Smart grid economic dispatch method based on fixed time distribution optimization

Abstract

The application discloses an intelligent power grid economic dispatching method based on fixed time distribution optimization. The method is divided into two stages. In the first stage, each generator group can converge to a preset value within a fixed time under any initial output power, and supply and demand balance is realized. In the second stage, the total power generation cost of the generator group is minimized within a fixed time under the premise of meeting the supply and demand balance and meeting the maximum output power limit. The application adopts a completely distributed control mode to adjust the output power of each generator group, effectively saving the calculation resource cost and communication cost of the intelligent power grid. The convergence time of the application is not affected by the initial output power of the generator group, and the task establishment time can be pre-allocated offline according to the task demand, so that the economic dispatching problem in the intelligent power grid is solved within a fixed time.

Description

Technical Field

[0001] The present invention relates to the field of smart grid economic dispatch, and in particular to a smart grid economic dispatch method based on fixed-time distributed optimization. Background Art

[0002] Studying economic dispatch in smart grids to maximize resource conservation and reduce the economic costs of power generation is of great significance. Centralized control strategies have been the most widely used control method for solving economic dispatch problems in smart grid systems over the past few decades. However, with the rapid growth in the scale of generator networks in recent years, the amount of data that smart grid systems need to process has increased significantly. Furthermore, practical systems often lack global information, making traditional centralized control increasingly inadequate. Consequently, distributed control has attracted widespread attention, aiming to efficiently achieve target tasks through the coordination and cooperation of generators by designing appropriate distributed cooperative controllers. However, most current distributed control methods can only achieve suboptimal solutions within a limited time, or the convergence time to the optimal solution is difficult to determine due to the influence of the system's initial state. Only a few research results can achieve the optimal solution within a fixed time, but these methods suffer from complex algorithmic structures, strict initial output power settings, high communication costs, and difficulty meeting practical constraints. Summary of the Invention

[0003] Purpose of the invention: In response to the shortcomings of the existing technology, the present invention proposes an economic dispatch method for smart grids based on fixed-time distributed optimization, taking into account the actual output power limitations and supply and demand balance constraints met by each generator set, and providing an optimal solution that can solve the economic dispatch problem in the smart grid within a fixed time.

[0004] Technical solution: To achieve the above-mentioned purpose, the present invention provides a smart grid economic dispatch method based on fixed-time distributed optimization, which specifically includes the following steps:

[0005] (1) The economic dispatch problem in smart grids refers to controlling the output power of generator sets to minimize the total power generation cost, and needs to meet two constraints: the supply and demand balance constraint and the output power constraint not exceeding the maximum output power constraint of the generator set. This problem is described by a mathematical expression;

[0006] (2) Constructing a connected undirected graph based on the communication topology of the smart grid system;

[0007] (3) Set the control parameters p, q, r1, r2, r3, s1, s2, s3, θ1, θ2, μ1, and μ2, where r1 and s1 are the control coefficients of the fixed-time control algorithm, θ1 and μ1 are the control power values ​​of the fixed-time control algorithm, r2, r3, s2, and s3 are the control coefficients of the fixed-time distributed optimization control algorithm, and p, q, θ2, and μ2 are the control power values ​​of the fixed-time distributed optimization control algorithm; these parameters must satisfy 0. <p<1,q> 1, r1, r2, r3, s1, s2, s3>0, 0<θ1<1, 0<θ2<1, μ1, μ2>1;

[0008] (4) Set the preset output power d according to the supply and demand balance constraint conditions described in step (1) i ;

[0009] (5) Calculate fixed times T1, T2, and T3 based on the connected undirected graph constructed in step (2) and the control parameters set in step (3), wherein the generator set is required to output a preset output power to meet the balance condition within time T1; secondly, the output power of the generator set is required to be within a safe output power range within time T2; and finally, the output power of the generator set is required to minimize the total power generation cost within time T3;

[0010] (6) When the task execution time t satisfies t≤T1, the connected undirected graph constructed in step (2) and the control parameters set in step (3) are used to control the output power of each generator set from the initial output power of the generator set to the preset output power d set in step (4) using a fixed time control algorithm. i , thus satisfying the supply and demand balance condition;

[0011] (7) When the task execution time t satisfies t>T1, the error e is calculated using the current output power of the generator set and its maximum output power. i (t);

[0012] (8) When the task execution time t satisfies t>T1, the connected undirected graph constructed in step (2), the control parameters set in step (3), and the error e calculated in step (7) are used to calculate the task execution time t>T1. i (t), based on step (6), a fixed-time distributed optimization control algorithm is used to first control the output power of the generator set within a safe range within a fixed time T2, that is, to meet the constraint that the output power does not exceed the maximum output power of the generator set, and then control the output power of the generator set within a fixed time T3, thereby minimizing the total power generation cost of the smart grid system, and at this time meeting the two constraints of the economic dispatch problem in the smart grid.

[0013] Furthermore, the control of the output power of the generator set in step (1) to minimize the total power generation cost, the supply and demand balance constraint, and the output power not exceeding the maximum output power constraint of the generator set can be described by mathematical expressions as follows:

[0014]

[0015]

[0016]

[0017] Where N is the number of generators in the smart grid, x i is the output power of generator set i, C i (x i ) is the output power x of generator set i i The power generation cost function, α i , β i , γ i is the cost coefficient of generator set i, C(x) is the total cost of power generation, D is the total electricity demand of users, is the maximum output power of generator set i; let is the initial output power of generator set i.

[0018] Furthermore, the specific method of constructing a connected undirected graph according to the communication topology structure of the smart grid system in step (2) is:

[0019] For a smart grid system consisting of N generators, a connected undirected graph G = (V, E, A) is constructed based on its communication topology, where V = {1, 2, ..., N} represents the set of N generators, and each generator is considered as an intelligent agent. Indicates the communication between agents, that is, (i, j)∈E indicates that agents i and j can communicate with each other, and the adjacency matrix A=[a ij ]∈R N×N is an N×N dimensional matrix, R N×N It is an N×N dimensional real vector space. If (i,j)∈E, then its element a ij =a ji =1, otherwise a ij =0, let N i ={j|(i,j∈E) is the set of neighboring nodes that can communicate with agent i, and the degree matrix Deg = diag[deg1,deg2,…,deg N ],in Laplace matrix L=Deg–A= [l ij ]∈R N×N, let the second smallest eigenvalue of the Laplace matrix L be λ2.

[0020] Furthermore, in step (4), the preset output power d is set i The specific method is:

[0021] Set the preset output power of generator set i according to the total electricity demand D of users in the supply and demand balance condition if Reset the preset output power The preset output power of generator set j except generator set i Until satisfied

[0022] Furthermore, the specific method for calculating the fixed times T1, T2, and T3 in step (5) is:

[0023]

[0024]

[0025]

[0026] where α=min{α1,α2,…,α N}.

[0027] Furthermore, the fixed time control algorithm described in steps (3) and (6) is expressed as follows:

[0028]

[0029]

[0030] where sig(·) z =sign(·)|·| z , sign(·) is the sign function, x i (t) is the output power of generator set i at task execution time t, x i (t+Δt) is the output power of generator set i at task execution time t+Δt, is the derivative of the output power of generator set i at task execution time t, Δt is the shortest update time of system data;

[0031] The algorithm controls the sum of the output power of the generator sets to be equal to the total electricity demand of the users within a fixed time T1, satisfying the supply and demand balance constraint.

[0032] Furthermore, the error e in step (7) i (t) is defined as:

[0033] When t>T1, if but like Then e i (t)=0.

[0034] Furthermore, the fixed-time distributed optimization control algorithm in steps (3) and (8) is expressed as follows:

[0035]

[0036]

[0037]

[0038]

[0039] where y i (t), z ij (t) is an auxiliary variable, x j (t) is the output power of generator set j at task execution time t, C i (x i (t)) represents the power generation cost of generator set i at task execution time t, C j (x j (t)) represents the power generation cost of generator set j at task execution time t, e j (t) is the error defined by generator set j at task execution time t;

[0040] The algorithm constrains the output power of the generator set within a safe output range within a fixed time T2, and minimizes the total power generation cost of the generator set within a fixed time T3, while satisfying the supply and demand balance constraints.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention places no restrictions on the initial output power of the generator set. It can be started at any time and converges to the optimal solution within a preset time, improving the practicality of the algorithm. Furthermore, the present invention only uses the generator set's own output power information when processing its initial output power, and does not require inter-generator communication. This avoids the use of an integrator, reduces communication overhead, and simplifies the algorithm structure.

[0043] 2. The control algorithm designed in the present invention is fully distributed, does not require any global information, and only uses the cost gradient data of the generator set neighbors, which greatly reduces the communication resource consumption of the entire system.

[0044] 3. This invention can obtain the optimal solution to the economic dispatch problem in smart grids within a fixed timeframe, pre-allocating task setup time offline based on task requirements. It also meets the practical maximum output power limit, helping to ensure the long-term safe and stable operation of the generator set. Furthermore, the generator set's output power meets the supply-demand balance requirement, eliminating resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is an overall scheme diagram of the economic dispatch method of smart grid based on fixed-time distributed optimization;

[0046] Figure 2 It is a flow chart of the economic dispatch method of smart grid based on fixed-time distributed optimization;

[0047] Figure 3 is a smart grid communication topology diagram of Example 1;

[0048] Figure 4 is a graph showing the output power of each generator set changing with time in Example 1;

[0049] Figure 5 is a graph showing the sum of the output power of each generator set in Example 1 changing over time;

[0050] Figure 6 is a graph showing the total power generation cost over time for Example 1;

[0051] Figure 7 is a smart grid communication topology diagram of Example 2;

[0052] Figure 8 is a graph showing the output power of each generator set changing over time in Example 2;

[0053] Figure 9 is a graph showing the sum of the output power of each generator set varying with time in Example 2;

[0054] Figure 10 This is a graph showing the total power generation cost changing over time in Example 2. DETAILED DESCRIPTION

[0055] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0056] The overall scheme of the smart grid economic dispatch method based on fixed-time distributed optimization is shown in the figure below: Figure 1As shown in the figure, the specific economic dispatch problem of the smart grid system is first described by a mathematical expression. According to the distribution of generator sets in the smart grid, an undirected connected graph of the multi-agent system is constructed, the control parameters are initialized, the preset output power is set, and the fixed times T1, T2, and T3 are calculated. Then, the generator set is controlled to output the preset output power within the time T1 to meet the balance condition. Secondly, the output power of the generator set is controlled within the safe output power range within the time T2. Finally, the output power of the generator set is controlled within the time T3 to minimize the total power generation cost.

[0057] Example 1:

[0058] The flow chart of the smart grid economic dispatch method based on fixed-time distributed optimization is as follows: Figure 2 As shown, the IEEE bus system is used for data transmission, which specifically includes the following steps:

[0059] (1) The economic dispatch problem in smart grid is described as follows:

[0060]

[0061]

[0062]

[0063] where x i is the output power of generator set i, c i (x i ) is the power generation cost function of generator set i, c(x) is the total power generation cost, d i is the preset output power, d is the total electricity demand of the user, is the maximum output power of generator set i, α i , β i , γ i is the cost coefficient of generator set i; let is the initial output power of generator set i, and the parameters of the generator set are given in Table 1.

[0064]

[0065] Table 1

[0066] (2) According to the distribution of generator sets in the smart grid, an undirected connected graph of the multi-agent system is constructed. The communication topology of the generator sets is as follows: Figure 3 As shown, a connected undirected graph G = (V, E, A) is constructed, where V = {1, 2, 3, 4, 5} represents the set of these five generators, and each generator is regarded as an intelligent agent. Indicates the communication between agents, that is, (i, j)∈E indicates that agents i and j can communicate with each other, and the adjacency matrix A=[a ij ]∈R n×n is an N×N dimensional matrix, R n×n It is an n×n dimensional real vector space. If (i,j)∈E, then its element a ij =a ji =1, otherwise a ij = 0. Let N i ={j|(i,j∈E) is the set of neighboring nodes that can communicate with agent j, and the degree matrix Deg = diag[deg1,deg2,…,deg N ],in Laplace matrix L = Deg – A = [l ij ]∈R n×n , let the second smallest eigenvalue of the Laplace matrix L be λ2. It can be calculated λ2=1.382。

[0067] (3) Initialize the control parameters p = 0.5, q = 1.5, r1 = 5, r2 = 0.5, r3 = 5, s1 = 5, s2 = 0.5, s3 = 5, θ1 = 0.5, θ2 = 0.5, μ1 = 1.5, μ2 = 1.5;

[0068] (4) Set the preset output power

[0069] (5) Calculate fixed time where α=min{α1,α2,…,α N}=0.8;

[0070] (6) When the task execution time t≤T1=0.81s, the following control algorithm is used to control the output power of generator set i to the preset output power d i =3kMW:

[0071]

[0072]

[0073] where sig(·) z =sign(·)|·| z , sign(·) is the sign function, x i (t) is the output power of generator set i at task execution time t, x i (t+Δt) is the output power of generator set i at task execution time t+Δt, is the derivative of the output power of generator set i at task execution time t, and Δt is the shortest update time of system data.

[0074] (7) Define the error e i (t): When t>T1, if but like Then e i (t)=0.

[0075] (8) When t>T1=0.81s, the following fixed-time distributed optimization control algorithm is used based on the results obtained in step (6):

[0076]

[0077]

[0078]

[0079]

[0080] where y i (t), z ij (t) is an auxiliary variable, x j (t) is the output power of generator set j at task execution time t, C i (x i (t)) represents the power generation cost of generator set i at task execution time t, C j (x j (t)) represents the power generation cost of generator set j at task execution time t, e j (t) is the error defined by generator j at task execution time t.

[0081] The specific effects of the invention are as follows: Figure 4-6 shown.

[0082] Figure 4 The output power of each generator set changes with time. When t≤T1=0.81s, the invention can control the output power of generator set i to the preset output power d i =3kMW, when t>T1+T2=2.56s, the invention can control the output power of generator set i not to exceed its maximum safe power range, and when t>T1+T2+T3=3.92s, the invention controls the output power of generator set i to its respective optimal state.

[0083] Figure 5The graph shows the change of the sum of the output power of each generator set over time. When t>T1=0.81s, the sum of the output power of all generator sets is equal to the total electricity demand of users D=15kMW.

[0084] Figure 6 The total power generation cost of each generator set varies with time. When t>T1+T2+T3=3.92s, the total power generation cost of all generator sets reaches the minimum value of 106.4k$ while satisfying the constraints.

[0085] Example 2:

[0086] The flow chart of the smart grid economic dispatch method based on fixed-time distributed optimization is as follows: Figure 2 As shown, the IEEE bus system is used for data transmission, which specifically includes the following steps:

[0087] (1) The economic dispatch problem in smart grid is described as follows:

[0088]

[0089]

[0090]

[0091] where x i is the output power of generator set i, C i (x i ) is the power generation cost function of generator set i, C(x) is the total power generation cost, d i is the preset output power, D is the total electricity demand of the user, is the maximum output power of generator set i, α i , β i , γ i is the cost coefficient of generator set i; let is the initial output power of generator set i.

[0092] Different from Example 1, this example further reduces the maximum output power limit of the generator set while keeping the cost coefficient of each generator set unchanged. The parameters of the generator set are given in Table 2.

[0093]

[0094] Table 2

[0095] (2) According to the distribution of generator sets in the smart grid, an undirected connected graph of the multi-agent system is constructed. The communication topology of the generator sets is as follows: Figure 7As shown, a connected undirected graph G = (V, E, A) is constructed, where V = {1, 2, 3, 4, 5} represents the set of these five generators, and each generator is regarded as an intelligent agent. Indicates the communication between agents, that is, (i, j)∈E indicates that agents i and j can communicate with each other, and the adjacency matrix A=[a ij ]∈R n×n is an N×N dimensional matrix, R n×n It is an n×n dimensional real vector space. If (i,j)∈E, then its element a ij =a ji =1, otherwise a ij = 0. Let N i ={j|(i,j∈E) is the set of neighboring nodes that can communicate with agent j, and the degree matrix Deg = diag[deg1,deg2,…,deg N ],in Laplace matrix L = Deg – A = [l ij ]∈R n×n , let the second smallest eigenvalue of the Laplace matrix L be λ2. It can be calculated λ2=0.5188。

[0096] The flow chart of the smart grid economic dispatch method based on fixed-time distributed optimization is as follows: Figure 2 As shown, the IEEE bus system is used for data transmission, which specifically includes the following steps:

[0097] (3) Initialize the control parameters p = 0.5, q = 1.5, r1 = 5, r2 = 3, r3 = 8, s1 = 5, s2 = 3, s3 = 8, θ1 = 0.5, θ2 = 0.5, μ1 = 1.5, μ2 = 1.5;

[0098] (4) Set the preset output power

[0099] (5) Calculate fixed time where α=min{α1,α2,…,α N}=0.8;

[0100] (6) When the task execution time t≤T1=0.81s, the following control algorithm is used to control the output power of generator set i to the preset output power d i =3kMW:

[0101]

[0102]

[0103] where sig(·) z =sign(·)|·| z , sign(·) is the sign function, x i (t) is the output power of generator set i at task execution time t, x i (t+Δt) is the output power of generator set i at task execution time t+Δt, is the derivative of the output power of generator set i at task execution time t, and Δt is the shortest update time of system data.

[0104] (7) Define the error e i (t): When t>T1, if but like Then e i (t)=0.

[0105] (8) When t>T1=0.81s, the following fixed-time distributed optimization control algorithm is used based on the results obtained in step (6):

[0106]

[0107]

[0108]

[0109]

[0110] where y i (t), z ij (t) is an auxiliary variable, x j (t) is the output power of generator set j at task execution time t, C i (x i (t)) represents the power generation cost of generator set i at task execution time t, C j (x j (t)) represents the power generation cost of generator set j at task execution time t, e j (t) is the error defined by generator j at task execution time t.

[0111] The specific effects of the invention are as follows: Figure 8-10 shown.

[0112] Figure 8 The output power of each generator set changes with time. When t≤T1=0.81s, the invention can control the output power of generator set i to the preset output power d i=3kMW, when t>T1+T2=3.93s, the invention can control the output power of generator set i not to exceed its maximum safe power range, and when t>T1+T2+T3=4.56s, the invention controls the output power of generator set i to its respective optimal state.

[0113] Figure 9 The graph shows the change of the sum of the output power of each generator set over time. When t>T1=0.81s, the sum of the output power of all generator sets is equal to the total electricity demand of users, 15kMW.

[0114] Figure 10 The total power generation cost of each generator set varies with time. When t>T1+T2+T3=4.56s, the total power generation cost of all generator sets reaches the minimum value of 106.8k$ while satisfying the constraints.

[0115] This paper proposes a fixed-time distributed optimization-based economic dispatch method for smart grids. This method addresses the economic dispatch problem in smart grids within a fixed timeframe, taking into account the actual output power limits of each generator set and the supply-demand balance constraints. Its convergence time is unaffected by the initial generator set output power, and it effectively reduces computing and communication costs within the smart grid.

[0116] The foregoing description is merely an embodiment of the present invention and is not intended to limit the present invention. Any equivalent substitutions made within the principles of the present invention are intended to be included within the scope of protection of the present invention. Any content not elaborated in detail herein is already known to those skilled in the art.

Claims

1. A smart grid economic dispatch method based on fixed-time distributed optimization, characterized in that: The method comprises the following steps: (1) The economic dispatch problem in smart grids refers to controlling the output power of generator sets to minimize the total power generation cost, and needs to meet two constraints: the supply and demand balance constraint and the output power constraint not exceeding the maximum output power constraint of the generator set. This problem is described by a mathematical expression; (2) Constructing a connected undirected graph based on the communication topology of the smart grid system; (3) Set the control parameters p, q, r1, r2, r3, s1, s2, s3, θ1, θ2, μ1, and μ2, where r1 and s1 are the control coefficients of the fixed-time control algorithm, θ1 and μ1 are the control power values ​​of the fixed-time control algorithm, r2, r3, s2, and s3 are the control coefficients of the fixed-time distributed optimization control algorithm, and p, q, θ2, and μ2 are the control power values ​​of the fixed-time distributed optimization control algorithm; these parameters must satisfy 0. <p<1,q> 1, r1, r2, r3, s1, s2, s3>0, 0<θ1<1, 0<θ2<1, μ1, μ2>1; (4) Set the preset output power d according to the supply and demand balance constraint conditions described in step (1) i ; (5) Calculate fixed times T1, T2, and T3 based on the connected undirected graph constructed in step (2) and the control parameters set in step (3), wherein the generator set is required to output a preset output power to meet the balance condition within time T1; secondly, the output power of the generator set is required to be within a safe output power range within time T2; and finally, the output power of the generator set is required to be controlled to minimize the total power generation cost within time T3; (6) When the task execution time t satisfies t≤T1, the connected undirected graph constructed in step (2) and the control parameters set in step (3) are used to control the output power of each generator set from the initial output power of the generator set to the preset output power d set in step (4) using a fixed time control algorithm. i , thus satisfying the supply and demand balance condition; (7) When the task execution time t satisfies t>T1, the error e is calculated using the current output power of the generator set and its maximum output power. i (t); (8) When the task execution time t satisfies t>T1, the connected undirected graph constructed in step (2), the control parameters set in step (3), and the error e calculated in step (7) are used to calculate the task execution time t>T1. i (t), based on step (6), a fixed-time distributed optimization control algorithm is used to first control the output power of the generator set within a safe range within a fixed time T2, that is, to meet the constraint condition that the output power does not exceed the maximum output power of the generator set, and then control the output power of the generator set within a fixed time T3, thereby minimizing the total power generation cost of the smart grid system, and at this time meeting the two constraints of the economic dispatch problem in the smart grid.

2. The method for economic dispatch of a smart grid based on fixed-time distributed optimization according to claim 1, characterized in that: The control of the generator set output power in step (1) to minimize the total power generation cost, the supply and demand balance constraint, and the output power not exceeding the maximum output power constraint of the generator set can be described by mathematical expressions as follows: Where N is the number of generators in the smart grid, x i is the output power of generator set i, C i (x i ) is the output power x of generator set i i The power generation cost function, α i , β i , γ i is the cost coefficient of generator set i, C(x) is the total cost of power generation, D is the total electricity demand of users, is the maximum output power of generator set i; let is the initial output power of generator set i.

3. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: The specific method of constructing a connected undirected graph according to the communication topology structure of the smart grid system in step (2) is: For a smart grid system consisting of N generators, a connected undirected graph G = (V, E, A) is constructed based on its communication topology, where V = {1, 2, ..., N} represents the set of N generators, and each generator is considered as an intelligent agent. Indicates the communication between agents, that is, (i, j)∈E indicates that agents i and j can communicate with each other, and the adjacency matrix A=[a ij ]∈R N×N is an N×N dimensional matrix, R N×N It is an N×N dimensional real vector space. If (i,j)∈E, then its element a ij =a ji =1, otherwise a ij =0, let N i ={j|(i,j∈E) is the set of neighboring nodes that can communicate with agent i, and the degree matrix Deg=diag[deg1,deg2,…,deg N ],in Laplace matrix L = Deg – A = [l ij ]∈R N×N , let the second smallest eigenvalue of the Laplace matrix L be λ2.

4. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: In step (4), the preset output power d is set i The specific method is: Set the preset output power of generator set i according to the total electricity demand D of users in the supply and demand balance condition if Reset the preset output power The preset output power of generator set j except generator set i Until satisfied 5. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: The specific method for calculating the fixed times T1, T2, and T3 in step (5) is: Where α=min{α1,α2,…,α N }。 6. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: The fixed time control algorithm described in steps (3) and (6) is expressed as follows: where sig(·) z =sign(·)|·| z , sign(·) is the sign function, x i (t) is the output power of generator set i at task execution time t, x i (t+Δt) is the output power of generator set i at task execution time t+Δt, is the derivative of the output power of generator set i at task execution time t, Δt is the shortest update time of system data; The algorithm controls the sum of the output power of the generator sets to be equal to the total electricity demand of the users within a fixed time T1, satisfying the supply and demand balance constraint.

7. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: The error e in step (7) i (t) is defined as: When t>T1, if but like Then e i (t)=0.

8. The smart grid economic dispatch method based on fixed-time distributed optimization according to claim 1, characterized in that: The fixed-time distributed optimization control algorithm in steps (3) and (8) is expressed as follows: where y i (t), z ij (t) is an auxiliary variable, x j (t) is the output power of generator set j at task execution time t, C i (x i (t)) represents the power generation cost of generator set i at task execution time t, C j (x j (t)) represents the power generation cost of generator set j at task execution time t, e j (t) is the error defined by generator set j at task execution time t; The algorithm constrains the output power of the generator set within a safe output range within a fixed time T2, and minimizes the total power generation cost of the generator set within a fixed time T3, while satisfying the supply and demand balance constraints.

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