Improved lo-based new energy microgrid vsg voltage regulation parameter optimization method and system

Abstract

The application relates to a new energy microgrid VSG voltage regulation parameter optimization method and system based on improved LO, and the method comprises the following steps: according to the VSG voltage control mechanism in the new energy microgrid, a group of voltage regulation parameters which have an influence on voltage regulation effect are selected; based on the voltage regulation parameters, a voltage regulation parameter optimization model is constructed by taking the minimization of voltage steady-state deviation, voltage regulation time and system active power loss as the target, considering active power balance constraints and photovoltaic output constraints; the voltage regulation parameters are taken as decision variables to be optimized, and the improved LO based on reverse learning and differential evolution is used to solve the voltage regulation parameter optimization model to obtain a group of optimal decision variables. The decision variables are solved by the improved LO, the global search ability and convergence precision of the algorithm are significantly improved, the optimal parameter combination considering multiple performance indexes can be obtained, and therefore the voltage stability and operation economy of the new energy microgrid are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, in particular to a new energy micro-grid VSG voltage regulation parameter optimization method and system based on improved LO. BACKGROUND

[0002] With the increasing penetration of wind energy, solar energy and other new energy in the power grid, its inherent randomness and volatility have brought serious challenges to the voltage stability of the power grid. The virtual synchronous machine (VSG) technology simulates the inertia and damping characteristics of the traditional synchronous generator, providing an effective way to solve the voltage stability problem of new energy grid-connected.

[0003] However, the voltage control effect of VSG is highly dependent on the setting of its internal control parameters. At present, researchers have used various intelligent optimization algorithms (such as particle swarm optimization PSO, genetic algorithm GA, etc.) to optimize VSG parameters. However, these traditional optimization algorithms often have the following defects when dealing with complex multi-objective optimization problems: 1) the algorithm relies on a high-quality initial population and has strong randomness; 2) the search strategy is limited and is prone to fall into local optimal solution, making it difficult to find the globally optimal parameter combination; 3) it cannot fully adapt to the multiple and dynamic demands of new energy micro-grid in terms of voltage stability, response speed and operating economy.

[0004] Therefore, how to develop a VSG parameter optimization technology with stronger global search ability, faster convergence speed and the ability to comprehensively balance multiple optimization objectives is a technical problem that needs to be solved in the current new energy micro-grid voltage control field. SUMMARY

[0005] The present application provides a new energy micro-grid VSG voltage regulation parameter optimization method and system based on improved LO to solve at least one of the above technical problems.

[0006] The technical solution of the present application to solve the above technical problems is as follows: a new energy micro-grid VSG voltage regulation parameter optimization method based on improved LO, comprising:

[0007] S1, according to the VSG voltage control mechanism in the new energy micro-grid, a group of voltage regulation parameters that affect the voltage regulation effect are selected;

[0008] S2, based on the voltage regulation parameters, a voltage regulation parameter optimization model is constructed to minimize the voltage steady-state deviation, voltage regulation time and system active power loss, considering the active power balance constraint and photovoltaic output constraint;

[0009] S3, the voltage regulation parameters are taken as decision variables to be optimized, and an improved LO based on reverse learning and differential evolution is used to solve the voltage regulation parameter optimization model to obtain a group of optimal decision variables.

[0010] On the basis of the above technical solutions, the application can be further improved as follows.

[0011] Further, in the S2, the voltage regulation parameter optimization model comprises a target function and a constraint condition; the target function comprises a voltage steady-state deviation target function, a voltage regulation time target function and a system active power loss target function; the constraint condition comprises a system active power balance constraint condition and a photovoltaic output constraint condition.

[0012] Further, the voltage steady-state deviation target function is expressed as:

[0013]

[0014] wherein F1 represents the voltage steady-state deviation, n represents the total number of nodes in the new energy microgrid, U i represents the voltage value of the i-th node in the voltage fluctuation process, U n represents the rated voltage;

[0015] The voltage regulation time target function is expressed as:

[0016]

[0017] wherein F2 represents the voltage regulation time, h -1 (·) represents the inverse function, t p represents the time used to reach the maximum value when the voltage deviates, f(t p ) represents the maximum value of the voltage deviation process;

[0018] The system active power loss target function is expressed as:

[0019]

[0020] wherein F3 represents the system active power loss, P device_loss represents the active power loss of the converter, U j represents the voltage value of the j-th node in the voltage fluctuation process, G ij represents the line conductance value between the i-th node and the j-th node, and L represents the total number of system lines.

[0021] Further, the system active power balance constraint condition is expressed as:

[0022] P PV + P ESS = P Loss + P Load ;

[0023] wherein P PV represents the active power output value of the photovoltaic module after being connected to the grid through the VSG; and PESS represents the active power output value of the energy storage module after being connected to the grid by the VSG; P Loss represents the active power loss in the system; P Load represents the active power consumed by the load;

[0024] The photovoltaic output constraint condition is represented as:

[0025]

[0026] wherein, S PV represents the total capacity of the photovoltaic output, Q PV represents the reactive power output value of the photovoltaic module, S PVmax represents the maximum limit of the total capacity of the photovoltaic output, P PVmax represents the maximum limit of the photovoltaic active output.

[0027] Further, the S3 is specifically:

[0028] S31, according to the number of decision variables, an LO individual representing a decision variable is initialized by using a reverse learning strategy;

[0029] S32, the fitness of each LO individual is calculated by using the voltage regulation parameter optimization model, and the LO individual with the optimal fitness is saved;

[0030] S33, an LO updating strategy improved based on a differential evolution algorithm is used, and each LO individual is iteratively updated in combination with the LO individual with the optimal fitness until a maximum value of the iteration number is reached, and a group of decision variables with the optimal voltage regulation effect is output.

[0031] Further, the S31 is specifically:

[0032] S311, the population size of LO and the value range of the decision variable are set, and a plurality of initial LO individuals are randomly generated in combination with the number of decision variables;

[0033] S312, for the decision variable in each of the initial LO individuals, a corresponding reverse variable is generated between the upper and lower limits of its value;

[0034] S313, for the decision variable in each of the initial LO individuals, the decision variable is compared with the corresponding reverse variable after being mixed, and the better variable is selected as the final decision variable to generate the corresponding LO individual.

[0035] Further, the decision variable in the initial LO individual is represented as:

[0036]

[0037] wherein, n represents the population size of the LO, d represents the number of the decision variables, x ij ′ represents the jth decision variable in the ith initial LO individual, and rand represents a random number with a value range of [0, 1]. j ub j represents the upper limit of the value range of the jth decision variable, and lb ij represents the lower limit of the value range of the jth decision variable.

[0038] The reverse variable corresponding to the decision variable is represented as:

[0039] x j + lb j -x ij ′.

[0040] wherein, n represents the population size of the LO, d represents the number of the decision variables, x ij ′ represents the jth decision variable in the ith initial LO individual, and rand represents a random number with a value range of [0, 1].

[0041] Further, the S33 specifically is:

[0042] S331, introducing a free risk rate according to the current iteration number, combining the free risk rate with the LO position updating strategy, and taking the decision variables in the LO individual as parent variables to perform a mutation operation to obtain initial child variables;

[0043] S332, crossing the parent variables with the initial child variables to generate crossover variables;

[0044] S333, selecting variables with optimal fitness from the parent variables and the crossover variables according to a “greedy” criterion to form final child variables;

[0045] S334, judging whether the current iteration number reaches a preset maximum iteration number, if not, increasing the current iteration number by one, taking the child variables obtained in the S333 as the decision variables in the LO individual, and returning to the S331 for cyclic iteration until a group of decision variables with optimal pressure regulating effect is outputted after the maximum iteration number is reached.

[0046] Further, the free risk rate is represented as:

[0047]

[0048] wherein, FRR represents the free risk rate, iter represents the current iteration number, iter max represents the maximum iteration number, FRR high represents the maximum value of the free risk rate, FRR low represents the minimum value of the free risk rate.

[0049] The LO position updating strategy is represented as:

[0050]

[0051] Wherein, rand represents a random number with a value range of [0, 1], x ij,G represents the jth decision variable in the ith LO individual as a parent variable, x near,G represents a neighboring solution, x global,G represents a global optimal solution, v ij,G+1 represents an initial child variable obtained by performing a mutation operation on the jth decision variable in the ith LO individual as a parent variable;

[0052] The formula for crossing the parent variable with the initial child variable is:

[0053]

[0054] Wherein, u ij,G+1 represents a crossover variable obtained by crossing v ij,G+1 and x ij,G , d represents the number of decision variables, and j rand represents a random integer in the range of [1, 2,..., d];

[0055] The child variable is represented as:

[0056]

[0057] Wherein, x ij,G+1 represents a child variable, f(u ij,G+1 ) represents the fitness of u ij,G+1 , and f(x ij,G ) represents the fitness of x ij,G .

[0058] On the basis of the above-mentioned new energy microgrid VSG voltage regulation parameter optimization method based on improved LO, the application further provides a new energy microgrid VSG voltage regulation parameter optimization system based on improved LO.

[0059] The new energy microgrid VSG voltage regulation parameter optimization system based on improved LO comprises a processor, a memory and a computer program stored in the memory, and the computer program is executed by the processor to realize the new energy microgrid VSG voltage regulation parameter optimization method based on improved LO as described above.

[0060] The beneficial effects of this invention are as follows: The improved LO-based method and system for optimizing the voltage regulation parameters of a new energy microgrid VSG uses an improved LO to solve for decision variables. This improved LO introduces a reverse learning strategy in the initialization stage to improve population quality, and integrates the mutation, crossover, and selection ideas of the differential evolution algorithm in the position update strategy. At the same time, it uses the free risk rate to dynamically balance global exploration and local development. By improving the LO to solve for decision variables, this invention significantly improves the global search capability and convergence accuracy of the algorithm, and can obtain the optimal parameter combination that takes into account multiple performance indicators, thereby effectively improving the voltage stability and operating economy of the new energy microgrid. Attached Figure Description

[0061] Figure 1 This is a flowchart of the method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO, as described in this invention.

[0062] Figure 2 VSG grid-connected topology diagram for new energy microgrids;

[0063] Figure 3 This is a schematic diagram of reactive power control.

[0064] Figure 4 This is a schematic diagram of voltage-current loop control;

[0065] Figure 5 This is a schematic diagram of the algorithm's convergence results;

[0066] Figure 6 The output is a voltage waveform diagram of the running result. Detailed Implementation

[0067] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0068] Example 1:

[0069] like Figure 1 As shown, the optimization method for VSG voltage regulation parameters of new energy microgrids based on improved LO includes:

[0070] S1. Based on the VSG voltage control mechanism in the new energy microgrid, select a set of voltage regulation parameters that affect the voltage regulation effect.

[0071] S2, Based on the voltage regulation parameters, with the goal of minimizing voltage steady-state deviation, voltage regulation time and system active power loss, and considering active power balance constraints and photovoltaic output constraints, a voltage regulation parameter optimization model is constructed.

[0072] S3, taking the voltage regulating parameters as decision variables to be optimized, and solving the voltage regulating parameter optimization model by using the improved LO based on reverse learning and differential evolution to obtain a set of optimal decision variables.

[0073] Wherein, LO is the abbreviation of Lemurs Optimizer, representing the Lemurs Optimizer algorithm. The improved Lemurs Optimizer algorithm based on reverse learning and differential evolution is used to solve the decision variables, which has the following advantages: 1. In the initialization population stage, the reverse learning strategy is used to improve the quality of the initialized population. 2. In the position updating strategy of the population individuals, a free risk rate is introduced as a dynamic adjustment parameter to achieve the dynamic balance between global search and local development. 3. The differential evolution algorithm is introduced to improve the position updating strategy of the LO to improve the search precision and achieve the solution of the global optimal solution.

[0074] The application will be specifically described below.

[0075] Figure 2 For the VSG grid-connected topology of the new energy microgrid, the embodiment is based on the topology structure shown in Figure 2 The simulation model (i.e. the voltage regulating parameter optimization model) is built in simulink to verify the topology structure, the rated voltage of the system is set to 380v, the rated frequency is set to 50Hz, the filter inductance of the VSG output side is set to 4mH, the filter capacitance is set to 10uF, the capacity of the photovoltaic power generation is set to 100kW, the rated voltage of the DC bus is set to 600V, and the voltage fluctuation caused by the change of the light intensity is set to optimize the VSG parameters.

[0076] In some embodiments of the S1:

[0077] Figure 3 For the reactive power control schematic diagram, Figure 4 For the voltage and current loop control schematic diagram, combined with Figure 3 and Figure 4 The control block diagram of the reactive power part of the PQ loop control and the voltage and current loop control is constructed, and four voltage regulating parameters, the reactive voltage droop coefficient Kq, the reactive integral coefficient K, the voltage loop PI control parameters K pv , K iv are selected as the decision variables.

[0078] In some embodiments of the S2:

[0079] The voltage regulating parameter optimization model includes an objective function and a constraint condition; the objective function includes a voltage steady-state deviation objective function, a voltage regulating time objective function and a system active power loss objective function; the constraint condition includes a system active power balance constraint condition and a photovoltaic output constraint condition. Wherein:

[0080] The voltage steady-state deviation objective function is expressed as:

[0081]

[0082] In the formula, F1 represents the voltage steady-state deviation, n represents the total number of nodes in the new energy microgrid, U i represents the voltage value of the i-th node in the voltage fluctuation process, U n represents the rated voltage.

[0083] The voltage regulation time target function is represented as:

[0084]

[0085] In the formula, F2 represents the voltage regulation time; h -1 (·) represents the inverse function, which is used to represent the time used in the voltage regulation process; t p represents the time used to reach the maximum value when the voltage deviation occurs; f(t p ) represents the maximum value of the voltage deviation process.

[0086] The system active loss target function is represented as:

[0087]

[0088] In the formula, F3 represents the system active loss, P device_loss represents the active loss of the converter, U j represents the voltage value of the j-th node in the voltage fluctuation process, G ij represents the line conductance value between the i-th node and the j-th node, and L represents the total number of system lines.

[0089] The system active balance constraint condition is represented as:

[0090] P PV +P ESS = P Loss +P Load ;

[0091] In the formula, P PV represents the active power output value of the photovoltaic module after being connected to the VSG; P ESS represents the active power output value of the energy storage module after being connected to the VSG; P Loss represents the active power loss in the system; and P Load represents the active power consumed by the load.

[0092] The photovoltaic output constraint condition is represented as:

[0093]

[0094] In the formula, S PV represents the total capacity of the photovoltaic output, and QPV represents a reactive power output value of a photovoltaic module, S PVmax represents a maximum limit of a total capacity of photovoltaic output, P PVmax represents a maximum limit of photovoltaic active output.

[0095] In some embodiments of the S3:

[0096] The S3 includes the following S31-S33:

[0097] S31, according to the number of decision variables, using a reverse learning strategy to initialize a plurality of LO individuals representing decision variables.

[0098] Specifically, the S31 includes the following S311-S313:

[0099] S311, setting the LO population size and the value range of the decision variables, and randomly generating a plurality of initial LO individuals in combination with the number of decision variables;

[0100] Wherein, the decision variables in the initial LO individuals are represented as:

[0101]

[0102] In the formula, n represents the LO population size (n can be set to 100 in this embodiment), d represents the number of decision variables (d is 4 in this embodiment, that is, four decision variables, also representing the dimension of the decision variables), x ij ′ represents the jth decision variable in the ith initial LO individual, rand represents a random number with a value range of [0, 1] (rand is used to ensure the randomness and diversity of the initial solution), ub j represents the upper limit of the value range of the jth decision variable, lb j represents the lower limit of the value range of the jth decision variable;

[0103] S312, for the decision variables in each of the initial LO individuals, generate corresponding reverse variables between the upper and lower limits of their values;

[0104] Wherein, the reverse variable corresponding to the decision variable is represented as:

[0105] x ij ″=ub j +lb j -x ij ′;

[0106] In the formula, x ij ″ represents the reverse variable corresponding to the jth decision variable in the ith initial LO individual.

[0107] S313, for each decision variable in the initial LO individual, the decision variable is compared with the corresponding reverse variable after mixing, and the better variable is selected as the final decision variable, and the corresponding LO individual is generated.

[0108] S32, the fitness of each LO individual is calculated by using the pressure regulating parameter optimization model, and the LO individual with the optimal fitness is saved.

[0109] Specifically, each group of decision variables representing the LO individual is substituted into the pressure regulating parameter optimization model, and the fitness of the LO individual can be calculated through the objective function.

[0110] S33, an LO updating strategy improved based on a differential evolution algorithm is used, and each LO individual is iteratively updated based on the LO individual with the optimal fitness until the maximum number of iterations is reached, and a group of decision variables with the optimal pressure regulating effect is output.

[0111] Specifically, the S33 includes the following S331-S334:

[0112] S331, according to the current iteration number, a free risk rate is introduced, based on the LO position updating strategy, the decision variable in the LO individual is used as the parent variable to perform a mutation operation, and an initial child variable is obtained;

[0113] The free risk rate is represented as:

[0114]

[0115] In the formula, FRR represents the free risk rate as a dynamic adjustment parameter in the algorithm; iter represents the current iteration number, iter max represents the maximum iteration number; FRR high represents the maximum value of the free risk rate, FRR low represents the minimum value of the free risk rate; iter max , FRR high and FRR low are parameters set in advance according to actual requirements, in the embodiment, FRR high is 0.5, FRR low is 0.1, and the maximum iteration number iter max is 100;

[0116] The LO position updating strategy is represented as:

[0117]

[0118] In the formula, rand represents a random number with a value range of [0, 1], x ij,Gx near,G x global,G v ij,G+1 x

[0119] S332, crossing the parent variable with the initial child variable to generate a crossover variable;

[0120] wherein the formula for crossing the parent variable with the initial child variable is:

[0121]

[0122] wherein u ij,G+1 x ij,G+1 and x ij,G is a crossover variable, d represents the number of decision variables, and j rand is a random integer in the range of [1, 2, …, d];

[0123] S333, selecting a variable with better fitness from the parent variable and the crossover variable according to a "greedy" rule to form a final child variable;

[0124] wherein the child variable is represented as:

[0125]

[0126] wherein x ij,G+1 is a child variable, f(u ij,G+1 ) represents the fitness of u ij,G+1 , and f(x ij,G ) represents the fitness of x ij,G ;

[0127] S334, determining whether the current iteration number reaches a preset maximum iteration number, if not, increasing the current iteration number by one, taking the child variable obtained in the S333 as a decision variable in the LO individual, and returning to the S331 for cyclic iteration until the maximum iteration number is reached and a set of decision variables with optimal voltage regulation effect is output.

[0128] In addition, the embodiment sets up a comparative experiment to compare the VSG voltage regulation results. Two groups are set up in the comparative experiment: one group uses a traditional particle swarm optimization (PSO) to optimize the VSG parameters, and the other group uses the improved LO to optimize the VSG parameters.

[0129] When the maximum iteration number is 100, the iteration convergence curve output by the comparative experiment is as shown in FIG. 3. Figure 5As shown. The results of the comparative experiments are as follows: ① When using PSO for optimization, the decision variables are 5.51, 0.52, 10.3, and 334, and the corresponding objective functions are: F1 is 6.9122V, F2 is 0.15s, and F3 is 5.3191kW; ② When using improved LO for optimization, the decision variables are 2.16, 0.47, 3.4, and 477, and the corresponding objective functions are: F1 is 4.6538V, which is 32.7% better than experimental group ①; F2 is 0.08s, which is 80ms shorter than experimental group ①; F3 is 5.1856kW, which is 133W less than experimental group ①.

[0130] Based on the experimental results, a simulation model of voltage regulation parameters was used to obtain the phase voltage waveforms at the PCC terminal for the two experimental groups, as shown below. Figure 6 As shown. By Figure 6 Analysis shows that the improved LO used in this invention has stronger global search capability and faster convergence speed. The optimized voltage regulation parameters can achieve faster response speed and more stable voltage regulation effect, and can comprehensively balance the optimization of multiple objectives.

[0131] Example 2:

[0132] Based on the above-mentioned method for optimizing the voltage regulation parameters of VSG in new energy microgrids based on improved LO, this invention also provides a system for optimizing the voltage regulation parameters of VSG in new energy microgrids based on improved LO.

[0133] The new energy microgrid VSG voltage regulation parameter optimization system based on improved LO includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the new energy microgrid VSG voltage regulation parameter optimization method based on improved LO as described above.

[0134] The application is based on a new energy microgrid VSG voltage regulation parameter optimization method and system, and aims to solve the problems that the existing VSG parameter optimization algorithm is easy to fall into local optimum, the optimization precision is insufficient, and it is difficult to balance the voltage regulation rapidity, stability and economy. In the method and system, first, the voltage control mechanism of the virtual synchronous machine is analyzed, and a group of key voltage regulation parameters are selected as decision variables; second, a comprehensive optimization model is constructed, taking the voltage steady-state deviation, voltage regulation time and the minimum system active power loss as the optimization objectives; finally, the improved LO is used to solve the model. The improved LO introduces a reverse learning strategy in the initialization stage to improve the population quality, and combines the mutation, crossover and selection ideas of the differential evolution algorithm in the position update strategy, and uses the free risk rate to dynamically balance global exploration and local development. The application solves the decision variables by the improved LO, significantly improves the global search ability and convergence precision of the algorithm, and can obtain the optimal parameter combination considering multiple performance indicators, thereby effectively improving the voltage stability and operation economy of the new energy microgrid.

[0135] The above only describes the preferred embodiments of the application and is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO, characterized in that, include: S1. Based on the VSG voltage control mechanism in the new energy microgrid, select a set of voltage regulation parameters that affect the voltage regulation effect. S2, Based on the voltage regulation parameters, with the goal of minimizing voltage steady-state deviation, voltage regulation time and system active power loss, and considering active power balance constraints and photovoltaic output constraints, a voltage regulation parameter optimization model is constructed. S3, the voltage regulation parameters are used as decision variables to be optimized, and the improved LO based on reverse learning and differential evolution is used to solve the voltage regulation parameter optimization model to obtain a set of optimal decision variables; Specifically, S3 is: S31, Based on the number of decision variables, a reverse learning strategy is used to initialize multiple LO individuals representing the decision variables; S32, calculate the fitness of each LO individual using the voltage regulation parameter optimization model, and save the LO individual with the best fitness; S33 adopts an improved LO update strategy based on differential evolution algorithm. It combines the LO individuals with the best fitness and iteratively updates each LO individual until the maximum number of iterations is reached, and outputs a set of decision variables with the best voltage regulation effect. Specifically, S31 is: S311, Set the LO population size and the range of values ​​for the decision variables, and randomly generate multiple initial LO individuals based on the number of decision variables; S312, For the decision variables in each of the initial LO individuals, generate corresponding inverse variables between the upper and lower limits of their values; S313, For the decision variables in each of the initial LO individuals, they are mixed with the corresponding inverse variables and compared. The better variable is selected as the final decision variable, and then the corresponding LO individual is generated. The decision variables in the initial LO individuals are represented as follows: ； in, Indicates the size of the LO population. This indicates the number of decision variables. Indicates the first In the initial LO individual, the first One decision variable, This represents a random number with a value in the range [0,1]. Indicates the first The upper limit of the range of values ​​for each decision variable. Indicates the first The lower limit of the range of values ​​for each decision variable; The inverse variable corresponding to the decision variable is represented as follows: ； in, Indicates the first In the initial LO individual, the first The inverse variables corresponding to each decision variable; Specifically, S33 is: S331, Based on the current iteration number, a free risk rate is introduced. Based on the LO position update strategy and combined with the free risk rate, the decision variables in the LO individuals are used as parent variables for mutation operation to obtain the initial child variables. S332, Interact the parent variable with the initial child variable to generate an interacting variable; S333, according to the "greedy" criterion, select the variable with better fitness from the parent variable and the cross variable to form the final child variable; S334, determine whether the current iteration count has reached the preset maximum iteration count. If not, increment the current iteration count by one, and use the offspring variables obtained in S333 as decision variables in the LO individual. Then return to S331 to iterate until the maximum iteration count is reached and output the set of decision variables with the best voltage regulation effect. The free risk rate is expressed as: ； in, This represents the free risk rate. This indicates the current iteration number. Indicates the maximum number of iterations. This represents the maximum value of the free risk rate. This represents the minimum value of the free risk rate; The LO position update strategy is expressed as follows: ； in, This represents a random number with a value in the range [0,1]. For the first Among the LO individuals, the first Each decision variable is represented as a parent variable. Indicates the nearest neighbor solution. This represents the globally optimal solution. Indicates the first Among the LO individuals, the first The initial child variables are obtained by mutating each decision variable as a parent variable. The formula for crossing the parent variable and the initial child variable is: ； in, Indicates by and The cross variables obtained from the crossover, This indicates the number of decision variables. Represents the interval [1,2,…,…] d A random integer within the range of ]; The child variable is represented as: ； in, Represents the offspring variable. express Adaptability, express The degree of adaptability.

2. The method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO, as described in claim 1, is characterized in that... In S2, the voltage regulation parameter optimization model includes an objective function and constraints; the objective function includes a voltage steady-state deviation objective function, a voltage regulation time objective function, and a system active power loss objective function; the constraints include a system active power balance constraint and a photovoltaic power output constraint.

3. The method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO, as described in claim 2, is characterized in that... The objective function for the steady-state voltage deviation is expressed as: ； in, Indicates the steady-state voltage deviation. This represents the total number of nodes in the new energy microgrid. Indicates the first step in the voltage fluctuation process The voltage value of each node, Indicates the rated voltage; The objective function for the voltage regulation time is expressed as: ； in, Indicates the voltage regulation time. Indicates the inverse function. This indicates the time it takes for the voltage to reach its maximum value when a deviation occurs. This represents the maximum value during the voltage deviation process; The objective function for the active power loss of the system is expressed as: ； in, This indicates the system's active power loss. This represents the active power loss of the converter. Indicates the first step in the voltage fluctuation process The voltage value of each node, Indicates the first The node to the first The line conductance between nodes L This indicates the total number of system lines.

4. The method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO, as described in claim 2, is characterized in that... The active power balance constraint of the system is expressed as follows: ； in, This indicates the active power output value of the photovoltaic module after it is connected to the grid via VSG; This indicates the active power output of the energy storage module after it is connected to the grid via VSG; This indicates the active power loss within the system; This represents the active power consumed by the load. The photovoltaic output constraint condition is expressed as follows: ； in, Indicates the total photovoltaic power output capacity. This indicates the reactive power output value of the photovoltaic module. This indicates the maximum limit on the total photovoltaic power output capacity. This indicates the maximum limit on the active power output of photovoltaic systems.

5. A new energy microgrid VSG voltage regulation parameter optimization system based on improved LO, characterized in that, It includes a processor, a memory, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the method for optimizing the voltage regulation parameters of a new energy microgrid VSG based on an improved LO as described in any one of claims 1 to 4.

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