A power dispatch method for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithm

Abstract

This invention discloses a power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms. The method includes: establishing a security-constrained unit combination objective function; inputting conventional security-constrained unit combination constraints into the objective function to construct an evaluation model of the dynamic coupling between the hybrid renewable energy system and the unit combination; setting dynamic stability constraints on the evaluation model based on the stability margin function of critical modes, and performing partitioning to construct a multi-objective function for the unit combination under dynamic stability constraints, and calculating the stability margin of the initial unit combination dispatching scheme; employing an adaptive power variable-step cross-tabulation algorithm to adjust unit output based on participation activity, updating the initial unit combination dispatching scheme and stability margin, calculating the corresponding generation cost, and performing power dispatching based on the generation cost. This invention can significantly improve the stability margin.

Description

Technical Field

[0001] This invention relates to the field of renewable energy technology, and in particular to a power dispatching method for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithms. Background Technology

[0002] The penetration rate of hybrid renewable energy in power systems has increased significantly. However, renewable energy sources such as wind and solar power are intermittent and volatile, and their grid connection can severely impact power system stability. Traditional safety-constrained unit combination schemes focus on static constraints and are ill-suited to address the dynamic changes brought about by hybrid renewable energy. When large-scale integration of renewable energy sources such as wind and solar power leads to frequent fluctuations in system power flow, conventional dispatch strategies cannot be adjusted in a timely manner, resulting in decreased power supply reliability and a significant increase in dispatch costs. Therefore, when considering renewable energy in unit combination schemes, it is essential to ensure the reliable operation of the power system.

[0003] Unit combination is a crucial economic and energy management issue in power systems. Currently, dynamic constraints are increasingly emphasized in power system dispatching; however, traditional safety-constrained unit combination schemes, when considering hybrid renewable energy sources, generally neglect the stability of converter drives. The dynamic interaction between converters and the system in hybrid renewable energy systems is highly complex. Existing schemes lack specific dynamic constraints, causing system operation to exceed stability margins, easily leading to oscillations and instability, threatening power system security. New methods are urgently needed to ensure system stability. Summary of the Invention

[0004] To overcome the problem in the prior art that it is difficult to ensure the stable operation of the power system when hybrid renewable energy is directly considered in the combination of safety-constrained units, the present invention proposes a power dispatching method for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithms. The method uses converter drive stability as a new constraint condition, so that the power system can be dispatched and operated stably and economically after considering hybrid renewable energy in the combination of safety-constrained units.

[0005] To achieve the above objectives, this invention provides a power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms, comprising:

[0006] Establish a safety-constrained unit combination objective function;

[0007] By inputting conventional safety-constrained unit combination constraints into the objective function of the safety-constrained unit combination, an evaluation model for the dynamic coupling of the hybrid renewable energy system and the unit combination is constructed.

[0008] Based on the stability margin function of critical modes, dynamic stability constraints are set for the evaluation model and partitioning is performed to construct a multi-objective function for unit combination under dynamic stability constraints, and the stability margin of the initial unit combination scheduling scheme is calculated.

[0009] An adaptive power variable step size cross-row algorithm is adopted to adjust the unit output based on the participation activity, update the initial unit combination scheduling scheme and stability margin, calculate the corresponding power generation cost, and perform power dispatch based on the power generation cost.

[0010] Preferably, the objective function for the safety constraint unit combination is:

[0011]

[0012] In the formula, F cost Safety-constrained unit combination objective function; N t N represents the number of time periods for unit scheduling; g Indicates the total number of units participating in the scheduling; F g (P gt ) is the fuel cost function; P gt S represents the active power output of unit g at time t; gt and R gt Representing the start-up cost and standby cost of the g-th generator unit at time t, respectively; binary variable u gt This indicates the on / off state of the g-th generator set at time t.

[0013] Preferably, the conventional safety constraint unit combination constraints include: generator output constraints, ramp output constraints, hot standby constraints, minimum start-up and minimum downtime constraints, and transmission capacity constraints.

[0014] Preferably, the evaluation model for the dynamic coupling of the hybrid renewable energy system and the unit combination includes:

[0015] The hybrid renewable energy system is equivalent to a static model with constant power. A static system state-space model is established, and the stability margin of the critical mode is solved. The hybrid renewable energy system consists of converter-type wind power generation and full converter-type photovoltaic power generation.

[0016] By connecting the hybrid renewable energy system with a full-power dynamic model, a dynamic system state-space model is constructed, and the stability margin of the critical mode is solved. The critical mode is the electromechanical oscillation mode in which the hybrid renewable energy system participates, and the stability margin is the damping ratio obtained from eigenvalue analysis and participation factor analysis.

[0017] The evaluation model of the dynamic coupling between the hybrid renewable energy system and the unit combination is obtained by subtracting the stability margin of the critical mode of the static system state space model from the stability margin of the critical mode of the dynamic system state space model.

[0018] Preferably, the stability margin function of the critical mode is:

[0019]

[0020] In the formula, DR t σ is the stability margin function of the critical mode; σ is the real part of the critical mode eigenvalue; ω is the imaginary part of the critical mode eigenvalue.

[0021] Preferably, the multi-objective function for unit combination under dynamic stability constraints is:

[0022]

[0023] In the formula, ρ represents a constant used to distinguish between weakly stable and strongly stable regions; F cost Represents the objective function for the safety-constrained unit combination; DR t ζ represents the damping ratio of the critical mode at time t; T This represents the set stability margin threshold; fit(cost,DR) is a multi-objective function for unit combination under dynamic stability constraints.

[0024] Preferably, the adaptive power variable step size cross-sectional algorithm includes:

[0025] S1. Initialize the size, dimensions, and number of iterations of the particle swarm;

[0026] S2. The cross-sectional algorithm is used to obtain the offspring particle swarm;

[0027] S3. Calculate the damping ratio of the particle swarm in the critical mode. If the damping ratio is greater than the stability margin threshold, then execute S5. If the damping ratio is less than the stability margin threshold, then execute S4.

[0028] S4. An adaptive power variable step size algorithm is used to adjust the unit output;

[0029] S5. Perform the next iteration and check if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, then execute S6; otherwise, execute S2.

[0030] S6: End the iteration and output the globally optimal particle after the iteration.

[0031] Preferably, in step S4, the adaptive power variable step size algorithm is used to adjust the unit output, including:

[0032] S4.1 Calculate the participation activity of all generators in the particle swarm under the critical mode. If the participation activity is greater than zero, increase the upper limit of particle exploration for the corresponding generator. If the participation activity is less than zero, decrease the lower limit of particle exploration for the corresponding generator, forming a new particle exploration region. Set a new number of iterations, population size and particle exploration region.

[0033] S4.2 Calculate the damping ratio of the newly generated particle in the critical mode. If the damping ratio is greater than the stability margin threshold, proceed to S4.7. If the damping ratio is less than the stability margin threshold, proceed to S4.3.

[0034] S4.3 Calculate the participation activity of the newly generated particle swarm generator in the critical mode;

[0035] S4.4 Calculate the power adjustment amount using the adaptive power variable step size calculation formula;

[0036] S4.5 Check whether the power adjustment amount meets the generator output limit of the generator set. If it does not meet the limit, update the power adjustment amount to the minimum value of the deviation between the generator output limit and the generator set power.

[0037] S4.6 Adjust the output of the generator set according to the power adjustment amount calculated in S4.4 or S4.5;

[0038] S4.7. Perform the next iteration and determine if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, execute S4.8; otherwise, execute S4.2.

[0039] S4.8. End the iteration and output the globally optimal particle after the iteration.

[0040] Preferably, the participation factor is:

[0041]

[0042] In the formula, λ j Indicates the magnitude of the eigenvalue; a ii Represents the diagonal elements of state matrix A;

[0043] The revised participation factor is:

[0044]

[0045] In the formula, θ λ The angle θ represents the critical modal eigenvalue. pij This represents the relationship between the j-th mode and the i-th state variable Δx. i The argument of the participating factors; This is the revised participation factor;

[0046] The participation activity level is:

[0047]

[0048] in, Represents all state variables related to the g-th generator. The sum, Represents all state variables related to a hybrid renewable energy system. The sum, Represents all state variables The sum, Pal g To increase participation activity.

[0049] Preferably, the power adjustment amount is calculated as follows:

[0050] ΔP kg =η*Δζ tk *P kg *Pal kg ,

[0051] Δζ tk =ζ T -DR tk ,

[0052] In the formula, η is the exploration factor; Δζ tk This represents the damping ratio DR before the k-th iteration of power transmission within time t. tk With the expected stability margin threshold ζ T The difference between them; ΔP kg Pal represents the power adjustment of the g-th generator in the k-th iteration; kg To represent the participation activity of the g-th generator in the k-th iteration, P kg Let g be the power of the g-th generator in the k-th iteration.

[0053] Compared with the prior art, the present invention has the following advantages and technical effects:

[0054] This invention introduces stability as a constraint in the safety-constrained unit combination problem, obtains a multi-objective function with the stability margin threshold as the boundary, and uses an adaptive power variable step size cross-multiplication algorithm to obtain the multi-objective scheduling result and adjust the output state of the original units. This method effectively overcomes the randomness problem of traditional artificial intelligence algorithms when improving stability margin, and ensures the safe and stable operation of the power system while taking into account economy. It has important significance in practical engineering applications. Attached Figure Description

[0055] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0056] Figure 1 This is a flowchart of a power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms, according to an embodiment of the present invention.

[0057] Figure 2 This is a flowchart of the adaptive power variable step size cross-sectional algorithm according to an embodiment of the present invention;

[0058] Figure 3 This is an iterative effect diagram of the adaptive power variable step size cross-sectional algorithm according to an embodiment of the present invention. Detailed Implementation

[0059] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0060] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment proposes a power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms, such as... Figure 1 ,include:

[0062] Establish a safety-constrained unit combination objective function;

[0063] By inputting conventional safety-constrained unit combination constraints into the objective function of the safety-constrained unit combination, an evaluation model for the dynamic coupling of the hybrid renewable energy system and the unit combination is constructed.

[0064] Based on the stability margin function of critical modes, dynamic stability constraints are set for the evaluation model and partitioning is performed to construct a multi-objective function for unit combination under dynamic stability constraints, and the stability margin of the initial unit combination scheduling scheme is calculated.

[0065] An adaptive power variable step size cross-row algorithm is adopted to adjust the unit output based on the participation activity, update the initial unit combination scheduling scheme and stability margin, calculate the corresponding power generation cost, and perform power dispatch based on the power generation cost.

[0066] Specifically, in this embodiment, the stability margin of the original unit combination is tested through a coupled model of the dynamic interaction between the hybrid renewable energy system and the unit combination. If the stability margin of the unit combination scheme does not reach the set threshold, it indicates that the unit combination poses a risk during the operation of the power system. In the case of insufficient stability margin, the proposed adaptive power variable step size cross-connection algorithm can obtain a unit combination with sufficient stability margin while minimizing the generation cost.

[0067] Furthermore, the objective function for the safety-constrained unit combination is:

[0068]

[0069] In the formula, F cost Safety-constrained unit combination objective function; N t N represents the number of time periods for unit scheduling; g Indicates the total number of units participating in the scheduling; F g (P gt ) is the fuel cost function; P gt S represents the active power output of unit g at time t; gt and R gt Representing the start-up cost and standby cost of the g-th generator unit at time t, respectively; binary variable u gt This indicates the on / off state of the g-th generator set at time t.

[0070] Furthermore, the conventional safety constraints of the unit combination constraints include: generator output constraints, ramp output constraints, hot standby constraints, minimum start-up and minimum downtime constraints, and transmission capacity constraints.

[0071] Specifically, the generator output constraint is as follows:

[0072]

[0073] In the formula, These represent the minimum and maximum power generation limits of the g-th generating unit, respectively;

[0074] The output constraint for climbing is:

[0075]

[0076] In the formula, RD g and RU g SD represents the output decrease and climb limit of the g-th generator set, respectively. g and SU g These correspond to the shutdown output and startup output limits of the g-th generator unit, respectively.

[0077] Hot standby constraints are:

[0078]

[0079] In the formula, This represents the total reserve cost at time t.

[0080] Furthermore, an evaluation model for the dynamic coupling of the hybrid renewable energy system and the unit combination is constructed, including:

[0081] The hybrid renewable energy system is equivalent to a static model with constant power. A static system state-space model is established, and the stability margin of the critical mode is solved. The hybrid renewable energy system consists of converter-type wind power generation and full converter-type photovoltaic power generation.

[0082] By connecting the hybrid renewable energy system with a full-power dynamic model, a dynamic system state-space model is constructed, and the stability margin of the critical mode is solved. The critical mode is the electromechanical oscillation mode in which the hybrid renewable energy system participates, and the stability margin is the damping ratio obtained from eigenvalue analysis and participation factor analysis.

[0083] The evaluation model of the dynamic coupling between the hybrid renewable energy system and the unit combination is obtained by subtracting the stability margin of the critical mode of the static system state space model from the stability margin of the critical mode of the dynamic system state space model.

[0084] Specifically, the stability margin function of the critical mode is:

[0085]

[0086] In the formula, DR t σ is the stability margin function of the critical mode; σ is the real part of the critical mode eigenvalue; ω is the imaginary part of the critical mode eigenvalue.

[0087] Using the aforementioned stability margin function as a dynamic stability constraint, in this embodiment, ζ is taken as... T With a threshold of 5%, the scheduling is divided into weakly stable and strongly stable regions. The multi-objective function of unit combination under dynamic stability constraints is as follows:

[0088]

[0089] In the formula, ρ represents a constant used to distinguish between weakly stable and strongly stable regions; F cost Represents the objective function for the safety-constrained unit combination; DR t ζ represents the damping ratio of the critical mode at time t; T This represents the set stability margin threshold; fit(cost,DR) is a multi-objective function for unit combination under dynamic stability constraints.

[0090] Furthermore, such as Figure 2 The adaptive power variable step size cross-sectional algorithm includes:

[0091] S1. Initialize the size, dimensions, and number of iterations of the particle swarm;

[0092] S2. The cross-sectional algorithm is used to obtain the offspring particle swarm;

[0093] S3. Calculate the damping ratio of the particle swarm in the critical mode. If the damping ratio is greater than the stability margin threshold, then execute S5. If the damping ratio is less than the stability margin threshold, then execute S4.

[0094] S4. An adaptive power variable step size algorithm is used to adjust the unit output;

[0095] S5. Perform the next iteration and check if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, then execute S6; otherwise, execute S2.

[0096] S6: End the iteration and output the globally optimal particle after the iteration. The effect of multiple iterations is as follows: Figure 3 As shown, the scheduling results are displayed over a time range of t=12. Under weak interaction conditions, a 1.09% economic sacrifice was made to restore stability to the power dispatch involving the hybrid renewable energy system. Under strong interaction conditions, the initial stability margin deteriorated significantly, requiring multiple iterations to reach stability, resulting in a 1.37% economic sacrifice. This small economic cost sacrifice is worthwhile, making the dispatch schemes robust.

[0097] Specifically, in S4, an adaptive power variable step size algorithm is used to adjust the unit output, including:

[0098] S4.1 Calculate the participation activity of all generators in the particle swarm under the critical mode. If the participation activity is greater than zero, increase the upper limit of particle exploration for the corresponding generator. If the participation activity is less than zero, decrease the lower limit of particle exploration for the corresponding generator, forming a new particle exploration region. Set a new number of iterations, population size and particle exploration region.

[0099] S4.2 Calculate the damping ratio of the newly generated particle in the critical mode. If the damping ratio is greater than the stability margin threshold, proceed to S4.7. If the damping ratio is less than the stability margin threshold, proceed to S4.3.

[0100] S4.3 Calculate the participation activity of the newly generated particle swarm generator in the critical mode;

[0101] S4.4 Calculate the power adjustment amount using the adaptive power variable step size calculation formula;

[0102] S4.5 Check whether the power adjustment amount meets the generator output limit of the generator set. If it does not meet the limit, update the power adjustment amount to the minimum value of the deviation between the generator output limit and the generator set power.

[0103] S4.6 Adjust the output of the generator set according to the power adjustment amount calculated in S4.4 or S4.5;

[0104] S4.7. Perform the next iteration and determine if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, execute S4.8; otherwise, execute S4.2.

[0105] S4.8. End the iteration and output the globally optimal particle after the iteration.

[0106] The adaptive power variable step-size algorithm can quickly select the optimal step size for each generator unit among different particles, realizing the redistribution of power output from the scheduling units. By introducing factors such as the difference between participation activity, damping ratio, and desired stability margin, the step size is determined, making power adjustment more targeted. During the scheduling algorithm, the goal is to improve the damping ratio of the system's critical mode; with each power adjustment, the damping ratio of the system's critical mode will increase.

[0107] Furthermore, the participation factor is:

[0108]

[0109] In the formula, λ j Indicates the magnitude of the eigenvalue; a ii Represents the diagonal elements of state matrix A;

[0110] The revised participation factor is:

[0111]

[0112] In the formula, θ λ The angle θ represents the critical modal eigenvalue. pij This represents the relationship between the j-th mode and the i-th state variable Δx. i The argument of the participating factors; This is the revised participation factor;

[0113] The participation activity level is:

[0114]

[0115] in, Represents all state variables related to the g-th generator. The sum, Represents all state variables related to a hybrid renewable energy system. The sum, Represents all state variables The sum, Pal g To increase participation activity.

[0116] The power adjustment amount is:

[0117] ΔP kg =η*Δζ tk *P kg *Pal kg ,

[0118] Δζ tk =ζ T -DR tk ,

[0119] In the formula, η is the exploration factor; Δζ tk This represents the damping ratio DR before the k-th iteration of power transmission within time t. tk With the expected stability margin threshold ζ T The difference between them; ΔP kg Pal represents the power adjustment of the g-th generator in the k-th iteration; kg To represent the participation activity of the g-th generator in the k-th iteration, P kg Let g be the power of the g-th generator in the k-th iteration.

[0120] This embodiment introduces stability as a constraint in the safety-constrained unit combination problem. It obtains a multi-objective function with the stability margin threshold as the boundary, and uses an adaptive power variable step size cross-multiplication algorithm to obtain the multi-objective scheduling result and adjust the output state of the original units. This method effectively overcomes the randomness problem of traditional artificial intelligence algorithms when improving stability margin. It ensures the safe and stable operation of the power system while taking into account economy, and has important significance in practical engineering applications.

[0121] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for power dispatch of hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithms, characterized by, include: Establish a safety-constrained unit combination objective function; By inputting the safety constraint unit combination constraints into the objective function of the safety constraint unit combination, an evaluation model for the dynamic coupling of the hybrid renewable energy system and the unit combination is constructed. Based on the stability margin function of critical modes, dynamic stability constraints are set for the evaluation model and partitioning is performed to construct a multi-objective function for unit combination under dynamic stability constraints, and the stability margin of the initial unit combination scheduling scheme is calculated. An adaptive power variable step size cross-row algorithm is adopted to adjust the unit output based on the participation activity, update the initial unit combination scheduling scheme and stability margin, calculate the corresponding power generation cost, and perform power dispatch based on the power generation cost. Constructing an evaluation model for the dynamic coupling of the hybrid renewable energy system and unit combination includes: The hybrid renewable energy system is equivalent to a static model with constant power. A static system state-space model is established, and the stability margin of the critical mode is solved. The hybrid renewable energy system consists of converter-type wind power generation and full converter-type photovoltaic power generation. By connecting the hybrid renewable energy system with a full-power dynamic model, a dynamic system state-space model is constructed, and the stability margin of the critical mode is solved. The critical mode is the electromechanical oscillation mode in which the hybrid renewable energy system participates, and the stability margin is the damping ratio obtained from eigenvalue analysis and participation factor analysis. The evaluation model of the dynamic coupling between the hybrid renewable energy system and the unit combination is obtained by subtracting the stability margin of the critical mode of the static system state space model from the stability margin of the critical mode of the dynamic system state space model. The participating factors are: ， wherein denotes the amplitude of the eigenvalue; denotes the diagonal elements of the state matrix A; The revised participation factor is: ， In the formula, The angle representing the critical modal eigenvalue; Indicates the first The modality and the first State variables The argument of the participating factors; This is the revised participation factor; The participation activity level is: ， in, Indicates all related to the first The state variables of the generator are related The sum, Represents all state variables related to a hybrid renewable energy system. The sum, Represents all state variables The sum, To increase participation activity; The calculated power adjustment amount is: ， ， In the formula, For exploration factors; Indicates time Inner Before the next iteration of power transmission, the damping ratio With the expected stability margin threshold The difference between them; In the first During the nth iteration The power adjustment amount of the generator; In the first During the nth iteration The level of participation and activity of the generators. For the first During the nth iteration The power of the generator.

2. The method of power dispatch for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithm according to claim 1, wherein, The objective function for the safety-constrained unit combination is: ； In the formula, Safety-constrained unit combination objective function; Indicates the number of time periods for unit scheduling; Indicates the total number of units participating in the scheduling; For fuel cost function; Indicates the unit In time Active power output; and Represent Time and the Start-up and standby costs of a generator set; binary variables Indicates the first The generator set at time The on / off status.

3. The power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms according to claim 1, characterized in that, The safety constraints on the generator set combination include: generator output constraints, ramp output constraints, hot standby constraints, minimum start-up and minimum downtime constraints, and transmission capacity constraints.

4. The power dispatching method for a hybrid renewable energy system based on dynamic stability constraints and adaptive algorithms according to claim 1, characterized in that, The stability margin function of the critical mode is: ， In the formula, This is the stability margin function for the critical mode; The real part of the critical mode eigenvalue; This represents the imaginary part of the critical mode eigenvalue.

5. The method of power dispatch for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithm according to claim 4, characterized in that, The multi-objective function for unit combination under dynamic stability constraints is: ， In the formula, represents a constant, used to distinguish the weak stability region and the strong stability region; represents a security constrained unit commitment objective function; represents the damping ratio of the critical mode at the time ; represents a set stability margin threshold value; is a multi-objective function of unit commitment under dynamic stability constraints.

6. The method of power dispatch for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithm according to claim 1, wherein, The adaptive power variable step size cross-sectional algorithm includes: S1. Initialize the size, dimensions, and number of iterations of the particle swarm; S2. The cross-sectional algorithm is used to obtain the offspring particle swarm; S3. Calculate the damping ratio of the particle swarm in the critical mode. If the damping ratio is greater than the stability margin threshold, then execute S5. If the damping ratio is less than the stability margin threshold, then execute S4. S4. An adaptive power variable step size algorithm is used to adjust the unit output; S5. Perform the next iteration and check if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, then execute S6; otherwise, execute S2. S6: End the iteration and output the globally optimal particle after the iteration.

7. The method of power dispatch for hybrid renewable energy systems based on dynamic stability constraints and adaptive algorithm according to claim 6, characterized in that, In step S4, an adaptive power variable step size algorithm is used to adjust the unit output, including: S4.1 Calculate the participation activity of all generators in the particle swarm under the critical mode. If the participation activity is greater than zero, increase the upper limit of particle exploration for the corresponding generator. If the participation activity is less than zero, decrease the lower limit of particle exploration for the corresponding generator, forming a new particle exploration region. Set a new number of iterations, population size and particle exploration region. S4.2 Calculate the damping ratio of the newly generated particle in the critical mode. If the damping ratio is greater than the stability margin threshold, proceed to S4.

7. If the damping ratio is less than the stability margin threshold, proceed to S4.

3. S4.3 Calculate the participation activity of the newly generated particle swarm generator in the critical mode; S4.4 Calculate the power adjustment amount using the adaptive power variable step size calculation formula; S4.5 Check whether the power adjustment amount meets the generator output limit of the generator set. If it does not meet the limit, update the power adjustment amount to the minimum value of the deviation between the generator output limit and the generator set power. S4.6 Adjust the output of the generator set according to the power adjustment amount calculated in S4.4 or S4.5; S4.

7. Perform the next iteration and determine if the number of iterations is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, execute S4.8; otherwise, execute S4.

2. S4.

8. End the iteration and output the globally optimal particle after the iteration.

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