A method, device and medium for comprehensively evaluating the stability of a distribution network based on the output of a light storage and charging microgrid

Abstract

The application discloses a kind of light storage charging microgrid output to power distribution network stability comprehensive evaluation method, equipment and medium, belong to electric power system and its automation technical field, including the pre-processing to light storage charging microgrid inside operation data, form the time series data section of unified whole network;Based on the time series data section, the potential maximum output range of microgrid is mapped by constructing dynamic virtual impedance model;Based on the potential maximum output range, multidimensional stability risk correlation evaluation is carried out, and dynamic stability influence factor atlas is generated;Through the stability influence factor atlas, false deduction is carried out, and the disturbance link is identified;For the disturbance link, evaluate local stability margin retention strategy;According to the evaluation result, simulate disturbance scene to generate optimal power supply trajectory, and evaluate the tracking ability of actual operation trajectory to optimal trajectory.The application realizes whole-process coverage, and provides a solution for friendly access and safe and stable operation of light storage charging microgrid.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, specifically to a method, equipment, and medium for comprehensive evaluation of the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network. Background Technology

[0002] Photovoltaic-storage-charging microgrids, as a typical form integrating distributed photovoltaics, energy storage systems, and electric vehicle charging loads, have become an important solution for improving energy flexibility and absorption capacity. However, the inherent intermittency and randomness of their output, as well as the rapid response characteristics of power electronic equipment control, also bring significant impacts to the voltage stability, frequency regulation, and power quality of the upstream distribution network. To address these challenges, accurately assessing the impact of microgrid output on distribution network stability has become a key focus for both academia and engineering.

[0003] However, existing technologies, based on data, mostly rely on minute-level or second-level data from the scheduling center, or assume strict data synchronization among nodes, neglecting the millisecond-level time asynchrony issues caused by communication delays and local clock differences within the micronetwork. This leads to a distorted understanding of the micronetwork's true operational status, making subsequent evaluations unstable.

[0004] Furthermore, in core modeling, traditional virtual impedance or equivalent models are often based on steady-state operating conditions or specific harmonic injection calculations, which makes it difficult to accurately characterize the time-varying coupling relationship between the output and the dynamic response of the common coupling point voltage of the photovoltaic-storage-charging microgrid during rapid power fluctuations. This can easily lead to a mismatch between the evaluation model and the actual dynamic external characteristics, and the derived stability boundary is either too conservative, limiting the microgrid's efficiency.

[0005] Furthermore, at the risk integration level, existing methods mostly focus on single stability issues, such as overvoltage or resonance, lacking a mechanism to correlate and integrate risks such as power limit exceedance and impedance-induced resonance from multiple dimensions. As a result, assessment results are often fragmented, making it difficult to provide an intuitive and unified stability threat level. Many assessments fail to simulate the true effectiveness of existing local control strategies under typical disturbance scenarios such as measurement information distortion and control malfunctions, and also fail to quantify the microgrid's ability to track the actual power supply trajectory and ideal resilience trajectory during disturbances. Consequently, the assessment value cannot be effectively translated into specific optimization measures to improve system resilience. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, this invention aims to overcome the problems of unreliable and impractical evaluation results caused by existing evaluation methods due to data asynchrony, model mismatch, risk fragmentation, and lack of resilience verification.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network, comprising, The operational data within the photovoltaic-storage-charging microgrid is preprocessed to form a unified time-series data profile for the entire network. Based on this time-series data profile, a dynamic virtual impedance model is constructed and mapped to the microgrid's potential maximum output range. Based on this potential maximum output range, a multi-dimensional stability risk correlation assessment is performed, generating a dynamic stability impact factor map. Misjudgment deduction is performed using the stability impact factor map to identify disturbance links. For the disturbance links, a local stability margin maintenance strategy is evaluated. Based on the evaluation results, a disturbance scenario is simulated to generate the optimal power supply trajectory, and the tracking capability of the actual operating trajectory to the optimal trajectory is evaluated.

[0009] As a preferred embodiment of the comprehensive evaluation method for the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the preprocessing includes: Collect local operating data when a reference electrical event occurs, and bind it to the reference electrical event and store it as a data unit; Time axis correction is performed by calculating the time offset of the current reference event based on each data unit. After time axis correction, the data from different nodes at the same time are validated, and the data that passes the validation are used to form the unified time-series data profile of the entire network.

[0010] As a preferred embodiment of the comprehensive evaluation method for the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the construction of the dynamic virtual impedance model includes: Extract key time series data sequences from a unified time series data section across the entire network; Define a sliding time window, calculate the dynamic virtual impedance corresponding to each sliding time window, and obtain the equivalent impedance of the distribution network at the point of common coupling based on the dynamic virtual impedance. The total equivalent impedance is obtained by adding the dynamic virtual impedance to the equivalent impedance of the distribution network using a complex number; The safety constraints between the photovoltaic-storage-charging microgrid and the voltage change at the point of common coupling are calculated based on the total equivalent impedance, and the upper and lower bounds of the safety constraints are taken as the potential maximum output range.

[0011] As a preferred embodiment of the comprehensive evaluation method for the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the multi-dimensional stability risk correlation assessment includes: Receive the dynamic virtual impedance and the corresponding potential maximum output range at each evaluation time; A multi-dimensional assessment of the correlation between stability risks was conducted, and a first risk factor, a second risk factor, and a third risk factor were assigned. The first risk factor, the second risk factor, and the third risk factor are weighted and combined to obtain the stability risk index at the common connection point at the current moment, and the stability impact factor map is obtained.

[0012] As a preferred embodiment of the comprehensive evaluation method for the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the stability influence factor spectrum includes: Construct a two-dimensional coordinate system with time as the horizontal axis and the electrical nodes of the photovoltaic-storage-charging microgrid extending to the distribution network as the vertical axis; By performing regression analysis on the currently collected real-time data, the sensitivity coefficient of the voltage change at the point of common coupling is obtained; Based on the sensitivity coefficient, the stability risk index is converted and mapped to the location of the corresponding node on the distribution network side to form a stability impact factor map.

[0013] As a preferred embodiment of the comprehensive evaluation method for the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the disturbance identification step includes: Analyze the stability influencing factor spectrum to determine the distorted data and its inferred location; Based on the determined simulation location, misjudgment simulations are performed to determine information-side disturbances and physical-side disturbances; Construct a digital simulation environment by injecting information-side and physical-side disturbances, simulate the deduction of misjudgments and erroneous commands based on distorted data, and record the fault deduction process; Based on the results of the fault simulation, key failure points are extracted and traced back to their specific locations in the actual power distribution network, where they are marked as disturbance links, and a stability impact factor map is generated.

[0014] As a preferred embodiment of the comprehensive evaluation method for the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, the evaluation strategy for maintaining local stability margin includes: All local control strategies are aggregated, and a stability margin benchmark value for safe operation is defined. For the disturbance link, the operating data and the electrical quantity degradation data during the disturbance process are extracted. Establish a digital simulation test environment, perform integrated tests on the disturbance links, calculate the stability margin erosion, and construct the corresponding mapping curve by combining the parameter changes of the local control strategy. Based on the mapping relationship curve, after generating the integrated optimization scheme, a second test is conducted in the digital simulation test environment. When the negative stability margin erosion becomes zero or positive after optimization, and the positive stability margin erosion does not decrease, the optimization ends and the local control strategy after configuring the integrated optimization scheme is output.

[0015] As a preferred embodiment of the comprehensive evaluation method for the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in this invention, wherein: the generation of the optimal power supply trajectory in the simulated disturbance scenario includes: In a digital simulation environment, configure the local control strategy after the integrated optimization scheme and record the actual capability trajectory; The actual capability trajectory is post-processed, smoothed, and optimized to form the optimal power supply trajectory; Under the same conditions, a simulation without post-processing smoothing and optimization is performed to obtain the actual running trajectory; Calculate the trajectory deviation index between the actual operating trajectory and the optimal power supply trajectory; The trajectory deviation index is compared with a preset resilience threshold to determine whether the power supply resilience is qualified. If the trajectory deviation index is greater than or equal to the preset resilience threshold, it is determined that the power supply resilience is qualified for the current disturbance scenario under the current control strategy.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the method for comprehensive evaluation of the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network.

[0018] The beneficial effects of this invention are as follows: This invention constructs a precisely synchronized time-series data profile through data preprocessing based on power frequency zero-crossing synchronization and logic verification; then, using sliding window cross-correlation analysis, it identifies the dynamic virtual impedance of the microgrid in real time and maps the output range that changes with the operating state; based on this, it quantifies the risks of injection / absorption exceeding limits and resonance, and generates a spatiotemporal visualization map, realizing the dynamic correlation assessment and intuitive display of multi-dimensional risks. Furthermore, this method proactively introduces information-physical hybrid disturbances for digital twin inference, reverse-locating the disturbance links in the system; subsequently, it systematically tests and optimizes the local control strategy parameters for the disturbance links, improving the system's autonomous robustness; by defining and evaluating the system's ability to track the optimal power supply trajectory under extreme disturbances, it forms a quantitative resilience index, completing a closed-loop process from assessment, location, optimization to verification, providing systematic and reliable support for the safe operation and proactive defense of microgrids. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The above is a flowchart of a method for comprehensively evaluating the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network, provided as an embodiment of the present invention.

[0021] Figure 2 The flowchart of the dynamic virtual impedance model for constructing a comprehensive evaluation method for the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network, provided in one embodiment of the present invention.

[0022] Figure 3 The flowchart of the disturbance identification link in a comprehensive evaluation method for the stability of distribution networks by the output of a photovoltaic-storage-charging microgrid is provided in one embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for comprehensively evaluating the impact of photovoltaic-storage-charging microgrid output on distribution network stability, including: S100: Preprocess the operating data within the photovoltaic-storage-charging microgrid to form a unified time-series data profile for the entire network; S200: Based on time-series data sections, a dynamic virtual impedance model is constructed and the potential maximum output range of the microgrid is mapped. S300, based on the potential maximum output range, conducts a multi-dimensional stability risk correlation assessment and generates a dynamic stability impact factor map; S400: Identify disturbance links by using stability influence factor maps to extrapolate misjudgments; S500: For disturbances, assess local stability margin maintenance strategies; S600: Based on the evaluation results, simulate disturbance scenarios to generate the optimal power supply trajectory, and evaluate the ability of the actual operating trajectory to track the optimal trajectory. It should be noted that existing technologies still have problems such as the inability of traditional steady-state or harmonic impedance models to accurately characterize the time-varying coupling relationship between the rapid power fluctuations of microgrids and the dynamic response of grid voltage; the current assessment is too singular and fragmented, lacking the ability to correlate and uniformly quantify multi-dimensional threats such as power limit exceedance risk and impedance resonance risk; and the current methods cannot actively simulate disturbance scenarios such as information distortion to locate system vulnerabilities. Therefore, in response to the aforementioned problems, through steps S100-S600, this invention starts with high-precision synchronous data, characterizes system characteristics through dynamic virtual impedance modeling, and then conducts multi-dimensional risk fusion and visualization assessment. On this basis, it proactively locates disturbance links through digital twin misjudgment deduction, and specifically tests and optimizes local control strategies to improve robustness. By defining and evaluating the power supply resilience of the system under extreme scenarios, a final closed-loop verification of the effectiveness of the entire set of assessment and optimization measures is formed.

[0025] Example 2, refer to Figures 1-3 This is one embodiment of the present invention, which provides a method for comprehensively evaluating the impact of photovoltaic-storage-charging microgrid output on distribution network stability, including: In this embodiment of the invention, step S100 involves preprocessing the operational data within the photovoltaic-storage-charging microgrid to form a unified time-series data profile for the entire network, including the following steps S101-S105: S101. Collect local operating data when a reference electrical event occurs, and bind it with the reference electrical event to store it as a data unit. The specific operation steps are as follows: Within the photovoltaic-storage-charging microgrid, agent nodes are deployed at three key locations; The key location can be the common connection point between the microgrid and the upper-level distribution network, or it can be the central point of the electrical connection within the microgrid. In addition, it can be the grid connection point of at least one unit with rapid power change characteristics, wherein the unit with rapid power change characteristics can be an energy storage system converter or a large photovoltaic inverter group. Each agent node continuously collects the corresponding instantaneous values ​​of three-phase voltage and three-phase current locally, as well as the status signals of the power electronic equipment associated with the current node. The status signals include the grid connection status of the equipment, fault alarm signals, and radiator temperature. All collected data is tagged with the current time of the current proxy node.

[0026] S102. Designate a proxy node deployed at the common connection point as a reference. The proxy node used as the reference continuously monitors the instantaneous value of the A-phase voltage. When it detects that the current instantaneous value of the A-phase voltage changes from a negative value to a positive value, it is determined to be the time when the reference electrical event occurs. Whenever a reference electrical event is detected, the agent node acting as the reference immediately generates the sequence number and event type code of the current event; The reference agent node then broadcasts the sequence number and event type code of the current event to other agent nodes in the network via the micronet's internal communication network.

[0027] S103. All non-referenced agent nodes within the micronet continuously collect data locally while monitoring the communication network. S1031. When the receiving node receives the sequence number and event type code from the proxy node used as a reference, it immediately records the current time as the local receiving time. S1032. Find the time of the nearest local A-phase voltage zero-crossing event before the current local time and record that time as the local event time. If not found, wait for the next local zero-crossing event to occur and record it; S1033. The latest electrical quantities and equipment status quantities in the current acquisition cycle, the original acquisition time tag of the local data, the received sequence number and event type code, and the received local time and the corresponding local event time are bound and stored as a data unit.

[0028] S104. All agent nodes package all data units from the past cycle and upload them to the central evaluation unit through the communication network. It should be noted that the data packets uploaded by the agent nodes used as references include the generated logical time-stamped sequence and locally collected electrical quantity data, while the data packets uploaded by other nodes include the bound data units.

[0029] S105. After receiving the data packets uploaded by all nodes, the central evaluation unit processes them and calculates the time offset of the reference event based on each data unit to perform timeline correction. The data from different nodes at the same time after timeline correction are then validated for validity. The data that passes the validity check forms the unified time-series data profile for the entire network. The specific operation steps are as follows: S1051. Sort all data globally according to the sequence number in the data packet, and data with the same sequence number are grouped into the same batch for processing.

[0030] S1052. For data belonging to the same batch, the central evaluation unit calculates the time offset relative to the current reference event for each non-reference proxy node. Specifically, the original time difference is obtained by subtracting the local event time from the local reception time recorded by the current node in this batch. The central evaluation unit continuously reads the raw time difference calculated from the most recent N batches of the node, where N is greater than or equal to 10, calculates the arithmetic mean of these N values, and uses the arithmetic mean as the current stable time offset of the node, reflecting the fixed deviation on the time axis between the local zero-crossing time recorded by the local clock of the current node and the zero-crossing time of the proxy node used as a reference.

[0031] S1053. After obtaining the stable time offset of each node, perform time axis correction on the original acquisition data of all nodes. For any raw data point reported by any non-reference proxy node, the local collection time tag is obtained. The corrected timestamp of the current data point, which is unified to the reference event time axis, is obtained by the difference between the local collection time tag and the time offset. For the data of the proxy node itself, which is used as a reference, the timestamp on the reference event timeline is directly equal to the locally collected time stamp.

[0032] The voltage, current, and state data collected from all nodes are mapped onto a unified logical timeline based on the zero-crossing event of the proxy node used as a reference.

[0033] S1054. Based on a unified logical timeline, the central evaluation unit performs validity checks on data from different nodes at the same time. The validity verification method is to select an electrical connection node based on the known microgrid topology and check whether the sum of the currents flowing into each branch of the current node matches Kirchhoff's current law within the tolerance range. The tolerance range can be selected to be within ±5%. In response to a mismatch with Kirchhoff's current law, the data of the relevant nodes at the current moment is marked as suspicious, and invalid data is marked.

[0034] S1055, The central evaluation unit outputs a time-series data profile that has been time-aligned and validated. The time-series data profile includes voltage and current waveform data from the common connection point to the internal nodes on a unified logical time axis, as well as the device's operating status data.

[0035] It should be noted that the main problem is to solve the millisecond-level time synchronization and bad data issues at the data foundation level. By using hardware-level logic synchronization based on power frequency zero crossing and Kirchhoff's law verification, a high-precision and high-reliability unified time-series data profile across the entire network is formed, which avoids evaluation bias caused by data distortion from the source.

[0036] In this embodiment of the invention, S200 involves constructing a dynamic virtual impedance model and mapping the potential maximum output range of the microgrid based on the time-series data profile, including the following steps S201-S204: S201. Extract key time series data sequences from the unified time series data section of the entire network; The extracted key time-series data sequences include the instantaneous three-phase voltage sequence, the instantaneous three-phase current sequence, and the decomposed time-series sequence of the total output within the microgrid at the common connection point between the microgrid and the distribution network; The decomposed time sequence of the total power output within the microgrid includes the total active power of photovoltaic power generation; the total active power output of the energy storage system (positive for discharge and negative for charging); and the corresponding total reactive power.

[0037] S202. Define a sliding time window, calculate the dynamic virtual impedance corresponding to each sliding time window, and obtain the equivalent impedance of the distribution network at the point of common coupling based on the dynamic virtual impedance. The specific operation steps are as follows: S021. Define a sliding time window with a length of M power frequency cycles. The sliding time window slides once per second. Within each window: Clark transform is performed on the instantaneous values ​​of the three-phase voltage and the three-phase current at the point of common coupling to obtain the voltage and current components in the two-phase stationary coordinate system. S2022. Based on the transformed voltage and current components, calculate the instantaneous active power sequence and instantaneous reactive power sequence injected by the microgrid into the distribution network at the point of common coupling within the current sliding time window. S2023. Calculate the net total active power output sequence and net total reactive power output sequence of the microgrid within the current window; calculate the average value of the net total active power output sequence at all sampling points within the current window and the standard deviation of the current sequence fluctuation component; similarly, calculate the average value of the net total reactive power output sequence within the current window and the standard deviation of the corresponding fluctuation component.

[0038] It should be noted that the net total active power output sequence of the microgrid is the algebraic sum of the total active power of photovoltaics, the total output active power of energy storage, and the total active power of electric vehicle charging load; The net total reactive power output sequence is the algebraic sum of the total reactive power of photovoltaic, energy storage, and charging. Since the charging load usually absorbs reactive power, the corresponding reactive power is taken as a positive value in the summation.

[0039] S2024. Calculate the average value of the composite vector amplitude sequence of the common connection point voltage within the current window in the two-phase stationary coordinate system, as well as the standard deviation of the fluctuation component of the current amplitude sequence.

[0040] S2025. Calculate the normalized cross-correlation characteristics between power fluctuation and voltage fluctuation within the current window. The difference between the instantaneous active power sequence and its average value within the current window is calculated to obtain the instantaneous active power fluctuation sequence; the difference between the voltage amplitude sequence and its average value within the same window is calculated to obtain the voltage amplitude fluctuation sequence. Furthermore, the correlation values ​​between the instantaneous active power fluctuation sequence and the voltage amplitude fluctuation sequence at different time offsets within the window are calculated. Find the time offset that causes the absolute value of the current cross-correlation coefficient to reach its maximum value, as well as the value and sign of the current maximum value itself; Similarly, calculate the maximum cross-correlation value between the instantaneous reactive power fluctuation sequence and the voltage amplitude fluctuation sequence, as well as the corresponding time offset and sign.

[0041] S203. Traditional virtual impedance calculation methods are mostly based on disturbance injection or steady-state harmonic analysis, which makes it difficult to capture the coupling relationship between the output of photovoltaic-storage-charging microgrids and the dynamic response of the power grid. Therefore, this invention is based entirely on conventional measurement data, without any additional perturbation, to identify the true dynamic external characteristics of the microgrid, and is used to directly calculate the dynamic virtual impedance corresponding to each sliding window. Where k is the window index: , in, It is the average amplitude of the voltage at the point of common connection within the current window. It is the average amplitude of the current at the common connection point within the current window; / The amplitude basis of the virtual impedance reflects the voltage-to-current amplitude ratio at the average operating point, where j is the imaginary unit; It should be noted that, As for the dynamic virtual impedance angle, it should also be noted that the dynamic virtual impedance angle is obtained by combining the expression of multiple characteristic quantities. First, the basic angle is calculated: take the standard deviation of the microgrid net reactive power output fluctuation sequence in the current window, multiply it by the positive and negative sign function value of the maximum cross-correlation coefficient of reactive power-voltage in the current window (i.e., positive one or negative one), and obtain the value reflecting the intensity and direction of reactive power fluctuation. Take the standard deviation of the net active power output fluctuation sequence of the microgrid within the current window, multiply it by the sign function value of the maximum cross-correlation coefficient of active power-voltage within the current window, and add one multiplied by ten to the power of negative six to prevent division by zero error, to obtain the value reflecting the intensity and direction of active power fluctuation. Divide the value reflecting the intensity and direction of reactive power fluctuations by the value reflecting the intensity and direction of active power fluctuations, and then perform an arctangent operation on the current ratio to obtain a preliminary angle expressed in radians. Furthermore, a time phase correction is performed on the preliminary angle: the time offset corresponding to the maximum cross-correlation coefficient of active power-voltage and the time offset corresponding to the maximum cross-correlation coefficient of reactive power-voltage within the current window are taken, and their arithmetic mean is calculated; the arithmetic mean is divided by the power frequency period of 0.02 seconds, and then multiplied by twice pi to obtain a phase correction amount; since the voltage response represented by the current time offset is lagging, the phase correction amount is subtracted from the preliminary angle to obtain the final dynamic virtual impedance angle.

[0042] S204. The total equivalent impedance is obtained by complex addition of the dynamic virtual impedance and the equivalent impedance of the distribution network. The specific operation steps are as follows: The real and imaginary parts of the dynamic virtual impedance are calculated based on the latest sliding window at the current moment, as well as the upper and lower limits of the common coupling voltage. The upper and lower limits are set according to the operating standard and are set to 107% and 93% of the rated voltage, respectively.

[0043] The equivalent impedance viewed from the point of common coupling to the distribution network side is obtained. In one optional embodiment, the equivalent impedance can be obtained from a power grid parameter database; in another optional embodiment, the equivalent impedance can be estimated based on historical operating data, typically simplified to pure inductive reactance.

[0044] The dynamic virtual impedance is added to the equivalent impedance of the distribution network by complex addition to obtain the total equivalent impedance from the equivalent power source inside the microgrid to the infinite system of the distribution network, and the corresponding resistance and reactance components are obtained.

[0045] Furthermore, based on the total equivalent impedance, the safety constraints between the photovoltaic-storage-charging microgrid and the voltage change at the point of common coupling are calculated, and the upper and lower bounds of the safety constraints are taken as the potential maximum output range. The specific steps are as follows: Based on a simplified circuit model and the principle of impedance voltage division, an approximate linear relationship is derived between the active and reactive power injected into the microgrid and the voltage change at the point of common coupling. In this approximate linear relationship, the voltage change is approximately equal to the injected active power multiplied by the total equivalent resistance component, plus the injected reactive power multiplied by the total equivalent reactance component, and then the value is divided by the reference voltage.

[0046] Using the approximate linear relationship, the constraint equations for the active and reactive power injected into the microgrid when the voltage at the point of common coupling reaches the upper and lower limits respectively are calculated. Solving the constraint equations yields a closed quadrilateral region enclosed by four straight lines in the active and reactive power coordinate system. The quadrilateral region represents the potential maximum output range of the microgrid under the current dynamic virtual impedance and system operating conditions. As long as the combination of active and reactive power injected by the microgrid falls within this area, the impact on the distribution network can be considered safe from the perspective of voltage amplitude.

[0047] The central evaluation unit records and outputs the dynamic virtual impedance and the corresponding potential maximum output range at each evaluation moment.

[0048] It should be noted that by using sliding window cross-correlation analysis, the dynamic virtual impedance reflecting the dynamic coupling and time delay characteristics of power and voltage can be determined, and the output range that changes with the operating state can be derived accordingly, thereby capturing the real dynamic external characteristics of the microgrid and mapping the safety boundary.

[0049] In an embodiment of the present invention, S300 involves a multi-dimensional stability risk correlation assessment based on the potential maximum output range, generating a dynamic stability impact factor map, including the following steps S301-S304: S301, Receive the dynamic virtual impedance and the corresponding potential maximum output range at each evaluation moment; Specifically, the dynamic virtual impedance value at the current moment at the microgrid's common connection point is used to obtain the corresponding virtual impedance phase angle.

[0050] Furthermore, the potential maximum output range of the microgrid under its current operating state is calculated, with the specific boundary values ​​being the maximum allowable injected power and the maximum allowable absorbed power; The maximum allowable injected power is the upper limit of active power that the microgrid can inject into the distribution network under the current virtual impedance and distribution network voltage constraints; the maximum allowable absorbed power is the upper limit of active power that the microgrid can absorb from the distribution network under the current virtual impedance and distribution network voltage constraints.

[0051] Furthermore, the current actual net active power at the microgrid's common connection point is the algebraic sum of the total photovoltaic output, total energy storage output, and total charging load. It should be noted that a positive current actual net active power indicates net injection of the microgrid into the grid, while a negative current actual net active power indicates net absorption.

[0052] S302. Based on the data received in S301, a multi-dimensional stability risk correlation assessment is conducted, and a first risk factor, a second risk factor, and a third risk factor are assigned. S3021. The first risk factor is the injection limit risk factor. Specifically, it is determined whether the current actual net active power is greater than zero. In response to the current actual net active power being greater than 0, the ratio of the current actual net active power to the maximum allowable injection power is calculated. To prevent the denominator from being zero, when the maximum allowable injection power is less than a positive number, such as 0.01 pu, the maximum allowable injection power is treated as this positive number. When the ratio of the current actual net active power to the maximum allowable injected power is less than 1, it indicates that the closer the current injected power is to the safety boundary, the higher the risk; when the ratio of the current actual net active power to the maximum allowable injected power is greater than or equal to 1, it indicates that the limit has been exceeded. To normalize the injection over-limit risk factor to the 0-1 range, the ratio of the current actual net active power to the maximum allowable injection power is limited: If the ratio of the current actual net active power to the maximum allowable injected power is greater than 1, then set the injection over-limit risk factor to 1; otherwise, set the injection over-limit risk factor to the ratio of the current actual net active power to the maximum allowable injected power.

[0053] If the current actual net active power is less than or equal to 0, then the current state is absorption, the injection risk is zero, that is, the injection over-limit risk factor is zero.

[0054] S3022. The second risk factor is the absorption limit risk factor. Specifically, it determines whether the current actual net active power is less than zero. If the current actual net active power is less than 0, the ratio of the absolute value of the current actual net active power to the absolute value of the maximum allowable absorbed power is calculated. It should be noted that, to prevent the denominator from being zero, when the maximum allowable absorbed power is less than a positive number, such as 0.01 pu, the maximum allowable absorbed power is considered to be this positive number. The same limit normalization process is applied to the ratio to obtain the absorption limit risk factor; If the current actual net active power is greater than or equal to 0, then the risk factor for absorbing the over-limit is 0.

[0055] S3023, the third risk factor is the resonance risk factor. Specifically, based on the virtual impedance phase angle obtained in S301, the resonance sensitivity angle range is set. It should be noted that, since resonance is prone to occur in power systems when inductive and capacitive impedances are complementary, a dangerous range boundary is set with a center of -90 degrees (pure capacitive) and +90 degrees (pure inductive). Define resonance risk factor The calculation formula is as follows: , in, It is the absolute value of the virtual impedance phase angle; This represents the difference between the absolute value of the current impedance phase angle and 90°. The preset safety angle threshold is between 10 and 30 degrees, and is set according to the harmonic background and stability requirements of the distribution network. For example, a typical value is 20 degrees. like A value of 0 indicates that the current impedance angle is not within the danger zone; if Greater than 0, and The larger the value, the higher the risk of resonance; S303. Weighted and combined risk factors 1, 2, and 3 are used to obtain the stability risk index at the common connection point at the current moment, and the stability impact factor map is obtained. The specific operation steps are as follows: The first risk factor, the second risk factor, and the third risk factor are weighted and summed to form a stability risk index at the current point of common connection. The larger the stability risk index, the greater the threat that the current microgrid output poses to the stability of the distribution network. The weights assigned to the three risk factors satisfy the condition that the sum of the weights is 1. The weight coefficients can be set according to the power grid operation experience and the degree of harm of different risks. For example, in voltage-sensitive areas, the weighting coefficients for injected risk and absorbed risk can be set high, such as 0.4 for injected risk, 0.4 for absorbed risk, and 0.2 for resonance risk; in areas with prominent harmonic problems, the weighting of resonance risk can be increased, such as 0.3 for injected risk, 0.3 for absorbed risk, and 0.4 for resonance risk.

[0056] S304. Generate a dynamic stability impact factor map based on stability risk indicators; S3041. Construct a two-dimensional coordinate system with time as the horizontal axis and the electrical nodes extending from the photovoltaic-storage-charging microgrid to the distribution network as the vertical axis. The specific operation steps are as follows: Generate a two-dimensional coordinate system, with the horizontal axis representing a real-time scrolling time window; The vertical axis represents the electrical impact hierarchy, from top to bottom: the beginning of the distribution network feeder, the middle node of the feeder, the point of common connection, and the main busbar inside the microgrid; It should be noted that the vertical axis reflects the electrical path of risk propagation from the microgrid to the distribution network.

[0057] S3042. Based on the numerical value of the stability risk index, map it to the corresponding color depth and graphic size. Define the color mapping rule as follows: stability risk index ∈ [0, 0.3) is green, [0.3, 0.6) is yellow, and [0.6, 1.0] is red; the radius of the circle is proportional to the R value.

[0058] S3043. Perform regression analysis using the currently collected real-time data to obtain the sensitivity coefficient of the voltage change at the point of common coupling. The specific operation steps are as follows: Using the voltage data of the agent nodes deployed at the key locations and the node voltage data obtained from the distribution network, an approximate value is obtained through recent data regression analysis to calculate the sensitivity coefficient of all node voltages relative to the change in the voltage of the point of common coupling under microgrid output disturbance. After normalization, the coefficient is less than or equal to 1. Based on the current stability risk indicators and sensitivity coefficients, estimate the starting and middle nodes of the distribution network feeders; Specifically, the risk indicators for the first and middle nodes of the distribution network feeder are the current stability risk indicators and the current voltage sensitivity coefficient, which are also mapped to the corresponding node and time coordinates in the graph using color and size.

[0059] S3044. Based on the aforementioned sensitivity coefficient, the stability risk index is converted and mapped to the location of the corresponding node on the distribution network side to form a stability impact factor map. The specific operation steps are as follows: The potential maximum output range is displayed as two dynamic curves superimposed on the graph and compared with the current actual power curve.

[0060] It should be noted that this technology primarily addresses issues at the risk assessment level. By quantifying multi-dimensional risk factors such as injection / absorption of limits and resonance, and integrating them to generate a spatiotemporal visualization map, it achieves a unified rating and intuitive dynamic display of stability threats.

[0061] In this embodiment of the invention, step S400 involves misjudging and deducing from the stability influence factor map to identify the disturbance element, including the following steps S401-S404: S401. Analyze the stability influencing factor spectrum to determine the distorted data and its inferred location. The specific steps are as follows: The current stability influencing factor map was analyzed. High-risk spatiotemporal areas in the map are marked by the intensity of heatmap colors and specific icons. The darker the color, the higher the risk. Based on the vertical axis reflecting the electrical path of risk propagation from the microgrid to the distribution network, these areas and paths were selected as the key focus areas and paths for this simulation.

[0062] For example, if the graph shows that at the current moment, any factor icon at the microgrid common connection point is red, and a risk transmission path pointing to any node on the distribution network side is significantly colored, then this node is set as the position for simulation.

[0063] S402. Based on the determined simulation location, perform misjudgment simulation to determine the information-side disturbance and the physical-side disturbance; S4021. Based on the key measurement or communication links associated with the predicted location determined in S401, preset information-side disturbances, the types of which include data distortion and control command anomalies: The data distortion specifically includes data loss, data freezing, and data errors; Wherein, data loss means the output value is zero; data freezing means the output value remains constant; data error means the output value is superimposed with a fixed deviation value, wherein the deviation value is a configurable value within ±10% to 20% of the maximum value of the current node's normal measurement range.

[0064] The control command anomaly is caused by a delay in the control command sent to the critical equipment, or by the command content being erroneously altered, for example, changing a discharge command to a charging command.

[0065] S4022. Based on the potential maximum output range of the microgrid and the current actual output point, a physical-side disturbance is set, wherein the physical-side disturbance simulates the change in output within the microgrid, and the direction of the change tends to exacerbate the high risks identified in the map.

[0066] S403. Construct a digital simulation environment by injecting information-side and physical-side disturbances, simulating the deduction of misjudgments and erroneous commands based on distorted data, and recording the fault deduction process. The specific operation steps are as follows: S4031. Perform a misjudgment deduction in a digital simulation environment. The initial state is completely consistent with the current stability influence factor spectrum. Make judgments and actions under distorted information and observe the chain reaction caused. Based on the location determined by S401 and the disturbance set by S402, two types of disturbances are simultaneously injected into the digital simulation environment. The information-side disturbance directly affects the data stream from the sensor to the controller, causing the key measurement data received by the controller to become a preset distortion value. For example, the preset distortion value includes the value that freezes the voltage measurement value at any point before the disturbance. The physical disturbance directly changes the power value of the microgrid power supply or load in the digital simulation environment.

[0067] S4032. Based on the received distorted data, in an optional embodiment, the control strategy calculates and issues a reactive power command that does not match the current actual demand according to the distortion value. The reactive power command is sent to the execution device through the simulation communication network. The execution device executes the command generated based on error perception, and the action is further superimposed on the changed physical system.

[0068] S4033. The digital simulation environment makes judgments based on real electrical quantities, i.e., undistorted data. As physical disturbances continue to worsen under the superposition of erroneous control actions, when the actual electrical quantity reaches the protection setting, the corresponding switch is tripped according to the original protection action. After the first switch trips, the digital simulation environment is automatically updated, which in turn triggers the next round of protection actions. This process is simulated cyclically until a new steady state or disconnection is reached.

[0069] S404. Based on the results of the fault simulation, key failure points are extracted and traced back to their specific locations in the actual distribution network, marked as disturbance links, and a stability impact factor map is generated. The specific operation steps are as follows: S4041. Analyze the simulation records of misjudgment and identify the direct causes that lead to a sharp deterioration in the status or failure of protection, including but not limited to key distorted data points, failed control strategies, and mismatched protection settings. It is important to understand which piece of information disturbance directly caused the control misjudgment at the key distorted data point; The failed control strategy is which control strategy becomes completely ineffective or has a counterproductive effect due to its reliance on distorted data. The mismatch protection setting refers to the protection device whose setting loses selective coordination with the upstream or downstream protection, resulting in an expansion of the power outage area.

[0070] S4042. Based on the determined direct cause, trace back to the specific location in the actual distribution network and mark it as the disturbance link, specific equipment, specific communication link, and specific control strategy logic block.

[0071] S4043. The location and type information of the identified disturbance elements are sent to the stability influence factor map. During the map update, these disturbance elements will be marked with a cross at the corresponding spatiotemporal coordinates.

[0072] It should be noted that the main problem is the inability to proactively locate vulnerabilities at the strategy verification level. By proactively injecting information-physical hybrid disturbances to perform digital twin simulations, the key failure points that lead to system deterioration are identified in a reverse engineering manner, deepening the assessment from phenomenon description to root cause localization.

[0073] In this embodiment of the invention, S500 evaluates the local stability margin preservation strategy for the disturbance element, including the following steps S501-S504: By aggregating all local control strategies and defining a stability margin benchmark for safe operation, and for the aforementioned disturbance, extracting operational data and electrical quantity degradation data during the disturbance process, the specific operation steps are as follows: S501. Collect all the local control policies that can be enabled by each power electronic device in the microgrid into a local stability margin maintenance policy library. At the same time, a stability margin benchmark value for safe operation is defined, wherein the stability margin benchmark value is the safety boundary; For example, for voltage, the safety boundary is 93% to 107% of the rated voltage, corresponding to the ±7% fluctuation allowed by national standards; for frequency, the safety boundary is 49.8Hz to 50.2Hz; for equipment, the safety boundary is 100% of the rated current; for networks, the safety boundary is 80% of the critical line transmission limit.

[0074] S502. Receive all disturbance links. For each disturbance link, extract the baseline normal data, which is the average value of the local measurements of the affected equipment within the power frequency cycle before the disturbance occurs. Extracting disturbance evolution data, i.e., electrical quantity time series curves recorded from the start of the disturbance, especially data that directly reflects the erosion of the stability margin, such as the lowest voltage point of the affected node being 90.2% of the rated voltage and the lowest frequency point being 49.52Hz.

[0075] S503. Establish a digital simulation test environment, perform integrated testing on the disturbance element, calculate the stability margin erosion, and construct the corresponding mapping curve based on the parameter changes of the local control strategy. The specific operation steps are as follows: S5031. Establish a digital simulation test environment, using the benchmark normal data as the initial state of the simulation, and perform integrated testing for each disturbance element. Reproduce the disturbance scenario that caused the current disturbance to be exposed in the digital simulation test environment; Run the corresponding local control strategy in a digital simulation test environment with the actual configured parameters, and record the timing of the deviation between the actual control output and the desired ideal control output of the local control strategy during the disturbance process; at the same time, record the response curves of voltage, frequency and current in the digital simulation test environment.

[0076] S5032. Compare the response curves of the voltage, frequency, and current with the stability margin reference value point by point; Calculate the difference between the voltage value at each point on the response curve and the lower limit of the safe voltage, and take the minimum value among all differences as the stability margin erosion amount of the current voltage in the current test scenario; if the voltage is higher than the lower limit, the stability margin erosion amount is positive; if the voltage is lower than the lower limit, the stability margin erosion amount is negative, and the absolute value represents the depth of voltage exceeding the limit.

[0077] Using the same calculation method, the frequency stability margin erosion is obtained based on the lower limit of frequency safety, and the current stability margin erosion is obtained based on the upper limit of current safety. It should be noted that the negative erosion value quantifies the specific degree of harm to stability caused by the current disturbance under a specific disturbance.

[0078] S5033. Analyze the impact of parameter changes on the stability margin erosion amount for the parameters of the local control strategies in the local stability margin maintenance strategy library. In this invention, the slope coefficient of any local control strategy parameter is kept constant in the simulation while maintaining the disturbance scenario. The slope coefficient is adjusted by traversing within the corresponding reasonable value range with a fixed step size. For each set slope coefficient, the test is repeated to calculate the new stability margin erosion amount. A mapping relationship curve showing the different values ​​of the slope coefficient and the corresponding stability margin erosion amounts is generated. By using the mapping curve, the range of parameter values ​​that maximizes the mitigation of all stability margin erosion amounts is identified; it should be noted that the maximization of mitigation means changing from a negative value to zero or a positive value, or minimizing the absolute value of a negative value.

[0079] S504. Based on the mapping relationship curve, after generating the integrated optimization scheme, a second test is conducted in the digital simulation test environment. Specifically, the first integrated optimization scheme is to optimize the parameter setting value; for each parameter of the tested local control strategy, an intermediate value is selected as the recommended setting value from the range of parameter values ​​that are most mitigated. The second integration optimization scheme is a supplement to the control logic and mode switching rules. If the local control strategy fails completely during integration testing, a new local control logic is specified to be added to the local stability margin maintenance strategy library. The central evaluation unit updates the generated integrated optimization scheme to the strategy library of the digital simulation test environment, performs another integrated test, and compares the stability margin erosion values ​​obtained under the same disturbance before and after applying the optimization scheme. In response to the optimization, all previously negative stability margin erosion values ​​must become zero or positive. If all previously positive erosion values ​​do not decrease, the optimization is considered complete.

[0080] It should be noted that the main problem is that the effectiveness of control strategies cannot be quantitatively tested and optimized. By defining the stability margin erosion index and conducting systematic parameter traversal tests, a quantitative method for evaluating the effectiveness of local control strategies and a scientific approach to parameter optimization are provided.

[0081] In an embodiment of the present invention, in step S600, based on the evaluation results, an optimal power supply trajectory is generated by simulating a disturbance scenario, and the tracking capability of the actual operating trajectory to the optimal trajectory is evaluated, including the following steps S601-S604: S601. In the digital simulation environment, configure the local control strategy after the integrated optimization scheme, and record the actual capability trajectory. The specific operation steps are as follows: In the digital simulation environment, the data setting state based on S101 is the steady state before the disturbance occurs. The verified local control strategy is configured on the corresponding device in the digital simulation environment, and the simulation is started. In the simulation, due to communication interruption, all devices enter the enhanced local autonomous mode according to the switching rules fixed in S504. The voltage and total active power of the nodes change over time throughout the simulation, reflecting the actual capacity trajectory provided to critical loads under the optimized local autonomous strategy. It should be noted that critical loads include, but are not limited to: medical emergency equipment, control and transmission equipment of communication base stations, emergency lighting and evacuation indication systems, key measurement and control devices for maintaining grid synchronization, and core equipment in user-specified uninterrupted production processes. The actual capability trajectory is post-processed, smoothed, and optimized to form the optimal power supply trajectory. The specific operation steps are as follows: S6011. Check the recorded voltage curve, find all voltage points that are lower than 93% of the rated voltage, and raise the voltage value to 93%. The additional reactive power required to raise the voltage of this segment is achieved by proportionally increasing the reactive power output of all devices with reactive power output capability in the current period. If the required reactive power exceeds the total capacity of the equipment, priority is given to raising the voltage of the lowest point.

[0082] S6012. Check the recorded active power curve to ensure that the value is greater than or equal to 95% of the total load power demand; if the active power is lower than this value at any time, it is considered that there is a power deficit. The specific priorities for making up for power deficits are as follows: disconnect non-critical loads in a preset priority order; increase the energy storage discharge power to the maximum value; if it is still insufficient, reduce the power of some non-critical loads in a preset priority order. It should be noted that the preset priority order can be based on a user-defined priority list in one embodiment, and can be preset according to the power supply protection level in the power grid operation procedure in another embodiment.

[0083] S6013. Calculate the trajectory after correction by S6011 and S6012. The total energy that the energy storage system needs to release is accumulated. Compare this total energy with the current remaining energy of the energy storage. If the accumulated energy to be released is greater than the remaining energy, start from the beginning of the time axis and reduce the power demand on the energy storage proportionally until the accumulated energy to be released is equal to 90% of the remaining energy, and retain 10% as a safety margin.

[0084] The obtained voltage-time curves and active power-time curves at the critical load points represent the optimal power supply trajectory.

[0085] S603. Perform a simulation without post-processing smoothing and optimization under the same conditions to obtain the actual running trajectory of the key load points. The specific operation steps are as follows: A second simulation was conducted under the same initial conditions. In this simulation, the equipment also operated according to the optimized local control strategy, but the optimal power supply trajectory was not applied. The voltage and power curves of the key load points obtained are the actual operating trajectories.

[0086] To avoid the one-sidedness of using only the maximum deviation or root mean square error, and to more comprehensively evaluate the resilience of the entire process, this invention introduces a trajectory deviation index to quantify the tracking ability of the actual operating trajectory to the optimal trajectory. The trajectory deviation index between the actual operating trajectory and the optimal power supply trajectory is calculated as follows: , in, It is the trajectory deviation index. The closer it is to 1, the stronger the tracking ability and the higher the resilience. This is the total evaluation period; It is the system's rated voltage. It is the voltage value in the optimal power supply trajectory at time t; It is the voltage value in the actual running trajectory at time t. This is the integral over time; It should be noted that, The normalization standard is mainly to solve the dimensional problem and make the calculation results comparable in different scenarios; dividing by T normalizes the time dimension, making the index independent of the evaluation duration, and making the trajectory deviation index comparable under different durations; dividing by... The voltage dimension can be normalized, making the indicators independent of the voltage level, and systems with different voltage levels can be compared uniformly. Simultaneously, by introducing the square of the voltage, a greater physical weight is given to the error; and by constraining the result to the [0,1] interval, it is easier to set a uniform toughness threshold. Furthermore, if the denominator is divided only by T or only by... When the voltage level is high or the integral value is large, the indicators may exceed the reasonable range, making it difficult to evaluate them uniformly. As a normalized benchmark, it ensures that the indicators are stable and comparable in various scenarios.

[0087] S604. Compare the trajectory deviation index with a preset resilience threshold to determine whether the power supply resilience is qualified. The specific determination is as follows: Based on the trajectory deviation index, the toughness qualification threshold is set to 0.85. It should be noted that S6011 of this invention states that when the voltage is lower than the safety limit of 93%, the erosion amount is negative. Through a large number of digital simulations, i.e. based on a typical photovoltaic-storage-charging microgrid model, under various disturbances, it was found that when the trajectory comprehensive deviation index R ≥ 0.85, the proportion of time when the voltage is lower than 93% is less than 5%; when R < 0.85, this proportion of time increases sharply; 0.85 is exactly the performance inflection point, so it was selected as the threshold.

[0088] If the trajectory deviation index is greater than or equal to 0.85, it is determined that the power supply resilience is qualified for the current disturbance scenario under the current optimization strategy. If the trajectory deviation index is less than 0.85, it is determined to be in need of improvement. The curves of the optimal trajectory and the actual running trajectory are plotted separately, and the time periods with large deviations are marked. The local control strategy is then optimized again until the power supply resilience is deemed acceptable.

[0089] It should be noted that this study primarily addresses the lack of quantitative resilience standards and methods in resilience assessment. By defining the optimal power supply trajectory and trajectory deviation index, a quantitative resilience standard is established to measure the ability of microgrids to maintain critical power supply under extreme disturbances.

[0090] Example 3: This example also provides an electronic device applicable to a method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network. The device includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as proposed in the above examples.

[0091] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a method for comprehensively evaluating the power output of a photovoltaic-storage-charging microgrid on the stability of the distribution network as proposed in the above embodiment.

[0092] The storage medium proposed in this embodiment and the method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of the distribution network proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0093] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for comprehensively evaluating the impact of photovoltaic-storage-charging microgrid output on distribution network stability, characterized in that: include, Preprocess the operational data within the photovoltaic-storage-charging microgrid to form a unified time-series data profile for the entire network; Based on the time-series data profile, a dynamic virtual impedance model is constructed and the potential maximum output range of the microgrid is mapped. Based on the potential maximum output range, a multi-dimensional stability risk correlation assessment is conducted to generate a dynamic stability impact factor map. By using the stability influencing factor map to extrapolate misjudgments, the perturbation process can be identified. For the aforementioned disturbance, evaluate the local stability margin preservation strategy; Based on the evaluation results, the optimal power supply trajectory is generated by simulating disturbance scenarios, and the tracking ability of the actual operating trajectory to the optimal trajectory is evaluated.

2. The method for comprehensive evaluation of the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 1, characterized in that, The preprocessing includes: Collect local operating data when a reference electrical event occurs, and bind it to the reference electrical event and store it as a data unit; Time axis correction is performed by calculating the time offset of the current reference event based on each data unit. After time axis correction, the data from different nodes at the same time are validated, and the data that passes the validation are used to form the unified time-series data profile of the entire network.

3. The method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 2, characterized in that, The construction of the dynamic virtual impedance model includes: Extract key time series data sequences from a unified time series data section across the entire network; Define a sliding time window, calculate the dynamic virtual impedance corresponding to each sliding time window, and obtain the equivalent impedance of the distribution network at the point of common coupling based on the dynamic virtual impedance. The total equivalent impedance is obtained by adding the dynamic virtual impedance to the equivalent impedance of the distribution network using a complex number; The safety constraints between the photovoltaic-storage-charging microgrid and the voltage change at the point of common coupling are calculated based on the total equivalent impedance, and the upper and lower bounds of the safety constraints are taken as the potential maximum output range.

4. The method for comprehensive evaluation of the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 3, characterized in that, The multi-dimensional stability risk correlation assessment includes: Receive the dynamic virtual impedance and the corresponding potential maximum output range at each evaluation time; A multi-dimensional assessment of the correlation between stability risks was conducted, and a first risk factor, a second risk factor, and a third risk factor were assigned. The first risk factor, the second risk factor, and the third risk factor are weighted and merged to obtain the stability risk index at the common connection point at the current moment, and the stability impact factor map is obtained.

5. The method for comprehensive evaluation of the output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 4, characterized in that, The stability influencing factor map includes: Construct a two-dimensional coordinate system with time as the horizontal axis and the electrical nodes of the photovoltaic-storage-charging microgrid extending to the distribution network as the vertical axis; By performing regression analysis on the currently collected real-time data, the sensitivity coefficient of the voltage change at the point of common coupling is obtained; Based on the sensitivity coefficient, the stability risk index is converted and mapped to the location of the corresponding node on the distribution network side to form a stability impact factor map.

6. The method for comprehensive evaluation of the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 5, characterized in that, The disturbance identification step includes: Analyze the stability influencing factor spectrum to determine the distorted data and its inferred location; Based on the determined simulation location, misjudgment simulations are performed to determine information-side disturbances and physical-side disturbances; Construct a digital simulation environment by injecting information-side and physical-side disturbances, simulate the deduction of misjudgments and erroneous commands based on distorted data, and record the fault deduction process; Based on the results of the fault simulation, key failure points are extracted and traced back to their specific locations in the actual power distribution network, where they are marked as disturbance links, and a stability impact factor map is generated.

7. The method for comprehensive evaluation of the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 6, characterized in that, The evaluation of the local stability margin preservation strategy includes: All local control strategies are aggregated, and a stability margin benchmark value for safe operation is defined. For the disturbance link, the operating data and electrical quantity degradation data during the disturbance process are extracted. Establish a digital simulation test environment, perform integrated tests on the disturbance link, calculate the stability margin erosion, and construct the corresponding mapping curve by combining the parameter changes of the local control strategy. Based on the mapping relationship curve, after generating the integrated optimization scheme, a second test is conducted in the digital simulation test environment. When the negative stability margin erosion becomes zero or positive after optimization, and the positive stability margin erosion does not decrease, the optimization ends and the local control strategy after configuring the integrated optimization scheme is output.

8. The method for comprehensive evaluation of the power output of a photovoltaic-storage-charging microgrid on the stability of a distribution network as described in claim 7, characterized in that, The simulated disturbance scenario generates the optimal power supply trajectory, including: In a digital simulation environment, configure the local control strategy after the integrated optimization scheme and record the actual capability trajectory; The actual capability trajectory is post-processed, smoothed, and optimized to form the optimal power supply trajectory; Under the same conditions, a simulation without post-processing smoothing and optimization is performed to obtain the actual running trajectory; Calculate the trajectory deviation index between the actual operating trajectory and the optimal power supply trajectory; The trajectory deviation index is compared with a preset resilience threshold to determine whether the power supply resilience is qualified. If the trajectory deviation index is greater than or equal to the preset resilience threshold, it is determined that the power supply resilience is qualified for the current disturbance scenario under the current control strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of the distribution network as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for comprehensively evaluating the output of a photovoltaic-storage-charging microgrid on the stability of the distribution network as described in any one of claims 1 to 8.

Images