Event-triggered consensus protocol based economic dispatch method for microgrid under noise condition

Abstract

The present application belongs to the field of smart grid energy saving and environmental protection economic dispatching, and particularly relates to a micro-grid economic dispatching method based on event-triggered consistency protocol under noise condition. First, an incremental cost consistency algorithm is designed through multi-agent consistency protocol, and a static event-triggered function is designed. Second, when the static event-triggered function is satisfied, the incremental cost consistency algorithm is used to update the communication incremental cost, and the generator output power is calculated according to the communication incremental cost. Finally, when the communication incremental cost reaches consistency, the optimal value of the incremental cost under the constraint condition of no generator output power is obtained, an auxiliary variable is introduced, the auxiliary variable is updated using the consistency protocol under the condition of noise, and the incremental cost is corrected using the auxiliary variable, and then the optimal solution is output. The present application not only realizes the economic dispatching of the micro-grid and the saving of communication resources, but also considers the communication noise interference, and improves the practicability of the method.

Description

Technical Field

[0001] This invention relates to the field of smart grid energy-saving, environmentally friendly, and economical dispatch, and in particular to a microgrid economical dispatch method based on an event-triggered consensus protocol under noise conditions. Background Technology

[0002] A microgrid is a small-scale power system typically composed of distributed energy sources (such as solar, wind, and fuel cells), energy storage devices, and loads. This system can operate independently of or connected to the traditional power grid, enabling flexible power management. Under normal circumstances, a microgrid can operate in parallel with the main grid, but in the event of a power outage or other emergency, it can automatically switch to islanded mode to ensure the continuity and stability of power supply. The economic dispatch of a microgrid aims to meet generator output power limitations and supply-demand balance constraints, thereby determining the optimal incremental cost and generator output power to improve overall operating efficiency.

[0003] To address the economic dispatch problem, early microgrid dispatching methods primarily employed centralized economic dispatching algorithms, such as genetic algorithms and particle swarm optimization. These centralized algorithms relied on a powerful central controller, and while effective dispatching was possible, their high cost and potential single point of failure limited their application. Therefore, researchers began to shift towards distributed economic dispatching algorithms. These algorithms, through information exchange between neighbors, avoid the need for a central controller, making microgrid dispatching more robust and informationally secure, and effectively addressing potential network failures.

[0004] Static event-triggered control is a specific form of event-triggered control designed to reduce unnecessary communication and computational burdens by triggering control updates when specific conditions are met. Current research largely assumes ideal communication networks, neglecting the impact of noise interference on information transmission. However, noise is ubiquitous in real-world communication environments, and this interference can lead to system instability. Therefore, researching distributed consensus algorithms that handle noisy environments is particularly important to ensure the stability and reliability of microgrids under various conditions. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a microgrid economic dispatch method based on an event-triggered consensus protocol under noisy conditions, which can economically dispatch the microgrid and save communication resources.

[0006] This invention employs the following scheme: a microgrid economic dispatch method based on an event-triggered consensus protocol under noisy conditions, comprising the following steps:

[0007] Step 1: Establish a microgrid economic dispatch model, which includes: the objective function of the economic dispatch problem; generator supply and demand balance constraints and generator output power limitation constraints; and set the initial values ​​of relevant parameters.

[0008] Step 2: Determine the adjacency matrix based on the microgrid communication topology, and construct the Laplace matrix based on the adjacency matrix;

[0009] Step 3: Design an incremental cost consensus algorithm using a multi-agent consensus protocol, and design a static event triggering function;

[0010] Step 4: Update the incremental cost using the incremental cost consistency algorithm; determine if the static event trigger function is greater than 0; if the static event trigger function is greater than 0, update the communication incremental cost; if the static event trigger function is less than or equal to 0, the communication incremental cost remains unchanged, and proceed to Step 5;

[0011] Step 5: Calculate the generator output power based on the incremental communication cost;

[0012] Step Six: Determine whether the incremental communication cost has reached a consensus; if the incremental communication cost has reached a consensus, obtain the optimal value of the incremental cost under the constraint of no generator output power limitation, introduce auxiliary variables, and set the initial value of the auxiliary variables; if the incremental communication cost has not reached a consensus, return to Step Four.

[0013] Step 7: Update the auxiliary variables using the multi-agent consensus protocol, and then update the incremental cost using the auxiliary variables;

[0014] Step 8: Process incremental costs and calculate generator output power;

[0015] Step 9: Determine if the auxiliary variables have reached a consensus; if the auxiliary variables have reached a consensus, output the incremental cost and generator output power; if the auxiliary variables have not reached a consensus, return to Step 7.

[0016] Furthermore, step one specifically includes:

[0017] The microgrid economic dispatch problem model is as follows:

[0018]

[0019]

[0020] p i_min ≤p i (t)≤p i_max

[0021] Where i = 1, 2, ..., n, and n is the number of generators in the microgrid; Let C be the objective function of the economic scheduling problem.i (p i (t) is the power generation cost function of the i-th generator and p i (t) represents the output power of the i-th generator at time t, α i >0, β i >0 and γ i >0 represents the power generation cost coefficient of the i-th generator; For generator supply and demand balance constraints; p i_min ≤p i (t)≤p i_max For generator output power limiting constraints; p d p represents the total power demand. i_min p represents the minimum output power of the i-th generator; i_max Let be the maximum output power of the i-th generator;

[0022] Set the initial values ​​for the relevant parameters:

[0023] Set the initial output power of the i-th generator to p. i (0), and also requires that the following conditions are met. Its corresponding initial incremental cost is λ i (0)=2α i p i (0)+β i The initial value of incremental communication cost is

[0024] Furthermore, step two specifically involves:

[0025] The adjacency matrix A is determined based on the microgrid communication topology, specifically as follows:

[0026]

[0027] Among them, a ij Represents the element in the i-th row and j-th column of adjacency matrix A, where j = 1, 2, ..., n; N i This represents the set of generators that communicate with the i-th generator.

[0028] The Laplacian matrix L is determined based on the adjacency matrix A, specifically as follows:

[0029]

[0030] Among them, l ij Let represent the element in the i-th row and j-th column of the Laplace matrix L.

[0031] Furthermore, step three specifically involves:

[0032] An incremental cost consensus algorithm is designed based on a multi-agent consensus protocol. The incremental cost is then updated based on this algorithm, specifically as follows:

[0033]

[0034] in, Let i be the time when the i-th generator is triggered for the kth time. For the i-th generator at Incremental cost per moment For the j-th generator at Incremental communication cost at any given moment; Let be the derivative of the incremental cost of the i-th generator at time t;

[0035] The static event trigger function is designed as follows:

[0036] f i (t)=|e i (t)|-σ i |z i (t)|-μ

[0037]

[0038] in, Let be the measurement error of the i-th generator at time t; Let f be the incremental cost error of the i-th generator at time t; i (t) is the triggering function of the i-th generator at time t; Let be the design parameters for the i-th generator, where λ max The largest eigenvalue of the Laplacian matrix L is represented by μ; μ > 0 is the dead zone threshold; h is the sampling period. inf is the lower bound.

[0039] Furthermore, step four specifically involves:

[0040] Check if the static event trigger function is greater than 0; if the static event trigger function f i If (t) is greater than 0, update the generator communication incremental cost, let

[0041] If the static event trigger function f i If (t) is less than or equal to 0, then the incremental communication cost remains unchanged, and proceed to step five.

[0042] Furthermore, step five specifically includes:

[0043] Based on communication incremental cost The generator output power is calculated as follows:

[0044]

[0045] Where, p i (t) represents the output power of the i-th generator at time t.

[0046] Furthermore, step six specifically involves:

[0047] Determine whether there is consensus on incremental communication costs; if This leads to a consensus on the incremental communication cost, resulting in the optimal incremental cost under the constraint of no generator output power limitation. Equal to the average of incremental communication costs Where Δ1>0 is the convergence coefficient. This represents the average cost of all incremental communications; auxiliary variables are introduced, namely XX. i (t) and YY i (t), and set the initial value of the auxiliary variable; the specific steps for setting the initial value of the auxiliary variable are as follows:

[0048]

[0049]

[0050] Where the output power of the i-th generator is p i (t) exceeds its generator's maximum output power p i_max , let p i (t)=p i_max If the output power of the i-th generator is p i (t) is lower than its generator's minimum output power p i_min , let p i (t)=p i_min ;

[0051] like If no consensus is reached on incremental costs, return to step four.

[0052] Furthermore, step seven specifically includes:

[0053] Considering noise, the auxiliary variable is updated using a multi-agent consensus protocol, specifically as follows:

[0054]

[0055]

[0056] in, and They represent auxiliary variables XX respectively. i (t) and YY ic(t) represents the derivative of c(t) at time t; c(t) represents the gain function at time t considering noise, specifically: in The time at which the auxiliary variable is introduced is represented by ρ; the noise delay coefficient is represented by ω. ij (t) represents the communication noise between the i-th generator and the j-th generator at time t; the incremental cost is updated using auxiliary variables as follows:

[0057]

[0058] Furthermore, step eight specifically includes:

[0059] The incremental cost of generators is handled as follows:

[0060]

[0061] Where, λ i_min and λ i_max Let λ represent the minimum and maximum incremental costs of the i-th generator, respectively. i_min =2α i p i_min +β i and λ i_max =2α i p i_max +β i ;

[0062] The generator output power is calculated as follows:

[0063]

[0064] Furthermore, step nine specifically includes:

[0065] Determine whether the auxiliary variables are consistent; if and in and Let them be represented as auxiliary variables XX respectively. i (t) and YY i (t) is the average value at time t. If Δ2>0 and Δ3>0 are the convergence coefficients, then the auxiliary variables reach consensus; output incremental cost and generator output power;

[0066] like or If the auxiliary variables do not reach a consensus, return to step seven.

[0067] This invention provides an economic dispatch method for microgrids based on an event-triggered consensus protocol under noisy conditions. Compared with existing technologies, the advantages of this invention are:

[0068] 1. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions proposed in this invention is fully distributed and is more flexible and scalable than the centralized microgrid economic dispatch method.

[0069] 2. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions proposed in this invention takes into account communication noise interference, thereby improving the practicality of the method.

[0070] 3. The generator output power obtained by the distributed economic dispatch method proposed in this invention satisfies both the generator supply and demand balance constraint and the generator output power limit constraint. Attached Figure Description

[0071] Figure 1 This is a flowchart of an economic dispatch method for microgrids based on an event-triggered consensus protocol under noisy conditions, according to the present invention.

[0072] Figure 2 This invention relates to the IEEE 24-node standard power network;

[0073] Figure 3 This is a communication topology diagram of each generator in the present invention;

[0074] Figure 4 The incremental cost λ of each generator in this invention i (t) changes over time;

[0075] Figure 5 The incremental communication cost of each generator in this invention Graph showing changes over time;

[0076] Figure 6 The output power p of each generator in this invention i (t) changes over time;

[0077] Figure 7 This is a graph showing the change of the total output power of the generator of the present invention over time;

[0078] Figure 8 This is a diagram showing the trigger times of static events for each generator in this invention;

[0079] Figure 9 The measurement error of each generator in this invention is |e i (t)|and the static event triggering function f i (t) Change over time;

[0080] Figure 10 XX is the auxiliary variable of this invention. i (t) changes over time;

[0081] Figure 11 YY is the auxiliary variable of this invention. i (t) Change over time. Detailed Implementation

[0082] 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 denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0083] Example 1:

[0084] The flowchart of a microgrid economic dispatch method based on an event-triggered consensus protocol under noisy conditions provided in this embodiment is as follows: Figure 1 As shown, the specific steps include:

[0085] Step 1: Establish a microgrid economic dispatch model, which includes: the objective function of the economic dispatch problem; generator supply and demand balance constraints and generator output power limitation constraints; and set the initial values ​​of relevant parameters.

[0086] Step 2: Determine the adjacency matrix based on the microgrid communication topology, and construct the Laplace matrix based on the adjacency matrix;

[0087] Step 3: Design an incremental cost consensus algorithm using a multi-agent consensus protocol, and design a static event triggering function;

[0088] Step 4: Update the incremental cost using the incremental cost consistency algorithm; determine if the static event trigger function is greater than 0; if the static event trigger function is greater than 0, update the communication incremental cost; if the static event trigger function is less than or equal to 0, the communication incremental cost remains unchanged, and proceed to Step 5;

[0089] Step 5: Calculate the generator output power based on the incremental communication cost;

[0090] Step Six: Determine whether the incremental communication cost has reached a consensus; if the incremental communication cost has reached a consensus, obtain the optimal value of the incremental cost under the constraint of no generator output power limitation, introduce auxiliary variables, and set the initial value of the auxiliary variables; if the incremental communication cost has not reached a consensus, return to Step Four.

[0091] Step 7: Update the auxiliary variables using the multi-agent consensus protocol, and then update the incremental cost using the auxiliary variables;

[0092] Step 8: Process incremental costs and calculate generator output power;

[0093] Step 9: Determine if the auxiliary variables have reached a consensus; if the auxiliary variables have reached a consensus, output the incremental cost and generator output power; if the auxiliary variables have not reached a consensus, return to Step 7.

[0094] In this embodiment, step one specifically includes:

[0095] The microgrid economic dispatch problem model is as follows:

[0096]

[0097]

[0098] p i_min ≤p i (t)≤p i_max

[0099] Where i = 1, 2, ..., n, and n is the number of generators in the microgrid; Let C be the objective function of the economic scheduling problem. i (p i (t) is the power generation cost function of the i-th generator and p i (t) represents the output power of the i-th generator at time t, α i >0, β i >0 and γ i >0 represents the power generation cost coefficient of the i-th generator; For generator supply and demand balance constraints; p i_min ≤p i (t)≤p i_max For generator output power limiting constraints; p d p represents the total power demand. i_min p represents the minimum output power of the i-th generator; i_max Let be the maximum output power of the i-th generator;

[0100] Set the initial values ​​for the relevant parameters:

[0101] Set the initial output power of the i-th generator to p. i (0), and also requires that the following conditions are met. Its corresponding initial incremental cost is λ i (0)=2α i p i (0)+β i The initial value of incremental communication cost is

[0102] In this embodiment, step two specifically includes:

[0103] The adjacency matrix A is determined based on the microgrid communication topology, specifically as follows:

[0104]

[0105] Among them, a ij Represents the element in the i-th row and j-th column of adjacency matrix A, where j = 1, 2, ..., n; N i This represents the set of generators that communicate with the i-th generator.

[0106] The Laplacian matrix L is determined based on the adjacency matrix A, specifically as follows:

[0107]

[0108] Among them, l ij Let represent the element in the i-th row and j-th column of the Laplace matrix L.

[0109] In this embodiment, step three specifically includes:

[0110] An incremental cost consensus algorithm is designed based on a multi-agent consensus protocol. The incremental cost is then updated based on this algorithm, specifically as follows:

[0111]

[0112] in, Let i be the time when the i-th generator is triggered for the kth time. For the i-th generator at Incremental cost per moment For the j-th generator at Incremental communication cost at any given moment; Let be the derivative of the incremental cost of the i-th generator at time t;

[0113] The static event trigger function is designed as follows:

[0114] f i (t)=|e i (t)|-σ i |z i (t)|-μ

[0115]

[0116] in, Let be the measurement error of the i-th generator at time t; Let f be the incremental cost error of the i-th generator at time t; i (t) is the triggering function of the i-th generator at time t; Let be the design parameters for the i-th generator, where λ maxThe largest eigenvalue of the Laplacian matrix L is represented by μ; μ > 0 is the dead zone threshold; h is the sampling period. inf is the lower bound.

[0117] In this embodiment, step four specifically includes:

[0118] Check if the static event trigger function is greater than 0; if the static event trigger function f i If (t) is greater than 0, update the generator communication incremental cost, let

[0119] If the static event trigger function f i If (t) is less than or equal to 0, then the incremental communication cost remains unchanged, and proceed to step five.

[0120] In this embodiment, step five specifically includes:

[0121] Based on communication incremental cost The generator output power is calculated as follows:

[0122]

[0123] Where, p i (t) represents the output power of the i-th generator at time t.

[0124] In this embodiment, step six specifically includes:

[0125] Determine whether there is consensus on incremental communication costs; if This leads to a consensus on the incremental communication cost, resulting in the optimal incremental cost under the constraint of no generator output power limitation. Equal to the average of incremental communication costs Where Δ1>0 is the convergence coefficient. This represents the average cost of all incremental communications; auxiliary variables are introduced, namely XX. i (t) and YY i (t), and set the initial value of the auxiliary variable; the specific steps for setting the initial value of the auxiliary variable are as follows:

[0126]

[0127]

[0128] Where the output power of the i-th generator is p i (t) exceeds its generator's maximum output power p i_max , let p i (t)=p i_max If the output power of the i-th generator is p i (t) is lower than its generator's minimum output power pi_min , let p i (t)=p i_min ;

[0129] like If no consensus is reached on incremental costs, return to step four.

[0130] In this embodiment, step seven specifically includes:

[0131] Considering noise, the auxiliary variable is updated using a multi-agent consensus protocol, specifically as follows:

[0132]

[0133]

[0134] in, and They represent auxiliary variables XX respectively. i (t) and YY i c(t) represents the derivative of c(t) at time t; c(t) represents the gain function at time t considering noise, specifically: in The time at which the auxiliary variable is introduced is represented by ρ; the noise delay coefficient is represented by ω. ij (t) represents the communication noise between the i-th generator and the j-th generator at time t; the incremental cost is updated using auxiliary variables as follows:

[0135]

[0136] In this embodiment, step eight specifically includes:

[0137] The incremental cost of generators is handled as follows:

[0138]

[0139] Where, λ i_min and λ i_max Let λ represent the minimum and maximum incremental costs of the i-th generator, respectively. i_min =2α i p i_min +β i and λ i_max =2α i p i_max +β i ;

[0140] The generator output power is calculated as follows:

[0141]

[0142] In this embodiment, step nine specifically includes:

[0143] Determine whether the auxiliary variables are consistent; if and in and Let them be represented as auxiliary variables XX respectively. i (t) and YY i (t) is the average value at time t. If Δ2>0 and Δ3>0 are the convergence coefficients, then the auxiliary variables reach consensus; output incremental cost and generator output power;

[0144] like or If the auxiliary variables do not reach a consensus, return to step seven.

[0145] Example 2:

[0146] Example 2 employs the proposed microgrid economic dispatch method based on an event-triggered consensus protocol under noisy conditions, and performs numerical simulation on an IEEE 24-node standard power network with a total of 10 generators. Figure 2 This is an IEEE 24-node standard power grid, and its communication topology is as follows: Figure 3 As shown.

[0147] Table 1 shows the power generation cost coefficient α for the i-th generator. i β i and γ i Generator minimum and maximum output power and design parameters σ i Total power demand p d =300 (MW), the initial values ​​of generator output power are p1(0) = 30 (MW), p2(0) = 15 (MW), p3(0) = 20 (MW), p4(0) = 40 (MW), p5(0) = 45 (MW), p6(0) = 20 (MW), p7(0) = 30 (MW), p8(0) = 35 (MW), p9(0) = 45 (MW) and p 10 (0) = 20(MW), the initial values ​​of the auxiliary variables are XX1(0) = 0, XX2(0) = 0, XX3(0) = 0, XX4(0) = 0, XX5(0) = 0, XX6(0) = 0, XX7(0) = 0, XX8(0) = 0, XX9(0) = 0, XX 10 (0)=17.62; YY1(0)=6.21, YY2(0)=7.16, YY3(0)=8.60, YY4(0)=7.71, YY 5(0)=8.30, YY6(0)=6.29, YY7(0)=8.59, YY8(0)=8.61, YY9(0)=7.08 and YY10 (0) = 0; the communication noise ω between the i-th generator and the j-th generator at time t. ij (t) Take Gaussian white noise with a normal distribution N[0,1]; the time interval h is 0.02(s); the noise delay coefficient ρ=0.02.

[0148] Based on the microgrid communication topology, the adjacency matrix A is determined as follows:

[0149]

[0150] The Laplace matrix L is constructed based on the adjacency matrix A as follows:

[0151]

[0152] Table 1

[0153] 1 0.080 2 51 0 60 0.037 2 0.070 3.5 31 20 75 0.027 3 0.058 2.5 78 15 40 0.037 4 0.065 4 42 10 60 0.023 5 0.060 3 43 15 46 0.037 6 0.080 2 51 0 60 0.037 7 0.058 2.5 15 15 40 0.027 8 0.058 2.5 15 15 40 0.037 9 0.070 3.5 41 20 75 0.037 10 0.038 3.6 26 0 20 0.037

[0154] Finally, the effectiveness of the invention is verified through numerical simulation using the given data.

[0155] Figure 4 The incremental cost λ of each generator in this invention i (t) changes over time; Figure 5 The incremental communication cost of each generator in this invention A graph showing the changes over time; from Figure 4 and Figure 5 It can be seen that, at a convergence time of t = 140 (s), the optimal incremental cost under the constraint of no generator output power limitation is obtained. Figure 4 The optimal value for incremental cost also changed from 6.63 ($ / MW) to 6.92 ($ / MW), where λ 10 (t) Because it exceeds the maximum value of incremental cost, changing from 6.63 ($ / MW) to 5.12 ($ / MW), it cannot converge to a uniformity; Figure 5 The optimal incremental cost for China Communications also changed from $6.63 / MW to $6.92 / MW, of which... Because the incremental cost exceeds the maximum value, changing from 6.63 ($ / MW) to 5.12 ($ / MW), it cannot converge to a uniformity.

[0156] Figure 6 The output power p of each generator in this invention i (t) changes over time; from Figure 6It can be seen that the optimal output power values ​​of the generator are p1 = 28.95 (MW), p2 = 22.38 (MW), p3 = 35.63 (MW), p4 = 20.26 (MW), p5 = 30.28 (MW), p6 = 28.96 (MW), p7 = 35.63 (MW), p8 = 35.63 (MW), p9 = 22.37 (MW) and p 10 =39.90 (MW), due to p 10 =39.90 (MW), exceeding the output power limit of generator 10; at t=140 (s), auxiliary variables are introduced, and finally p1=30.77 (MW), p2=24.45 (MW), p3=38.12 (MW), p4=22.48 (MW), p5=32.69 (MW), p6=30.77 (MW), p7=38.12 (MW), p8=38.12 (MW), p9=24.45 (MW) and p 10 =20.00 (MW) satisfies the generator output power limit constraint.

[0157] Figure 7 This is a graph showing the change of the total output power of the generator of the present invention over time; Figure 8 This is a diagram showing the trigger times of static events for each generator in this invention; Figure 9 The measurement error of each generator in this invention is |e i (t)|and the static event triggering function f i (t) Change over time; Figure 10 XX is the auxiliary variable of this invention. i (t) changes over time; Figure 11 YY is the auxiliary variable of this invention. i (t) Change over time.

[0158] It should be noted that the above description is merely a preferred embodiment 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 foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A microgrid economic dispatch method based on an event-triggered consensus protocol under noisy conditions, characterized in that, Includes the following steps: Step 1: Establish a microgrid economic dispatch model, which includes: the objective function of the economic dispatch problem; generator supply and demand balance constraints and generator output power limitation constraints; and set the initial values ​​of relevant parameters. Step 2: Determine the adjacency matrix based on the microgrid communication topology, and construct the Laplace matrix based on the adjacency matrix; Step 3: Design an incremental cost consensus algorithm using a multi-agent consensus protocol, and design a static event triggering function; Step 4: Update the incremental cost using the incremental cost consistency algorithm; determine if the static event trigger function is greater than 0; if the static event trigger function is greater than 0, update the communication incremental cost; if the static event trigger function is less than or equal to 0, the communication incremental cost remains unchanged, and proceed to Step 5; Step 5: Calculate the generator output power based on the incremental communication cost; Step Six: Determine whether the incremental communication cost has reached a consensus; if the incremental communication cost has reached a consensus, obtain the optimal value of the incremental cost under the constraint of no generator output power limitation, introduce auxiliary variables, and set the initial value of the auxiliary variables; if the incremental communication cost has not reached a consensus, return to Step Four. Step 7: Update the auxiliary variables using the multi-agent consensus protocol, and then update the incremental cost using the auxiliary variables; Step 8: Process incremental costs and calculate generator output power; Step 9: Determine if the auxiliary variables have reached a consensus; if the auxiliary variables have reached a consensus, output the incremental cost and generator output power; if the auxiliary variables have not reached a consensus, return to Step 7. Step one specifically involves: The microgrid economic dispatch problem model is as follows: Where i = 1, 2, ..., n, and n is the number of generators in the microgrid; Let the objective function of the economic scheduling problem be... Let be the power generation cost function of the i-th generator and , Let be the output power of the i-th generator at time t. , and Let be the power generation cost coefficient of the i-th generator; For generator supply and demand balance constraints; These are constraints limiting the generator's output power. Total power demand; Let be the minimum output power of the i-th generator; This represents the maximum output power of the i-th generator; Set the initial values ​​for the relevant parameters: Set the initial output power of the i-th generator to [value]. At the same time, it is required to meet the following requirements. The corresponding initial value of incremental cost is The initial value of incremental communication cost is ; Step two specifically involves: The adjacency matrix A is determined based on the microgrid communication topology, specifically as follows: in, ij This represents the element in the i-th row and j-th column of the adjacency matrix A, where j = 1, 2, ..., n; N i This represents the set of generators that communicate with the i-th generator. The Laplacian matrix L is determined based on the adjacency matrix A, specifically as follows: in, This represents the element in the i-th row and j-th column of the Laplace matrix L; Step three specifically involves: An incremental cost consensus algorithm is designed based on a multi-agent consensus protocol. The incremental cost is then updated based on this algorithm, specifically as follows: in, , For the i-th generator at the k-th trigger time, For the i-th generator at Incremental cost per moment For the j-th generator at Incremental communication cost at any given moment; Let be the derivative of the incremental cost of the i-th generator at time t; The static event trigger function is designed as follows: Where l = 1, 2, ...; Let be the measurement error of the i-th generator at time t; Let be the incremental cost error of the i-th generator at time t; Let be the trigger function for the i-th generator at time t; Let be the design parameters for the i-th generator, where It is represented by the largest eigenvalue of the Laplace matrix L; The dead zone threshold is h; the sampling period is h. ; The lower bound.

2. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 1, characterized in that, Step four specifically involves: Check if the static event trigger function is greater than 0; if the static event trigger function... 0, update generator communication incremental cost, making = , = ; If a static event trigger function If the value is less than or equal to 0, the incremental communication cost remains unchanged, and proceed to step five.

3. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 2, characterized in that, Step five specifically involves: Based on communication incremental cost The generator output power is calculated as follows: in, Let be the output power of the i-th generator at time t.

4. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 3, characterized in that, Step six specifically involves: Determine whether there is consensus on incremental communication costs; if Then the incremental communication cost reaches a consensus, making the incremental cost optimal under the constraint of no generator output power limitation. Equal to the average of incremental communication costs in The convergence coefficient is . The average cost of all incremental communication costs is given; auxiliary variables are introduced as follows: and And set the initial value of the auxiliary variable; the specific steps for setting the initial value of the auxiliary variable are as follows: Where the output power of the i-th generator is Exceeding its generator's maximum output power ,make = If the output power of the i-th generator Below its generator's minimum output power ,make = ; like If no consensus is reached on incremental costs, return to step four.

5. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 4, characterized in that, Step seven specifically involves: Considering noise, the auxiliary variable is updated using a multi-agent consensus protocol, specifically as follows: in, and They represent auxiliary variables respectively. and The derivative at time t; This represents the gain function at time t considering noise, specifically: ,in This indicates the moment when the auxiliary variable is introduced; Represented as the noise delay factor; The communication noise between the i-th generator and the j-th generator at time t; the incremental cost is updated using auxiliary variables: 。 6. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 5, characterized in that, Step eight specifically involves: The incremental cost of generators is handled as follows: in, and Let these represent the minimum and maximum incremental costs of the i-th generator, respectively. and ; The generator output power is calculated as follows: 。 7. The microgrid economic dispatch method based on event-triggered consensus protocol under noisy conditions according to claim 6, characterized in that, Step nine specifically involves: Determine whether the auxiliary variables are consistent; if and ,in and They are respectively represented as auxiliary variables and The average value at time t. and If the convergence coefficient is used, then the auxiliary variables will reach a consensus; output incremental cost and generator output power; like or If the auxiliary variables do not reach a consensus, return to step seven.

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