Distributed economic dispatch method for smart grid based on dynamic event triggering

Abstract

The present invention belongs to the field of smart grid energy-saving and environmental protection economic dispatch, and specifically relates to a distributed economic dispatch method for smart grid based on dynamic event triggering. The method comprises: first, designing a finite time consistency economic dispatch algorithm based on multi-agent consistency, and designing dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm. Secondly, when the dynamic event triggering conditions are met, the finite time consistency economic dispatch algorithm is used to update the incremental cost of the generator and calculate the output power. Finally, an auxiliary variable is introduced, and the finite time consistency economic dispatch algorithm is used to update the auxiliary variable, and the incremental cost is updated with the auxiliary variable, and the output power is calculated so that the output power meets the generator power output power limit. The present invention can perform economic dispatch on the smart grid within a limited time and save communication resources.

Description

Technical Field

[0001] The present invention belongs to the field of smart grid energy conservation and environmental protection economic dispatch, and in particular to a smart grid distributed economic dispatch method based on dynamic event triggering. Background Art

[0002] Smart grid is the intelligence of power grid, also known as "grid 2.0". It is built on the basis of integrated, high-speed two-way communication network. Through the application of advanced control methods and advanced decision support system technology, it realizes the goals of reliable, safe, economical, efficient, environmentally friendly and safe use of power grid. The economic dispatch problem is one of the key optimization problems in the field of smart grid. Its purpose is to obtain the optimal incremental cost and optimal output power of the generator under the constraints of generator output power limit and supply and demand balance. In order to solve the economic dispatch problem, smart grid dispatch initially adopted centralized economic dispatch algorithms, such as genetic algorithm, particle swarm optimization, etc. These centralized economic dispatch algorithms require a powerful central controller, which leads to high cost and easy single point failure of centralized economic dispatch algorithms.

[0003] In order to overcome the shortcomings of the centralized economic dispatch algorithm, a distributed economic dispatch algorithm is used to perform economic dispatch of the smart grid. The distributed economic dispatch algorithm exchanges information between neighbors and can complete the economic dispatch of the smart grid without a central controller. It has good robustness and information confidentiality advantages.

[0004] Most distributed economic dispatch algorithms are based on time-triggered mechanisms, which leads to a large amount of information exchange in the system. In order to save resources and reduce the frequency of controller updates, event-triggered mechanisms have been widely studied in economic dispatch. Compared with the traditional time-triggered mechanism, the trigger under the event-triggered mechanism depends on a trigger condition related to the system measurement quantity, rather than a fixed sampling time. Only when the trigger condition is met, the current system state is sampled and the controller is updated. The control quantity at other times is the same as at the last trigger moment. The dynamic event trigger mechanism is different from the static event trigger. The dynamic event trigger mechanism depends not only on the state of the system, but also on an additional internal dynamic variable. This dynamic variable is time-varying and the derivative has adjustable parameters, so the trigger mechanism will change dynamically over time, thereby reducing the number of triggers. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a distributed economic dispatch method for smart grid based on dynamic event triggering, which can perform economic dispatch of smart grid within a limited time and save communication resources.

[0006] The present invention is implemented by the following scheme: a distributed economic dispatching method of a smart grid based on dynamic event triggering, comprising the following steps:

[0007] Step S1: Establish a smart grid economic dispatch problem model, including the smart grid power generation cost objective function, generator supply and demand balance constraints and generator output power limit constraints, and set the initial values ​​of relevant parameters;

[0008] The step S1 specifically includes the following steps:

[0009] The economic dispatch problem model of smart grid is:

[0010] C i (p i (t)) = α i p i 2 (t)+β i p i (t)+γ i

[0011]

[0012]

[0013]

[0014] Where i = 1, 2, ..., n; n is the number of generators in the smart grid; C i (p i (t)) is the power generation cost function of the i-th generator; p i (t) is the output power of the i-th generator; α i >0,β i >0 and γ i >0 is the power generation cost coefficient; is the objective function; Generator supply and demand balance constraints and is the generator output power limit constraint, p D is the total power demand, is the minimum output power limit of the i-th generator, is the maximum output power limit of the i-th generator; set the initial values ​​of the required parameters, and the initial output power of the generator is p i (0), the initial value of the incremental cost is λ i (0) = 2α i p i (0)+β i and the initial value of the internal dynamic variable χ i (0)>0;

[0015] Step S2: design a finite-time consistent economic dispatch algorithm through multi-agent consistency, and design dynamic event triggering conditions based on the finite-time consistent economic dispatch algorithm;

[0016] The step S2 specifically includes the following steps:

[0017] Step S21: Design a finite-time consensus economic dispatch algorithm through multi-agent consensus, specifically:

[0018]

[0019] Where j = 1, 2, ..., n; λ i (t) and λ j (t) are the incremental costs of the i-th and j-th generators at time t, is the derivative of the incremental cost of the i-th generator at time t; is the kth triggering time of the i-th generator; κ is the convergence coefficient; a ij is the adjacency weight of generator i and generator j; μ∈(0,1) is the ratio of two positive odd numbers;

[0020] Step S22: Design dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm, specifically:

[0021]

[0022]

[0023]

[0024] in, is the measurement error of the i-th generator; is the incremental cost error of the i-th generator; is the average value of the incremental cost of all generators; h i (t) is the trigger function of the i-th generator; θ>0 and σ∈(0,1) are design parameters; is the derivative of the internal dynamic variables of the ith generator at time t, χ i (t) is the internal dynamic variable of the i-th generator; δ∈(0,1), γ1>0 and γ2>0 are design parameters; inf is the lower bound;

[0025] Step S3: Use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost, and determine whether the incremental cost is consistent; if the incremental cost is consistent, introduce auxiliary variables; otherwise, continue to use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost;

[0026] Step S3 is specifically as follows:

[0027] When the i-th generator h i (t)≥χ i (t), the dynamic event triggering condition is met, let The incremental cost of the generator is updated using the finite time consistency economic dispatch algorithm; the incremental cost is used to calculate the output power of the generator:

[0028]

[0029] The condition for the incremental cost to reach consistent convergence is:

[0030]

[0031] Among them, Δ1>0 is the convergence coefficient;

[0032] Step S4: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables, otherwise output the incremental cost and output power of the generator; use the finite-time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator based on the incremental cost; determine whether the output power of each generator exceeds the limit value: if the generator output power is greater than its maximum value, set the generator output power to the maximum value; if the generator output power is less than its minimum value, set the generator output power to the minimum value, otherwise the generator output power remains unchanged; then determine whether the auxiliary variables are consistent. If the auxiliary variables are consistent, output the incremental cost and output power of the generator, otherwise use the finite-time consistency economic dispatch algorithm to update the auxiliary variables again.

[0033] Furthermore, the step S4 specifically includes the following steps:

[0034] Step S41: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables. Otherwise, output the incremental cost and output power of the generator. The initial value of the auxiliary variables is specifically set as follows:

[0035] The initial values ​​of the two auxiliary variables are

[0036] Step S42: Use the finite time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator according to the incremental cost, specifically:

[0037] The auxiliary variables are updated using the finite time consistency economic dispatch algorithm as follows:

[0038]

[0039]

[0040] in, and Represent auxiliary variables and ψ i (t) for the derivative at time t; the incremental cost of auxiliary variable update is:

[0041]

[0042] in, The incremental cost after the incremental cost reaches consistency convergence; the incremental cost calculates the output power of the generator as:

[0043]

[0044] Step S43: Determine whether the output power of each generator exceeds the limit value: if the output power of the generator is greater than its maximum value, set the output power of the generator to the maximum value; if the output power of the generator is less than its minimum value, set the output power of the generator to the minimum value; otherwise, the output power of the generator remains unchanged, specifically:

[0045]

[0046] Step S44: Determine whether the auxiliary variables have reached consistency. If the auxiliary variables have reached consistency convergence, the incremental cost and output power of the generator are output. Otherwise, return to step S42 and use the finite time consistency economic dispatch algorithm to update the auxiliary variables again. The conditions for the auxiliary variables to reach consistency convergence are specifically:

[0047] The conditions for the auxiliary variables to achieve consistent convergence are:

[0048]

[0049]

[0050] in, and Auxiliary variables and ψ i The average value of (t), Δ2>0 and Δ3>0 are the convergence coefficients.

[0051] The present invention provides a distributed economic dispatching method for a smart grid based on dynamic event triggering. Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The distributed economic dispatch method proposed in the present invention is completely distributed and can quickly find the optimal solution within a limited time. Compared with the centralized economic dispatch method, the proposed distributed economic dispatch method is more flexible and scalable.

[0053] 2. Compared with ordinary static event trigger conditions, the dynamic event trigger conditions proposed in this article introduce an additional internal dynamic variable to adjust the trigger mechanism over time. Combined with the finite time economic scheduling algorithm, when solving the problem of minimizing the total power generation cost of the generator, it can save communication resources and avoid energy loss.

[0054] 3. The generator output power solved by the distributed economic dispatch method proposed in the present invention satisfies both the generator supply and demand balance constraint and the generator output power limit constraint. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The IEEE 30-node standard power network of the present invention;

[0056] Figure 2 The communication topology diagram of each generator of the present invention;

[0057] Figure 3 is the incremental cost λ of each generator of the present invention i (t) Changes over time;

[0058] Figure 4 is the output power p of each generator of the present invention i (t) time-varying graph;

[0059] Figure 5 The total output power of the generator of the present invention is and the total power demand P D Time-varying graphs;

[0060] Figure 6 It is a triggering time diagram of each generator dynamic event of the present invention;

[0061] Figure 7 It is a triggering time diagram of each generator static event of the present invention;

[0062] Figure 8 is the auxiliary variable of the present invention Plot of changes over time;

[0063] Fig. 9 is the auxiliary variable ψ of the present inventioni (t) Changes over time;

[0064] Fig.10 The present invention is a flow chart of the distributed economic dispatching method of smart grid based on dynamic event triggering. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0066] Embodiment 1:

[0067] The process of the smart grid distributed economic dispatching method based on dynamic event triggering provided in this embodiment is as follows: Fig.10 As shown, the specific steps include:

[0068] Step S1: Establish a smart grid economic dispatch problem model, including the smart grid power generation cost objective function, generator supply and demand balance constraints and generator output power limit constraints, and set the initial values ​​of relevant parameters;

[0069] The step S1 specifically includes the following steps:

[0070] The economic dispatch problem model of smart grid is:

[0071] C i (p i (t)) = α i p i 2 (t)+β i p i (t)+γ i

[0072]

[0073]

[0074]

[0075] Where i = 1, 2, ..., n; n is the number of generators in the smart grid; C i (p i (t)) is the power generation cost function of the i-th generator; p i (t) is the output power of the i-th generator; α i >0,β i >0 and γ i >0 is the power generation cost coefficient; is the objective function; Generator supply and demand balance constraints and is the generator output power limit constraint, p D is the total power demand, is the minimum output power limit of the i-th generator, is the maximum output power limit of the i-th generator; set the initial values ​​of the required parameters, and the initial output power of the generator is p i (0), the initial value of the incremental cost is λ i (0) = 2α i p i (0)+β i and the initial value of the internal dynamic variable χ i (0)>0;

[0076] Step S2: design a finite-time consistent economic dispatch algorithm through multi-agent consistency, and design dynamic event triggering conditions based on the finite-time consistent economic dispatch algorithm;

[0077] The step S2 specifically includes the following steps:

[0078] Step S21: Design a finite-time consensus economic dispatch algorithm through multi-agent consensus, specifically:

[0079]

[0080] Where j = 1, 2, ..., n; λ i (t) and λ j (t) are the incremental costs of the i-th and j-th generators at time t, is the derivative of the incremental cost of the i-th generator at time t; is the kth triggering time of the i-th generator; κ is the convergence coefficient; a ij is the adjacency weight of generator i and generator j; μ∈(0,1) is the ratio of two positive odd numbers;

[0081] Step S22: Design dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm, specifically:

[0082]

[0083]

[0084]

[0085] in, is the measurement error of the i-th generator; is the incremental cost error of the i-th generator; is the average value of the incremental cost of all generators; h i (t) is the trigger function of the i-th generator; θ>0 and σ∈(0,1) are design parameters; is the derivative of the internal dynamic variables of the ith generator at time t, χ i (t) is the internal dynamic variable of the i-th generator; δ∈(0,1), γ1>0 and γ2>0 are design parameters; inf is the lower bound;

[0086] Step S3: Use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost, and determine whether the incremental cost is consistent; if the incremental cost is consistent, introduce auxiliary variables; otherwise, continue to use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost;

[0087] Step S3 is specifically as follows:

[0088] When the i-th generator h i (t)≥χ i (t), the dynamic event triggering condition is met, let The incremental cost of the generator is updated using the finite time consistency economic dispatch algorithm; the incremental cost is used to calculate the output power of the generator:

[0089]

[0090] The condition for the incremental cost to reach consistent convergence is:

[0091]

[0092] Among them, Δ1>0 is the convergence coefficient;

[0093] Step S4: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables, otherwise output the incremental cost and output power of the generator; use the finite-time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator based on the incremental cost; determine whether the output power of each generator exceeds the limit value: if the generator output power is greater than its maximum value, set the generator output power to the maximum value; if the generator output power is less than its minimum value, set the generator output power to the minimum value, otherwise the generator output power remains unchanged; then determine whether the auxiliary variables are consistent. If the auxiliary variables are consistent, output the incremental cost and output power of the generator, otherwise use the finite-time consistency economic dispatch algorithm to update the auxiliary variables again.

[0094] In this embodiment, step S4 specifically includes the following steps:

[0095] Step S41: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables. Otherwise, output the incremental cost and output power of the generator. The initial value of the auxiliary variables is specifically set as follows:

[0096] The initial values ​​of the two auxiliary variables are

[0097] Step S42: Use the finite time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator according to the incremental cost, specifically:

[0098] The auxiliary variables are updated using the finite time consistency economic dispatch algorithm as follows:

[0099]

[0100]

[0101] in, and Represent auxiliary variables and ψ i (t) for the derivative at time t; the incremental cost of auxiliary variable update is:

[0102]

[0103] in, The incremental cost after the incremental cost reaches consistency convergence; the incremental cost calculates the output power of the generator as:

[0104]

[0105] Step S43: Determine whether the output power of each generator exceeds the limit value: if the output power of the generator is greater than its maximum value, set the output power of the generator to the maximum value; if the output power of the generator is less than its minimum value, set the output power of the generator to the minimum value; otherwise, the output power of the generator remains unchanged, specifically:

[0106]

[0107] Step S44: Determine whether the auxiliary variables have reached consistency. If the auxiliary variables have reached consistency convergence, the incremental cost and output power of the generator are output. Otherwise, return to step S42 and use the finite time consistency economic dispatch algorithm to update the auxiliary variables again. The conditions for the auxiliary variables to reach consistency convergence are specifically:

[0108] The conditions for the auxiliary variables to achieve consistent convergence are:

[0109]

[0110]

[0111] in, and Auxiliary variables and ψ i The average value of (t), Δ2>0 and Δ3>0 are the convergence coefficients.

[0112] Embodiment 2:

[0113] Example 2 uses the proposed smart grid distributed economic dispatch method based on dynamic event triggering to perform numerical simulation on the IEEE 30-node standard power network, with a total of 6 generators. Figure 1 It is an IEEE 30-node standard power network, and its communication topology is as follows Figure 2 shown.

[0114] The objective function of power generation cost is C i (p i (t)) = α i p i 2 (t)+β i p i (t)+γ i , Table 1 shows the power generation cost coefficient α i , β i and γ i Table 2 shows the output power limit of the generator; input the initial value and system related parameters, the total power demand p D =120MW, the initial values ​​of the generator output power are p1(0)=10MW, p2(0)=15MW, p3(0)=20MW, p4(0)=40MW, p5(0)=45MW and p6(0)=50MW, calculated by the formula λ i (0) = 2α i p i +β i , the initial values ​​of incremental cost are λ1(0)=3.60$ / MW, λ2(0)=5.60$ / MW, λ3(0)=4.82$ / MW, λ4(0)=9.20$ / MW, λ5(0)=8.40$ / MW and λ6(0)=7.40$ / MW, and the initial values ​​of auxiliary variables are ψ1(0)=6.25,ψ2(0)=7.14,ψ3(0)=8.62,ψ4(0)=7.69,ψ5(0)=8.33 and ψ6(0)=0,the initial values ​​of internal dynamic variables are χ1(0)=7,χ2(0)=7,χ3(0)=7,χ4(0)=7,χ5(0)=7 and χ6(0)=7;convergence coefficient κ=10,μ=5 / 7,design parameters θ=1,σ=0.009,δ=0.2,γ1=8.5 and γ2=8,the adjacency matrix is ​​obtained according to the communication topology graph

[0115] Table 1

[0116] Generator <![CDATA[α i ]]> <![CDATA[β i ]]> <![CDATA[γ i ]]> 1 0.08 2 51 2 0.07 3.5 31 3 0.058 2.5 78 4 0.065 4 42 5 0.06 3 43 6 0.038 3.6 56

[0117] Table 2

[0118]

[0119] Substituting the above parameters into the finite time consistency economic dispatch algorithm, we can see that

[0120]

[0121]

[0122]

[0123] When h i (t)≥χ i (t), the dynamic event triggering condition is met make Update the incremental cost λ of the i-th generator using the finite time consistency economic dispatch algorithm i (t), with incremental cost λ i (t) Calculate the output power of the generator Then, auxiliary variables are introduced, the auxiliary variables are calculated through the consistency algorithm, and the incremental cost is updated with the auxiliary variables, and the output power of each generator is recalculated with the updated auxiliary variables. Determine whether the output power of each generator meets the generator output power limit constraint. If the generator output power meets the generator output power limit constraint and does not exceed the limit value, the generator output power remains unchanged. Otherwise, the generator output power is the maximum or minimum value. The auxiliary variables are calculated again through the consistency algorithm, and the incremental cost is updated with the auxiliary variables until the auxiliary variables reach consistency convergence, and the incremental cost and generator output power are output.

[0124] Finally, the effectiveness of the present invention is verified through numerical simulation based on the given data. Figure 3 is the incremental cost λ of each generator of the present invention i(t) Changes over time; Figure 4 is the output power p of each generator of the present invention i (t) time-varying graph; Figure 5 The total output power of the generator of the present invention is and the total power demand P D Time-varying graphs; Figure 6 It is a triggering time diagram of each generator dynamic event of the present invention; Figure 7 It is a triggering time diagram of each generator static event of the present invention; Figure 8 is the auxiliary variable of the present invention Plot of changes over time;

[0125] Fig. 9 is the auxiliary variable ψ of the present invention i (t) changes over time. Figure 3 It can be seen that at the convergence time t = 0.27s, the optimal value of incremental cost λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = 6.67$ / MW is obtained; Figure 4 It can be seen that the optimal values ​​of generator output power are p1 = 29.69MW, p2 = 22.67MW, p3 = 35.99MW, p4 = 20.57MW, p5 = 30.62MW and p6 = 40.46MW. Because p6 = 40.46MW exceeds the output power limit of generator 6, after the first second, the auxiliary variable is introduced to adjust the generator output power, and p1 = 32.62MW, p2 = 26.57MW, p3 = 40.MW, p4 = 24.76MW, p5 = 36.05MW and p6 = 20.00MW are obtained, which meets the generator output power limit constraint; Figure 3 The optimal value of the incremental cost also changed from 6.67$ / MW to 7.22$ / MW. Figure 5 Shows the total generator output power Total power demand p D =180MW, satisfying the generator supply and demand balance constraint. Figure 6 The triggering time of each generator under the dynamic event triggering condition is shown. Figure 7 The triggering time of each generator under the static event triggering condition is shown. Table 3 shows the statistical dynamic event triggering times of each generator and the static event triggering times of each generator. It can be seen from the table that the dynamic event triggering times of each generator are significantly less than the static event triggering times, which shows that when each generator adopts the dynamic event triggering conditions for communication, it can effectively save communication resources. Figure 8 Shows auxiliary variables As time goes by, consistency converges. Fig. 9 The auxiliary variable ψ is shown i (t) changes with time and reaches a consistent convergence.

[0126] Table 3

[0127]

[0128] It should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed economic dispatching method for smart grid based on dynamic event triggering, characterized in that: The following steps are involved: Step S1: Establish a smart grid economic dispatch problem model, including the smart grid power generation cost objective function, generator supply and demand balance constraints and generator output power limit constraints, and set the initial values ​​of relevant parameters; The step S1 specifically includes the following steps: The economic dispatch problem model of smart grid is: C i (p i (t))=a i p i 2 (t)+β i p i (t)+γ i Where i = 1, 2, ..., n; n is the number of generators in the smart grid; C i (p i (t)) is the power generation cost function of the i-th generator; p i (t) is the output power of the i-th generator; α i >0,β i >0 and γ i >0 is the power generation cost coefficient; is the objective function; Generator supply and demand balance constraints and is the generator output power limit constraint, p D is the total power demand, is the minimum output power limit of the i-th generator, is the maximum output power limit of the i-th generator; set the initial values ​​of the required parameters, and the initial output power of the generator is p i (0), the initial value of the incremental cost is λ i (0) = 2α i p i (0)+β i and the initial value of the internal dynamic variable χ i (0)>0; Step S2: design a finite-time consistent economic dispatch algorithm through multi-agent consistency, and design dynamic event triggering conditions based on the finite-time consistent economic dispatch algorithm; The step S2 specifically includes the following steps: Step S21: Design a finite-time consensus economic dispatch algorithm through multi-agent consensus, specifically: Where, j = 1, 2, ..., n; λ i (t) and λ j (t) are the incremental costs of the i-th and j-th generators at time t, is the derivative of the incremental cost of the i-th generator at time t; is the kth triggering time of the i-th generator; κ is the convergence coefficient; a ij is the adjacency weight of generator i and generator j; μ∈(0,1) is the ratio of two positive odd numbers; Step S22: Design dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm, specifically: in, is the measurement error of the i-th generator; is the incremental cost error of the i-th generator; is the average value of the incremental cost of all generators; h i (t) is the trigger function of the i-th generator; θ>0 and σ∈(0,1) are design parameters; is the derivative of the internal dynamic variables of the ith generator at time t, χ i (t) is the internal dynamic variable of the i-th generator; δ∈(0,1), γ1>0 and γ2>0 are design parameters; inf is the lower bound; Step S3: Use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost, and determine whether the incremental cost is consistent; if the incremental cost is consistent, introduce auxiliary variables; otherwise, continue to use the dynamic event triggering condition and the finite time consistency economic dispatch algorithm to update the incremental cost of the generator, and obtain the output power of the generator based on the incremental cost; Step S3 is specifically as follows: When the i-th generator h i (t)≥χ i (t), the dynamic event triggering condition is met, let The incremental cost of the generator is updated using the finite time consistency economic dispatch algorithm; the incremental cost is used to calculate the output power of the generator: The condition for the incremental cost to reach consistent convergence is: Among them, Δ1>0 is the convergence coefficient; Step S4: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables. Otherwise, output the incremental cost and output power of the generator. Use the finite-time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator based on the incremental cost. Determine whether the output power of each generator exceeds the limit value: if the generator output power is greater than its maximum value, set the generator output power to the maximum value; if the generator output power is less than its minimum value, set the generator output power to the minimum value, otherwise the generator output power remains unchanged. Then determine whether the auxiliary variables are consistent. If the auxiliary variables are consistent, output the incremental cost and output power of the generator. Otherwise, use the finite-time consistency economic dispatch algorithm to update the auxiliary variables again. The step S4 specifically comprises the following steps: Step S41: Determine whether to introduce auxiliary variables. If so, set the initial value of the auxiliary variables. Otherwise, output the incremental cost and output power of the generator. The initial value of the auxiliary variables is specifically set as follows: The initial values ​​of the two auxiliary variables are Step S42: Use the finite time consistency economic dispatch algorithm to update the auxiliary variables, use the auxiliary variables to update the incremental cost, and obtain the output power of the generator according to the incremental cost, specifically: The auxiliary variables are updated using the finite time consistency economic dispatch algorithm as follows: in, and Represent auxiliary variables and ψ i (t) for the derivative at time t; the incremental cost of auxiliary variable update is: in, The incremental cost after the incremental cost reaches consistency convergence; the incremental cost calculates the output power of the generator as: Step S43: Determine whether the output power of each generator exceeds the limit value: if the output power of the generator is greater than its maximum value, set the output power of the generator to the maximum value; if the output power of the generator is less than its minimum value, set the output power of the generator to the minimum value; otherwise, the output power of the generator remains unchanged, specifically: Step S44: Determine whether the auxiliary variables have reached consistency. If the auxiliary variables have reached consistency convergence, the incremental cost and output power of the generator are output. Otherwise, return to step S42 and use the finite time consistency economic dispatch algorithm to update the auxiliary variables again. The conditions for the auxiliary variables to reach consistency convergence are specifically: The conditions for the auxiliary variables to achieve consistent convergence are: in, and Auxiliary variables and ψ i The average value of (t), Δ2>0 and Δ3>0 are the convergence coefficients.

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