Wind-solar-hydrogen storage micro-grid system capacity optimization configuration method based on MPSO

Abstract

The invention relates to the technical field of energy storage systems, in particular to a wind-solar-hydrogen storage micro-grid system capacity optimization configuration method based on MPSO, which comprises the following steps: setting a parameter acquisition period, acquiring power generation parameters in a wind power generation device and a photovoltaic power generation device based on the acquisition period, and synchronously acquiring tie line power parameters based on the acquisition period; filtering the collected power generation parameters and tie line power parameters; power generation parameters and tie line power parameters after filtering processing are obtained, and a learning model is built through an MPSO algorithm by combining the power generation parameters and the tie line power parameters; according to the method, more reasonable capacity configuration can be carried out on the wind-light-hydrogen storage micro-grid system, the power generation power, hydrogen storage system parameters, load requirements and other conditions of the device are reliably measured according to specific conditions, an improved multi-target optimization particle swarm optimization model is established, and the optimal capacity of the wind-light-hydrogen storage micro-grid system is obtained. And carrying out reliable capacity optimization configuration on the micro-grid system by taking the overall benefits of all parties and the economical efficiency and the reliability of the operation of the micro-grid system as optimization targets.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a method for optimizing the capacity of a wind-solar-hydrogen storage microgrid system based on MPSO. Background Art

[0002] The wind-solar-hydrogen-storage microgrid system is a microgrid that integrates wind power generation, solar photovoltaic power generation, hydrogen power generation and energy storage systems. This system uses two clean energy sources, wind and solar energy, to generate electricity, and uses the excess electricity generated to electrolyze water to produce hydrogen, storing the energy in a more convenient form of hydrogen. When electricity demand increases or renewable energy generation is insufficient, the stored hydrogen can be converted into electricity through fuel cells to meet electricity demand. This system can improve energy utilization efficiency and enhance the stability and reliability of the power grid. In addition, while being self-sufficient, the wind-solar-hydrogen-storage microgrid system can also transmit excess electricity back to the grid, or obtain electricity from the grid when electricity is insufficient to maintain a balance between supply and demand.

[0003] In recent years, with the increasing attention paid to renewable energy generation, people have paid more and more attention to how to stabilize the instability of wind and solar power generation to reduce energy waste and power outages. In order to meet these challenges, the wind-solar-hydrogen storage microgrid system came into being. By integrating wind, solar and hydrogen energy, it effectively improves the utilization efficiency and economic benefits of renewable energy, while promoting the clean production and storage of energy. Although this system shows great potential, it still faces challenges in terms of technological maturity, and further research and innovation are needed to enhance its performance and stability.

[0004] When operating this system, a core issue is how to determine its optimal capacity configuration, which requires careful design based on actual energy demand. Currently, research on the optimal capacity configuration of this system is not perfect enough, and many studies focus on the overall system benefits.

[0005] To this end, a capacity optimization configuration method for wind-solar-hydrogen storage microgrid system based on MPSO was proposed. Summary of the invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a capacity optimization configuration method for a wind-solar-hydrogen storage microgrid system based on MPSO, which solves the technical problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method includes:

[0009] Set the parameter collection cycle, collect power generation parameters in wind power generation devices and photovoltaic power generation devices based on the collection cycle, and synchronously collect interconnection line power parameters based on the collection cycle; filter the collected power generation parameters and interconnection line power parameters; obtain the filtered power generation parameters and interconnection line power parameters, and combine the two to build a learning model through the MPSO algorithm; set the capacity configuration target of the wind-solar-hydrogen storage microgrid system, and import the parameters of the configuration target into the learning model; start the learning model, repeat the iterative search process of the particle swarm, and continuously find the optimal capacity configuration plan that meets the target.

[0010] Furthermore, the power generation parameters include wind power generation parameters and photovoltaic power generation parameters. The wind power generation parameters include wind speed, wind direction, wind rotor speed, power output, air density and turbulence intensity. The photovoltaic power generation parameters include light intensity, temperature, open circuit voltage, short circuit current, maximum power point power and filling factor of the photovoltaic cell. The tie line power parameters include active power, power direction, power fluctuation rate and power factor.

[0011] Among them, the parameter collection cycle is set based on historical power generation parameters.

[0012] Furthermore, the setting logic of the parameter collection period is expressed as:

[0013]

[0014] Where: T collect is the parameter acquisition period; ω, γ is the weight; F is the fluctuation factor of the power generation equipment; R is the response factor of the power generation equipment; S is the factor based on storage limitation;

[0015] Among them, the weight ω, γ are all positive numbers, and the weights ω, The sum of γ is 1, the weight value is customized by the user, and the weight is initially set to 0.5, 0.3, and 0.2.

[0016] Furthermore, the calculation formulas of F, R, and S are:

[0017]

[0018] Where: α, β are weights; σ w , σ s is the standard deviation of wind power generation fluctuation and photovoltaic power generation fluctuation; is the average power of wind power generation in the historical period, and the average power of photovoltaic power generation in the historical period; T w , T s is the cumulative response time of wind power generation equipment and photovoltaic power generation equipment; M maxis the maximum acceptable data storage capacity per unit time; m is the average storage bytes of a single data point; T tolal is the corresponding duration of the union of the historical time periods of wind power generation and photovoltaic power generation;

[0019] Among them, the weights α and β are customized by the user end, and the weights α and β are both in the range of [0, 1]. The wind power generation in the historical time period and the photovoltaic power generation in the historical time period are customized by the user end. max , m comes from the storage device used after the power generation parameters are collected.

[0020] Furthermore, before filtering the power generation parameters and the interconnection line power parameters, the parameters are initially processed by any one of the matrices including the state transfer matrix, the observation matrix, the process noise covariance matrix, and the measurement noise covariance matrix. After the parameters are initialized, the parameters that have completed the initialization processing are further filtered based on the Kalman filtering algorithm.

[0021] Furthermore, when the filtered power generation parameters and tie line power parameters are processed by the MPSO algorithm, they are optimized by introducing dynamic inertia weights or adding Gaussian disturbances. After the MPSO algorithm completes the optimization, the corresponding learning model is constructed.

[0022] Among them, the optimization of the MPSO algorithm is preferably optimized by introducing dynamic inertia weights. The optimization steps include:

[0023] Set the dynamic inertia weight function:

[0024] ω′=ω max -(ω max -ω min )*(iter / iter max );

[0025] Where: ω′ is the current inertia weight; ω max is the maximum value of the set inertia weight; ω min is the minimum value of the set inertia weight; iter is the current number of iterations; iter max is the total maximum number of iterations;

[0026] The dynamic inertia weight obtained based on the dynamic inertia weight function is further embedded into the particle velocity update part of the MPSO algorithm, that is, introduced into the particle velocity update formula:

[0027]

[0028] Where: v[i][j] is the velocity of the ith particle in the jth dimension; c1 and c2 are learning factors; rand() is a random number generation function; pbest[i][j] and gbest[i][j] are the components of the individual optimal position of the ith particle in the jth dimension and the group optimal position in the jth dimension; x[i][j] is the component of the current position of the ith particle in the jth dimension;

[0029] Among them, rand(), ω max ,ω max Customized by the user.

[0030] Furthermore, the steps of building the learning model include:

[0031] In the learning model, the particle position vector is set to represent the capacity of the components of the wind-solar-hydrogen-storage microgrid system, and the velocity vector of the corresponding particle is used to control the movement state of the particle in the search space. According to the expected goal of system capacity configuration, a fitness function is constructed to determine the quality of the capacity configuration scheme represented by different particles.

[0032] Set the value range of penalty factor and learning factor;

[0033] Divide the preprocessed data into training sets and validation sets in appropriate proportions, use the validation set to validate the model, check the fit between the capacity configuration results output by the model and the actual expected results, coordinate the penalty factor and learning factor of the model based on the validation results, and finally output the learning model;

[0034] Among them, the components of the wind, solar, hydrogen and storage microgrid system include wind turbines, photovoltaic panels, and hydrogen storage equipment. The goals that the system capacity configuration expects to achieve include cost control and power supply reliability. The cost control among the goals that the system capacity configuration expects to achieve includes electricity value and fees. The power supply reliability among the goals that the system capacity configuration expects to achieve includes: the failure frequency of the wind, solar, hydrogen and storage microgrid system and the average energy storage capacity of the wind, solar, hydrogen and storage microgrid system.

[0035] Furthermore, the fitness function is:

[0036]

[0037] Where: F(x) is the fitness value; C(x) is the cost objective function; T l is the duration of power outage; T is the total duration of the system in a given time period; E out E is the effective electric energy output by the system within a certain period of time; w is the total wind energy input; E s is the total solar energy input;

[0038] Among them, the cost objective function C(x) = C inv +C om +C f , C inv , C om , C f It includes equipment investment cost, operation and maintenance cost, and fuel cost.

[0039] Furthermore, the wind-solar-hydrogen-storage microgrid system capacity configuration objectives include: utilization rate, cost, power load, and energy loss rate;

[0040] Among them, the user-side customized cycle or the master control iterates the capacity configuration target of the wind, solar, hydrogen and storage microgrid system. When the capacity configuration target of the wind, solar, hydrogen and storage microgrid system changes, the refresh step is executed and the learning model is synchronously iterated and rebuilt.

[0041] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0042] The present invention provides a capacity optimization configuration method for a wind-solar-hydrogen-storage microgrid system based on MPSO. During the execution of the method, a more reasonable capacity configuration can be performed on the wind-solar-hydrogen-storage microgrid system. According to specific circumstances, the power generation power, hydrogen storage system parameters, load demand and other conditions of the device are reliably measured, and an improved multi-objective optimization particle swarm algorithm model is established. The overall benefits of all parties, the economy and reliability of the operation of the microgrid system are taken as optimization objectives, and reliable capacity optimization configuration is performed on such a microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flow chart of the MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method;

[0045] Figure 2 This is a schematic diagram of the structure of the wind-solar-hydrogen storage microgrid system in the present invention;

[0046] Figure 3 It is a schematic diagram of the overall operation principle of the wind-solar-hydrogen storage microgrid system in the present invention;

[0047] Figure 4 It is a schematic diagram of the overall process of the multi-objective optimization particle swarm algorithm in the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The present invention will be further described below in conjunction with the embodiments.

[0050] Example:

[0051] The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method of this embodiment is as follows: Figure 1 As shown, including:

[0052] Setting a parameter collection cycle, collecting power generation parameters in the wind power generation device and the photovoltaic power generation device based on the collection cycle, and synchronously collecting the interconnection line power parameters based on the collection cycle;

[0053] The setting logic of the parameter acquisition cycle is expressed as:

[0054]

[0055] Where: T collect is the parameter acquisition period; ω, γ is the weight; F is the fluctuation factor of the power generation equipment; R is the response factor of the power generation equipment; S is the factor based on storage limitation;

[0056] Among them, the weight ω, γ are all positive numbers, and the weights ω, The sum of γ is 1, and the weight value is customized by the user. The weight is initially set to 0.5, 0.3, and 0.2;

[0057] The calculation formulas for F, R, and S are:

[0058]

[0059] Where: α, β are weights; σ w , σ s is the standard deviation of wind power generation fluctuation and photovoltaic power generation fluctuation; is the average power of wind power generation in the historical period, and the average power of photovoltaic power generation in the historical period; T w , T s is the cumulative response time of wind power generation equipment and photovoltaic power generation equipment; M maxis the maximum acceptable data storage capacity per unit time; m is the average storage bytes of a single data point; T tolal is the corresponding duration of the union of the historical time periods of wind power generation and photovoltaic power generation;

[0060] Among them, the weights α and β are customized by the user end, and the weights α and β are both in the range of [0, 1]. The wind power generation in the historical time period and the photovoltaic power generation in the historical time period are customized by the user end. max , m comes from the storage device used after the power generation parameters are collected;

[0061] Filter the collected power generation parameters and tie line power parameters;

[0062] Obtain the power generation parameters and tie line power parameters after filtering, and combine them to build a learning model through the MPSO algorithm;

[0063] Set the capacity configuration target of the wind-solar-hydrogen-storage microgrid system and import the parameters of the configuration target into the learning model;

[0064] Start the learning model and repeat the iterative search process of the particle swarm to continuously find the optimal capacity configuration solution that meets the goal;

[0065] When the filtered power generation parameters and tie-line power parameters are processed by the MPSO algorithm, they are optimized by introducing dynamic inertia weights or adding Gaussian disturbances. After the MPSO algorithm completes the optimization, the corresponding learning model is constructed.

[0066] Among them, the optimization of the MPSO algorithm is preferably optimized by introducing dynamic inertia weights. The optimization steps include:

[0067] Set the dynamic inertia weight function:

[0068] ω′=ω max -(ω max -ω min )*(iter / iter max );

[0069] Where: ω′ is the current inertia weight; ω max is the maximum value of the set inertia weight; ω min is the minimum value of the set inertia weight; iter is the current number of iterations; iter max is the total maximum number of iterations;

[0070] The dynamic inertia weight obtained based on the dynamic inertia weight function is further embedded into the particle velocity update part of the MPSO algorithm, that is, introduced into the particle velocity update formula:

[0071]

[0072] Where: v[i][j] is the velocity of the ith particle in the jth dimension; c1 and c2 are learning factors; rand() is a random number generation function; pbest[i][j] and gbest[i][j] are the components of the individual optimal position of the ith particle in the jth dimension and the group optimal position in the jth dimension; x[i][j] is the component of the current position of the ith particle in the jth dimension;

[0073] Among them, rand(), ω max ,ω max Customized by the user;

[0074] The steps to build a learning model include:

[0075] In the learning model, the particle position vector is set to represent the capacity of the components of the wind-solar-hydrogen-storage microgrid system, and the velocity vector of the corresponding particle is used to control the movement state of the particle in the search space. According to the expected goal of system capacity configuration, a fitness function is constructed to determine the quality of the capacity configuration scheme represented by different particles.

[0076] Set the value range of penalty factor and learning factor;

[0077] Divide the preprocessed data into training sets and validation sets in appropriate proportions, use the validation set to validate the model, check the fit between the capacity configuration results output by the model and the actual expected results, coordinate the penalty factor and learning factor of the model based on the validation results, and finally output the learning model;

[0078] Among them, the components of the wind-solar-hydrogen-storage microgrid system include wind turbines, photovoltaic panels, and hydrogen storage equipment. The goals expected to be achieved by the system capacity configuration include cost control and power supply reliability. The goals expected to be achieved by the system capacity configuration include cost control, electricity value, and fees. The goals expected to be achieved by the system capacity configuration include power supply reliability, including: the fault frequency of the wind-solar-hydrogen-storage microgrid system, and the average energy storage capacity of the wind-solar-hydrogen-storage microgrid system;

[0079] The fitness function is:

[0080]

[0081] Where: F(x) is the fitness value; C(x) is the cost objective function; T l is the duration of power outage; T is the total duration of the system in a given time period; E out E is the effective electric energy output by the system within a certain period of time; w is the total wind energy input; E s is the total solar energy input;

[0082] Among them, the cost objective function C(x) = C inv +C om +C f , C inv , C om , C f Equipment investment cost, operation and maintenance cost, and fuel cost;

[0083] The capacity configuration targets of wind-solar-hydrogen-storage microgrid systems include: utilization rate, cost, power load, and energy loss rate;

[0084] Among them, the user-side customized cycle or the master control iterates the capacity configuration target of the wind, solar, hydrogen and storage microgrid system. When the capacity configuration target of the wind, solar, hydrogen and storage microgrid system changes, the refresh step is executed and the learning model is synchronously iterated and rebuilt.

[0085] In this embodiment, an improved multi-objective optimization particle swarm algorithm model is established, the overall benefits of all parties, the economy and reliability of the microgrid system operation are taken as optimization goals, and reliable capacity optimization configuration is performed on such a microgrid system.

[0086] See also Figure 3 As shown in the figure, new energy generation devices such as wind power generation and photovoltaic power generation provide electricity to the microgrid. When the wind power generation and photovoltaic power generation are greater than the load demand, the excess electricity is stored in the form of hydrogen through electrolyzer hydrogen production and other methods. The excess electricity can also be sold to the power grid to improve the economic benefits of the microgrid system. When the wind power generation and photovoltaic power generation are less than the load demand, the fuel cell uses the previously stored hydrogen energy to generate electricity to make up for the load difference, maintain the reliability of the microgrid system operation, and avoid load interruption. If the load demand is still not met, electricity can be purchased from the power grid through the interconnection line.

[0087] like Figure 1 As shown, the power generation parameters include wind power generation parameters and photovoltaic power generation parameters. The wind power generation parameters include wind speed, wind direction, wind rotor speed, power output, air density and turbulence intensity. The photovoltaic power generation parameters include light intensity, temperature, open circuit voltage of photovoltaic cells, short circuit current, maximum power point power, and filling factor. The tie line power parameters include active power, power direction, power fluctuation rate, and power factor.

[0088] Among them, the parameter collection cycle is set based on historical power generation parameters.

[0089] Through the above settings, the contents of power generation parameters and interconnection line power parameters are further limited.

[0090] On the other hand, mathematical modeling of wind-solar-hydrogen-storage microgrid system:

[0091] In terms of economic benefits, we need to consider the situation of three investors: wind power, photovoltaic power, and hydrogen storage. The three investors form a non-cooperative game relationship in the entire capacity allocation process, with both competition and cooperation. The set of game parties can be expressed as shown in formula (3-1):

[0092] N = {WT, PV, H} (3-1);

[0093] The profit function of each investor is shown in formula (3-2):

[0094] G all =G sell -G buy -G punish -G run (3-2)

[0095] In the above formula, G all Represents the total return of the investor, G sell Represents the investor’s electricity sales income, G buy Represents the cost of electricity purchased by the investor, G punish Represents the penalty factor introduced by the investor due to wind and solar power abandonment or load interruption, G run Represents the investor's operation and maintenance costs.

[0096] The revenue from electricity sales is shown in formula (3-3):

[0097]

[0098] Where A represents the electricity price at that time, P sell To sell electrical power, T represents the system operating period.

[0099] The electricity purchase cost is shown in formula (3-4), P buy represents the electric power purchased from the power grid, and B represents the electricity price at that time.

[0100]

[0101] The penalty factor is shown in formula (3-5):

[0102] G punish =C×W1+D×W2 (3-5)

[0103] Among them, C is the penalty factor corresponding to the wind and solar power abandonment behavior, D is the penalty factor corresponding to the load interruption behavior, W1 is the amount of electricity wasted due to the wind and solar power abandonment behavior, and W2 is the amount of electricity not supplied due to the load interruption behavior.

[0104] The investment party's operation and maintenance costs are shown in formula (3-6):

[0105] G run =E×G inv (3-6)

[0106] Where: E is the operation and maintenance cost coefficient, G inv is the total investment cost.

[0107] In combination with actual usage, it is necessary to restrict the number of devices installed. The number of devices is limited as shown in formula (3-7):

[0108] N min ≤N≤N max (3-7)

[0109] N min 、N max The lower and upper limits for the number of devices.

[0110] The following further explains the process of filtering through the Kalman filter algorithm after initializing the parameters:

[0111] Prediction stage:

[0112] State prediction: Based on the state equation of the system, the state estimate at the previous moment k is used To predict the current state value For linear systems, the state equation is usually expressed as Among them, F k is the state transfer matrix, which describes the transition law of the system state from time k-1 to time k; is the optimal state estimate after filtering at the previous moment; B k is the control input matrix, u k It is the control input at the current moment (if any). For example, in wind power generation parameter filtering, the state variables may include the changes of relevant physical quantities such as wind speed and wind direction over time, and their dynamic evolution laws are reflected through the state transfer matrix for prediction.

[0113] Error covariance prediction:

[0114] At the same time, the error covariance matrix P of the state estimation kk-1 It is also predicted, and its calculation formula is Here P k-1 is the error covariance matrix of the previous moment, which reflects the uncertainty of the state estimation at the previous moment; Yes F k The transpose of Q k It is the process noise covariance matrix, which is used to describe the uncertainty caused by random interference factors in the system during the state transfer process. For example, the impact of random fluctuations of natural factors such as wind speed and light in the actual environment on power generation parameters. This formula can be used to predict the error covariance of the state prediction at the current moment.

[0115] Update phase:

[0116] Calculate the Kalman gain: First, calculate the Kalman gain K k , and its calculation formula is Among them, H k It is the observation matrix, which establishes the connection between state variables and observation variables. For example, the corresponding relationship between the observation values ​​such as power generation measured by sensors and the actual state variables such as wind speed and light is reflected by the observation matrix; R k is the measurement noise covariance matrix, which reflects the uncertainty caused by sensor measurement errors during the observation process. The Kalman gain is determined by comprehensively considering factors such as prediction error covariance, observation relationship, and measurement error through the above formula. This gain is used to weigh the weights of the predicted value and the observed value.

[0117] State update: Using Kalman gain K k The predicted state value Update to get the optimal state estimate after filtering at the current moment The calculation formula is Here k is the actual observation value at the current moment, that is, the actual measurement data collected by sensors, such as power generation and tie line power. This formula represents the difference between the observation value and the observation prediction value based on the predicted state, and corrects the predicted state after weighting by the Kalman gain, so as to obtain a more accurate state estimate.

[0118] Error covariance update: Update the error covariance matrix. The updated error covariance matrix P k The calculation formula is P k =(IK k H k ) k|k-1 , where I is the identity matrix. The error covariance update reflects the uncertainty of the current state estimate after the state update, preparing for the next filtering cycle.

[0119] like Figure 1 As shown, before the power generation parameters and the interconnection line power parameters are filtered, the parameters are initially processed by any one of the matrices including the state transfer matrix, the observation matrix, the process noise covariance matrix, and the measurement noise covariance matrix. After the parameters are initialized, the parameters that have completed the initialization processing are further filtered based on the Kalman filtering algorithm.

[0120] Through the above settings, further execution logic support is provided for the execution of the method in this embodiment, ensuring the effectiveness and reliability of the execution of the method steps in this embodiment.

[0121] In summary, the method in the above embodiment can reduce the system's load supply interruption rate and wind and solar abandonment rate as much as possible while ensuring the highest overall benefits of each part (wind power generation, photovoltaic power generation, and hydrogen storage), and can make a more reasonable capacity configuration for the wind, photovoltaic, and hydrogen storage microgrid system. According to specific circumstances, the device's power generation power, hydrogen storage system parameters, load demand, etc. are reliably measured, and an improved multi-objective optimization particle swarm algorithm model is established. The overall benefits of all parties, the economy and reliability of the microgrid system operation are taken as optimization goals, and reliable capacity optimization configuration is performed on such microgrid system.

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A capacity optimization configuration method for a wind-solar-hydrogen-storage microgrid system based on MPSO, characterized in that: include: Setting a parameter collection cycle, collecting power generation parameters in the wind power generation device and the photovoltaic power generation device based on the collection cycle, and synchronously collecting the interconnection line power parameters based on the collection cycle; Filtering the collected power generation parameters and tie line power parameters; Obtain the power generation parameters and tie line power parameters after filtering, and combine them to build a learning model through the MPSO algorithm; Set the capacity configuration target of the wind-solar-hydrogen-storage microgrid system and import the parameters of the configuration target into the learning model; Start the learning model and repeat the iterative search process of the particle swarm to continuously find the optimal capacity configuration solution that meets the goal.

2. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: The power generation parameters include wind power generation parameters and photovoltaic power generation parameters. The wind power generation parameters include wind speed, wind direction, wind rotor speed, power output, air density and turbulence intensity. The photovoltaic power generation parameters include light intensity, temperature, open circuit voltage, short circuit current, maximum power point power and filling factor of photovoltaic cells. The tie line power parameters include active power, power direction, power fluctuation rate and power factor. Among them, the parameter collection cycle is set based on historical power generation parameters.

3. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: The setting logic of the parameter acquisition cycle is expressed as: Where: T collect is the parameter acquisition period; ω, γ is the weight; F is the fluctuation factor of the power generation equipment; R is the response factor of the power generation equipment; S is the factor based on storage limitation; Among them, the weight ω, γ are all positive numbers, and the weights ω, The sum of γ is 1, the weight value is customized by the user, and the weight is initially set to 0.5, 0.3, and 0.

2.

4. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 3 is characterized in that: The calculation formulas of F, R and S are: Where: α, β are weights; σ w , σ s is the standard deviation of wind power generation fluctuation and photovoltaic power generation fluctuation; is the average power of wind power generation in the historical period, and the average power of photovoltaic power generation in the historical period; T w , T s is the cumulative response time of wind power generation equipment and the cumulative response time of photovoltaic power generation equipment; M max is the maximum acceptable data storage capacity per unit time; m is the average storage bytes of a single data point; T tolal is the corresponding duration of the union of the historical time periods of wind power generation and photovoltaic power generation; Among them, the weights α and β are customized by the user end, and the weights α and β are both in the range of [0, 1]. The wind power generation in the historical time period and the photovoltaic power generation in the historical time period are customized by the user end. max , m comes from the storage device used after the power generation parameters are collected.

5. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: Before filtering the power generation parameters and the interconnection line power parameters, the parameters are initially processed by any one of the matrices including the state transfer matrix, the observation matrix, the process noise covariance matrix, and the measurement noise covariance matrix. After the parameters are initialized, the parameters that have completed the initialization processing are further filtered based on the Kalman filtering algorithm.

6. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: When the filtered power generation parameters and tie line power parameters are processed by the MPSO algorithm, they are optimized by introducing dynamic inertia weights or adding Gaussian disturbances. After the MPSO algorithm completes the optimization, the corresponding learning model is constructed. Among them, the optimization of the MPSO algorithm is preferably optimized by introducing dynamic inertia weights. The optimization steps include: Set the dynamic inertia weight function: ω′=ω max -(oh max -oh min )*(iter / iter max ); Where: ω′ is the current inertia weight; ω max is the maximum value of the set inertia weight; ω min is the minimum value of the set inertia weight; iter is the current number of iterations; iter max is the total maximum number of iterations; The dynamic inertia weight obtained based on the dynamic inertia weight function is further embedded into the particle velocity update part of the MPSO algorithm, that is, introduced into the particle velocity update formula: Where: v[i][j] is the velocity of the ith particle in the jth dimension; c1 and c2 are learning factors; rand() is a random number generation function; pbest[i][j] and gbest[i][j] are the components of the individual optimal position of the ith particle in the jth dimension and the group optimal position in the jth dimension; x[i][j] is the component of the current position of the ith particle in the jth dimension; Among them, rand(), ω max ,ω max Customized by the user.

7. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: The steps of building the learning model include: In the learning model, the particle position vector is set to represent the capacity of the components of the wind-solar-hydrogen-storage microgrid system, and the velocity vector of the corresponding particle is used to control the movement state of the particle in the search space. According to the expected goal of system capacity configuration, a fitness function is constructed to determine the quality of the capacity configuration scheme represented by different particles. Set the value range of penalty factor and learning factor; Divide the preprocessed data into training sets and validation sets in appropriate proportions, use the validation set to validate the model, check the fit between the capacity configuration results output by the model and the actual expected results, coordinate the penalty factor and learning factor of the model based on the validation results, and finally output the learning model; Among them, the components of the wind, solar, hydrogen and storage microgrid system include wind turbines, photovoltaic panels, and hydrogen storage equipment. The goals that the system capacity configuration expects to achieve include cost control and power supply reliability. The cost control among the goals that the system capacity configuration expects to achieve includes electricity value and fees. The power supply reliability among the goals that the system capacity configuration expects to achieve includes: the failure frequency of the wind, solar, hydrogen and storage microgrid system and the average energy storage capacity of the wind, solar, hydrogen and storage microgrid system.

8. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 7 is characterized in that: The fitness function is: Where: F(x) is the fitness value; C(x) is the cost objective function; T l is the duration of power outage; T is the total duration of the system in a given time period; E out E is the effective electric energy output by the system within a certain period of time; w is the total wind energy input; E s is the total solar energy input; Among them, the cost objective function C(x) = C inv +C om +C f , C inv , C om , C f It includes equipment investment cost, operation and maintenance cost, and fuel cost.

9. The MPSO-based wind-solar-hydrogen-storage microgrid system capacity optimization configuration method according to claim 1 is characterized in that: The capacity configuration targets of the wind-solar-hydrogen-storage microgrid system include: utilization rate, cost, power load, and energy loss rate; Among them, the user-side customized cycle or the master control iterates the capacity configuration target of the wind, solar, hydrogen and storage microgrid system. When the capacity configuration target of the wind, solar, hydrogen and storage microgrid system changes, the refresh step is executed and the learning model is synchronously iterated and rebuilt.

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