Method, device and equipment for evaluating accommodation capacity of optical storage micro-grid

Abstract

The application provides a kind of light storage micro-grid accommodation capacity evaluation method, device and equipment, it is related to micro-grid planning technical field.The method comprises: obtaining the potential operation data of the light storage micro-grid to be evaluated, the potential operation data includes the load power, distributed power and energy storage charge-discharge power corresponding to each working condition;Based on the potential operation data, and the constraint condition of safe operation of light storage micro-grid, data validity screening and combination are carried out, to obtain effective data combination under each working condition, each effective data combination includes a group of load power, distributed power and energy storage charge-discharge power;Based on the effective data combination under each working condition, the total load and accommodation value of each working condition are calculated respectively;Based on the total load and accommodation value of each working condition, combined with the real-time operation data of light storage micro-grid, accommodation capacity evaluation is carried out, to obtain evaluation result.The application can improve the evaluation precision of micro-grid accommodation capacity.

Description

Technical Field

[0001] This invention relates to the field of microgrid planning technology, and in particular to a method, apparatus and equipment for assessing the absorption capacity of a photovoltaic-storage microgrid. Background Technology

[0002] The assessment of absorption capacity is an important foundation for the planning and operation analysis of microgrids. Therefore, accurately assessing the absorption capacity of the system is of great significance and value for its planning, transformation and safe and stable operation.

[0003] Most existing microgrid absorption capacity assessment methods focus only on solving and analyzing the maximum absorption capacity. However, the maximum absorption capacity can only be achieved when the microgrid reaches specific load distribution and distributed generation (DG) access layout conditions. As a result, existing absorption capacity assessment methods can only provide a single-dimensional limit value reference of the maximum absorption capacity, which leads to a disconnect between the absorption capacity assessment results and actual operation requirements, resulting in insufficient assessment accuracy. Summary of the Invention

[0004] This invention provides a method, apparatus, and equipment for assessing the absorption capacity of photovoltaic-storage microgrids, in order to solve the problem of low accuracy in existing microgrid absorption capacity assessments.

[0005] In a first aspect, embodiments of the present invention provide a method for assessing the absorption capacity of a photovoltaic-storage microgrid, comprising: acquiring potential operating data of the photovoltaic-storage microgrid to be assessed, the potential operating data including load power, distributed generation power, and energy storage charging and discharging power corresponding to each operating condition; based on the potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid, performing data validity screening and combination to obtain effective data combinations under each operating condition, each effective data combination including a set of load power, distributed generation power, and energy storage charging and discharging power; calculating the total load and absorption value for each operating condition based on the effective data combinations for each operating condition; and assessing the absorption capacity based on the total load and absorption value for each operating condition, combined with the real-time operating data of the photovoltaic-storage microgrid, to obtain the assessment result.

[0006] In one possible implementation, the constraints include distributed generation boundary constraints and energy storage power constraints. Distributed generation boundary constraints include N-1 safety constraints and reverse power flow constraints. The N-1 safety constraint indicates that after a single component failure in the photovoltaic-storage microgrid, reliable load transfer is achieved through network reconfiguration, and the rated capacity of all lines and substation main transformers after reconfiguration is not less than the absolute value of their net transmission power. The reverse power flow constraint is a reverse power flow equation constraint, covering all distributed generation variables, indicating that when the reverse power flow of any system component reaches its capacity limit, any increase in the power of any distributed generation will lead to a reverse power flow exceeding the limit. The energy storage power constraint indicates that the energy storage charging and discharging power does not exceed its rated capacity, and the state of charge is maintained within the allowable range. Based on potential operating data and the constraints for the safe operation of the photovoltaic-storage microgrid, data validity is screened and combined to obtain effective data combinations for each operating condition. This includes: for each set of potential operating data, if it satisfies the distributed generation boundary constraints and energy storage power constraints, the set of potential operating data is deemed valid; based on the valid potential operating data, the effective data combinations corresponding to each operating condition are summarized.

[0007] In one possible implementation, for each set of potential operating data, if it satisfies the distributed power source boundary constraints and energy storage power constraints, then the set of potential operating data is determined to be valid. This includes: for each set of potential operating data, if the set of potential operating data satisfies the N-1 security constraints and the reverse power flow constraints, then the energy storage power constraints are judged; if the set of potential operating data satisfies the energy storage power constraints, then the set of potential operating data is determined to be valid.

[0008] In one possible implementation, for each set of potential operating data, if it satisfies the distributed power source boundary constraints and energy storage power constraints, then the set of potential operating data is determined to be valid. This includes: for each set of potential operating data, if the set of potential operating data does not satisfy the N-1 security constraints, then the set of potential operating data is determined to be invalid and removed, and the reverse power flow constraints and energy storage power constraints are no longer checked; if the set of potential operating data satisfies the N-1 security constraints but does not satisfy the reverse power flow constraints, then the set of potential operating data is determined to be invalid and removed, and the energy storage power constraints are no longer checked.

[0009] In one possible implementation, the total load and absorption value for each operating condition are calculated based on the effective data combination for each operating condition, including: calculating the total load corresponding to the effective data combination based on the load power in each effective data combination; and calculating the total output of the distributed power source corresponding to the effective data combination based on the distributed power source power in the effective data combination, which is used as the absorption value.

[0010] In one possible implementation, the absorption capacity is assessed based on the total load and absorption value of each operating condition, combined with the real-time operation data of the photovoltaic-storage microgrid, to obtain the assessment results, including: plotting a visualization curve of absorption capacity versus total load with the total load corresponding to the effective data combination as the abscissa and the absorption value corresponding to the total load as the ordinate; and determining the current absorption capacity of the photovoltaic-storage microgrid based on the visualization curve and the real-time operation data of the photovoltaic-storage microgrid.

[0011] In one possible implementation, the current absorption capacity of the photovoltaic-storage microgrid is determined based on a visualized curve and real-time operational data. This includes: calculating the Euclidean distance between the key parameters corresponding to the real-time operational data of the photovoltaic-storage microgrid and all valid data combinations in the visualized curve; the key parameters include the current total load power, the actual output of distributed power sources, and the real-time charging and discharging power of energy storage; sorting all valid data combinations in ascending order of Euclidean distance to obtain a sorting result; determining the first valid data combination in the sorting result as the valid data closest to the operating condition of the photovoltaic-storage microgrid; and determining the current absorption capacity corresponding to the real-time operational data of the photovoltaic-storage microgrid based on the absorption value corresponding to the valid data closest to the operating condition.

[0012] In one possible implementation, before obtaining potential operational data for various operating scenarios of the photovoltaic-storage microgrid to be evaluated, the following steps are included: Based on the basic parameters of the photovoltaic-storage microgrid to be evaluated, including the microgrid network structure, rated capacity of lines and substation main transformers, power range of each load node, power range of each distributed power generation node, and energy storage device parameters; energy storage device parameters include energy storage capacity, upper and lower limits of state of charge, maximum charging and discharging power, charging and discharging time, power factor, and charging and discharging efficiency; based on the basic parameters and combined with historical operational limit data of the photovoltaic-storage microgrid, determining the value boundaries of load power, distributed power generation power, and energy storage charging and discharging power; sampling within the value boundaries of load power, distributed power generation power, and energy storage charging and discharging power at a preset step size to obtain multiple sample combinations, including sampled values ​​of load nodes, distributed power generation, and energy storage charging and discharging power; and obtaining potential operational data based on the sample combinations.

[0013] Secondly, embodiments of the present invention provide a device for evaluating the absorption capacity of a photovoltaic-storage microgrid, comprising: a communication module for acquiring potential operating data of the photovoltaic-storage microgrid to be evaluated, the potential operating data including load power, distributed power generation power, and energy storage charging and discharging power corresponding to each operating condition; a processing module for filtering and combining data based on the potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid to obtain effective data combinations under each operating condition, each effective data combination including a set of load power, distributed power generation power, and energy storage charging and discharging power; calculating the total load and absorption value for each operating condition based on the effective data combinations for each operating condition; and evaluating the absorption capacity based on the total load and absorption value for each operating condition, combined with the real-time operating data of the photovoltaic-storage microgrid, to obtain the evaluation result.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0015] This invention first filters potential operating data through constraints, retaining valid data combinations corresponding to different operating conditions that meet safe operation requirements. This ensures the safety and rationality of the assessment data, laying the foundation for accurate energy absorption assessment. Next, this invention calculates the total load and absorption value for each operating condition, overcoming the limitation of existing absorption assessment methods that can only obtain a single maximum absorption capacity, and achieving a quantitative representation of the absorption energy value for different operating conditions. Finally, based on the absorption value and total load for each operating condition, combined with real-time operating data of the photovoltaic-storage microgrid, this invention assesses the absorption capacity of the photovoltaic-storage microgrid under its current operating conditions. This ensures that the absorption capacity assessment results closely match the actual operating state of the microgrid, improving the accuracy of the absorption assessment. Attached Figure Description

[0016] Figure 1 This is an application scenario diagram of the photovoltaic-storage microgrid absorption capacity assessment method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the implementation of the photovoltaic-storage microgrid absorption capacity assessment method provided in this embodiment of the invention. Figure 3 This is a visual curve diagram illustrating the method for evaluating the absorption capacity of a photovoltaic-storage microgrid provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of the photovoltaic-storage microgrid absorption capacity assessment device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This diagram illustrates an application scenario of the photovoltaic-storage microgrid absorption capacity assessment method provided in this embodiment of the invention. (Example:) Figure 1 As shown, the photovoltaic-storage microgrid includes two substation main transformers (T1 and T2), a tie switch (TS), load nodes, distributed generation (DG) nodes, and energy storage devices. These components work together to ensure safe system operation and renewable energy absorption. The two substation main transformers are the core power supply equipment, responsible for voltage level conversion and power transmission. The tie switch (TS) interconnects the main transformers and their associated lines, providing a flexible topology for load transfer during faults. Load nodes are the power consumption terminals, and their power demand directly affects the absorption capacity assessment. DG nodes are renewable energy generation units, and their output is the core assessment object for absorption capacity. Energy storage devices smooth out DG output fluctuations through charge and discharge regulation, and assist in load transfer under N-1 fault scenarios, maintaining their state of charge within the allowable range of 15%-100%, ensuring system operational stability.

[0019] See Figure 2 The document illustrates a flowchart of the implementation of the photovoltaic-storage microgrid absorption capacity assessment method provided in this embodiment of the invention, detailed below: Step 201: Obtain potential operating data of the photovoltaic-storage microgrid to be evaluated. Potential operating data includes load power, distributed power generation power, and energy storage charging and discharging power corresponding to each operating condition.

[0020] In some embodiments, potential operating data refers to the set of key parameters related to energy balance and operational constraints of a photovoltaic-storage microgrid under various possible operating conditions.

[0021] For example, potential operational data includes load power, distributed generation power, and energy storage charging / discharging power corresponding to each operating condition. Load power is the power consumed by all load nodes in a photovoltaic-storage microgrid under a specific operating condition, such as the power value of residential electricity load, industrial electricity load, etc. Distributed generation power is the output power of distributed power sources in the microgrid, such as photovoltaic and wind power, under a specific operating condition. Energy storage charging / discharging power is the charging or discharging power of energy storage devices under a specific operating condition.

[0022] Step 202: Based on potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid, data validity is screened and combined to obtain effective data combinations under each operating condition. Each effective data combination includes a set of load power, distributed power generation power, and energy storage charging and discharging power.

[0023] In some embodiments, the constraints include distributed generation boundary constraints and energy storage power constraints. Distributed generation boundary constraints include N-1 safety constraints and reverse power flow constraints. The N-1 safety constraint indicates that after a single component failure occurs in the photovoltaic-storage microgrid, the load can be reliably transferred after network reconfiguration, and the rated capacity of all lines and substation main transformers after reconfiguration is not less than the absolute value of their net transmission power. The reverse power flow constraint is a reverse power flow equation constraint, covering all distributed generation variables, indicating that when the reverse power flow of system components reaches the capacity limit, any increase in the power of any distributed generation will lead to the reverse power flow exceeding the limit. The energy storage power constraint indicates that the energy storage charging and discharging power does not exceed the rated capacity and the state of charge is maintained within the allowable range.

[0024] For example, constraints are the set of rules that a photovoltaic-storage microgrid must meet for safe operation, and they are the core basis for screening valid potential operational data. Constraints serve as the criteria for judging the validity of data, eliminating parameter combinations that do not conform to the logic of safe microgrid operation from massive amounts of potential operational data, ensuring that subsequent assessments are based on real and feasible operational scenarios, and avoiding distortion of assessment results.

[0025] For example, distributed generation boundary constraints are key rules that limit the output of distributed generation (DG).

[0026] For example, the N-1 security constraint is a core principle for the safe operation of power systems. The N-1 security constraint ensures the fault redundancy capability of microgrids, ensuring that the assessed absorption capacity is the absorption capacity under safe conditions, rather than a one-sided result that only considers normal operation and ignores fault scenarios, thus improving the practicality and reliability of the assessment.

[0027] For example, reverse power flow constraints prevent excessive DG output from causing reverse power transmission beyond the equipment capacity, prevent grid voltage and frequency fluctuations, ensure microgrid power quality and equipment safety, and set precise upper limit thresholds for DG output.

[0028] For example, energy storage power constraints are rules that limit the charging and discharging behavior of energy storage devices, taking into account key factors such as energy storage capacity, charging and discharging time, power factor, and charging and discharging efficiency. Energy storage power constraints ensure that the charging and discharging behavior of energy storage devices conforms to their physical characteristics and safety requirements, avoiding damage to the equipment due to overcharging, over-discharging, or exceeding power limits. At the same time, they ensure that the energy storage effectively mitigates fluctuations in distributed generation (DG) output, so that the assessment results reflect the true absorption capacity of energy storage in coordinated operation with DG and load.

[0029] As one possible implementation, step 202 can be specifically implemented as steps A11-A12.

[0030] A11: For each set of potential operating data, if the distributed power source boundary constraints and energy storage power constraints are satisfied, then the set of potential operating data is deemed valid.

[0031] As one possible implementation, step A11 can be specifically implemented as steps B11-B12.

[0032] B11: For each set of potential operating data, if the set of potential operating data satisfies the N-1 safety constraint and the reverse power flow constraint, then energy storage power constraint judgment is performed.

[0033] B12: If the potential operating data of this group meets the energy storage power constraint, then the potential operating data of this group is deemed valid.

[0034] In some embodiments, the set of potential operating data is a combination of potential operating data that meets the safe operation requirements of a photovoltaic-storage microgrid after being verified layer by layer by N-1 security constraints, reverse power flow constraints, and energy storage power constraints.

[0035] This invention clarifies the forward judgment logic and verification sequence for the validity of potential operational data. By setting layer-by-layer, fully compliant screening rules, it ensures that the selected valid data not only meets the safety operation requirements of the microgrid but also supports the accurate assessment of subsequent complete absorption capacity. The data validity judgment follows a progressive verification sequence of N-1 safety constraints, reverse power flow constraints, and energy storage power constraints. Only when potential operational data sequentially meets these three constraints can it be judged as valid potential operational data. This process avoids chaotic verification logic, ensuring that each set of valid data undergoes multi-layer verification of safety baseline, power flow safety, and equipment safety, guaranteeing data compliance and reliability from the source. Addressing the shortcomings of existing technologies that neglect safety constraints and focus only on maximum absorption capacity, this invention ensures that valid data covers the core safety dimensions of microgrid operation through forward verification of multi-layer constraints. For example, N-1 safety constraints guarantee power supply reliability under fault scenarios, reverse power flow constraints prevent power flow exceeding limits due to unreasonable DG output, and energy storage power constraints ensure the safe operation of energy storage devices. The combined effect of these three factors ensures that the operating conditions corresponding to valid data are all safe and feasible, preventing invalid data from interfering with the assessment results. Through rigorous positive screening, the selected valid data covers various compliant operation scenarios of photovoltaic-storage microgrids, providing comprehensive foundational data for subsequent steps. The total load and absorption values ​​calculated based on this data, along with the visualized absorption capacity curves and quantified absorption capacity ranges, accurately reflect the complete absorption potential of the microgrid under different safety conditions, overcoming the limitations of existing technologies that can only describe a single maximum absorption capacity.

[0036] As one possible implementation, step A11 can also be implemented as steps B21-B22.

[0037] B21: For each set of potential operating data, if the set of potential operating data does not meet the N-1 safety constraint, the set of potential operating data is deemed invalid and removed, and the reverse power flow constraint and energy storage power constraint are no longer checked.

[0038] B22: If the potential operating data in this group meets the N-1 safety constraint but does not meet the reverse power flow constraint, then the potential operating data in this group is deemed invalid and removed, and the energy storage power constraint will no longer be verified.

[0039] In some embodiments, the group of potentially invalid operating data is a combination of potentially operating data that fails to meet the N-1 security constraints after N-1 security constraints, or fails to meet the safe operation requirements of the photovoltaic-storage microgrid after being verified layer by layer by N-1 security constraints and reverse power flow constraints, and is therefore directly eliminated.

[0040] This invention clarifies the negative judgment logic and priority of potential operational data validity. By setting a layer-by-layer verification and rejection rule for non-compliant data, it optimizes the data screening process, simplifies the effective data pool, and ensures the safety and accuracy of subsequent absorption capacity assessments. It defines the N-1 safety constraint as the bottom-line constraint for microgrid safe operation, and the reverse power flow constraint as a secondary safety constraint, both having higher priority than energy storage power constraints. If potential operational data does not meet the N-1 safety constraint (e.g., inability to achieve load transfer after a fault, equipment overload), or meets the N-1 safety constraint but not the reverse power flow constraint (e.g., increased DG power leading to reverse power flow exceeding limits), it is directly deemed invalid and rejected without further verification of energy storage power constraints. This logic avoids redundant calculations for obviously non-compliant data, significantly improving the screening efficiency of massive amounts of potential operational data. Addressing the shortcomings of existing technologies that only focus on maximum absorption capacity and ignore safety constraints, the rule of prioritizing the rejection of bottom-line constraints ensures that all valid data ultimately participating in the assessment meets core safety requirements. For example, if data does not meet the N-1 safety constraint, even if parameters such as energy storage power and DG output are compliant, it will be excluded due to the risk of power supply failure in fault scenarios. This avoids the assessment results including absorption capacity under safety risks, ensuring the reliability and practicality of the assessment results. By strictly eliminating invalid data that does not meet the core safety constraints, the redundancy of subsequent data processing is reduced, ensuring that all data used in total load calculation, absorption value calculation, and visualization curve plotting are safe and compliant valid data. This process avoids the interference of invalid data on the assessment results, enabling the final quantified absorption capacity range and visualization curve to truly reflect the absorption potential of the photovoltaic-storage microgrid under various safety conditions, further compensating for the shortcomings of existing technologies in describing complete absorption capacity.

[0041] A12: Based on the effective potential operating data, the effective data combinations corresponding to each operating condition are summarized.

[0042] The embodiments of the present invention can clarify the compliance judgment criteria for the evaluation of the absorption capacity of photovoltaic-storage microgrids, provide clear and enforceable constraints for the effective screening of subsequent potential operating data, and ultimately ensure the safety, accuracy and comprehensiveness of the absorption capacity evaluation results.

[0043] Step 203: Based on the effective data combination under each operating condition, calculate the total load and absorption value for each operating condition.

[0044] As one possible implementation, step 203 can be specifically implemented as steps A21-A22.

[0045] A21: Based on the load power of each effective data combination, calculate the total load corresponding to that effective data combination.

[0046] A22: Based on the power of each distributed power source in the effective data combination, the total output of the distributed power source corresponding to the effective data combination is calculated and used as the absorption value.

[0047] In some embodiments, the effective data combination is a potential operating data combination that satisfies the N-1 security constraint, reverse power flow constraint, and energy storage power constraint after being verified layer by layer by constraint conditions.

[0048] For example, a valid data set includes a compliant set of load power, distributed generation (DG) power, and energy storage charging and discharging power. A valid data set ensures that the calculation results are based on real-world operating conditions under the premise of safe microgrid operation, avoiding invalid data, such as data that violates the N-1 safety constraint, which would lead to calculation distortion, and providing reliable basic data for subsequent absorption capacity assessment.

[0049] In some embodiments, total load is the total power consumption of the photovoltaic-storage microgrid under a given operating condition, calculated by summing the power values ​​of all load nodes in a single set of valid data. It is the sum of the power of all load nodes and is a key parameter reflecting the scale of electricity demand in the microgrid. Total load establishes the correspondence between electricity demand and absorption capacity. For example, the absorption capacity of a microgrid varies under different total load levels. The calculation of total load provides horizontal axis data support for subsequently plotting a visualization curve of total load versus absorption value and analyzing the correlation between the two.

[0050] In some embodiments, the absorption value is the total output of distributed generation (DG) under a given operating condition, calculated by summing the power values ​​of all DG nodes in a single valid data set. This value, representing the sum of the absolute power values ​​of all DG nodes, is a core quantitative indicator characterizing the microgrid's ability to absorb renewable energy. The absorption value transforms the microgrid's absorption capacity into a specific numerical value. By calculating the absorption value for each valid data set, the absorption capacity of the microgrid under various compliant operating conditions can be aggregated.

[0051] This invention establishes a bridge between effective data and quantitative indicators of absorption capacity. By clarifying the calculation logic of total load and absorption value, it transforms scattered effective data combinations into core quantitative parameters that can be used for evaluation, providing a foundation for the subsequent visualization and interval quantification of complete absorption capacity. For the scattered load power and distributed generation (DG) power parameters in the effective data combinations, the calculation rules for total load and absorption value are clarified, transforming each set of compliant operating parameter combinations into intuitive quantitative indicators. This transformation solves the problem that the original effective data parameters are scattered and cannot directly represent absorption capacity, ensuring that each set of effective operating conditions corresponds to a clear scale of electricity demand and absorption capacity. Based on the total load and absorption value of all effective data combinations, it can cover various compliant operating scenarios of photovoltaic-storage microgrids, such as the absorption status under different load levels and DG output combinations.

[0052] Step 204: Based on the total load and absorption value of each operating condition, and combined with the real-time operation data of the photovoltaic-storage microgrid, the absorption capacity is assessed to obtain the assessment results.

[0053] As one possible implementation, step 204 can be specifically implemented as steps A31-A32.

[0054] A31: Plot a visualization curve of absorption capacity versus total load, with the total load corresponding to the effective data combination as the horizontal axis and the absorption value corresponding to the total load as the vertical axis.

[0055] A32: Based on the visualized curves and combined with the real-time operation data of the photovoltaic-storage microgrid, determine the current absorption capacity of the photovoltaic-storage microgrid.

[0056] As one possible implementation, step A32 can be specifically implemented as steps B31-B34.

[0057] B31: Based on the key parameters corresponding to the real-time operation data of the photovoltaic-storage microgrid, calculate the Euclidean distance between the key parameters and all valid data combinations in the visualization curve; the key parameters include the current total load power, the actual output of distributed power sources, and the real-time charging and discharging power of energy storage.

[0058] B32: Combine all valid data and sort them in ascending order according to Euclidean distance to obtain the sorting result.

[0059] B33: The first valid data in the sorting results is combined and determined as the valid data that is closest to the real-time operating conditions of the photovoltaic-storage microgrid.

[0060] B34: Based on the absorption value corresponding to the effective data closest to the operating condition, determine the current absorption capacity corresponding to the real-time operation data of the photovoltaic-storage microgrid.

[0061] In some embodiments, the visualization curve of absorption capacity-total load is formed by plotting the total load of all effective operating conditions as the horizontal axis and the corresponding absorption value as the vertical axis, visually presenting the complete pattern of microgrid absorption capacity changing with total load. This curve visually presents the changes in microgrid absorption capacity under different total loads, allowing technicians to quickly grasp the correlation between absorption capacity and total load, providing a visual reference for real-time assessment.

[0062] In some embodiments, the real-time operating data of the photovoltaic-storage microgrid is a set of key parameters under the current actual operating status of the photovoltaic-storage microgrid.

[0063] For example, real-time operating data of photovoltaic-storage microgrids includes current total load power, actual DG output, and real-time charging and discharging power of energy storage, which serve as a practical basis for determining current absorption capacity. Establishing a correlation between historical compliant operating conditions and current actual operating conditions, and using this data to find the most matching reference operating condition on the curve, ensures that the determination of current absorption capacity closely matches the actual operating state, avoiding a disconnect between assessment and reality.

[0064] In some embodiments, the current absorption capacity corresponding to the real-time operation data of the photovoltaic-storage microgrid is determined based on visualized curves and real-time operation data, assessing the microgrid's potential for renewable energy absorption under safe and compliant conditions during its current actual operation. This provides a decision-making basis for the real-time scheduling and operation optimization of the microgrid, addressing the core need to assess real-time absorption potential based on historical compliance data. It also provides accurate and actionable decision-making basis for the real-time scheduling of the microgrid, addressing the core need for rapid quantification of absorption capacity under real-time operating conditions.

[0065] In some embodiments, key parameters are core evaluation metrics extracted from real-time operating data, specifically including current total load power, actual output of distributed power sources, and real-time charging and discharging power of energy storage. These are core parameters used for comparison with effective data combinations in the visualization curve.

[0066] In some embodiments, Euclidean distance is a mathematical indicator that measures the similarity between key parameters of real-time operating data and parameters corresponding to a valid data combination in a visualization curve. The smaller the distance, the closer the operating conditions of the two sets of parameters are.

[0067] In some embodiments, the sorting result is a sequence formed by combining all valid data in the visualization curve and arranging them in ascending order according to their Euclidean distance from the key parameters of the real-time running data. This sequence is used to filter the most matching reference operating condition. By sorting the data from smallest to largest distance, the similarity levels between each historical compliant operating condition and the real-time operating condition are clearly presented, providing an intuitive basis for quickly locating the most matching operating condition and improving matching efficiency.

[0068] In some embodiments, the most closely related valid data is the first valid data combination in the ranking results. Its corresponding operating condition has the highest similarity to the current real-time operating condition of the microgrid and is the core reference for determining the current absorption capacity. The most closely related valid data serves as a reference template for real-time absorption capacity, and its corresponding absorption value is the core basis for determining the current absorption capacity. Since this data combination has passed multi-layered safety constraint verification, its absorption value can be directly mapped to the safe absorption potential under the current operating condition, ensuring the reliability of the determination result.

[0069] This invention utilizes Euclidean distance quantification and sorting to locate the reference scenario that best matches the real-time operating status from historical compliant operating conditions on the visualized curve, ensuring the objectivity, accuracy, and efficiency of the current absorption capacity assessment. Addressing the core challenge of how to correlate real-time operating data with massive amounts of historical compliant data, this invention introduces Euclidean distance as a mathematical tool. It quantifies and compares real-time key parameters with the corresponding parameters of all valid data combinations in the visualized curve, using distance to objectively characterize the similarity of operating conditions, avoiding errors from subjective judgments, and achieving a precise mapping between real-time and historical compliant operating conditions. Through the process of sorting and filtering the optimal matching operating conditions by distance, it ensures that the reference data ultimately used to determine the current absorption capacity is a valid data combination that has undergone multi-layer verification under N-1 safety constraints, reverse power flow constraints, and energy storage power constraints. The corresponding absorption value represents the absorption potential under the premise of safety and compliance, avoiding distortion of the current absorption capacity assessment due to non-compliant reference operating conditions, and ensuring that the results both closely match the real-time operating status and meet the safety operation requirements of the microgrid.

[0070] The embodiments of the present invention can intuitively reflect the complete absorption pattern through a visual curve, and anchor the current absorption capacity by combining real-time operation data, thus solving the defect of the prior art that can only provide a single maximum absorption capacity and cannot support actual operation decisions.

[0071] This invention first filters potential operating data through constraints, retaining valid data combinations corresponding to different operating conditions that meet safe operation requirements. This ensures the safety and rationality of the assessment data, laying the foundation for accurate energy absorption assessment. Next, this invention calculates the total load and absorption value for each operating condition, overcoming the limitation of existing absorption assessment methods that can only obtain a single maximum absorption capacity, and achieving a quantitative representation of the absorption energy value for different operating conditions. Finally, based on the absorption value and total load for each operating condition, combined with real-time operating data of the photovoltaic-storage microgrid, this invention assesses the absorption capacity of the photovoltaic-storage microgrid under its current operating conditions. This ensures that the absorption capacity assessment results closely match the actual operating state of the microgrid, improving the accuracy of the absorption assessment.

[0072] As one possible implementation, steps A41-A44 can be performed before step 201.

[0073] A41: Based on the basic parameters of the photovoltaic-storage microgrid to be evaluated, the basic parameters include the microgrid network structure, the rated capacity of the main transformers of the lines and substations, the power range of each load node, the power range of each distributed power generation node, and the parameters of the energy storage equipment; the parameters of the energy storage equipment include the energy capacity of the energy storage, the upper and lower limits of the state of charge, the maximum charging and discharging power, the charging and discharging time, the power factor, and the charging and discharging efficiency.

[0074] A42: Based on the basic parameters and combined with the historical operating limit data of the photovoltaic-storage microgrid, determine the value boundaries of load power, distributed power generation power, and energy storage charging and discharging power.

[0075] A43: Samples are taken within the boundary range of load power, distributed power, and energy storage charging and discharging power using a preset step size to obtain multiple sample combinations. The sample combinations include the sampled values ​​of the load node, the sampled values ​​of the distributed power source, and the sampled values ​​of the charging and discharging power of the energy storage.

[0076] A44: Based on sample combinations, potential operational data is obtained.

[0077] In some embodiments, the basic parameters of the photovoltaic-storage microgrid to be evaluated are the set of core parameters inherent to the photovoltaic-storage microgrid that determine its operating boundary, and serve as the basis for generating potential operating data.

[0078] In some embodiments, the historical operating limit data of the photovoltaic-storage microgrid is a record of the extreme values ​​reached by the load power, DG power, and energy storage charging and discharging power during the past operation of the photovoltaic-storage microgrid, which is used to calibrate the value boundaries of potential operating data.

[0079] In some embodiments, the value boundaries are determined based on basic parameters and historical operating limit data, and the reasonable range of values ​​for load power, DG power, and energy storage charging and discharging power is ensured to ensure that the potential operating data generated by subsequent sampling does not exceed the physical limits and safe operating range of the equipment.

[0080] In some embodiments, the preset step size is the interval at which uniform sampling is performed within the boundary range of each power value when generating potential running data, such as a power step size of 0.1 MVA, which is used to control the number and density of sampling points and balance data coverage and computational efficiency.

[0081] In some embodiments, the sample combination is a combination of load node sample values, DG node sample values, and energy storage charging and discharging power sample values ​​obtained by sampling within each power value boundary at a preset step size. It is the basic building block of potential operating data.

[0082] This invention, by clarifying the generation logic and process of potential operating data, ensures that the raw data covers various safe and feasible operating conditions of photovoltaic-storage microgrids, laying a reliable foundation for subsequent effective data screening, absorption capacity calculation, and visualization evaluation.

[0083] As one possible implementation, steps A51-A52 can be performed after step A31.

[0084] A51: Based on the maximum and minimum values ​​of the absorption values ​​among all valid data combinations of the visualized curve, determine the maximum and minimum absorption capacity indicators.

[0085] A52: Based on the absorption value in all valid data combinations, the arithmetic mean of all absorption values ​​is calculated to obtain the average absorption capacity index of the photovoltaic-storage microgrid.

[0086] In some embodiments, the maximum absorption capacity index is the maximum value selected from all valid data combinations covered by the visualization curve. It represents the highest absorption potential that the microgrid can achieve under various safe operating conditions. Clearly defining the upper limit of microgrid absorption within the safe operating boundary provides an upper limit reference for microgrid planning and operation scheduling, avoiding overestimation of absorption potential.

[0087] In some embodiments, the minimum absorption capacity index is the minimum value selected from all valid data combinations covered by the visualization curve. It is a quantitative value of the minimum absorption potential of the microgrid under various safe operating conditions. Clearly defining the lower limit of microgrid absorption within the safe operating boundary provides a baseline basis for the access and dispatch of new energy sources under extreme conditions, such as peak load and low DG output fluctuations, ensuring operational safety.

[0088] In some embodiments, the average absorption capacity index is the result of calculating the arithmetic average of the absorption values ​​of all valid data combinations. It is a quantitative value of the average absorption potential of the microgrid under various safe operating conditions. It reflects the average absorption level of the microgrid under various compliant operating conditions, providing a balanced reference for the long-term operation planning of the microgrid, such as the planning of new energy installed capacity and the formulation of annual absorption targets, taking into account both safety and economy.

[0089] This invention achieves accurate definition and efficient application of complete absorption capacity by extracting the maximum, minimum, and average absorption capacity indicators.

[0090] As one possible implementation, the following processing can also be performed before step B33: If there are multiple first-order valid data combinations with equal Euclidean distances in the sorting results, the arithmetic mean of the multiple first-order valid data combinations is calculated; the arithmetic mean is used as the current absorption capacity of the photovoltaic-storage microgrid.

[0091] In some embodiments, the first valid data combination with equal Euclidean distance is a group of valid data combinations in the sorting results that have the same Euclidean distance value as the key parameters of the real-time running data and are all at the top of the sequence. The operating conditions corresponding to these combinations have the same and highest similarity to the real-time operating conditions.

[0092] The embodiments of the present invention ensure the uniqueness, objectivity and rationality of the current absorption capacity results through quantitative processing of arithmetic average.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0094] The above embodiments are in Figure 2 Based on the method shown, each step will be discussed in detail. To facilitate understanding of the complete execution process, the overall method flow will be discussed below with reference to an embodiment.

[0095] This invention implements a method based on a given photovoltaic-storage microgrid network structure and parameters. It outlines the objective function of a photovoltaic-storage microgrid absorption capacity assessment model, progressively detailing DG boundary constraints and energy storage power constraints. Sampling points are obtained by uniformly sampling within the load and DG power range at a certain step size. Based on these sampling points, effective sampling points that satisfy the photovoltaic-storage microgrid absorption capacity assessment model are selected. The total load and total DG output of the effective sampling points are calculated to obtain the absorption capacity of each effective sampling point. The absorption capacities of the effective sampling points are sorted from smallest to largest. Using the total load after sorting as the x-axis and the absorption capacity of the effective sampling points as the y-axis, the complete absorption capacity of the photovoltaic-storage microgrid is visualized as a curve. The maximum value of the absorption capacity of the effective sampling points is further selected to obtain the maximum absorption capacity index; the minimum value of the absorption capacity of the effective sampling points is selected to obtain the minimum absorption capacity index; and the average value of the absorption capacity of all effective sampling points is calculated to obtain the average absorption capacity index.

[0096] Example 1 A method for assessing the absorption capacity of photovoltaic-storage microgrids considering N-1 security constraints, see [link to relevant documentation]. Figure 1 and Figure 2 This includes the following steps: 101: For a given photovoltaic-storage microgrid, write the objective function of the photovoltaic-storage microgrid absorption capacity assessment model; Based on the given grid structure and parameters of the photovoltaic-storage microgrid, the total load of the operating point is used as the horizontal axis, and the sum of the corresponding DG output (hereinafter referred to as total DG output) is used as the vertical axis. The DG output is sorted from small to large, and the absorption capacity of the operating point after sorting is used as the objective function, as shown in Equation (1).

[0097] (1) In the formula, Represents the complete absorption capacity of a photovoltaic-storage microgrid; the total load at the operating point. The sum of the loads at the operating points is equal to the sum of the apparent power at all load nodes. ); absorption capacity of operating points The total output of the DG at the operating point is equal to the sum of the absolute values ​​of the apparent power of all DG nodes. ); S L,n For the first n Load node L n Apparent power; S DG,l For the first l DG nodes l Apparent power; C L,m The first is composed of the apparent power of the load nodes. m The load vector of each running point; C DG,m The first is composed of the apparent power of DG nodes. m DG vectors for each running point; j The number of DG nodes; k This represents the number of load nodes.

[0098] 102: List the DG boundary constraints; All distributed generation (DG) variables in a photovoltaic-storage microgrid are subject to reverse power flow equality constraints. These constraints state that when the reverse power flow of a system component reaches its capacity limit, any increase in DG power will violate the equality constraints, resulting in a reverse power flow exceeding the limit. On the other hand, to ensure compliance with the N-1 safety constraint, the photovoltaic-storage microgrid must reconfigure the network to reliably transfer loads after a single component failure. The reconfigured operating state must ensure that the rated capacity of all lines and substation transformers is not less than the absolute value of their net transmission power. The N-1 safety constraint and the equality constraint together constitute the DG boundary constraints, as shown in equation (2).

[0099] (2) In the formula, Indicates the run point At the DG boundary of the microgrid, For the first m A point running on the DG boundary, determined by the load node power vector. and DG node power vector composition; For the first in microgrid c Apparent power of each load node; For the first in microgrid d Apparent power of each DG node; For the first The DG boundary of a microgrid consists of all operating points that satisfy the strict criticality constraints of the DG.

[0100] To account for the strict criticality constraint of distributed generation (DG) in energy storage, it means that all DG variables in the microgrid are subject to the reverse power flow equality constraint, i.e., the reverse power flow equality constraint covers all DG variables. In this case, any increase in any DG will inevitably cause a reverse power flow overload. In the formula, For the first in microgrid b Apparent power of each energy storage node; p The number of energy storage nodes in the microgrid; v The number of reverse power flow equality constraints in the microgrid; w Let be the total number of reverse power flow equality constraints and inequality constraints in the microgrid; when the th c The element in the first... z Time coefficient in each constraint When the first c The element is not in the first position. z Time coefficient in each constraint ; and These are the first in the microgrid. z The and the first s The capacity of each component; This indicates a reverse power flow equality constraint. This represents the constraint of the power flow inequality; This indicates that the sum of the coefficients of the DG variables in the reverse power flow equality constraint is greater than zero, thereby ensuring that the reverse power flow equality constraint covers all DG variables and guarantees that the strict criticality of DG is satisfied.

[0101] 103: List the energy storage power constraints; The energy storage charging and discharging power in a photovoltaic-storage microgrid is subject to energy storage power constraints to ensure that the charging and discharging power of the energy storage at any time does not exceed its rated capacity, while the state of charge is maintained within the allowable range. The constraints take into account factors such as the energy storage capacity, state of charge, charging and discharging time, power factor, and charging and discharging efficiency, as shown in Equation (3).

[0102] (3) In the formula, For the first The operating power of each energy storage unit; The charging power for energy storage; The discharge power of the stored energy; It is an integer variable that takes the value 0 or 1; This is the maximum charging power for energy storage; This represents the maximum discharge power of the stored energy. Energy storage capacity; The state of charge of energy storage; This represents the maximum value of the energy storage state of charge. This represents the minimum state of charge of the energy storage. The charging and discharging time for energy storage; The power factor for energy storage connected to the microgrid; For energy storage charging efficiency; The discharge efficiency of energy storage.

[0103] 104: Construct an evaluation model for the absorption capacity of photovoltaic-storage microgrids considering N-1 security constraints; Equations (1) to (3) obtained by combining steps 101 to 103 yield the photovoltaic-storage microgrid absorption capacity assessment model that simultaneously considers the objective function of the absorption capacity assessment model, DG boundary constraints, and energy storage power constraints, as shown in equation (4).

[0104] (4) 105: Calculate the absorption capacity of a photovoltaic-storage microgrid considering N-1 security constraints; (1) Within the power range of load, DG, and energy storage, in step size Sampling and generation sampling points In the formula, for The upper limit, for The upper limit, for The upper limit.

[0105] (2) For each sampling point obtained from the sampling, identify whether it meets the DG boundary constraints in the photovoltaic-storage microgrid absorption capacity assessment model considering N-1 security constraints obtained in step 104. If the condition is met, then the sampling point is a valid sampling point.

[0106] (3) Based on the valid sampling points obtained in (2), calculate the total load of all valid sampling points. and DG's total output The absorption capacity of all valid sampling points is obtained. The capacity to absorb all valid sampling points Sort by smallest to largest, and use the total load after sorting. The x-axis represents the absorption capacity of all valid sampling points. Using the vertical axis as the ordinate, the complete absorption capacity of the microgrid is visualized as a curve, which accurately reflects the relationship between the complete absorption capacity of the photovoltaic-storage microgrid and the total load.

[0107] (4) Based on the absorption capacity of all valid sampling points The maximum absorption capacity of the effective sampling points is selected to obtain the maximum absorption capacity (denoted as ). The minimum absorption capacity (denoted as ) is selected from the effective sampling points; the minimum absorption capacity is obtained. The index is calculated by averaging the absorption capacity of all valid sampling points to obtain the average absorption capacity (denoted as ). The indicator quantifies the complete absorption capacity of the active distribution network into a specific range value based on the absorption capacity indicator.

[0108] For any given photovoltaic-storage microgrid, the method for assessing the absorption capacity of a photovoltaic-storage microgrid considering N-1 security constraints includes the following steps: First, for a given photovoltaic-storage microgrid, write the objective function of the photovoltaic-storage microgrid absorption capacity assessment model; second, write the DG boundary constraints; third, write the energy storage power constraints; fourth, construct an absorption capacity assessment model for the photovoltaic-storage microgrid considering N-1 security constraints; fifth, calculate the absorption capacity of the photovoltaic-storage microgrid considering N-1 security constraints.

[0109] Example 2 The following uses a specific example (see...) Figure 1 and Figure 3 The feasibility of the solution in Example 1 is verified, and the details are as follows: 1. Basic Information of the Case Study See the example of a microgrid containing photovoltaic and energy storage. Figure 1 The system comprises two 35 / 10kV substation main transformers (T1 and T2). The two main transformers and their lines are interconnected via a tie switch (TS), forming a network structure capable of flexibly transferring loads. The substation main transformer capacity is 4.0 MVA, the line capacity is 2.0 MVA, the distribution transformer capacity is 2.0 MVA, the load capacity is 2.0 MVA (i.e., the power range of a single load node is [0, 1.0] MVA), and the DG capacity is 2.0 MVA (i.e., the power range of a single DG node is [-2.0, 0] MVA). The maximum value of the energy storage state of charge is... =100%, the minimum value of the energy storage state of charge. =15%, State of charge of energy storage =50%, energy storage capacity =4.0 MWh, maximum charge / discharge power =2.0 MVA, energy storage charge / discharge time =1h, energy storage charging and discharging efficiency =0.85, power factor of energy storage connected to a microgrid =0.95.

[0110] 2. Implementation steps of the present invention 1) For a given photovoltaic-storage microgrid, write the objective function of the photovoltaic-storage microgrid absorption capacity assessment model. For a given Figure 1 For the photovoltaic-storage microgrid, the objective function of the photovoltaic-storage microgrid absorption capacity assessment model is written according to Equation (1), and the result is shown in Equation (5).

[0111] (5) 2) Write the DG boundary constraints Write according to formula (2) Figure 1 The DG boundary constraints for the photovoltaic-storage microgrid example are shown in Equation (6).

[0112] (6) 3) List the energy storage power constraints Write according to formula (3) Figure 1 The energy storage power constraint in the photovoltaic-storage microgrid example is that all DG variables in the photovoltaic-storage microgrid are subject to the reverse power flow equation constraint. In order to avoid the reverse power flow exceeding the limit, the energy storage should operate in the charging state. The result is shown in equation (7). The solution yields the energy storage discharge power range of [0, 2.0] MVA.

[0113] (7) 4) Construct an evaluation model for the absorption capacity of photovoltaic-storage microgrids considering N-1 security constraints. Combining equations (5) to (7), we obtain the result that simultaneously considers the objective function, DG boundary constraints, and energy storage power constraints. Figure 1 The absorption capacity assessment model of the photovoltaic-storage microgrid example is shown in Equation (8).

[0114] (8) 5) Calculate the absorption capacity of the photovoltaic-storage microgrid considering N-1 security constraints. (1) Within the power range of load [0,1.0] MVA, DG [-2.0, 0] MVA and energy storage [0,2.0] MVA, in steps Perform uniform sampling to obtain One sampling point.

[0115] (2) For the 12,326,391 sampling points obtained by uniform sampling, identify the DG boundary constraints that satisfy the photovoltaic-storage microgrid absorption capacity assessment model considering N-1 security constraints obtained in step 104. The results of 108,937 valid sampling points are shown in Table 1.

[0116] Table 1

[0117] (3) Based on the valid sampling points in Table 1, calculate the total load of all valid sampling points. and DG's total output The results are shown in the last two columns of Table 1, which yields the absorption capacity of all valid sampling points. The capacity to absorb all valid sampling points Sort by smallest to largest, and use the total load after sorting. The x-axis represents the absorption capacity of all valid sampling points. Using the vertical axis as the ordinate, the complete absorption capacity of the photovoltaic-storage microgrid is visualized as a curve. The results are shown in [Figure 1]. Figure 3 As shown.

[0118] (4) Based on the absorption capacity of the effective sampling points in the last column of Table 1 The maximum and minimum absorption capacities of the effective sampling points are selected to obtain the maximum absorption capacity. Indicators and minimum absorption capacity The indicator is the absorption capacity of all valid sampling points calculated in the last column of Table 1. The average value is used to obtain the average absorption capacity. The results of the absorption capacity indicators are shown in Table 2.

[0119] Table 2

[0120] Depend on Figure 3 It can be seen that the complete absorption capacity range of the photovoltaic-storage microgrid example considering N-1 security constraints is a curve, rather than a single maximum absorption capacity (6.0 MVA). The curve ranges from [2.0, 6.0] MVA, indicating that considering N-1 security constraints... Figure 1 The absorption capacity of the photovoltaic-storage microgrid is between 2.0 and 6.0 MVA under a load power range of [0.0, 1.0] MVA. Specifically, the operating points in the second to last row of Table 1 are... C 108936 For example, with a total load of 2.0 MVA, the absorption capacity of the photovoltaic-storage microgrid is 5.8 MVA.

[0121] On the other hand, Table 2 also considers the N-1 safety constraint based on the absorption capacity index. Figure 1 The complete absorption capacity of the photovoltaic-storage microgrid example is quantified into a defined range of values ​​([2.0, 6.0] MVA), with a maximum absorption capacity of 6.0 MVA, a minimum absorption capacity of 2.0 MVA, and an average absorption capacity of 3.7 MVA.

[0122] This invention addresses photovoltaic-storage microgrids, considering N-1 security constraints, and presents an evaluation model for the absorption capacity of photovoltaic-storage microgrids that simultaneously considers the objective function, DG boundary constraints, and energy storage power constraints. For example, regarding... Figure 1 The results of the absorption capacity assessment model for the photovoltaic-storage microgrid example are shown in Equation (8).

[0123] 2. To address the inability of existing technologies to accurately assess the complete absorption capacity of a photovoltaic-storage microgrid considering N-1 security constraints, this invention proposes a method for assessing the absorption capacity of such microgrids. This method accurately assesses the range of complete absorption capacity of a photovoltaic-storage microgrid considering N-1 security constraints, visualizing the complete absorption capacity as a curve. This curve accurately reflects the relationship between the complete absorption capacity and the total load, allowing for the calculation of absorption capacity indices. These indices quantify the complete absorption capacity of the photovoltaic-storage microgrid into defined range values. For example, for a given... Figure 1 A photovoltaic-storage microgrid example is provided, using the method proposed in this invention to calculate the N-1 security constraint. Figure 1 The complete absorption capacity range of the photovoltaic-storage microgrid case study under various total load conditions is: Figure 3 The curve shown is not a single maximum absorption capacity (6.0 MVA). Based on the absorption capacity index, the complete absorption capacity of the case study is also quantified into a specific range of values ​​(see [2.0, 6.0] MVA in Table 2), where the maximum absorption capacity is 6.0 MVA, the minimum absorption capacity is 2.0 MVA, and the average absorption capacity is 3.7 MVA.

[0124] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0125] Figure 4 A schematic diagram of the structure of the photovoltaic-storage microgrid absorption capacity assessment device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 4 As shown, the photovoltaic-storage microgrid absorption capacity assessment device 4 includes: Communication module 41 is used to acquire potential operating data of the photovoltaic-storage microgrid to be evaluated. The potential operating data includes load power, distributed power generation power and energy storage charging and discharging power corresponding to each operating condition. The processing module 42 is used to screen and combine data based on potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid to obtain effective data combinations under each operating condition. Each effective data combination includes a set of load power, distributed power, and energy storage charging and discharging power. Based on the effective data combinations under each operating condition, the total load and absorption value of each operating condition are calculated. Based on the total load and absorption value of each operating condition, combined with the real-time operating data of the photovoltaic-storage microgrid, the absorption capacity is evaluated to obtain the evaluation result.

[0126] Figure 5This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 5 As shown, the electronic device 5 of this embodiment includes a processor 50 and a memory 51. The memory 51 stores a computer program 52. When the processor 50 executes the computer program 52, it implements the steps in the various method embodiments described above. Alternatively, when the processor 50 executes the computer program 52, it implements the functions of each module / unit in the various device embodiments described above.

[0127] For example, computer program 52 may be divided into one or more modules / units, which are stored in memory 51 and executed by processor 50 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 52 in electronic device 5.

[0128] Electronic device 5 may include, but is not limited to, processor 50 and memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 5 may also include input / output devices, network access devices, buses, etc.

[0129] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0130] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for assessing the absorption capacity of a photovoltaic-storage microgrid, characterized in that, include: Acquire potential operational data of the photovoltaic-storage microgrid to be evaluated, including load power, distributed power generation power, and energy storage charging and discharging power for each operating condition; Based on the potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid, data validity is screened and combined to obtain effective data combinations under various operating conditions. Each effective data combination includes a set of load power, distributed power generation power, and energy storage charging and discharging power. Based on the effective data combination under each operating condition, the total load and absorption value of each operating condition are calculated respectively. Based on the total load and absorption value under each operating condition, combined with the real-time operation data of the photovoltaic-storage microgrid, the absorption capacity is assessed, and the assessment results are obtained.

2. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 1, characterized in that, The constraints include distributed generation boundary constraints and energy storage power constraints. The distributed generation boundary constraints include N-1 safety constraints and reverse power flow constraints. The N-1 safety constraints indicate that after a single component failure in the photovoltaic-storage microgrid, reliable load transfer is achieved through network reconfiguration, and the rated capacity of all lines and substation main transformers after reconfiguration is not less than the absolute value of their net transmission power. The reverse power flow constraints are reverse power flow equation constraints, covering all distributed generation variables, indicating that when the reverse power flow of system components reaches the capacity limit, any increase in the power of any distributed generation will lead to the reverse power flow exceeding the limit. The energy storage power constraints indicate that the energy storage charging and discharging power does not exceed the rated capacity and the state of charge is maintained within the allowable range. Based on the potential operational data and the constraints on the safe operation of the photovoltaic-storage microgrid, data validity is screened and combined to obtain effective data combinations for each operating condition, including: For each set of potential operating data, if the distributed power source boundary constraints and energy storage power constraints are satisfied, then the set of potential operating data is determined to be valid. Based on the effective potential operating data, the effective data combinations corresponding to each operating condition are summarized.

3. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 2, characterized in that, For each set of potential operating data, if it satisfies the distributed power source boundary constraints and energy storage power constraints, then the set of potential operating data is determined to be valid, including: For each set of potential operating data, if the set of potential operating data satisfies the N-1 security constraint and the reverse power flow constraint, then energy storage power constraint judgment is performed. If the set of potential operating data meets the energy storage power constraint, then the set of potential operating data is deemed valid.

4. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 3, characterized in that, For each set of potential operating data, if it satisfies the distributed power source boundary constraints and energy storage power constraints, then the set of potential operating data is determined to be valid, including: For each set of potential operating data, if the set of potential operating data does not meet the N-1 security constraints, the set of potential operating data is deemed invalid and removed, and the reverse power flow constraints and the energy storage power constraints are no longer verified. If the set of potential operating data satisfies the N-1 security constraint but does not satisfy the reverse power flow constraint, then the set of potential operating data is deemed invalid and removed, and the energy storage power constraint verification is no longer performed.

5. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 1, characterized in that, The total load and absorption value for each operating condition are calculated based on the effective data combination under each operating condition, including: Based on the load power of each effective data combination, the total load corresponding to that effective data combination is calculated. Based on the power of each distributed power source in the effective data combination, the total output of the distributed power source corresponding to the effective data combination is calculated and used as the absorption value.

6. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 1, characterized in that, The absorption capacity is assessed based on the total load and absorption value under each operating condition, combined with the real-time operation data of the photovoltaic-storage microgrid, to obtain the assessment results, including: Plot a visualization curve of absorption capacity versus total load with the total load corresponding to the effective data combination as the horizontal axis and the absorption value corresponding to the total load as the vertical axis. Based on the visualized curve and combined with the real-time operating data of the photovoltaic-storage microgrid, the current absorption capacity of the photovoltaic-storage microgrid is determined.

7. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 6, characterized in that, The determination of the current absorption capacity of the photovoltaic-storage microgrid based on the visualized curve and combined with the real-time operating data of the photovoltaic-storage microgrid includes: Based on the key parameters corresponding to the real-time operation data of the photovoltaic-storage microgrid, the Euclidean distance between the key parameters and all valid data combinations in the visualization curve is calculated; the key parameters include the current total load power, the actual output of distributed power sources, and the real-time charging and discharging power of energy storage. Combine all valid data and sort them in ascending order according to the Euclidean distance to obtain the sorting result; The first valid data group in the sorting results is determined as the valid data that is closest to the real-time operating conditions of the photovoltaic-storage microgrid. Based on the absorption value corresponding to the effective data closest to the operating condition, the current absorption capacity corresponding to the real-time operation data of the photovoltaic-storage microgrid is determined.

8. The method for assessing the absorption capacity of a photovoltaic-storage microgrid according to claim 1, characterized in that, Before obtaining potential operational data for various operating scenarios of the photovoltaic-storage microgrid to be evaluated, the following steps are also included: Based on the basic parameters of the photovoltaic-storage microgrid to be evaluated, the basic parameters include the microgrid grid structure, the rated capacity of the main transformers of the lines and substations, the power range of each load node, the power range of each distributed power generation node, and the energy storage device parameters; the energy storage device parameters include the energy storage capacity, upper and lower limits of state of charge, maximum charging and discharging power, charging and discharging time, power factor, and charging and discharging efficiency. Based on the aforementioned basic parameters and combined with the historical operating limit data of the photovoltaic-storage microgrid, the value boundaries of load power, distributed power generation power, and energy storage charging and discharging power are determined. With a preset step size, sampling is performed within the boundary range of load power, distributed power, and energy storage charging and discharging power to obtain multiple sample combinations. The sample combinations include the sampled values ​​of load nodes, distributed power sources, and energy storage charging and discharging power. Based on the sample combination, the potential operational data is obtained.

9. A device for assessing the absorption capacity of a photovoltaic-storage microgrid, characterized in that, include: The communication module is used to acquire potential operating data of the photovoltaic-storage microgrid to be evaluated. The potential operating data includes load power, distributed power generation power, and energy storage charging and discharging power corresponding to each operating condition. The processing module is used to screen and combine data based on the potential operating data and the constraints of safe operation of the photovoltaic-storage microgrid to obtain effective data combinations under various operating conditions. Each effective data combination includes a set of load power, distributed power, and energy storage charging and discharging power. Based on the effective data combination under each operating condition, the total load and absorption value of each operating condition are calculated respectively; based on the total load and absorption value of each operating condition, combined with the real-time operation data of the photovoltaic-storage microgrid, the absorption capacity is evaluated, and the evaluation result is obtained.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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