Micro-grid optical storage capacity optimal configuration method and system

Abstract

The invention provides a micro-grid optical storage capacity optimal configuration method and system, and relates to the technical field of micro-grid optimization, and the method comprises the steps: setting a sectional capacity configuration parameter according to a load characteristic parameter and an energy access condition of a micro-grid; setting a first compensation correction value and a second compensation correction value based on impedance matching characteristics of the micro-grid and charge and discharge efficiency parameters of the energy storage unit; reversely analyzing the capacity optimization configuration parameter combination obtained through optimization into the sectional capacity configuration parameters; and collecting real-time monitoring data in real time through an industrial Ethernet, and performing dynamic refreshing and rolling optimization on a capacity optimization configuration parameter combination according to a preset power balance threshold value and an actual operation deviation of an optical storage cooperative configuration execution scheme. According to the invention, the technical problem of capacity redundancy or insufficiency caused by static and fixed configuration of the optical storage capacity of the micro-grid in the prior art is solved, and the configuration precision of the optical storage capacity of the micro-grid is improved.

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 in 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 satisfy 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 being 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 through 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 present 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 through 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 present application have at least the following technical effects or advantages: Based on the load characteristic parameters and energy access conditions of the microgrid, segmented capacity configuration parameters are set, which are associated with photovoltaic output segments, energy storage regulation segments, and load response segments. Based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage units, a first compensation correction value and a second compensation correction value are set. Based on the first compensation correction value and the second compensation correction value, the optimized capacity configuration parameter combination obtained through optimization is back-analyzed into the segmented capacity configuration parameters to generate a photovoltaic-energy storage collaborative configuration execution scheme. Real-time monitoring data, including voltage, current, and SOC status data, are collected in each power balance influence area via industrial Ethernet. Based on the preset power balance threshold of the photovoltaic-energy storage collaborative configuration execution scheme and the actual operating deviation, the optimized capacity configuration parameter combination is dynamically refreshed and continuously optimized. In other words, by setting segmented capacity configuration parameters and introducing compensation correction values ​​to correct capacity configuration errors, real-time monitoring data of each power balance-affected area is collected in real time through industrial Ethernet. Based on the preset power balance threshold of the photovoltaic-storage collaborative configuration execution scheme and the actual operating deviation, the combination of capacity optimization configuration parameters is dynamically refreshed and rolled to improve the accuracy of photovoltaic-storage capacity configuration in the microgrid, thereby enhancing the power balance capability and power supply reliability of the microgrid under different operating conditions.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the microgrid photovoltaic-storage capacity optimization configuration method of this application.

[0020] Figure 2 This is a schematic diagram of the microgrid photovoltaic-storage capacity optimization configuration system of this application.

[0021] Figure labeling: Parameter configuration module 11, compensation and correction module 12, scheme generation module 13, deviation optimization module 14. Detailed Implementation

[0022] This application provides a method and system for optimizing the configuration of photovoltaic (PV) and energy storage (ESS) capacity in microgrids. This addresses the technical problem in existing technologies where statically fixed PV / ESS capacity configuration leads to capacity redundancy or insufficiency, thus affecting the reliability of microgrid power supply. By setting segmented capacity configuration parameters and introducing compensation correction values ​​to correct capacity configuration errors, and by collecting real-time monitoring data from various power balance-affected areas via industrial Ethernet, the application dynamically refreshes and continuously optimizes the combination of capacity optimization configuration parameters based on the preset power balance threshold of the PV / ESS collaborative configuration execution scheme and the actual operational deviation. This improves the accuracy of PV / ESS capacity configuration in microgrids, thereby enhancing the power balance capability and power supply reliability of microgrids under different operating conditions.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a method for optimizing the configuration of photovoltaic and energy storage capacity in a microgrid. The method is applied to a microgrid photovoltaic and energy storage capacity optimization configuration system, and specifically includes the following steps: Based on the load characteristic parameters and energy access conditions of the microgrid, segmented capacity configuration parameters are set that are associated with the photovoltaic output segment, energy storage regulation segment, and load response segment.

[0025] Furthermore, this application also includes the following steps: based on the load characteristic parameters and energy access conditions of the microgrid, performing time-series equivalent analysis and sensitive node identification on the load fluctuation amplitude and new energy output prediction error, and calculating the power deficit and supply-demand balance critical point for each time period; through the power deficit and supply-demand balance critical point for each time period, dividing the microgrid dispatch cycle into photovoltaic output segment, energy storage regulation segment and load response segment.

[0026] Specifically, the load characteristic parameters of a microgrid are key parameters describing the load variation patterns within the microgrid, such as the peak-to-valley ratio of the load curve, maximum load power, minimum load power, and load change rate; energy access conditions are the physical and operational conditions for new energy sources such as photovoltaics and wind power, as well as energy storage systems, to access the microgrid, including access capacity, access point location, inverter capacity, and access voltage level.

[0027] Based on the load characteristic parameters and energy access conditions of the microgrid, a time-series equivalent analysis is performed on the load fluctuation amplitude and the prediction error of renewable energy output. This involves using time-series data to equalize the load fluctuation amplitude and the prediction error of renewable energy output, compressing long time series data into a typical daily profile, and identifying power surplus and power deficit segments according to the time series. Power flow calculations and sensitivity analyses are performed on the microgrid topology to identify sensitive nodes and locate the key electrical nodes in the microgrid whose voltage is most susceptible to fluctuation and has the greatest impact on system stability. At each time step, the power deficit is calculated and the supply-demand balance critical point is calibrated. Using these critical points as boundaries, the scheduling cycle is divided into photovoltaic output segments, energy storage regulation segments, and load response segments, and initial capacity configuration parameters are assigned to each segment.

[0028] Power deficit is the difference between load demand and renewable energy generation output within a certain period. When the load exceeds generation, a positive deficit occurs, requiring energy storage or grid supplementation. The supply-demand balance critical point is the turning point from a safe and stable operating state to an unsafe and unstable state.

[0029] The photovoltaic output segment is the period when photovoltaic energy output matches the initial power, corresponding to the time window for adjusting photovoltaic module layout and output fluctuation smoothing; the energy storage regulation segment is the period when power imbalance occurs and the power deficit must be continuously compensated by energy storage charging and discharging to avoid voltage fluctuations and frequency deviations; the load response segment is the period when system balance is maintained by managing load-side resources, including setting flexible load priorities, interruptible load thresholds, and local reactive power compensation measures to align power balance and power quality indicators across different periods.

[0030] For example, suppose a microgrid in a region has a peak load of 150kW and 200kW of photovoltaic power. Through power flow calculations, node A at the end of the 10kV feeder is identified as a voltage-sensitive point, with a normal operating voltage of 10.2kV and a safety lower limit of 9.8kV. From 18:00 to 19:00, the load is 130kW, the photovoltaic output is 0kW, and the power deficit is +130kW. At this time, energy storage needs to discharge. If the rated discharge power of the energy storage is 80kW, there is still a 50kW power deficit that needs to be supplemented by the grid or the load reduced. Simulations show that after the energy storage continuously discharges at 80kW for 15 minutes, its SOC drops from 60% to 45%, while the voltage at node A slowly drops from 10.1kV to 9.85kV, approaching the critical point of 9.8kV. Therefore, 18:15 is a critical point for supply and demand balance. Without intervention, the voltage will exceed the limit. The period from 18:00 to 18:15 is defined as the energy storage regulation segment, where energy storage is set to discharge at its maximum power of 80kW to delay voltage drop and SOC decline. After 18:15, the period is defined as the load response segment, which is automatically activated upon detecting a critical point. Preset parameters are used for startup: first, the lowest priority 20kW interruptible load is disconnected; then, the 50kvar reactive power compensation device near node A is activated. After these measures are implemented, the power deficit decreases to 110kW, and the voltage at node A rises to 9.92kV, restoring stability.

[0031] By identifying critical points and implementing differentiated strategies in different segments, a shift from passive response to proactive prevention has been achieved. This significantly improves the utilization efficiency of energy storage and load resources while ensuring power supply reliability and power quality, and avoids unnecessary capacity waste.

[0032] Based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage unit, a first compensation correction value and a second compensation correction value are set.

[0033] Furthermore, this application also includes the following steps: based on the impedance matching characteristics of the microgrid and combined with power quality requirements, perform full-dimensional detection of grid impedance parameters and power transmission efficiency to obtain a first compensation correction value; based on the segmented capacity configuration parameters, simulate and set the power flow distribution of the photovoltaic output fluctuation process using power simulation to obtain photovoltaic output gradient parameters that meet the impedance matching stability constraints.

[0034] Specifically, the equivalent impedance of key grid-connected points is determined by online impedance measurement and measured short-circuit capacity. Simultaneously, power transmission efficiency is evaluated and transmission losses at different power levels are calibrated. Sensitivity analysis is performed on impedance-induced voltage sensitivity, constrained by voltage deviation, frequency deviation, and harmonic limits. The sensitivity results are compared with power quality limits to determine the power margin required to meet voltage / frequency / harmonic constraints, thus calculating the first compensation correction value. For example, suppose a microgrid's main line has a high impedance, with a measured average resistance of 0.15Ω and a reactance of 0.10Ω. Power quality requirements stipulate that the voltage deviation at all user nodes must not exceed -10%, and the rated line voltage is 400V. A planned photovoltaic capacity of 100kW is configured. Through power simulation, at a peak load of 150kW and with 100kW of photovoltaic power generated at full capacity, due to line impedance voltage drop, the voltage at the furthest node is 0.92pu, or 368V, which is close to the critical value of 0.9pu, i.e., the critical value is 360V. To establish a reliability margin, the target voltage boost is set at 0.945 pu, or 378V. Therefore, the required voltage boost is 0.945 - 0.92 = 0.025 pu, corresponding to an actual voltage of 10V. In high-voltage / medium-voltage power distribution, line voltage drop is mainly affected by reactive power and reactance. Theoretically, it can be approximated by: Actual Voltage = (Reactive Power Required) / (Reactive Power Required) The reactive power required is calculated by substituting the rated line voltage (400V), actual voltage (10V), and reactance (0.10Ω) into the formula. This yields a reactive power injection of 40kvar. Therefore, to stably increase the terminal voltage from 0.92pu to 0.945pu, approximately 40kvar of reactive power support needs to be continuously provided at the photovoltaic grid connection point. Originally, the photovoltaic inverter was planned to be configured with a rated active power of only 100kW. To provide sufficient voltage support margin, calculations show that an additional 8% capacity of the photovoltaic inverter is needed to provide reactive power compensation. Therefore, the first compensation correction value is determined to be 8%, meaning the apparent power capacity of the photovoltaic inverter should not be less than 108kVA, rather than the initially planned 100kW.

[0035] Impedance matching characteristics refer to the impedance relationship between power sources, lines, and loads in a microgrid. Mismatch can lead to decreased power transmission efficiency, voltage fluctuations, and even oscillation instability. Power quality requirements are technical indicators for stable grid operation, including voltage deviation, frequency deviation, and harmonic suppression requirements. Voltage deviation is the deviation between the actual voltage and the rated voltage, and is generally required to be no more than ±7%; frequency deviation is the deviation between the actual frequency and the rated frequency, and is required to be no more than ±0.2Hz; harmonic suppression requirements limit the high-frequency harmonic content in current and voltage. Grid impedance parameters are the impedance modulus and phase at the grid connection point or key bus, usually expressed as equivalent impedance. Power transmission efficiency is the efficiency of transmitting functional quantities from the generator to the receiver along a specific power flow path, considering line losses, transformer losses, and commutation / inverter conversion losses. Full-dimensional testing involves measuring grid impedance parameters and power transmission efficiency across the entire operating range, from the grid connection point to the end load, rather than sampling only at individual points. The first compensation correction value is an additional capacity margin added to the theoretically calculated photovoltaic-storage capacity to offset voltage drops and power losses caused by line impedance.

[0036] The segmented capacity configuration parameters are substituted into the power simulation model. A photovoltaic (PV) output disturbance sequence is applied during the simulation, and the rate of change of PV output is gradually adjusted. The maximum permissible PV output gradient under given impedance and power quality constraints is determined. This gradient represents the engineering limit that restricts instantaneous PV changes and prevents excessive voltage drops or frequency disturbances. The simulation results, together with impedance sensitivity, determine the final first compensation correction value and gradient constraint, ensuring that the capacity configuration satisfies both utilization and power quality requirements. The PV output gradient parameter is the maximum permissible limit set for the rate of change of PV power generation. Its function is to ensure that when the actual PV power change exceeds this limit, the rate of change must be reduced through energy storage smoothing or active curtailment to ensure system impedance matching stability and prevent voltage exceedance.

[0037] By detecting grid impedance and power transmission efficiency in all dimensions and calculating the first compensation correction value based on power quality constraints, as well as the photovoltaic output gradient parameters obtained through power simulation, it is possible to pre-limit the instantaneous output changes of photovoltaics and reserve the necessary power margin in capacity configuration and operation control. This will prevent grid connection point voltage from exceeding limits, limit frequency fluctuations, and suppress harmonic problems caused by large active power changes when facing rapid cloud shadows or sudden output changes.

[0038] Furthermore, this application also includes the following steps: based on the charge and discharge efficiency parameters of the energy storage unit and combined with historical charge and discharge cycle data, dynamically extrapolate the capacity decay in different SOC intervals to obtain a second compensation correction value; based on the segmented capacity configuration parameters, optimize the energy storage access point of the microgrid using topology optimization to obtain a reactive power compensation gap that meets the power balance constraints.

[0039] Specifically, based on the charge / discharge efficiency parameters of the energy storage unit and its historical charge / discharge cycle data, a refined battery degradation model considering the State of Charge (SOC) range 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; historical charge / discharge cycle data is a database recording the past charge / discharge processes of the energy storage system, including records of the number of charge / discharge cycles experienced in actual operation, average depth of discharge, SOC operating range, power changes, temperature, etc., used to assess lifetime degradation and capacity reduction.

[0040] A refined battery degradation model is used to dynamically extrapolate the capacity degradation of energy storage systems in different State of Charge (SOC) ranges. The SOC range represents the percentage of energy currently stored relative to the rated capacity, such as 0-20% for low SOC, 20-80% for medium SOC, and 80-100% for high SOC. For example, the extrapolation results show that due to frequent deep charging and discharging at high temperatures, the average annual degradation rate of batteries in the low SOC range is as high as 3%. Based on the extrapolation results, the additional capacity required in the initial configuration to meet power supply demands at the end of the entire lifespan is calculated, i.e., a second compensation correction value is obtained. The second compensation correction value is an additional capacity margin added to the theoretical configuration capacity to compensate for efficiency losses and capacity degradation of the energy storage system throughout its lifespan. For example, in a specific configuration case, it is assumed that the microgrid requires the energy storage system to still be able to release 500 kWh of net energy per day at the end of the 10th year. First, energy conversion losses are considered: if the system's average cycle efficiency is 92%, the initial energy storage required is 500 / 0.92≈543.5kWh. Efficiency losses alone require an initial capacity increase of approximately 8.7% over the net output demand. Next, a physics-empirical battery aging model is used to simulate degradation. This model must be able to simulate calendar aging and cycle aging. Calendar aging is related to time, temperature, and average state of charge (SOC), while cycle aging is related to the number of cycles and average depth of discharge (DOC). Based on typical annual operating plans or historical data of the microgrid, the future operating conditions of the energy storage system are statistically analyzed or predicted and used as model input. Key data include an average annual ambient temperature of 25℃, an average annual equivalent full charge-discharge cycle count of 300, an average DOC of 60%, and an average SOC of 50%. These key data are input into a calibrated aging model for simulation. At the end of the 10th year (end of life), the battery's capacity retention rate decreases to 60%, meaning the total degradation rate is 40%. Based on this projection, to ensure the provision of efficiency-compensated stored energy at the end of its lifespan, the required initial rated capacity needs to meet the requirement of 543.5 kWh / 0.6 ≈ 905.8 kWh. This is defined as the percentage increase in initial capacity relative to the baseline requirement considering only net discharge energy. In other words, compared to the theoretical configuration of 500 kWh considering only net energy, the actual required increase in capacity is [percentage missing]. Taking into account the model's uncertainties and to reserve a safety margin for the system, the second compensation correction value can be determined to be 85% in engineering practice. The initial installed capacity of the energy storage system should be configured with a margin of approximately 85% based on the baseline energy demand of 500kWh, that is, reaching approximately 925kWh.

[0041] Based on the power requirements of each segment defined by the segmented capacity configuration parameters, the microgrid network structure is analyzed using a topology optimization algorithm. With the objective functions of minimizing network loss and maximizing voltage stability, and constrained by power flow equations, the optimal electrical location of the energy storage connection point is determined. After determining the optimal connection point, the reactive power deficit of each segment is analyzed through power flow calculations to obtain the reactive power compensation gap that meets the power balance constraints. The reactive power compensation gap represents the additional reactive power capacity that needs to be provided or absorbed while maintaining active power balance. This clarifies the required reactive power compensation capacity of the energy storage inverter to work collaboratively with the connection point and ensure system voltage stability.

[0042] The first compensation correction value is a power margin or capacity correction coefficient calculated based on the microgrid impedance matching characteristics and power quality constraints. It is used to compensate for voltage or frequency fluctuation risks during capacity optimization. The second compensation correction value is a compensation coefficient based on the capacity decay and insufficient charging and discharging efficiency of energy storage in different SOC ranges. It is used to reserve effective energy storage capacity in capacity configuration. By setting the first and second compensation correction values, voltage sensitivity and energy storage capacity loss can be quantitatively compensated before microgrid capacity optimization, thereby improving capacity utilization and system power supply reliability.

[0043] Based on the first compensation correction value and the second compensation correction value, the optimized capacity configuration parameter combination obtained by optimization is reverse-analyzed into the segmented capacity configuration parameters to generate a photoelectric storage collaborative configuration execution scheme.

[0044] Furthermore, this application also includes the following steps: constructing an association mapping matrix based on the first compensation correction value and photovoltaic output gradient parameters, the second compensation correction value and reactive power compensation gap; using the coupling influence coefficient of the association mapping matrix as the fitness function weight, minimizing the voltage deviation at the grid connection point corresponding to the microgrid, maximizing the capacity utilization of the energy storage cluster, and minimizing the response delay of the flexible load aggregate as optimization objectives.

[0045] Specifically, based on the first compensation correction value, photovoltaic output gradient parameters, the second compensation correction value, and the reactive power compensation gap of energy storage, a mapping matrix is ​​established between the capacity configuration parameters of the microgrid and the power balance influence areas. The matrix rows represent correction parameters or capacity control elements, including impedance matching compensation elements, power transmission efficiency compensation elements, high SOC segment attenuation compensation elements, and charge / discharge cycle compensation elements; each row corresponds to a specific quantifiable parameter. The matrix columns represent the influence areas of the microgrid, including photovoltaic grid connection points, energy storage clusters, and flexible load aggregations; each column represents an area whose performance needs to be evaluated. The value within each cell represents its correlation strength, i.e., the coupling influence coefficient. The coupling influence coefficient quantifies the specific numerical value of the degree to which a change in one parameter in the matrix affects another parameter or target. Specifically, in the microgrid operation simulation model, keeping other parameters constant, only the parameter associated with a certain compensation element is subjected to a series of discretized perturbations within its typical range. The change in key performance indicators of another power balance area under each perturbation is recorded. By linear regression or by calculating the ratio of the change in this performance indicator to the parameter perturbation, the original sensitivity coefficient of one parameter to another parameter is obtained. Since different performance indicators have different dimensions and orders of magnitude, all original sensitivity coefficients are normalized so that they fall within the range of [0,1] and the sum of all coefficients in the same column is 1. The normalized value is the coupling effect coefficient.

[0046] When invoking the optimization algorithm, the correlation mapping matrix is ​​used as the core, and the coupling influence coefficients of the correlation mapping matrix are used as the fitness function weights to optimize the capacity configuration combination. That is, the sum of all coupling influence coefficients in each column of the matrix is ​​calculated, reflecting the comprehensive influence potential of all compensation elements on the region. The larger the sum, the more sensitive the region's performance is to the overall configuration strategy. According to the microgrid operation regulations, each affected region is assigned a basic priority coefficient related to power supply security. This coefficient is usually set based on the system risk level caused by performance degradation in that region; the higher the risk level, the larger the coefficient. For example, grid connection point voltage stability is directly related to the risk of system collapse, so its basic priority coefficient is the highest; load response delay is related to comfort, so its basic priority coefficient is relatively low. The total regional influence is combined with the basic priority coefficient and normalized to obtain an objective fitness function weight: the comprehensive influence of each region is multiplied by the corresponding basic safety priority coefficient to obtain a weighted comprehensive value. The weighted comprehensive values ​​of all power balance affected regions are summed to obtain the total. The final fitness function weight of each region is equal to the proportion of its weighted comprehensive value to the total weighted comprehensive value of all regions. After normalization, the sum of all weights is finally 1.

[0047] The optimization objective is to simultaneously minimize the voltage deviation at the grid connection point of the microgrid, maximize the capacity utilization of the energy storage cluster, and minimize the response delay of the flexible load aggregation. Therefore, the objective function is... Grid connection point voltage deviation Energy storage cluster capacity utilization The response delay of the flexible load aggregation is considered. For example, assuming a photovoltaic gradient parameter of -25 kW / min and an energy storage compensation value of 15% for a certain scheme, simulation verification yields a grid connection voltage deviation of 0.02 pu, an energy storage cluster capacity utilization rate of 85%, and a flexible load aggregation response delay of 25 ms. Assuming weights are set as follows: grid connection voltage deviation 0.35, energy storage cluster capacity utilization rate 0.45, and flexible load aggregation response delay 0.2, normalization yields a grid connection voltage deviation of 0.4, an energy storage cluster capacity utilization rate of 0.3, and a flexible load aggregation response delay of 0.5. Substituting these into the objective function... .

[0048] The grid connection point voltage deviation is the difference between the actual voltage and the rated voltage at the connection point between the microgrid 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 its total available capacity during the dispatch cycle; the flexible load aggregation response delay is the time required from the issuance of the dispatch command to the overall flexible load reaching the predetermined adjustment target. By introducing an association mapping matrix and weighted multi-objective optimization, the coordinated configuration of photovoltaics and energy storage is ensured to be more reasonable, and the overall power balance, energy storage regulation, and load response of the system reach a dynamically optimized state, further improving the stability and power supply reliability of the microgrid.

[0049] Furthermore, this application also 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-discharge cycle compensation element category; the matrix column dimension of the correlation mapping matrix is ​​the power balance influence area of ​​the microgrid, including the grid connection point of the photovoltaic output segment, the energy storage cluster of the energy storage regulation segment, and the flexible load aggregation of the load response segment.

[0050] Specifically, the matrix row dimension of the correlation mapping matrix includes four categories of compensation elements: impedance matching compensation element category, power transmission efficiency compensation element category, high SOC section attenuation compensation element category, and charge / discharge cycle compensation element category, corresponding to the first compensation correction value and the second compensation correction value, respectively. The impedance matching compensation element category considers specific aspects to compensate for the impact of line impedance, including voltage compensation amount and reactive power support requirements; the power transmission efficiency compensation element category considers specific aspects to compensate for the impact of resistance losses, including active power loss and derating factor; the high SOC section attenuation compensation element category considers factors that exacerbate capacity attenuation when the energy storage battery is operating in the high-charge state range, including capacity attenuation rate and internal resistance growth rate; and the charge / discharge cycle compensation element category considers lifetime attenuation factors caused by the energy storage charging and discharging behavior itself, including equivalent cycle count and average depth of discharge.

[0051] The matrix columns cover all power balance impact areas in the microgrid, namely the grid connection point of the photovoltaic output segment, the energy storage cluster of the energy storage regulation segment, and the flexible load aggregation of the load response segment. The grid connection point of the photovoltaic output segment is the common connection point for the photovoltaic power generation system to connect to the microgrid, and it is a key location for monitoring photovoltaic power injection and its impact on the grid; the energy storage cluster of the energy storage regulation segment is a collection of all energy storage units operating in a unified and coordinated manner, and it is the main body for energy transfer and power support; the flexible load aggregation of the load response segment virtualizes numerous adjustable loads into a unified load that can be dispatched.

[0052] After clarifying the row and column dimensions of the correlation mapping matrix, the matrix is ​​filled, which involves numerically representing the contribution of each compensation element to each power balance region, forming a coupling influence coefficient. For example, the contribution of the attenuation compensation element category in the high SOC segment to the energy storage cluster in the energy storage regulation segment may be 0.9, indicating a significant impact on the available energy storage capacity, while the contribution to the grid connection point in the photovoltaic output segment may only be 0.1.

[0053] Furthermore, this application also includes the following steps: taking the range of values ​​for the first compensation correction value and the photovoltaic output gradient parameter, and the range of values ​​for the second compensation correction value and the reactive power compensation gap as the particle search space, and introducing adaptive inertial weights for iterative optimization; the capacity configuration parameter combination generated in each round of optimization is input into the microgrid operation simulation model for verification; if the simulation verification result meets the power quality requirements, the current capacity configuration parameter combination is dynamically refreshed, and the optimized capacity configuration parameter combination is output.

[0054] Specifically, the ranges of the first compensation correction value and the photovoltaic output gradient parameter, and the ranges of the second compensation correction value and the reactive power compensation gap are used as the particle search space, that is, the upper and lower limits of the four key variables are used as the boundaries of the algorithm's search. In the particle swarm optimization algorithm, each possible solution is regarded as a particle, and the range of all particle activities is the particle search space.

[0055] A swarm of particles is initialized, with each particle's position representing a random combination of capacity configuration parameters. In the iterative optimization process incorporating adaptive inertia weights, the adjustment strategy typically employs a linear decreasing method. Initially, the weights are set to a large value to encourage extensive exploration within the search space; as the number of iterations increases, the inertia weights gradually decrease to a lower limit until the maximum number of iterations is reached (e.g., a maximum value of 0.9, a lower limit of 0.4, and a maximum of 700 iterations). In each iteration, each particle updates its movement speed and direction based on its own historical best position and the historical best position of the entire swarm. This adaptive adjustment of the inertia weights ensures the intelligence of the search. After updating the particle positions, a boundary constraint mechanism ensures the feasibility of the solution: if a particle's position component exceeds the preset variable value range, the component is forcibly set to the corresponding boundary value, i.e., an absorbing boundary strategy. A penalty function method is used to transform the constrained optimization problem into an unconstrained problem. For capacity configuration combinations that do not meet power quality requirements or power balance constraints, a large penalty term, such as 10, is added to their fitness value. 6 This significantly reduces the probability of it being selected as the optimal solution, 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 velocity from the previous time step. Adaptive means that this weight is not fixed but dynamically adjusted according to the optimization process: a larger weight in the early stages facilitates global exploration and helps escape local optima, while a smaller weight in the later stages facilitates fine-grained local search and rapid convergence.

[0056] Each round of optimization generates a combination of capacity configuration parameters, which is then input into a microgrid operation simulation model for verification. This model operates based on load and solar radiation data from typical days throughout the year, analyzing the capacity configuration parameter combinations and outputting key operational indicators, particularly power quality indicators. If the simulation results meet the power quality requirements, the capacity configuration parameter combination is considered a feasible solution. Power quality requirements are the technical parameter standards and limits necessary to ensure the normal operation of electrical equipment and the safe and stable operation of the power system. These are power quality assessment standards, including voltage deviation, frequency deviation, voltage fluctuation and flicker, and harmonic distortion rate. For example, it assesses whether the per-unit voltage of the grid connection point and key user nodes remains within the allowable range of 0.95 pu to 1.05 pu; whether the frequency remains within the allowable range of 49.8 Hz to 50.2 Hz; the voltage variation limit caused by photovoltaic output fluctuations (medium-voltage systems typically require voltage variation not to exceed 3%); and whether the total harmonic distortion rate of the grid connection point current and the content of each harmonic are lower than the standard limit of 5%. A capacity configuration parameter combination is considered feasible only if the voltage, frequency, and harmonic parameters of all nodes simultaneously meet the power quality assessment criteria in the simulation results of all typical daily scenarios. The system dynamically refreshes the current capacity configuration parameter combination, updates the global optimal solution, and finally outputs the capacity optimization configuration parameter combination with the highest overall fitness among all feasible solutions.

[0057] The microgrid operation simulation model simulates the dynamic operation of a microgrid under different capacity configurations and regulation strategies by establishing its various components. The model is built based on modular modeling principles, and its specific structure includes a power supply module, a load module, a network module, and a control module. The power supply module comprises a photovoltaic array and an energy storage system. The photovoltaic array's input is a typical daily irradiance and temperature sequence, and its output characteristics follow the PV curves provided by the manufacturer. The energy storage system includes a battery equivalent circuit, a bidirectional converter, and its control logic; its charging and discharging characteristics are directly controlled by the capacity configuration parameters. The load module uses constant power, constant impedance, or time-series load models, with inputs being hourly or minute-by-minute active and reactive load curves corresponding to a typical day. The network module establishes a node admittance matrix including line impedance and transformer parameters based on the actual microgrid topology for power flow calculations. The control module integrates photovoltaic maximum power point tracking, power / voltage support strategy settings, and load response logic. The power supply module, load module, network module, and control module are coupled through power flow equations. During simulation, within each time step (typically 1 to 15 minutes), the voltage, current, power, and energy storage SOC state of all network nodes are calculated iteratively to simulate quasi-steady-state operation. The simulation relies on the following explicit boundary condition inputs: clearly defined initial SOC of each energy storage unit and initial network power flow solution at the start of the simulation. In multi-day continuous simulations, the initial SOC for each day is the final value at the end of the previous day's simulation. The voltage at the main grid connection point is clearly defined; the connection point between the microgrid and the main grid is considered 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 connection point can be set as a constant voltage source or simulate the dynamics of the external power grid. The input data consists of dynamic configuration parameter combinations generated in each round of optimization, specifically including the first compensation correction value, photovoltaic output gradient parameters, the second compensation correction value, reactive power compensation gap, and time-series scenario data, including typical daily load curves throughout the year, photovoltaic irradiance and temperature sequences, and electricity price indicator lights. The model output is a feasibility assessment index, namely the power quality verification result, which determines whether the voltage deviation of all nodes meets the power quality requirements throughout the entire simulation cycle, thus determining the feasibility of the scheme. In addition, it also includes detailed operational status data, such as voltage and frequency curves, power flow distribution, and energy storage operation trajectory.

[0058] The capacity configuration parameters generated by capacity optimization are input into the microgrid operation simulation model. Combined with historical load data and photovoltaic output prediction data, the simulation calculates voltage, current, power flow, and energy storage state at each time step. The simulation model can be implemented using existing tools, and a modular modeling approach is used to model photovoltaics, energy storage, load, and grid impedance separately. The system state is then solved through power flow iteration and time step integration.

[0059] For example, 50 particles are initialized. Particle A's initial position is set to 8% first compensation, -30 kW / min gradient, 12% second compensation, and 80 kvar reactive power gap. An adaptive inertia weight is introduced, initially set to 0.9. The parameter combination of particle A is substituted into a microgrid operation simulation model for verification. Simulation results show that on a summer afternoon, due to a surge in load and photovoltaic fluctuations, the grid connection voltage briefly drops to 0.93 pu, failing to meet power quality requirements. Therefore, particle A's scheme is eliminated, and its fitness is set to a very poor value. In subsequent iterations, particle B's position is updated to 10% first compensation, -25 kW / min gradient, 18% second compensation, and 120 kvar reactive power gap. Simulation verification shows that under this configuration, the lowest grid voltage is 0.952 pu, fully meeting the 0.95 pu requirement. Simultaneously, its comprehensive fitness is calculated to be 0.35, better than the current record. Therefore, a dynamic refresh is performed, recording particle B's parameter combination and fitness of 0.35 as the new global optimal solution.

[0060] By combining intelligent optimization algorithms with high-fidelity simulation models and introducing adaptive inertia weights, the search performance of the algorithm is improved, avoiding premature convergence. A dynamic refresh mechanism based on simulation verification ensures that each final output scheme is technically feasible, strictly meeting power quality requirements, thus enhancing the engineering practicality and reliability of the entire optimization configuration scheme.

[0061] Furthermore, this application also includes the following steps: if the simulation verification results do not meet the power quality requirements, then based on the positioning deviation contribution of the correlation mapping matrix, the positioning deviation contribution is 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; then, the optimized capacity configuration parameter combination obtained by optimization is reverse-analyzed into the segmented capacity configuration parameters to generate a photovoltaic-storage collaborative configuration execution scheme including a dynamic output smoothing curve for photovoltaic output segments, a stepped charging and discharging scheme for energy storage regulation segments, and a zoned load control strategy for load response segments.

[0062] Specifically, if the simulation results do not meet the power quality requirements, the solution will not be discarded directly; instead, a diagnostic procedure will be initiated. Based on the correlation mapping matrix, a location deviation contribution analysis is performed to determine which compensation elements have the highest influence coefficients at specific times and locations where voltage exceedances occur, thereby calculating and ranking a sequence of compensation element categories. The location deviation contribution is used to determine the degree of responsibility each compensation element category in the correlation mapping matrix should bear for a specific deviation when power quality is substandard. A higher contribution indicates that the element is the main cause of the problem. After sorting the compensation element categories according to their location deviation contribution from high to low, a sequence of compensation element categories is obtained, defining the priority order for fault repair.

[0063] Local optimization is performed based on the compensation element category sequence. Low-contribution capacity configuration parameter combinations are temporarily frozen, while high-contribution combinations are optimized locally, such as adjusting high-SOC energy storage capacity, charge / discharge cycle strategies, or impedance matching parameters. Power balance and voltage deviation are gradually improved while keeping other elements constant. Local optimization iterates until a preset threshold is reached to ensure convergence. The preset threshold is a safety limit set to prevent the optimization process from getting stuck in an infinite loop or taking too long.

[0064] The optimized capacity configuration parameter combination obtained at this point is then reverse-analyzed into the segmented capacity configuration parameters. This involves mapping the optimized capacity combination parameters back to the specific capacity configurations for the photovoltaic output segment, energy storage regulation segment, and load response segment. This generates a photovoltaic-energy storage collaborative configuration execution scheme that includes the dynamic output smoothing curve for the photovoltaic output segment, the stepped charge-discharge scheme for the energy storage regulation segment, and the zoned load control strategy for the load response segment. The photovoltaic-energy storage collaborative configuration execution scheme incorporates the dynamic output smoothing curve, the stepped charge-discharge scheme, and the zoned load control strategy.

[0065] By utilizing the contribution of positioning deviation to guide local optimization, fine-tuning of compensation factors that mainly affect the power quality of microgrids is achieved. The optimization results are then analyzed in reverse to generate segmented capacity configuration, resulting in an executable photovoltaic-storage coordinated control scheme. This scheme enables photovoltaic output smoothing, efficient energy storage regulation, and timely load response matching, significantly improving the power supply reliability, voltage stability, and overall power balance accuracy of the microgrid, while reducing capacity redundancy and unnecessary energy storage regulation overhead.

[0066] Real-time monitoring data, including voltage, current, and SOC status data, is collected in various power balance-affected areas via industrial Ethernet. Based on the preset power balance threshold and actual operating deviation of the optical-storage collaborative configuration execution scheme, the combination of capacity optimization configuration parameters is dynamically refreshed and continuously optimized.

[0067] Furthermore, this application also includes the following steps: using the parameter sensitivity of the correlation mapping matrix as the network input weight, and using the difference between the target value and the power quality requirement as the network input layer variable; after each round of refresh, substituting 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 region; if the deviation improvement rate is lower than the preset improvement rate threshold, then updating the boundary constraints of the particle search space with the compensation element category sequence.

[0068] Specifically, Industrial Ethernet is a high-speed, reliable communication network used in industrial fields to connect various measurement points and control devices in a microgrid, supporting real-time data transmission. Through sensors distributed throughout the power balance impact areas of the microgrid, data such as voltage, current, and state of charge (SOC) are collected in real-time at millisecond speeds via Industrial Ethernet, forming real-time monitoring data. The power balance impact areas are collections of physical nodes or devices in the microgrid that play a crucial role in global power balance and stability, including photovoltaic grid-connected points, energy storage regulation clusters, and flexible load aggregations.

[0069] The real-time monitoring data is compared with the preset power balance threshold in the photovoltaic-storage collaborative configuration execution scheme to calculate the current operating deviation. The actual operating deviation is the difference between the real-time monitoring data and the preset power balance threshold. For example, if the real-time monitoring voltage is 9.7kV and the lower limit of the preset power balance threshold is 10.5kV, the deviation is -0.8kV.

[0070] Once the actual operational deviation exceeds the allowable range, the rolling optimization engine is triggered. Based on the latest voltage, current, and SOC status, the photovoltaic output gradient, energy storage charging and discharging strategies, and load response priorities are updated to ensure the microgrid continuously meets power quality requirements. Rolling optimization uses a certain time window for iterative adjustments, and the updated configuration parameters are applied immediately after each optimization round to achieve adaptive adjustment of the microgrid's operating status, ensuring that the synergistic effect of photovoltaic and energy storage takes effect in real time. The refreshed capacity optimization configuration parameter combination is immediately sent to the corresponding execution units, and the entire microgrid then operates according to the new strategy, which is more adapted to the current situation.

[0071] The parameter sensitivity of the correlation mapping matrix is ​​used as the network input weights. Higher weights are assigned to the differences in target values ​​corresponding to parameters with high sensitivity. Simultaneously, the differences between the actual operating data and the power quality target values ​​of each power balance influence region are used as network input layer variables to calculate the direction of the next round of capacity parameter adjustments. The parameter sensitivity of the correlation mapping matrix represents the magnitude and gradient of the influence coefficient of each compensation element category on the power balance influence region. High sensitivity means that a small change in this parameter can have a significant impact on microgrid performance, and vice versa.

[0072] After each refresh, the next global optimization is not performed immediately. Instead, the updated capacity optimization configuration parameter combination is first substituted into the correlation mapping matrix for reverse verification. This predicts the impact of the new parameters on each power balance influence region and calculates the predicted deviation improvement rate. The deviation improvement rate is usually calculated as the ratio of the difference between the deviation in the previous round and the deviation in the current round to the deviation in the previous round. The calculated deviation improvement rate is compared with a preset improvement rate threshold. If the deviation improvement rate is lower than the preset improvement rate threshold, it indicates that within the currently defined particle search space, the algorithm is finding it difficult to find a significantly better solution, possibly trapped in a local optimum or the search space itself is unreasonable. The preset improvement rate threshold is the minimum 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 allowable value range of each parameter in the particle swarm optimization algorithm. For example, suppose that after a rolling optimization, the capacity optimization configuration parameter combination is refreshed, and the reactive power compensation gap is adjusted from 100kvar to 110kvar. Substituting the new capacity optimization configuration parameter combination of 110kvar into the correlation mapping matrix, this change improves the prediction of the grid connection point voltage by 0.003 pu. Before the refresh, the voltage deviation was 0.05 pu, and after the refresh, the deviation is 0.047 pu, resulting in a deviation improvement rate of 0.06. Assuming the preset improvement rate threshold is 0.1, and since 0.06 < 0.1, the improvement rate is lower than the preset improvement rate threshold. Therefore, the search space is updated by calling the compensation element category sequence obtained from historical analysis.

[0073] By combining parameter sensitivity and target value differences to construct input layer weights, fine-grained control of each compensation element is achieved. Reverse verification ensures that the optimization scheme is actually effective in each power balance region, and the convergence efficiency of iterative optimization is improved by dynamically adjusting the particle search space boundary.

[0074] In summary, the microgrid photovoltaic-storage capacity optimization configuration method provided in this application has the following technical effects: Based on the load characteristic parameters and energy access conditions of the microgrid, segmented capacity configuration parameters are set, which are associated with photovoltaic output segments, energy storage regulation segments, and load response segments. Based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage units, a first compensation correction value and a second compensation correction value are set. Based on the first compensation correction value and the second compensation correction value, the optimized capacity configuration parameter combination obtained through optimization is back-analyzed into the segmented capacity configuration parameters to generate a photovoltaic-energy storage collaborative configuration execution scheme. Real-time monitoring data, including voltage, current, and SOC status data, are collected in each power balance influence area via industrial Ethernet. Based on the preset power balance threshold of the photovoltaic-energy storage collaborative configuration execution scheme and the actual operating deviation, the optimized capacity configuration parameter combination is dynamically refreshed and continuously optimized. In other words, by setting segmented capacity configuration parameters and introducing compensation correction values ​​to correct capacity configuration errors, real-time monitoring data of each power balance-affected area is collected in real time through industrial Ethernet. Based on the preset power balance threshold of the photovoltaic-storage collaborative configuration execution scheme and the actual operating deviation, the combination of capacity optimization configuration parameters is dynamically refreshed and rolled to improve the accuracy of photovoltaic-storage capacity configuration in the microgrid, thereby enhancing the power balance capability and power supply reliability of the microgrid under different operating conditions.

[0075] Example 2: Based on the same inventive concept as the microgrid photovoltaic-storage capacity optimization configuration method in Example 1, this application also provides a microgrid photovoltaic-storage capacity optimization configuration system. Please refer to the appendix. Figure 2 The microgrid photovoltaic-storage capacity optimization configuration system includes: The parameter configuration module 11 is used to set segmented capacity configuration parameters associated with photovoltaic output segments, energy storage regulation segments, and load response segments based on the load characteristic parameters and energy access conditions of the microgrid; the compensation correction module 12 is used to set a first compensation correction value and a second compensation correction value based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage unit; the scheme generation module 13 is used to reverse-analyze the optimized capacity 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; the deviation optimization module 14 is used to collect real-time monitoring data, including voltage, current, and SOC status data, of each power balance influence area through industrial Ethernet, and dynamically refresh and continuously optimize the capacity optimization configuration parameter combination according to the preset power balance threshold and actual operating deviation of the photovoltaic-storage collaborative configuration execution scheme.

[0076] Furthermore, the parameter configuration module 11 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: perform time-series equivalent analysis and sensitive node identification on the load fluctuation amplitude and new energy output prediction error based on the load characteristic parameters and energy access conditions of the microgrid, and calculate the power deficit and supply-demand balance critical point for each time period; and divide the microgrid scheduling cycle into photovoltaic output segment, energy storage regulation segment and load response segment through the power deficit and supply-demand balance critical point for each time period.

[0077] Furthermore, the compensation correction module 12 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: based on the impedance matching characteristics of the microgrid and combined with power quality requirements, perform full-dimensional detection of grid impedance parameters and power transmission efficiency to obtain a first compensation correction value; based on the segmented capacity configuration parameters, simulate and set the power flow distribution of the photovoltaic output fluctuation process using power simulation to obtain photovoltaic output gradient parameters that meet the impedance matching stability constraints.

[0078] Furthermore, the compensation correction module 12 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: dynamically extrapolate the capacity attenuation in different SOC intervals based on the charging and discharging efficiency parameters of the energy storage unit and combined with historical charging and discharging cycle data, and obtain a second compensation correction value; and optimize the energy storage access point of the microgrid based on the segmented capacity configuration parameters using topology optimization to obtain a reactive power compensation gap that meets the power balance constraints.

[0079] Furthermore, the scheme generation module 13 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: construct an association mapping matrix based on the first compensation correction value and photovoltaic output gradient parameters, the second compensation correction value and reactive power compensation gap; and use the coupling influence coefficient of the association mapping matrix as the fitness function weight to minimize the voltage deviation of the grid connection point corresponding to the microgrid, maximize the capacity utilization of the energy storage cluster, and minimize the response delay of the flexible load aggregation as optimization objectives.

[0080] Furthermore, the scheme generation module 13 in the microgrid photovoltaic-storage capacity optimization configuration system is also used for: 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-discharge cycle compensation element category; the matrix column dimension of the correlation mapping matrix is ​​the power balance influence area of ​​the microgrid, including the grid connection point of the photovoltaic output segment, the energy storage cluster of the energy storage regulation segment, and the flexible load aggregation of the load response segment.

[0081] Furthermore, the scheme generation module 13 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: take the range of values ​​of the first compensation correction value and the photovoltaic output gradient parameter, and the range of values ​​of the second compensation correction value and the reactive power compensation gap as the particle search space, and introduce adaptive inertial weights for iterative optimization; the capacity configuration parameter combination generated in each round of optimization is input into the microgrid operation simulation model for verification; if the simulation verification result meets the power quality requirements, the current capacity configuration parameter combination is dynamically refreshed, and the capacity optimization configuration parameter combination is output.

[0082] Furthermore, the scheme generation module 13 in the microgrid photovoltaic-storage capacity optimization configuration system is also used for: if the simulation verification results do not meet the power quality requirements, then based on the positioning deviation contribution of the correlation mapping matrix, sorting the positioning deviation contribution from high to low to obtain a compensation element category sequence; performing local optimization based on the compensation element category sequence until the number of iterations reaches a preset threshold, and then back-analyzing the optimized capacity configuration parameter combination obtained by optimization into the segmented capacity configuration parameters to generate a photovoltaic-storage collaborative configuration execution scheme including a dynamic output smoothing curve for photovoltaic output segments, a stepped charging and discharging scheme for energy storage regulation segments, and a zoned load control strategy for load response segments.

[0083] Furthermore, the deviation optimization module 14 in the microgrid photovoltaic-storage capacity optimization configuration system is also used to: use the parameter sensitivity of the correlation mapping matrix as the network input weight, and use the difference between the target value and the power quality requirement as the network input layer variable; after each round of refresh, 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 the preset improvement rate threshold, then update the boundary constraints of the particle search space with the compensation element category sequence.

[0084] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The microgrid photovoltaic-storage capacity optimization configuration method and specific examples in the aforementioned embodiment one are also applicable to the microgrid photovoltaic-storage capacity optimization configuration system of this embodiment. Through the foregoing detailed description of the microgrid photovoltaic-storage capacity optimization configuration method, those skilled in the art can clearly understand the microgrid photovoltaic-storage capacity optimization configuration system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0085] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not 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.

[0086] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the configuration of photovoltaic and energy storage capacity in microgrids, characterized in that, include: Based on the load characteristic parameters and energy access conditions of the microgrid, segmented capacity configuration parameters are set that are associated with the photovoltaic output segment, energy storage regulation segment, and load response segment. Based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage unit, a first compensation correction value and a second compensation correction value are set. Based on the first compensation correction value and the second compensation correction value, the optimized capacity configuration parameter combination obtained by optimization is reverse-analyzed into the segmented capacity configuration parameters to generate a photoelectric storage collaborative configuration execution scheme. Real-time monitoring data, including voltage, current, and SOC status data, is collected in various power balance-affected areas via industrial Ethernet. Based on the preset power balance threshold and actual operating deviation of the optical-storage collaborative configuration execution scheme, the combination of capacity optimization configuration parameters is dynamically refreshed and continuously optimized.

2. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 1, characterized in that, The method further includes setting segmented capacity configuration parameters associated with photovoltaic output segments, energy storage regulation segments, and load response segments, and setting segmented capacity configuration parameters: Based on the load characteristic parameters and energy access conditions of the microgrid, time-series equivalent analysis and sensitive node identification are performed on the load fluctuation amplitude and the prediction error of new energy output, and the power deficit and supply-demand balance critical point are calculated for each time period. Based on the power deficit and supply-demand balance critical points in each time period, the microgrid dispatch cycle is divided into photovoltaic output segment, energy storage regulation segment, and load response segment.

3. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 1, characterized in that, The method further includes setting a first compensation correction value: Based on the impedance matching characteristics of microgrids and combined with power quality requirements, the impedance parameters and power transmission efficiency of the grid 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 and set using power simulation to obtain the photovoltaic output gradient parameters that meet the impedance matching stability constraints.

4. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 3, characterized in that, The method further includes setting a second compensation correction value: Based on the charge and discharge efficiency parameters of the energy storage unit and combined with historical charge and discharge cycle data, the capacity decay in different SOC ranges is dynamically extrapolated to obtain a second compensation correction value. Based on the segmented capacity configuration parameters, the energy storage access point of the microgrid is optimized using topology optimization to obtain reactive power compensation gaps that meet power balance constraints.

5. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 4, characterized in that, Based on the first compensation correction value and the second compensation correction value, the optimized capacity configuration parameter combination obtained through optimization is reverse-analyzed into the segmented capacity configuration parameters. The method further includes: Based on the first compensation correction value and photovoltaic output gradient parameters, the second compensation correction value and reactive power compensation gap, construct an association mapping matrix; Using the coupling influence coefficient of the aforementioned correlation mapping matrix as the fitness function weight, the optimization objectives are to minimize the voltage deviation at the grid connection point corresponding to the microgrid, maximize the capacity utilization of the energy storage cluster, and minimize the response delay of the flexible load aggregate.

6. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 5, characterized in that, 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 column dimension of the correlation mapping matrix is ​​the power balance influence area of ​​the microgrid, including the grid connection point of the photovoltaic output segment, the energy storage cluster of the energy storage regulation segment, and the flexible load aggregation of the load response segment.

7. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 6, characterized in that, The method includes: The range of values ​​for the first compensation correction value and the photovoltaic output gradient parameter, the range of values ​​for the second compensation correction value and the reactive power compensation gap are used as the particle search space, and adaptive inertial weights are introduced for iterative optimization. Each round of optimization generates a combination of capacity configuration parameters, which is then input into a microgrid operation simulation model for verification. If the simulation verification results meet the power quality requirements, the current combination of capacity configuration parameters is dynamically refreshed, and the optimized capacity configuration parameter combination is output.

8. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 7, characterized in that, The method further includes: If the simulation verification results do not meet the power quality requirements, then based on the positioning deviation contribution of the correlation mapping matrix, the compensation element category sequence is obtained by sorting the positioning deviation contribution from high to low. Local optimization is performed based on the compensation element category sequence until the number of iterations reaches a preset threshold. Then, the optimized capacity configuration parameter combination obtained by optimization is reverse-analyzed into the segmented capacity configuration parameters to generate a photovoltaic-storage collaborative configuration execution scheme that includes a dynamic output smoothing curve for photovoltaic output segments, a stepped charging and discharging scheme for energy storage regulation segments, and a zoned load control strategy for load response segments.

9. The microgrid photovoltaic-storage capacity optimization configuration method as described in claim 8, characterized in that, The method includes dynamically refreshing and continuously optimizing the combination of capacity optimization configuration parameters, wherein the method comprises: The parameter sensitivity of the correlation mapping matrix is ​​used as the network input weight, and the difference between the target value and the power quality requirement is used as the network input layer variable. After each round of refresh, the updated capacity optimization configuration parameters are substituted into the associated 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 the preset improvement rate threshold, the boundary constraints of the particle search space are updated using the compensation element category sequence.

10. A microgrid photovoltaic-storage capacity optimization configuration system, characterized in that, The step of implementing the microgrid photovoltaic-storage capacity optimization configuration method according to any one of claims 1 to 9, wherein the microgrid photovoltaic-storage capacity optimization configuration system comprises: The parameter configuration module is used to set segmented capacity configuration parameters associated with photovoltaic output segment, energy storage regulation segment, and load response segment based on the load characteristic parameters and energy access conditions of the microgrid. The compensation correction module is used to set a first compensation correction value and a second compensation correction value based on the impedance matching characteristics of the microgrid and the charging and discharging efficiency parameters of the energy storage unit. The scheme generation module is used to reverse-parse the optimized capacity 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, and generate a photoelectric storage collaborative configuration execution scheme. The deviation optimization module is used to collect real-time monitoring data, including voltage, current, and SOC status data, of various power balance-affected areas via industrial Ethernet. Based on the preset power balance threshold of the optical-storage collaborative configuration execution scheme and the actual operating deviation, the module dynamically refreshes and continuously optimizes the combination of capacity optimization configuration parameters.

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