Micro-grid light storage capacity optimization configuration method and system

Abstract

The application provides a micro-grid light storage capacity optimization configuration method and system, relates to the micro-grid optimization technical field, and the method comprises the following steps: setting segmented capacity configuration parameters according to the load characteristic parameters and energy access conditions of a micro-grid; setting a first compensation correction value and a second compensation correction value based on the impedance matching characteristics of the micro-grid and the charging and discharging efficiency parameters of the energy storage unit; combining the capacity optimization configuration parameters obtained by optimization and inversely analyzing the segmented capacity configuration parameters; collecting real-time monitoring data in real time through an industrial Ethernet, and dynamically refreshing and rolling optimizing the capacity optimization configuration parameter combination according to the preset power balance threshold and the actual operation deviation of the light storage collaborative configuration execution scheme. The application solves the technical problem that the capacity redundancy or deficiency is caused due to the static fixation of the micro-grid light storage capacity configuration in the prior art, and improves the configuration precision of the micro-grid light storage capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid optimization, in particular to a micro-grid light storage capacity optimization configuration method and system. BACKGROUND

[0002] At present, the existing micro-grid light storage capacity configuration method generally relies on typical historical data for static planning, that is, the rated capacity of photovoltaic and energy storage is determined according to long-term average meteorological data and typical load curve in the design stage. The static fixed configuration mode is difficult to adapt to the fluctuation changes in actual operation, leading to the problems of capacity redundancy or insufficient capacity in actual operation. Capacity redundancy will cause waste of initial investment and resource idling, while insufficient capacity will directly cause power supply interruption risk. Since it cannot be corrected according to the actual performance, the micro-grid continuously bears the performance loss and operation risk caused by configuration mismatch in the whole life cycle, which seriously restricts the power supply reliability of the micro-grid.

[0003] In summary, the existing technology has the technical problem that the static fixation of micro-grid light storage capacity configuration leads to capacity redundancy or insufficient capacity, thereby affecting the power supply reliability of the micro-grid. SUMMARY

[0004] The purpose of the present application is to provide a micro-grid light storage capacity optimization configuration method and system to solve the technical problem in the prior art that the static fixation of micro-grid light storage capacity configuration leads to capacity redundancy or insufficient capacity, thereby affecting the power supply reliability of the micro-grid.

[0005] In order to achieve the above purpose, the present application provides a micro-grid light storage capacity optimization configuration method and system.

[0006] In a first aspect, the present application provides a micro-grid light storage capacity optimization configuration method, which is realized by a micro-grid light storage capacity optimization configuration system, wherein the micro-grid light storage capacity optimization configuration method comprises: setting a segmented capacity configuration parameter associated with photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation according to the load characteristic parameters and energy access conditions of the micro-grid; setting a first compensation correction value and a second compensation correction value based on the impedance matching characteristics of the micro-grid and the charge and discharge efficiency parameters of the energy storage unit; combining the capacity optimization configuration parameter obtained by optimization into the segmented capacity configuration parameter based on the first compensation correction value and the second compensation correction value, to generate a light storage collaborative configuration execution scheme; collecting real-time monitoring data of voltage, current and SOC state data of each power balance influence area in real time through industrial Ethernet, and dynamically refreshing and rolling optimizing the capacity optimization configuration parameter combination according to the preset power balance threshold and actual operation deviation of the light storage collaborative configuration execution scheme.

[0007] Optionally, based on the load characteristic parameters of the micro-grid and the energy access conditions, the load fluctuation amplitude and the new energy output prediction error are analyzed in time sequence, and sensitive nodes are identified, and the power shortage and supply-demand balance critical point of each period is calculated; through the power shortage and supply-demand balance critical point of each period, the micro-grid dispatching period is divided into photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation.

[0008] Optionally, based on the impedance matching characteristics of the micro-grid, the power grid impedance parameters and the power transmission efficiency are detected in full dimension combined with the power quality demand, and a first compensation correction value is obtained; based on the segmented capacity configuration parameters, the power flow distribution of the photovoltaic output fluctuation process is simulated by power simulation, and a photovoltaic output gradient parameter meeting the impedance matching stability limitation is obtained.

[0009] Optionally, based on the charge-discharge efficiency parameters of the energy storage unit, the capacity attenuation amount of different SOC intervals is dynamically deduced combined with historical charge-discharge cycle data, and a second compensation correction value is obtained; based on the segmented capacity configuration parameters, the energy storage access point of the micro-grid is optimized by topology optimization, and a reactive power compensation gap meeting the power balance limitation is obtained.

[0010] Optionally, according to the first compensation correction value and the photovoltaic output gradient parameter, the second compensation correction value and the reactive power compensation gap, an associated mapping matrix is constructed; taking the coupling influence coefficient of the associated mapping matrix as the fitness function weight, the minimum of the grid-connected point voltage deviation of the micro-grid, the maximum of the capacity utilization rate of the energy storage cluster and the minimum of the response delay of the flexible load aggregate are taken as the optimization target.

[0011] Optionally, the matrix row dimension of the associated mapping matrix is the impedance matching compensation element category associated with the first compensation correction value, the power transmission efficiency compensation element category, the high SOC segment attenuation compensation element category associated with the second compensation correction value, and the charge-discharge cycle compensation element category; the matrix column dimension of the associated mapping matrix is the power balance influence area of the micro-grid, including the grid-connected point of the photovoltaic output segmentation, the energy storage cluster of the energy storage regulation segmentation, and the flexible load aggregate of the load response segmentation.

[0012] Optionally, the value range of the first compensation correction value and the photovoltaic output gradient parameter, the value range of the second compensation correction value and the reactive power compensation gap are taken as the particle search space, and the adaptive inertia weight is introduced for iterative optimization; the capacity configuration parameter combination generated by each round of optimization is input into the micro-grid operation simulation model for verification, and if the simulation verification result meets the power quality demand, the current capacity configuration parameter combination is dynamically refreshed, and the capacity optimization configuration parameter combination is output.

[0013] Optionally, if the simulation verification result does not meet the power quality demand, a compensation element category sequence is obtained based on a positioning deviation contribution degree of the correlation mapping matrix, the positioning deviation contribution degrees are sorted from high to low, local optimization is performed based on the compensation element category sequence until the number of iterations reaches a preset threshold, and then the capacity optimization configuration parameter combination obtained by optimization is inversely analyzed into the segmented capacity configuration parameter to generate a photovoltaic storage collaborative configuration execution scheme including a dynamic output smoothing curve of photovoltaic output segmentation, a step-type charging and discharging scheme of energy storage adjustment segmentation, and a partitioned load regulation strategy of load response segmentation.

[0014] Optionally, the parameter sensitivity of the correlation mapping matrix is taken as the network input weight, and the difference between the target value of the power quality demand is taken as the network input layer variable; after each round of refreshing is completed, the updated capacity optimization configuration parameter combination is substituted into the correlation mapping matrix for reverse verification to determine the deviation improvement rate of each power balance influence area; if the deviation improvement rate is lower than a preset improvement rate threshold, the boundary constraint of the particle search space is updated based on the compensation element category sequence.

[0015] In a second aspect, the application further provides a micro-grid photovoltaic storage capacity optimization configuration system for executing the micro-grid photovoltaic storage capacity optimization configuration method of the first aspect, wherein the micro-grid photovoltaic storage capacity optimization configuration system comprises: a parameter configuration module configured to set segmented capacity configuration parameters associated with photovoltaic output segmentation, energy storage adjustment segmentation, and load response segmentation according to load characteristic parameters and energy access conditions of a micro-grid; a compensation correction module configured to set a first compensation correction value and a second compensation correction value based on impedance matching characteristics of the micro-grid and charging and discharging efficiency parameters of the energy storage unit; a scheme generation module configured to inversely analyze a capacity optimization configuration parameter combination obtained by optimization into the segmented capacity configuration parameters based on the first compensation correction value and the second compensation correction value to generate a photovoltaic storage collaborative configuration execution scheme; and a deviation optimization module configured to collect real-time monitoring data including voltage, current, and SOC state data of each power balance influence area in real time through an industrial Ethernet, and perform dynamic refreshing and rolling optimization on the capacity optimization configuration parameter combination according to a preset power balance threshold and an actual operation deviation of the photovoltaic storage collaborative configuration execution scheme.

[0016] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0017] By setting the segmented capacity configuration parameters associated with the photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation according to the load characteristic parameters and energy access conditions of the micro-grid; based on the impedance matching characteristics of the micro-grid and the charging and discharging efficiency parameters of the energy storage unit, the first compensation correction value and the second compensation correction value are set; based on the first compensation correction value and the second compensation correction value, the capacity optimization configuration parameter combination obtained by optimization is combined and inversely analyzed into the segmented capacity configuration parameter to generate a photovoltaic storage collaborative configuration execution scheme; through industrial Ethernet real-time acquisition of real-time monitoring data of each power balance influence area including voltage, current and SOC state data, according to the preset power balance threshold and actual operation deviation of the photovoltaic storage collaborative configuration execution scheme, the capacity optimization configuration parameter combination is dynamically refreshed and optimized. That is, by setting the segmented capacity configuration parameters, introducing the compensation correction value to correct the capacity configuration error, and through industrial Ethernet real-time acquisition of real-time monitoring data of each power balance influence area, according to the preset power balance threshold and actual operation deviation of the photovoltaic storage collaborative configuration execution scheme, the capacity optimization configuration parameter combination is dynamically refreshed and optimized, which improves the micro-grid photovoltaic storage capacity configuration precision, thereby improving the power balance capability and power supply reliability of the micro-grid under different operating conditions.

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating laborious work on the basis of the provided drawings.

[0020] Figure 1 The flowchart of the micro-grid photovoltaic storage capacity optimization configuration method of the present application.

[0021] Figure 2 The structure diagram of the micro-grid photovoltaic storage capacity optimization configuration system of the present application.

[0022] Explanation of reference signs: parameter configuration module 11, compensation correction module 12, scheme generation module 13, deviation optimization module 14. DETAILED DESCRIPTION

[0023] The application provides a micro-grid light storage capacity optimization configuration method and system, which solves the technical problem of the prior art that the static fixed configuration of the light storage capacity of the micro-grid leads to capacity redundancy or deficiency, thereby affecting the power supply reliability of the micro-grid. By setting segmented capacity configuration parameters, introducing a compensation correction value to correct the capacity configuration error, and collecting real-time monitoring data of each power balance influencing area in real time through an industrial Ethernet, the capacity optimization configuration parameter combination is dynamically refreshed and optimized according to the preset power balance threshold and the actual operation deviation of the light storage collaborative configuration execution scheme, the capacity configuration accuracy of the micro-grid light storage is improved, and the power balance capability and power supply reliability of the micro-grid under different operating conditions are improved.

[0024] In the following, the technical solutions in the application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the example embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings, rather than all parts.

[0025] Embodiment one, please refer to the accompanying drawings Figure 1 The application provides a micro-grid light storage capacity optimization configuration method, which is applied to a micro-grid light storage capacity optimization configuration system and specifically includes the following steps:

[0026] According to the load characteristic parameters and energy access conditions of the micro-grid, segmented capacity configuration parameters associated with photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation are set.

[0027] Further, the application further includes the following steps: based on the load characteristic parameters and energy access conditions of the micro-grid, the load fluctuation amplitude and new energy output prediction error are subjected to time sequence equivalent analysis and sensitive node identification, and the power shortage and supply-demand balance critical point of each period is calculated; by the power shortage and supply-demand balance critical point of each period, the micro-grid dispatching period is divided into photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation.

[0028] Specifically, the load characteristic parameters of the micro-grid are key parameters for describing the change law of the internal load of the micro-grid, such as the peak-valley ratio of the load curve, the maximum load power, the minimum load power, the load change rate, etc.; the energy access conditions are the physical and operating conditions of photovoltaic, wind power and other new energy and energy storage systems accessing the micro-grid, including access capacity, access point location, inverter capacity, access voltage level, etc.

[0029] Based on the load characteristic parameters and energy access conditions of micro-grid, the load fluctuation amplitude and new energy output prediction error are analyzed by time sequence equivalence, that is, the load fluctuation amplitude and new energy output prediction error are processed by time sequence data equivalence, and the long time sequence is compressed into typical daily profile. The power surplus section and power shortage section are identified according to time sequence. The power flow calculation and sensitivity analysis are carried out on the micro-grid topology to identify sensitive nodes, locate the key electrical nodes in the micro-grid which are most likely to fluctuate voltage and have the greatest impact on system stability. The power shortage is calculated at each time step and the supply-demand balance critical point is calibrated. These critical points are used as boundaries to divide the dispatching period into photovoltaic output segment, energy storage regulation segment and load response segment, and initial capacity configuration parameters are assigned to each segment.

[0030] The power shortage is the difference between the load demand and the new energy generation output in a certain period. When the load is greater than the power generation, positive shortage occurs, which needs to be supplemented by energy storage or power grid. The supply-demand balance critical point is the turning point from safe and stable operation state to unsafe and unstable state.

[0031] The photovoltaic output segment is the period when photovoltaic energy output matches the initial power, which corresponds to the regulation time window of photovoltaic component layout and output fluctuation suppression. The energy storage regulation segment is the period when power imbalance occurs continuously, and energy storage charging and discharging must be used to continuously compensate the power shortage to avoid voltage fluctuation and frequency deviation. The load response segment is the period when the system balance is maintained by managing the load side resources, including setting flexible load priority, interruptible load threshold and local reactive power compensation measures to align the power balance and power quality index of each period.

[0032] Exemplarily, assuming that the peak load of a micro-grid in a region is 150 kW, and a 200 kW photovoltaic is installed. Through power flow calculation, it is determined that the node A at the end of the 10 kV feeder is a voltage sensitive point, the normal operating voltage is 10.2 kV, and the lower limit of safety is 9.8 kV. The load is 130 kW and the photovoltaic output is 0 kW from 18:00 to 19:00, and the power shortage is +130 kW. At this time, the energy storage needs to be discharged. If the rated discharge power of the energy storage is 80 kW, there will still be a 50 kW power shortage that needs to be supplemented by the power grid or load reduction. Through simulation, it is found that when the energy storage is continuously discharged at 80 kW for 15 minutes, the SOC decreases from 60% to 45%, and at the same time, the voltage of node A slowly drops from 10.1 kV to 9.85 kV, close to the critical point of 9.8 kV. Therefore, 18:15 is a critical point of supply and demand balance. If no intervention is made, the voltage will be out of limit. 18:00-18:15 is defined as an energy storage adjustment section, and the energy storage is set to discharge at the maximum power of 80 kW to delay the voltage drop and SOC decrease; 18:15 is defined as a load response section, and the critical point is identified, and the section is automatically activated. The preset parameter starts: first, the 20 kW interruptible load with the lowest priority is cut off; then, the 50 kvar reactive power compensation device near node A is put into operation. After the measures are implemented, the power shortage is reduced to 110 kW, and the voltage of node A rises to 9.92 kV, and the system is restored to stability.

[0033] By identifying the critical point and implementing different strategies in different sections, the transformation from passive response to active prevention is realized, thereby significantly improving the utilization efficiency of energy storage and load resources under the premise of ensuring power supply reliability and power quality, and avoiding unnecessary capacity waste.

[0034] Based on the impedance matching characteristics of the micro-grid and the charge and discharge efficiency parameters of the energy storage unit, a first compensation correction value and a second compensation correction value are set.

[0035] Further, the application further includes the following steps: based on the impedance matching characteristics of the micro-grid, the power grid impedance parameters and the power transmission efficiency are detected in all dimensions in combination with the power quality demand, and a first compensation correction value is obtained; based on the segmented capacity configuration parameters, the power flow distribution of the photovoltaic output fluctuation process is simulated by power simulation, and a photovoltaic output gradient parameter meeting the impedance matching stability limitation is obtained.

[0036] Specifically, the equivalent impedance of the critical point of grid-connection is calculated by online impedance measurement and measured short-circuit capacity. Meanwhile, the power transfer efficiency is evaluated and the transfer loss at different power levels is calibrated. With voltage deviation, frequency deviation and harmonic upper limit as constraints, the sensitivity analysis of voltage sensitivity caused by impedance is carried out. The sensitivity results are compared with the power quality limit value, and the power margin required to ensure the voltage / frequency / harmonic constraints is obtained, so as to calculate the first compensation correction value. For example, assuming that the main line impedance of a certain microgrid is large, the measured average resistance is 0.15Ω, and the reactance is 0.10Ω. The power quality requirement stipulates that the voltage deviation of all user nodes cannot exceed-10%, and the rated line voltage is 400V. The planned configuration of photovoltaic capacity is 100kW, and through power simulation, when the load peak is 150kW and the photovoltaic full load is 100kW, due to the line impedance voltage drop, the voltage of the most remote node is 0.92pu, i.e. 368V, which is close to the critical value of 0.9pu, i.e. the critical value is 360V. In order to establish a reliable margin, the voltage lifting target value is set to 0.945pu, i.e. 378V, so the voltage value that needs to be lifted is 0.945-0.92=0.025pu, which corresponds to the actual voltage of 10V. In high-voltage / middle-voltage power distribution, the line voltage drop is mainly affected by the reactive power and the reactance, and theoretically it can be approximately calculated by actual voltage=(the reactive power that needs to be injected reactance) / rated line voltage. By substituting the rated line voltage 400V, the actual voltage 10V and the reactance 0.10Ω into the formula, the reactive power that needs to be injected is 40kvar. Therefore, in order to stabilize the voltage at the end from 0.92pu to 0.945pu, about 40kvar of reactive power support needs to be continuously provided at the photovoltaic grid-connection point. The original plan is to configure the photovoltaic inverter only according to the rated active power 100kW. In order to provide sufficient voltage support margin, it is calculated that the capacity of the photovoltaic inverter needs to be additionally configured by 8% to provide the reactive power compensation capability, so the first compensation correction value is determined to be 8%, which means that the apparent power capacity of the photovoltaic inverter should not be less than 108kVA, instead of the initial plan of 100kW.

[0037] Impedance matching characteristics refer to the impedance relationship among power sources, lines and loads in a micro-grid, and mismatching can lead to power transmission efficiency reduction, voltage fluctuation and even oscillation instability. Power quality requirements are technical indicators for stable operation of a power grid, including voltage deviation, frequency deviation and harmonic suppression requirements. Voltage deviation refers to the deviation of actual voltage from rated voltage, which is generally not more than ± 7%; frequency deviation refers to the deviation of actual frequency from rated frequency, which is not more than ± 0.2 Hz; harmonic suppression requirements refer to the limitation of high-frequency harmonic content in current and voltage. Grid impedance parameters are impedance modulus and phase at a grid connection point or a key bus, which are usually expressed by equivalent impedance. Power transmission efficiency is the transmission efficiency of functional energy from the sending end to the receiving end on a specific power flow path, considering line loss, transformer loss and commutation / inverter conversion loss. Full-dimensional detection is the measurement of grid impedance parameters and power transmission efficiency in the full domain and under all operating conditions, i.e. from the grid connection point to the end load, rather than only sampling at individual points. The first compensation correction value is the capacity margin additionally increased on the basis of theoretically calculated optical storage capacity to offset voltage drop and power loss caused by line impedance.

[0038] The segmented capacity configuration parameters are substituted into the power simulation model, a photovoltaic output disturbance sequence is applied in the simulation, and the photovoltaic output change rate is gradually adjusted to solve the maximum photovoltaic output gradient that can be allowed under given impedance and power quality constraints, i.e. the engineering limit value for limiting photovoltaic instantaneous change and avoiding causing excessive voltage drop or frequency disturbance. The simulation results and impedance sensitivity jointly determine the final first compensation correction value and gradient constraint, so that the capacity configuration meets both utilization and power quality. The photovoltaic output gradient parameter is the maximum allowed limit value set for the photovoltaic power change rate, which functions to reduce the change rate through energy storage smoothing or active light rejection when the actual photovoltaic power change exceeds this limit value, so as to ensure system impedance matching stability and avoid voltage overrun.

[0039] Through full-dimensional detection of grid impedance and power transmission efficiency and the first compensation correction value calculated based on power quality constraints, and through the photovoltaic output gradient parameter obtained by power simulation, the instantaneous output change of photovoltaic and the necessary power margin can be predefined in capacity configuration and operation control, so as to avoid grid connection point voltage overrun, limit frequency fluctuation and suppress harmonic problems caused by large active power change when facing rapid cloud shadow or sudden output change.

[0040] Further, the application further includes the following steps: based on the charge and discharge efficiency parameters of the energy storage unit, combining historical charge and discharge cycle data, dynamically deducing the capacity attenuation amount in different SOC intervals to obtain a second compensation correction value; based on the segmented capacity configuration parameters, optimizing the energy storage access point of the micro-grid by topology optimization to obtain a reactive power compensation gap that meets the power balance limit.

[0041] Specifically, based on the charge-discharge efficiency parameters of the energy storage unit and combined with its historical charge-discharge cycle data, a refined battery degradation model considering the SOC interval is established. The charge-discharge efficiency parameters of the energy storage unit are indicators describing the energy conversion capability of the energy storage system, mainly including charge-discharge efficiency and cycle efficiency; the historical charge-discharge cycle data is a database recording the past charge-discharge process of the energy storage system, including the number of charge-discharge cycles, average discharge depth, SOC operating interval, power change, temperature and other records experienced in actual operation, for evaluating life degradation and capacity decline.

[0042] The capacity degradation of the energy storage system in different SOC intervals is dynamically derived using a refined battery degradation model. The SOC interval is the state of charge range of the energy storage, which represents the percentage of the current stored energy relative to the rated capacity, such as the low SOC interval 0-20%, the medium SOC interval 20-80%, and the high SOC interval 80-100%. For example, the derivation result shows that the annual average degradation rate of the battery in the low SOC interval is as high as 3% due to frequent deep charging and discharging at high temperatures. According to the derivation result, the additional capacity that needs to be increased initially to meet the power supply demand at the end of the full life cycle is calculated, i.e., the second compensation correction value is obtained. The second compensation correction value is the capacity margin that is additionally increased based on the theoretical configuration capacity to compensate for the efficiency loss and capacity degradation of the energy storage system during the full life cycle. For example, in a specific configuration case, it is assumed that the microgrid requires the energy storage system to still be able to discharge 500 kWh of electricity per day at the end of the 10th year. First, the energy conversion loss is taken into account: if the average cycle efficiency of the system is 92%, the initial storage energy is 500 / 0.92≈543.5 kWh, and the initial capacity needs to be increased by about 8.7% due to efficiency loss compared to the net output demand. Then, the physical-empirical-based battery aging model is used for degradation derivation, which needs to simulate calendar aging and cycle aging. The calendar aging is related to time, temperature, and average state of charge, and the cycle aging is related to cycle number and average discharge depth. Based on the typical annual operation plan or historical data of the microgrid, the future operation conditions of the energy storage system are counted or predicted as the model input. The key data includes an annual average ambient temperature of 25°C, an annual average equivalent full charge and discharge cycle number of 300 times, an average discharge depth of 60%, and an average state of charge of 50%. The key data is input into the calibrated aging model for simulation and derivation. At the end of the 10th year (at the end of life), the capacity retention rate of the battery decreases to 60%, i.e., the total degradation rate is 40%. According to this derivation result, to ensure that the storage energy after efficiency compensation can still be provided at the end of life, the initial rated capacity needs to meet 543.5 kWh / 0.6≈905.8 kWh, which is defined as the percentage of the initial capacity that needs to be additionally increased relative to the baseline demand considering only the net discharge energy. That is, compared to the theoretical configuration of 500 kWh considering only the net energy, the actual capacity needs to be increased by . Considering the model uncertainty and the safety margin reserved for the system, the second compensation correction value can be determined as 85% in engineering. The initial installation capacity of the energy storage system should be configured with about 85% margin based on the baseline energy demand of 500 kWh, i.e., about 925 kWh.

[0043] According to the segmented power requirement defined by the segmented capacity configuration parameter, the network structure of the micro-grid is analyzed by a topology optimization algorithm, the optimal access point of the energy storage is optimized and set, and the best electrical position is determined, with the minimum network loss and the best voltage stability as the objective function and the power flow equation as the constraint. After determining the optimal access point, the reactive power shortage of each segment is analyzed through power flow calculation, so as to obtain the reactive power compensation gap under the power balance limit. The reactive power compensation gap is the additional reactive power capacity that needs to be provided or absorbed under the premise of meeting the active power balance, which clearly defines how much reactive power compensation capacity the energy storage inverter needs to have to work cooperatively with the access point and ensure the voltage stability of the system.

[0044] The first compensation correction value is a power margin or capacity correction coefficient calculated according to the impedance matching characteristics of the micro-grid and the power quality constraints, which is used to compensate for the risk of voltage or frequency fluctuation during capacity optimization; the second compensation correction value is a compensation coefficient based on the capacity attenuation of the energy storage in different SOC intervals and the insufficient charging and discharging efficiency, which is used to reserve the effective capacity of the energy storage in capacity configuration. By setting the first compensation correction value and the second compensation correction value, the voltage sensitivity and energy storage capacity loss can be quantitatively compensated before micro-grid capacity optimization, improving the capacity utilization rate and system power supply reliability.

[0045] Based on the first compensation correction value and the second compensation correction value, the capacity optimization configuration parameters obtained by optimization are combined and inversely analyzed into the segmented capacity configuration parameters to generate a photovoltaic and energy storage collaborative configuration execution scheme.

[0046] Further, the present application further comprises the following steps: constructing an associated mapping matrix according to the first compensation correction value and the photovoltaic output gradient parameter, the second compensation correction value and the reactive power compensation gap; taking the coupling influence coefficient of the associated mapping matrix as the fitness function weight, and taking the minimization of the voltage deviation of the corresponding grid-connected point of the micro-grid, the maximization of the capacity utilization rate of the energy storage cluster, and the minimization of the response delay of the flexible load aggregate as the optimization objective.

[0047] Specifically, based on the first compensation correction value, the photovoltaic output gradient parameter, the second compensation correction value, and the energy storage reactive compensation gap, a mapping relationship matrix between each capacity configuration parameter of the microgrid and the power balance influence area is established. The matrix row represents the correction parameter or the capacity control element, including the impedance matching compensation element, the power transmission efficiency compensation element, the high SOC segment attenuation compensation element, and the charge and discharge cycle compensation element, each row corresponding to a specific quantifiable parameter; the matrix column represents each influence area of the microgrid, including the photovoltaic grid-connected point, the energy storage cluster, and the flexible load aggregate, each column representing an area that needs to be evaluated for performance; the value in the cell represents the correlation strength, that is, the coupling influence coefficient. The coupling influence coefficient quantifies the specific value of the influence degree of the change of one parameter in the matrix on another parameter or target. Specifically, in the microgrid operation simulation model, keeping other parameters unchanged, only a series of discrete perturbations are made to the parameters associated with a certain compensation element within its typical range, and the change in the key performance indicator of another power balance area under each perturbation is recorded. By linear regression or calculating the ratio of the change in the performance indicator to the amount of parameter perturbation, the original sensitivity coefficient of the change of one parameter on another parameter is obtained. Since the dimensions and orders of magnitude of different performance indicators are different, all original sensitivity coefficients are normalized to fall within the [0, 1] interval, and the sum of all coefficients in the same column is 1. The normalized value is the coupling influence coefficient.

[0048] When calling the optimization algorithm, the associated mapping matrix is taken as the core, and the coupling influence coefficient of the associated mapping matrix is taken as the fitness function weight to optimize the capacity configuration combination. That is, the sum of all coupling influence coefficients in each column of the matrix reflects the comprehensive influence potential of all compensation elements on the region, and the larger the sum is, the more sensitive the performance of the region is to the overall configuration strategy. According to the microgrid operation regulations, a basic priority coefficient related to power supply safety is assigned to each influence area, which is usually set according to the risk level caused by the performance deterioration of the area. The higher the risk level is, the larger the coefficient is. For example, the voltage stability of the grid-connected point directly relates to the system collapse risk, and its basic priority coefficient is the highest; the load response delay relates to comfort, and its basic priority coefficient is relatively low. The total influence degree of the region is combined with the basic priority coefficient and normalized to obtain an objective fitness function weight: the comprehensive influence degree of each region is multiplied by the basic safety priority coefficient corresponding to the region to obtain a weighted comprehensive value, and the weighted comprehensive values of all power balance influence areas are added to obtain a total sum. The final fitness function weight of each region is equal to the proportion of the weighted comprehensive value of the region in the total sum of the weighted comprehensive values of all regions. Through normalization processing, the total sum of all weights is 1.

[0049] The optimization target is to simultaneously satisfy the minimization of the micro-grid corresponding to the grid point voltage deviation, the maximization of the energy storage cluster capacity utilization rate, and the minimization of the flexible load aggregate response delay. Therefore, the objective function is The grid point voltage deviation The energy storage cluster capacity utilization rate The flexible load aggregate response delay. For example, assuming that the photovoltaic gradient parameter of a certain scheme is -25 kW / min and the energy storage compensation value is 15%, after being substituted into the simulation verification, the grid point voltage deviation is 0.02 pu, the energy storage cluster capacity utilization rate is 85%, and the flexible load aggregate response delay is 25 ms. Assuming that the set weights are the grid point voltage deviation weight 0.35, the energy storage cluster capacity utilization rate weight 0.45, and the flexible load aggregate response delay weight 0.2, after normalization, the grid point voltage deviation is 0.4, the energy storage cluster capacity utilization rate is 0.3, and the flexible load aggregate response delay is 0.5. Substituting into the objective function .

[0050] The grid point voltage deviation is the difference between the actual voltage and the rated voltage at the connection point of the micro-grid and the main grid; the energy storage cluster capacity utilization rate is the ratio of the actual capacity used by the energy storage system to the total available capacity within the dispatching period; and the flexible load aggregate response delay is the time required from the issuance of the dispatching instruction to the overall flexible load reaching the predetermined adjustment target. By introducing the correlation mapping matrix and the weighted multi-objective optimization, the photovoltaic and energy storage collaborative configuration is more reasonable, the overall power balance of the system, the energy storage regulation, and the load response reach a dynamic optimization state, and the micro-grid stability and power supply reliability are further improved.

[0051] Further, the application further includes the following steps: the matrix row dimension of the correlation mapping matrix is the impedance matching compensation element category associated with the first compensation correction value, the power transmission efficiency compensation element category, the high SOC segment attenuation compensation element category associated with the second compensation correction value, and the charge and discharge cycle compensation element category; and the matrix column dimension of the correlation mapping matrix is the power balance influence area of the micro-grid, including the grid point of the photovoltaic output section, the energy storage cluster of the energy storage regulation section, and the flexible load aggregate of the load response section.

[0052] Specifically, the matrix row dimension of the correlation mapping matrix includes four types of compensation elements, namely, an impedance matching compensation element category, a power transmission efficiency compensation element category, a high SOC segment attenuation compensation element category, and a charge-discharge cycle compensation element category, which correspond to the first compensation correction value and the second compensation correction value, respectively. The impedance matching compensation element category is a specific aspect that needs to be considered to compensate for the impact of line impedance, including voltage compensation amount, reactive power support demand, etc.; the power transmission efficiency compensation element category is a specific aspect that needs to be considered to compensate for the impact of resistance loss, including active loss amount, derating operation coefficient, etc.; the high SOC segment attenuation compensation element category is a factor that aggravates the capacity attenuation of the energy storage battery when it is operated in a high state of charge interval, including capacity attenuation rate, internal resistance growth rate, etc.; and the charge-discharge cycle compensation element category is a life attenuation factor caused by the charge-discharge behavior of the energy storage itself, including equivalent cycle number and average discharge depth, etc.

[0053] The matrix column dimension covers all power balance influencing areas in the microgrid, namely, a grid-connected point of a photovoltaic output segment, an energy storage cluster of an energy storage adjustment segment, and a flexible load aggregate of a load response segment. The grid-connected point of the photovoltaic output segment is a common connection point of the photovoltaic power generation system accessing the microgrid, and is a key position for monitoring the photovoltaic power injection and the impact on the power grid; the energy storage cluster of the energy storage adjustment segment is a collection of all energy storage units as a unified coordinated operation, and is an execution subject for energy transfer and power support; and the flexible load aggregate of the load response segment is a virtual whole load that can be uniformly dispatched.

[0054] After the matrix row dimension and the matrix column dimension of the correlation mapping matrix are determined, the matrix is filled, that is, the contribution degree of each compensation element to each power balance area is numerized to form a coupling influence coefficient. For example, the contribution of the high SOC segment attenuation compensation element category to the energy storage cluster of the energy storage adjustment segment can be 0.9, indicating a greater impact on the available capacity of the energy storage, and the contribution to the grid-connected point of the photovoltaic output segment can be only 0.1.

[0055] Further, the application further includes the following steps: taking the first compensation correction value and the value range of the photovoltaic output gradient parameter, and the second compensation correction value and the value range of the reactive power compensation gap as a particle search space, introducing an adaptive inertia weight for iterative optimization; the capacity configuration parameter combination generated in each round of optimization is input into a microgrid operation simulation model for verification, and if the simulation verification result meets the power quality demand, the current capacity configuration parameter combination is refreshed dynamically, and the capacity optimization configuration parameter combination is output.

[0056] Specifically, the first compensation correction value and the value range of the photovoltaic output gradient parameter, the second compensation correction value and the value range of the reactive power compensation gap are taken as the particle search space, that is, the upper and lower limits of the four key variables are taken as the boundary of the algorithm search. Each possible solution in the particle swarm optimization algorithm is regarded as a particle, and the range of all particle activities is the particle search space.

[0057] A group of particles is initialized, and the position of each particle represents a random combination of capacity configuration parameters. In the iterative optimization process with adaptive inertia weight, the adjustment strategy usually adopts a linear decreasing method. The weight is set to a larger value at initialization to encourage the particles to explore widely in the search space; as the number of iterations increases, the inertia weight gradually decreases to the lower limit value until the maximum number of iterations is reached, such as a larger value of 0.9, a lower limit value of 0.4, and a maximum number of iterations of 700. In each iteration, each particle updates its moving speed and direction according to its historical optimal position and the historical optimal position of the entire population, and the adaptive adjustment of the inertia weight ensures the intelligence of the search. After updating the particle position, a boundary constraint processing mechanism is used to ensure the feasibility of the solution: if the position component of a particle exceeds the preset variable value range, the component is forced to be set to the corresponding boundary value, i.e. the absorbing boundary strategy. The penalty function method is used to convert the constrained optimization problem into an unconstrained problem. For capacity configuration combinations that do not meet the power quality requirements or power balance constraints, a large penalty term such as 10 6 is added to their fitness value, significantly reducing the probability of being selected as the optimal solution and ensuring that infeasible solutions are eliminated in the comparison. The adaptive inertia weight is a key parameter that controls the particle's ability to inherit its speed at the previous time. Adaptive means that this weight is not fixed, but is dynamically adjusted according to the optimization process: a larger weight at the beginning is beneficial for global exploration, jumping out of local optimum, and a smaller weight at the later stage is beneficial for local fine search and rapid convergence.

[0058] The capacity configuration parameter combination generated in each round of optimization is input into the microgrid operation simulation model for verification. The microgrid operation simulation model runs the capacity configuration parameter combination based on the load and illumination data of a typical day throughout the year and outputs key operation indicators, especially power quality indicators. If the simulation verification results meet the power quality requirements, the capacity configuration parameter combination is considered a feasible solution. The power quality requirements are technical parameter standards and limits necessary to ensure the normal operation of electrical equipment and the safety and stability of the power system, i.e., power quality evaluation standards, including voltage deviation, frequency deviation, voltage fluctuation and flicker, and harmonic distortion rate. For example, whether the voltage at the grid-connected point and key user nodes remains within the allowed range of 0.95pu to 1.05pu throughout; whether the frequency remains within the allowed range of 49.8Hz to 50.2Hz throughout; the voltage variation limit caused by photovoltaic output fluctuation, which is usually required to be no more than 3% for a medium-voltage system; and whether the total harmonic distortion rate of the current at the grid-connected point and the harmonic content rate of each harmonic are lower than the standard limit of 5%. Only when the voltage, frequency, and harmonic indicators of all nodes in the simulation results of all typical day scenarios simultaneously meet the power quality evaluation standards is the capacity configuration parameter combination considered a feasible solution. The current capacity configuration parameter combination is dynamically refreshed to update the global optimal solution, and the capacity optimization configuration parameter combination with the highest comprehensive fitness among all feasible solutions is finally output.

[0059] The microgrid operation simulation model simulates the dynamic operation of the microgrid under different capacity configurations and regulation strategies by establishing each component of the microgrid. The microgrid operation simulation model is constructed based on the principle of modular modeling, and its specific structure includes power supply module, load module, network module, and control module. The power supply module includes photovoltaic array and energy storage system, the input of photovoltaic array is typical daily irradiance and temperature sequence, and the output characteristic follows the P-V curve provided by the manufacturer. The energy storage system includes battery equivalent circuit, bidirectional converter and its control logic, and its charging and discharging characteristics are directly controlled by capacity configuration parameters. The load module adopts constant power, constant impedance or time sequence load model, and the input is the hourly or minute-by-minute active and reactive load curve corresponding to the typical day. The network module establishes the node admittance matrix including line impedance and transformer parameters according to the actual topology of the microgrid, which is used for power flow calculation. The control module integrates photovoltaic maximum power point tracking, set power / voltage support strategy, and load response logic. The power supply module, load module, network module, and control module are coupled through power flow equations, and during simulation, the voltage, current, power and energy storage SOC state of each node in the whole network are calculated by iterative solution at each time step, usually 1 minute to 15 minutes, to simulate the quasi-steady state operation. The simulation relies on the following explicit boundary condition inputs, such as the initial SOC of each energy storage unit at the start of the simulation, the initial solution of network power flow, etc. In multi-day continuous simulation, the initial SOC of each day is the final value at the end of the previous day simulation. The voltage of the main grid connection point is defined, i.e. the grid connection point of the microgrid and the main grid is regarded as an ideal power source or set to a fixed value, and the power exchange limit between the microgrid and the main grid is defined. The grid connection point can be set as a constant voltage source or simulate the dynamics of the external grid. The input data is the dynamic configuration parameter combination generated by each round of optimization, including the first compensation correction value, the photovoltaic output gradient parameter, the second compensation correction value, the reactive power compensation gap, and the time sequence scenario data, including the annual typical day load curve, the photovoltaic irradiance and temperature sequence, and the price signal lamp. The model output is the feasibility judgment index, i.e. the power quality verification result, which judges whether the voltage deviation of all nodes meets the power quality demand throughout the simulation period, i.e. whether the scheme is feasible. In addition, it also carries some detailed running state data, including voltage and frequency curve, power flow distribution, energy storage operation trajectory, etc.

[0060] The capacity optimization generates a capacity configuration parameter combination, which is input into the microgrid operation simulation model, and combined with historical load data and photovoltaic output prediction data to run simulation to calculate the voltage, current, power flow and energy storage state at each time step. The simulation model can be implemented using existing tools, and the photovoltaic, energy storage, load and grid impedance are modeled respectively through modular modeling, and the system state is solved through power flow iteration and time step integration.

[0061] Exemplarily, 50 particles are initialized, one of which is particle A with initial position of first compensation 8%, gradient -30 kW / min, second compensation 12%, and reactive gap 80 kvar. An adaptive inertia weight is introduced, and the initial value is set to 0.9. The parameter combination of particle A is substituted into the microgrid operation simulation model for verification. The simulation result shows that, in the afternoon of a summer day, due to the surge of load and the fluctuation of photovoltaic, the grid-connected point voltage drops to 0.93 pu for a short time, which does not meet the demand of power quality. Therefore, the scheme of particle A is eliminated, and the fitness is set to a very poor value. In the subsequent iteration, the position of particle B is updated to first compensation 10%, gradient -25 kW / min, second compensation 18%, and reactive gap 120 kvar. Simulation verification shows that, under this configuration, the minimum voltage of the whole network is 0.952 pu, which fully meets the demand of 0.95 pu. At the same time, the comprehensive fitness after calculation is 0.35, which is better than the current record. Therefore, dynamic refreshing is performed, and the parameter combination and fitness 0.35 of particle B are recorded as the new global optimal solution.

[0062] By combining the intelligent optimization algorithm with the high-fidelity simulation model, the adaptive inertia weight is introduced to improve the search performance of the algorithm and avoid premature convergence. The dynamic refreshing mechanism based on simulation verification ensures that each scheme output is technically feasible, i.e., strictly meets the power quality requirements, thereby enhancing the engineering practicality and reliability of the whole optimization configuration scheme.

[0063] Further, the application further includes the following steps: if the simulation verification result does not meet the power quality demand, based on the positioning deviation contribution degree of the correlation mapping matrix, a compensation element category sequence is obtained by sorting the positioning deviation contribution degrees from high to low; and based on the compensation element category sequence, local optimization is performed until the number of iterations reaches a preset threshold, and then the capacity optimization configuration parameter combination obtained by optimization is inversely analyzed into the segmented capacity configuration parameters to generate a photovoltaic and energy storage collaborative configuration execution scheme including a dynamic output smoothing curve of photovoltaic output segmentation, a stepwise charging and discharging scheme of energy storage adjustment segmentation, and a partitioned load regulation strategy of load response segmentation.

[0064] Specifically, if the simulation verification result does not meet the power quality demand, the scheme is not directly discarded, but a diagnosis program is started. Based on the correlation mapping matrix, the positioning deviation contribution degree is analyzed to determine which compensation element has the highest influence coefficient in the specific period and location where the voltage exceeds the limit, so as to calculate and sort the compensation element category sequence. The positioning deviation contribution degree is used to determine the responsibility of each compensation element category in the correlation mapping matrix for the specific deviation. The higher the contribution degree, the more likely the element is the main cause of the problem. After sorting the compensation element categories from high to low according to the positioning deviation contribution degree, the compensation element category sequence is obtained, which defines the priority processing order for fault repair.

[0065] Based on the local optimization of the compensation element category sequence, the capacity configuration parameter combination with low contribution degree is temporarily frozen, and the capacity configuration parameter combination with high contribution degree is locally optimized, such as adjusting the high SOC section energy storage capacity, the charge and discharge cycle strategy or the impedance matching parameter, to gradually improve the power balance and voltage deviation while keeping other elements unchanged. The local optimization is iterated until a preset number threshold is reached to ensure the convergence of the optimization process. The preset threshold is a safety upper limit set to prevent the optimization process from falling into an infinite loop or taking too long.

[0066] The capacity optimization configuration parameter combination obtained by optimization at this time is inversely analyzed into the segmented capacity configuration parameter, i.e., the optimized capacity combination parameter is mapped back to the specific capacity configuration of the photovoltaic output segmentation, energy storage adjustment segmentation and load response segmentation, to generate a photovoltaic storage collaborative configuration execution scheme including a dynamic output smoothing curve of the photovoltaic output segmentation, a step-type charge and discharge scheme of the energy storage adjustment segmentation and a partitioned load regulation strategy of the load response segmentation. The photovoltaic storage collaborative configuration execution scheme includes the dynamic output smoothing curve, the step-type charge and discharge scheme and the partitioned load regulation strategy.

[0067] By using the positioning deviation contribution degree to guide the local optimization, fine adjustment of the compensation elements that mainly affect the power quality of the microgrid is realized. The optimization results are inversely analyzed into the segmented capacity configuration to generate an executable photovoltaic storage collaborative regulation scheme, realize photovoltaic output smoothing, efficient energy storage adjustment and timely load response matching, significantly improve the power supply reliability, voltage stability and overall power balance accuracy of the microgrid, and reduce capacity redundancy and unnecessary energy storage adjustment overhead.

[0068] Through industrial Ethernet, real-time collection of real-time monitoring data of voltage, current, SOC state data of each power balance influencing area is realized, and according to the preset power balance threshold and actual operation deviation of the photovoltaic storage collaborative configuration execution scheme, the capacity optimization configuration parameter combination is dynamically refreshed and optimized.

[0069] Further, the application further includes the following steps: taking the parameter sensitivity of the correlation mapping matrix as the network input weight, and taking the difference between the target value of the power quality demand as the network input layer variable; after completing a round of refreshing, the updated capacity optimization configuration parameter combination is substituted into the correlation mapping matrix for reverse verification to determine the deviation improvement rate of each power balance influencing area; if the deviation improvement rate is lower than a preset improvement rate threshold, the boundary constraint of the particle search space is updated based on the compensation element category sequence.

[0070] Specifically, the industrial Ethernet is a high-speed and reliable communication network for industrial sites, used to connect various measurement points and control devices of the micro-grid, supporting real-time data transmission. Through sensors distributed in various power balance influence areas of the micro-grid, real-time data such as voltage, current, SOC, etc. are collected at millisecond level by industrial Ethernet, forming real-time monitoring data. The power balance influence area is a set of physical nodes or devices in the micro-grid that play a key role in global power balance and stability, including photovoltaic output grid connection point, energy storage regulation cluster and flexible load aggregation.

[0071] The real-time monitoring data are compared with the preset power balance threshold in the photovoltaic and energy storage collaborative configuration execution scheme to calculate the current operation deviation. The actual operation deviation is the difference between the real-time monitoring data and the preset power balance threshold. For example, the real-time monitoring voltage is 9.7 kV, and the lower limit of the preset power balance threshold is 10.5 kV, then the deviation is -0.8 kV.

[0072] Once the actual operation deviation exceeds the allowed range, the rolling optimization engine is triggered. According to the latest voltage, current and SOC state, the photovoltaic output gradient, energy storage charging and discharging strategy and load response priority are updated to ensure that the micro-grid continuously meets the power quality requirements. The rolling optimization adopts a certain time window for iterative adjustment, and the updated configuration parameters are applied immediately after each optimization, realizing adaptive adjustment of the micro-grid operation state and ensuring that the photovoltaic and energy storage collaborative effect takes effect in real time. The refreshed capacity optimization configuration parameter combination is immediately issued to the corresponding execution unit, and the entire micro-grid immediately operates according to the new strategy that is more suitable for the current situation.

[0073] The parameter sensitivity of the associated mapping matrix is used as the network input weight, and the difference between the target value of the parameter corresponding to the high sensitivity and the actual running data of each power balance influence area is given a higher weight. The difference between the target value of the parameter corresponding to the high sensitivity and the actual running data of each power balance influence area is used as the network input layer variable, which is used to calculate the adjustment direction of the capacity parameters in the next round. The parameter sensitivity of the associated mapping matrix is the size and change gradient of the influence coefficient of each compensation element category in the matrix on the power balance influence area. High sensitivity means that a small change in the parameter will have a huge impact on the performance of the micro-grid, and vice versa.

[0074] After each round of refreshing, the next global optimization is not immediately performed, but the updated capacity optimization configuration parameter combination is substituted into the correlation mapping matrix for reverse verification, to predict how much influence the new parameter combination will have on each power balance influence area, and to calculate the predicted deviation improvement rate. The deviation improvement rate is usually calculated by the ratio of the difference between the previous round deviation and the current round deviation to the previous round deviation. The calculated deviation improvement rate is compared with the preset improvement rate threshold. If the deviation improvement rate is lower than the preset improvement rate threshold, it indicates that the algorithm has been difficult to find significantly better solutions within the current defined particle search space, and may be trapped in local optimum or the search space itself is unreasonable. The preset improvement rate threshold is the lowest acceptable improvement standard. The compensation element category sequence is used as a guide to update the boundary constraints of the particle search space, that is, to define the allowed value range of each parameter in the particle swarm optimization algorithm. For example, it is assumed that after one rolling optimization, the capacity optimization configuration parameter combination is refreshed, and the reactive power compensation gap is adjusted from 100 kvar to 110 kvar. The new capacity optimization configuration parameter combination 110 kvar is substituted into the correlation mapping matrix, and the predicted improvement of the grid-connected point voltage is 0.003 pu. Before refreshing, the voltage deviation is 0.05 pu, and the predicted deviation after refreshing is 0.047 pu, so the deviation improvement rate is 0.06. It is assumed that the preset improvement rate threshold is 0.1. Currently 0.06<0.1, the improvement rate is lower than the preset improvement rate threshold, and the compensation element category sequence obtained by historical analysis is called to update the search space.

[0075] By combining the parameter sensitivity and the target value difference to construct the input layer weight, fine control of each compensation element is realized, reverse verification ensures that the optimization scheme is actually effective in each power balance area, and the convergence efficiency of iterative optimization is improved by dynamically adjusting the particle search space boundary.

[0076] In summary, the microgrid light storage capacity optimization configuration method provided in the present application has the following technical effects:

[0077] The segmented capacity configuration parameters associated with the photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation are set according to the load characteristic parameters and energy access conditions of the microgrid; the first compensation correction value and the second compensation correction value are set based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage unit; the capacity optimization configuration parameter combination obtained by optimization is inversely analyzed into the segmented capacity configuration parameters based on the first compensation correction value and the second compensation correction value, to generate a photovoltaic and energy storage collaborative configuration execution scheme; real-time monitoring data of each power balance influence area including voltage, current and SOC state data is collected in real time through an industrial Ethernet, and the capacity optimization configuration parameter combination is dynamically refreshed and rolling optimized according to the preset power balance threshold and actual operation deviation of the photovoltaic and energy storage collaborative configuration execution scheme. That is, by setting the segmented capacity configuration parameters, introducing the compensation correction value to correct the capacity configuration error, collecting the real-time monitoring data of each power balance influence area in real time through the industrial Ethernet, and dynamically refreshing and rolling optimizing the capacity optimization configuration parameter combination according to the preset power balance threshold and actual operation deviation of the photovoltaic and energy storage collaborative configuration execution scheme, the microgrid photovoltaic and energy storage capacity configuration precision is improved, thereby improving the power balance capability and power supply reliability of the microgrid under different operating states.

[0078] In the second embodiment, based on the same inventive concept as the microgrid photovoltaic and energy storage capacity optimization configuration method in the first embodiment, the present application also provides a microgrid photovoltaic and energy storage capacity optimization configuration system, please refer to the attached Figure 2 The microgrid photovoltaic and energy storage capacity optimization configuration system comprises:

[0079] The parameter configuration module 11 is configured to set segmented capacity configuration parameters associated with photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation according to load characteristic parameters and energy access conditions of the microgrid; the compensation correction module 12 is configured to set a first compensation correction value and a second compensation correction value based on impedance matching characteristics of the microgrid and charging and discharging efficiency parameters of the energy storage unit; the scheme generation module 13 is configured to inversely analyze a capacity optimization configuration parameter combination obtained by optimization into the segmented capacity configuration parameters based on the first compensation correction value and the second compensation correction value, to generate a photovoltaic and energy storage collaborative configuration execution scheme; and the deviation optimization module 14 is configured to collect real-time monitoring data of each power balance influence area including voltage, current and SOC state data in real time through an industrial Ethernet, and dynamically refresh and rolling optimize the capacity optimization configuration parameter combination according to a preset power balance threshold and actual operation deviation of the photovoltaic and energy storage collaborative configuration execution scheme.

[0080] Further, the parameter configuration module 11 in the micro-grid light storage capacity optimization configuration system is further used for: based on the load characteristic parameters and energy access conditions of the micro-grid, performing time sequence equivalent analysis and sensitive node identification on the load fluctuation amplitude and new energy output prediction error, calculating power shortage and supply-demand balance critical point of each period; by the power shortage and supply-demand balance critical point of each period, the micro-grid dispatching period is divided into photovoltaic output segmentation, storage regulation segmentation and load response segmentation.

[0081] Further, the compensation correction module 12 in the micro-grid light storage capacity optimization configuration system is further used for: based on the impedance matching characteristics of the micro-grid, combining the power quality demand, performing full-dimensional detection on the grid impedance parameters and power transmission efficiency, obtaining a first compensation correction value; based on the segmented capacity configuration parameters, simulating the power flow distribution of the photovoltaic output fluctuation process by power simulation, obtaining photovoltaic output gradient parameters under the impedance matching stability limitation.

[0082] Further, the compensation correction module 12 in the micro-grid light storage capacity optimization configuration system is further used for: based on the charge-discharge efficiency parameters of the energy storage unit, combining the historical charge-discharge cycle data, dynamically deducing the capacity attenuation amount in different SOC intervals, obtaining a second compensation correction value; based on the segmented capacity configuration parameters, optimizing the energy storage access point of the micro-grid by topology optimization, obtaining a reactive power compensation gap under the power balance limitation.

[0083] Further, the scheme generation module 13 in the micro-grid light storage capacity optimization configuration system is further used for: according to the first compensation correction value and the photovoltaic output gradient parameters, the second compensation correction value and the reactive power compensation gap, constructing an associated mapping matrix; taking the coupling influence coefficient of the associated mapping matrix as the fitness function weight, minimizing the grid-connected point voltage deviation of the micro-grid, maximizing the capacity utilization rate of the energy storage cluster, and minimizing the response delay of the flexible load aggregate as the optimization target.

[0084] Further, the scheme generation module 13 in the micro-grid light storage capacity optimization configuration system is further used for: the matrix row dimension of the associated mapping matrix is the impedance matching compensation element category associated with the first compensation correction value, the power transmission efficiency compensation element category, the high SOC segment attenuation compensation element category associated with the second compensation correction value, and the charge-discharge cycle compensation element category; the matrix column dimension of the associated mapping matrix is the power balance influence area of the micro-grid, including the grid-connected point of the photovoltaic output segmentation, the energy storage cluster of the energy storage regulation segmentation, and the flexible load aggregate of the load response segmentation.

[0085] Further, the scheme generation module 13 in the micro-grid light storage capacity optimization configuration system is further configured to: take the first compensation correction value and the value range of the photovoltaic output gradient parameter, the second compensation correction value and the value range of the reactive power compensation gap as a particle search space, introduce an adaptive inertia weight for iterative optimization; input the capacity configuration parameter combination generated in each round of optimization into the micro-grid operation simulation model for verification, if the simulation verification result meets the power quality demand, refresh the current capacity configuration parameter combination dynamically, and output the capacity optimization configuration parameter combination.

[0086] Further, the scheme generation module 13 in the micro-grid light storage capacity optimization configuration system is further configured to: if the simulation verification result does not meet the power quality demand, based on the positioning deviation contribution degree of the correlation mapping matrix, sort the compensation element categories in descending order of the positioning deviation contribution degree to obtain a compensation element category sequence; based on the compensation element category sequence, perform local optimization until the number of iterations reaches a preset threshold, and then inversely analyze the capacity optimization configuration parameter combination obtained by optimization into a segmented capacity configuration parameter to generate a light storage collaborative configuration execution scheme including a dynamic output smoothing curve of photovoltaic output segmentation, a step charging and discharging scheme of energy storage adjustment segmentation, and a partitioned load regulation strategy of load response segmentation.

[0087] Further, the deviation optimization module 14 in the micro-grid light storage capacity optimization configuration system is further configured to: take the parameter sensitivity of the correlation mapping matrix as a network input weight, and take the difference between the target value of the power quality demand as a network input layer variable; after each round of refreshing is completed, substitute the updated capacity optimization configuration parameter combination into the correlation mapping matrix for reverse verification to determine the deviation improvement rate of each power balance influence area; if the deviation improvement rate is lower than a preset improvement rate threshold, update the boundary constraint of the particle search space according to the compensation element category sequence.

[0088] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The micro-grid light storage capacity optimization configuration method and specific examples in the first embodiment are also applicable to the micro-grid light storage capacity optimization configuration system of the present embodiment. Through the foregoing detailed description of the micro-grid light storage capacity optimization configuration method, those skilled in the art can clearly know the micro-grid light storage capacity optimization configuration system in the present embodiment. Therefore, in order to make the specification concise, the micro-grid light storage capacity optimization configuration system in the present embodiment is not described in detail.

[0089] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the inventive faculty. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0090] It will be readily apparent to one skilled in the art that varying substitutions and modifications can be made to the application disclosed herein without departing from the scope and spirit of the application. Accordingly, it is intended that all such alterations and modifications be considered as within the scope of the application.

Claims

1. A method for optimizing configuration of optical storage capacity in a microgrid, characterized in that, The method comprises the following steps: According to the load characteristic parameters and energy access conditions of the micro-grid, set the segmented capacity configuration parameters associated with the photovoltaic output segmentation, energy storage regulation segmentation and load response segmentation; Based on the impedance matching characteristics of the micro-grid and the charge and discharge efficiency parameters of the energy storage unit, set the first compensation correction value and the second compensation correction value; Based on the first compensation correction value and the second compensation correction value, combine the capacity optimization configuration parameters obtained by optimization into the segmented capacity configuration parameters to generate a photovoltaic and energy storage collaborative configuration execution scheme; Real-time acquisition of real-time monitoring data of each power balance influencing area including voltage, current and SOC state data through industrial Ethernet, dynamic refreshing and rolling optimization of the capacity optimization configuration parameter combination according to the preset power balance threshold and actual operation deviation of the photovoltaic and energy storage collaborative configuration execution scheme; Wherein, setting the first compensation correction value further comprises: Based on the impedance matching characteristics of the micro-grid, and combining the power quality demand, the grid impedance parameters and the power transmission efficiency are detected in all dimensions to obtain the first compensation correction value; Based on the segmented capacity configuration parameters, the power flow distribution of the photovoltaic output fluctuation process is simulated by power simulation to obtain the photovoltaic output gradient parameters under the impedance matching stability limitation; Wherein, setting the second compensation correction value further comprises: Based on the charge and discharge efficiency parameters of the energy storage unit, and combining the historical charge and discharge cycle data, the capacity attenuation amount in different SOC intervals is dynamically deduced to obtain the second compensation correction value; Based on the segmented capacity configuration parameters, the energy storage access point of the micro-grid is optimized by topology optimization to obtain the reactive power compensation gap under the power balance limitation; Wherein, based on the first compensation correction value and the second compensation correction value, combining the capacity optimization configuration parameters obtained by optimization into the segmented capacity configuration parameters further comprises: According to the first compensation correction value and the photovoltaic output gradient parameters, and the second compensation correction value and the reactive power compensation gap, an associated mapping matrix is constructed; Taking the coupling influence coefficient of the associated mapping matrix as the fitness function weight, the minimization of the grid-connected point voltage deviation of the micro-grid, the maximization of the energy storage cluster capacity utilization rate, and the minimization of the response delay of the flexible load aggregate are taken as the optimization objectives; Wherein, the matrix row dimension of the associated mapping matrix is the impedance matching compensation element category associated with the first compensation correction value, the power transmission efficiency compensation element category, the high SOC segment attenuation compensation element category associated with the second compensation correction value, and the charge and discharge cycle compensation element category; The matrix column dimension of the associated mapping matrix is the power balance influencing area of the micro-grid, including the grid-connected point of the photovoltaic output segmentation, the energy storage cluster of the energy storage regulation segmentation, and the flexible load aggregate of the load response segmentation. 2.The micro-grid optical storage capacity optimization configuration method of claim 1, wherein, Set the segmented capacity configuration parameters associated with the photovoltaic output segmentation, the energy storage regulation segmentation and the load response segmentation, and the method further comprises: Based on the load characteristic parameters and energy access conditions of the micro-grid, perform time sequence equivalent analysis and sensitive node identification on the load fluctuation amplitude and new energy output prediction error to calculate the power shortage and supply-demand balance critical point of each period; The micro-grid scheduling period is divided into a photovoltaic output segment, a storage regulation segment and a load response segment by the power shortage of each period and the supply-demand balance critical point. 3.The micro-grid optical storage capacity optimization configuration method of claim 1, wherein, The method comprises: The first compensation correction value and the value range of the photovoltaic output gradient parameter, the second compensation correction value and the value range of the reactive power compensation gap are taken as the particle search space, and an adaptive inertia weight is introduced for iterative optimization; The capacity configuration parameter combination generated in each round of optimization is input into a micro-grid operation simulation model for verification, and if the simulation verification result meets the power quality demand, the current capacity configuration parameter combination is dynamically refreshed, and the capacity optimization configuration parameter combination is output. 4.The method of claim 3, wherein, The method further comprises: If the simulation verification result does not meet the power quality demand, the positioning deviation contribution degree is obtained based on the positioning deviation contribution degree of the correlation mapping matrix, and the positioning deviation contribution degrees are sorted from high to low to obtain a compensation element category sequence; Based on the compensation element category sequence, local optimization is performed until the number of iterations reaches a preset threshold, and then the capacity optimization configuration parameter combination obtained by optimization is inversely analyzed into the segmented capacity configuration parameter to generate a photovoltaic storage collaborative configuration execution scheme including a dynamic output smoothing curve of the photovoltaic output segment, a step-type charging and discharging scheme of the storage regulation segment and a partitioned load regulation strategy of the load response segment.

5. The microgrid optical storage capacity optimization configuration method of claim 4, wherein, The capacity optimization configuration parameter combination is dynamically refreshed and rolled, and the method comprises: The parameter sensitivity of the correlation mapping matrix is taken as the network input weight, and the difference between the target value and the power quality demand is taken as the network input layer variable; After each round of refreshing is completed, the updated capacity optimization configuration parameter combination is substituted into the correlation mapping matrix for reverse verification to determine the deviation improvement rate of each power balance influence area; If the deviation improvement rate is lower than a preset improvement rate threshold, the boundary constraint of the particle search space is updated based on the compensation element category sequence.

6. A micro-grid optical storage capacity optimization configuration system, characterized in that, The micro-grid photovoltaic storage capacity optimization configuration system for implementing the steps of the micro-grid photovoltaic storage capacity optimization configuration method in any one of claims 1 to 5 comprises: A parameter configuration module is configured to set the segmented capacity configuration parameter associated with the photovoltaic output segment, the storage regulation segment and the load response segment according to the load characteristic parameter and the energy access condition of the micro-grid. A compensation correction module is configured to set the first compensation correction value and the second compensation correction value based on the impedance matching characteristic of the micro-grid and the charging and discharging efficiency parameter of the storage unit. A scheme generation module is configured to inversely analyze the capacity optimization configuration parameter combination obtained by optimization into the segmented capacity configuration parameter based on the first compensation correction value and the second compensation correction value, and generate a photovoltaic storage collaborative configuration execution scheme. A deviation optimization module is configured to collect real-time monitoring data including voltage, current and SOC state data of each power balance influence area in real time through an industrial Ethernet, and dynamically refresh and roll the capacity optimization configuration parameter combination according to a preset power balance threshold and an actual operation deviation of the photovoltaic storage collaborative configuration execution scheme.

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