S6. All generators calculate their generated power based on their own incremental costs and cost parameters; S7. Determine whether the power generation of all generators is within the power limit range. If so, complete the economic dispatch; if not, introduce auxiliary variables to update the incremental costs of all generators again, and return to step S6.

2. The smart grid economic dispatch method based on fixed-time dynamic event triggering according to claim 1 is characterized in that: Among them, λ i (t) means when The incremental cost of the i-th generator when λ j (t) means when The incremental cost of the j-th generator when ; Indicates that the i-th generator is The interaction result of incremental cost with all neighbor nodes at the moment, sig i (·) means |·| i ·sign(·)., where sign(·) is the sign function.

3. The smart grid economic dispatch method based on fixed-time dynamic event triggering according to claim 1, characterized in that: In step S6, when the output power of the i-th generator is not within the power limit range, the auxiliary variable is introduced to update the incremental cost again, which is expressed as: in, Indicates when When , the incremental cost of updating the i-th generator after introducing the auxiliary variable; λ i (t) indicates when The incremental cost of the i-th generator when ; The i-th generator is Auxiliary variables at time ψ i (t) represents the number of generators i Auxiliary variables at time 4. The smart grid economic dispatch method based on fixed-time dynamic event triggering according to claim 3 is characterized in that: The calculation formula of auxiliary variables is: Among them, α i represents the cost parameter of the i-th generator, k1, k2 and k3 represent gain coefficients and are all positive numbers, μ1 represents a constant in the interval (0,1), μ2 represents a constant in the interval (1,∞), Represents the auxiliary variable of the i-th generator when it is triggered for the kth time a ij represents the communication weight between the i-th generator and the j-th generator, Represents the auxiliary variable of the i-th generator when it is triggered for the kth time 5. The smart grid economic dispatch method based on fixed-time dynamic event triggering according to claim 3 is characterized in that: At the initial time t0, each generator calculates its generated power based on its own incremental cost and its own cost parameters; then it is determined whether the generated power of all generators is within the power limit. If so, economic dispatch is completed; if not, the unsatisfied generators are placed in the power unsatisfied set, and the satisfied generators are placed in the power satisfied set, and the following operations are performed at the same time: If the i∈Ω p If the power generation power of the ith generator is greater than the upper limit of the power limit range, the power generation power of the ith generator is set to the upper limit of the power limit range; if the power generation power of the ith generator is less than the lower limit of the power limit range, the power generation power of the ith generator is set to the lower limit of the power limit range; where Ω p Indicates that the power does not satisfy the set; At the initial moment, different auxiliary variables are introduced to update the incremental costs of all generators, and then the process returns to step S6. The auxiliary variables at the initial moment are expressed as: Among them, Ω p Indicates that the power at the initial moment does not satisfy the set, represents the auxiliary variable of the i-th generator at the initial moment ψ i (t) represents the auxiliary variable of the i-th generator at the initial moment λ i (0) represents the incremental cost of the i-th generator at the initial moment, α i , β i represents the cost parameter of the i-th generator, P i represents the power generated by the i-th generator.

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