Black-start capability evaluation method and system for network-forming type new energy and energy storage device

Abstract

The invention relates to a black-start capability evaluation method and system for a network construction type new energy and energy storage device. Detecting fault nodes of the power grid according to the real-time running state of the power grid; and reconstructing a power grid topological graph according to the fault nodes. In the reconstruction of the power grid topology, calculating a recovery priority score of a load node, and calculating the real-time output capability of each new energy and energy storage device; and converting the reconstructed power grid topological graph into a weighted graph to restore the priority, the output capability and the path cost of an intermediate node, and correcting the edge weight and the node weight of the weighted graph. Based on the corrected weighted graph, searching a black-start path and deploying the black-start path in a simulation environment to execute simulation; and recording the operation data of each new energy and energy storage device in each simulation process, and evaluating the black-start capability of a single device. The method can accurately reflect the actual connectivity and operation constraint when the power grid is powered off, and provides black-start capability evaluation closer to the real operation condition for each new energy and energy storage device.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of new power systems and new energy technologies, and specifically relates to a black start capability evaluation method and system for grid-forming new energy and energy storage devices. BACKGROUND

[0002] In the absence of external power sources, new energy devices (such as wind power or photovoltaic) or energy storage devices can start and provide initial power to support the start of key facilities in the power grid. The black start capability of new energy or energy storage devices ensures that even without traditional power sources, they can play a role in restoring power supply, ensuring emergency recovery and stable operation of the power grid.

[0003] The patent application with publication number CN118487269A establishes and selects key evaluation indicators, establishes a black start decision-making model and simplifies the input-output relationship, and finally sorts the black start scheme through weighted evaluation and fuzzy evaluation method to select the optimal scheme to evaluate the black start capability of the energy storage system after large-scale power failure, ensuring rapid recovery of power supply in the power grid. The patent application with publication number CN114862150A proposes a distribution network black start capability evaluation method based on distributed power sources, which uses an entropy weight fuzzy comprehensive evaluation model to obtain and standardize the evaluation indicators, calculate the subjective and objective weights; combined with the fuzzy evaluation method, the black start capability evaluation value of the distributed power source is finally obtained. The patent application with publication number CN115906615A analyzes the key technical factors of new energy participating in the initial black start, defines the black start space-time support capability, and combines LSTM neural network to model the time series data to evaluate the black start capability of the new energy system. This method considers the space-time volatility of new energy units and can optimize the black start scheme to improve the efficiency of power grid recovery.

[0004] The above researches propose different technical paths and evaluation methods in the black start capability evaluation of new energy and energy storage combined power generation systems. However, there are still some deficiencies for grid-forming new energy and energy storage devices. The above methods mainly focus on the single role of new energy and energy storage devices in black start, but the complexity of the power grid structure is also an important factor affecting the black start capability. The change of power grid topology, coordination of different types of distributed energy, load change and distribution, etc. will also affect the black start process. The black start evaluation of grid-forming new energy needs to consider the interconnection and cooperation between different power sources, so as to more comprehensively evaluate the black start capability. SUMMARY

[0005] To solve the problems in the prior art, the application provides a black start capability evaluation method and system for network-constructed new energy and energy storage devices, which dynamically and accurately evaluates the black start capability of each new energy and energy storage device. The method first detects the fault of the power grid and reconstructs the power grid topology. Then, based on the reconstructed topology, the load node recovery priority and the real-time output capability of the new energy / energy storage device are calculated, which are combined with the intermediate node path cost to form a weighted graph, and the optimal black start path is searched based on the graph. Finally, the black start is performed in a simulation environment, the device operation data is recorded, the start time, the available power, the voltage / frequency support capability and the influence intensity are calculated, and a comprehensive score is generated.

[0006] The first aspect of the application discloses a black start capability evaluation method for network-constructed new energy and energy storage devices, which adopts the following technical scheme: According to the real-time operation state of the power grid, a double-layer fault judgment method is adopted to detect the fault nodes of the power grid, and the connection relationship of the nodes of the power grid is updated according to the fault nodes to obtain a reconstructed power grid topology. In the reconstructed power grid topology, the recovery priority score of each load node is calculated, and the real-time output capability of each new energy and energy storage device is calculated. The reconstructed power grid topology is converted into a weighted graph, and the edge weight and node weight of the weighted graph are corrected according to the recovery priority, the output capability and the path cost of the intermediate nodes. Based on the corrected weighted graph, the optimal black start path is searched. The black start path is deployed in a simulation environment to perform black start simulation, the operation data of each new energy and energy storage device in each simulation process is recorded, and the black start capability of a single new energy / energy storage device is evaluated according to the operation data.

[0007] Further, the double-layer fault judgment method comprises: For each node of the power grid, the preliminary detection of the operation parameters is performed in different load periods. When the preliminary detection result exceeds the trigger threshold, the fault detection is triggered. For the nodes of the power grid that trigger the fault detection, the instantaneous change value of the operation parameters is calculated. If the instantaneous change value exceeds the fault detection threshold, the node of the power grid is judged as a fault node. The fault detection threshold is the sum of the upper limit value of the trigger threshold and the offset amount, the offset amount is the product of the standard deviation of the operation parameters of the fault detection corresponding load period and the offset weight, and the offset weight is the ratio of the parameter instantaneous change value to the interval.

[0008] Further, the step of obtaining the reconstructed power grid topology comprises: The positions, device types, connection lines and line attributes of all nodes in the power grid are obtained, and the power grid topology is constructed by using an undirected graph structure. The edge weight of the power grid topology graph is a weighted sum of multiple factors, including line impedance, line capacity, line length, voltage level, and restoration cost; wherein the line capacity and voltage level are inversely calculated in the weighted calculation; A fault state marker is set for the fault node, and edges connected to the fault node are marked as disconnected; in the power grid topology graph, the topology connection relationship is updated according to the fault state marker and the disconnected marker, and a reconstructed power grid topology graph is obtained.

[0009] Further, the calculation method of the restoration priority of the load node is a weighted sum of the restoration urgency of the load node, the power demand level, and the restoration difficulty; Wherein, the restoration urgency is scored according to the load type; the power demand level is represented as the ratio of the maximum demand power of the load node to the maximum single-node demand power among all load nodes; The restoration difficulty is the result of subtracting the weighted sum of three deduction items from the reference value 10; The deduction items include the device state score, the standby power available capacity of the load node; the restoration time required is the ratio of the estimated restoration time required by the load node to the longest tolerable power outage time.

[0010] Further, the real-time output capacity of new energy and energy storage devices in the reconstructed power grid topology graph is calculated; including: For wind power devices in new energy, a wind speed-power conversion model based on power curve and meteorological parameter correction is used to calculate the instantaneous available power, and a real-time correction is introduced according to the grid operation state constraint; For photovoltaic devices in new energy, the theoretical output is calculated through the light intensity, and is corrected according to the component temperature; For energy storage devices, the actual discharge power that the energy storage system can provide in real time is calculated according to the real-time state of charge, the rated capacity and the maximum charge and discharge power, and is represented as the minimum power constraint corrected by the discharge efficiency; The minimum power constraint is the minimum value between the maximum allowed discharge power and the available power determined by the state of charge; the available power determined by the state of charge is calculated as the product of the rated capacity and a fraction; the numerator of the fraction is the current available capacity, and the denominator is the discharge time window.

[0011] Further, for the discharge time window, dynamic correction is made in combination with load forecasting and safety constraints; the corrected discharge time window is a weighted sum of the theoretical maximum discharge time window, the energy-load ratio and the temperature safety function; The energy-load ratio is the ratio of the available energy of the energy storage device to the predicted load.

[0012] Further, the weighted graph includes load nodes, power source nodes and intermediate nodes; The weight of the load node is represented as the inverse of the restoration priority, the weight of the power generation source node is represented as the inverse of the real-time output capacity, and the weight of the intermediate node is represented as the inverse of the path cost; For the edges between the load nodes and the power generation source nodes, the edge weights are corrected by the endpoint comprehensive values; according to the node weights and the corrected edge weights in the weighted graph, the shortest path is searched as the optimal black-start path.

[0013] Further, the correction of the edge weights by the endpoint comprehensive values comprises: For the edges between the load nodes and the power generation source nodes, the endpoint comprehensive values are calculated as the product of the node balance term and the path cost suppression term; The node balance term is the weighted sum of the restoration priority normalized value and the real-time output capacity normalized value; The path cost suppression term is 1 minus the path cost normalized value; Then, the corrected edge weight is the ratio of the original edge weight in the power grid topology graph to the endpoint comprehensive value; in the calculation, the endpoint comprehensive value is adjusted by the influence intensity.

[0014] Further, in the multiple simulation black-start processes, the operation data of the new energy and energy storage devices are recorded in real time; for each new energy and energy storage device, a single evaluation index is calculated, including the average start-up time, the average available start-up power, the voltage and frequency support capacity, and the influence intensity coefficient; According to the single evaluation index and the single evaluation index reference value, a black-start capability comprehensive score of each new energy / energy storage device under the optimal black-start path is calculated.

[0015] The second aspect of the present application discloses a black-start capability evaluation system of network-constructed new energy and energy storage devices, which runs the black-start capability evaluation method as described in the first aspect of the present application, and the system comprises: A topology reconstruction module is used to detect the fault nodes of the power grid according to the real-time operation state of the power grid, and to obtain the reconstructed power grid topology graph by using a double-layer fault judgment mode in the load period and updating the node connection relationship of the power grid according to the fault nodes; A restoration capability evaluation module is used to calculate the restoration priority score of each load node and the real-time output capacity of each new energy and energy storage device in the reconstructed power grid topology; A black-start path search module is used to convert the reconstructed power grid topology graph into a weighted graph, correct the edge weights and node weights of the weighted graph by using the restoration priority, the output capacity, and the path cost of the intermediate nodes, and search the optimal black-start path based on the corrected weighted graph. A black start capability evaluation module; a black start simulation is performed by deploying a black start path in a simulation environment; the operation data of each new energy and energy storage device during each simulation process is recorded; the black start capability of a single new energy / energy storage device is evaluated according to the operation data.

[0016] Compared with the prior art, the technical scheme provided in the application has at least one of the following beneficial effects, 1. The application obtains the operation state of the power grid in real time, performs double-layer fault judgment according to the load period, identifies the fault node in time, and updates the connection relationship of the nodes of the power grid according to the fault node, so as to generate a reconstructed power grid topology graph. Through this dynamic topology, the actual connectivity and operation constraints of the power grid under different fault conditions can be accurately reflected, and the black start capability evaluation of each new energy and energy storage device is provided under more realistic operation conditions.

[0017] 2. On the basis of the reconstructed power grid topology, the application forms a weighted graph by comprehensively considering the priority of the load node, the real-time output capability of the power source node and the path cost of the intermediate node, searches for an optimal black start path, and makes the key load be restored in priority and fully exert the output potential of each device. The path optimization not only ensures the feasibility and stability of the black start process, but also can more accurately evaluate the starting capability and power support performance of each new energy or energy storage device under actual power grid conditions.

[0018] 3. In multiple black start simulations, the application records the starting time, the available power, the voltage and frequency support capability and the influence intensity on the system of each new energy and energy storage device in real time, and comprehensively scores the black start capability of a single new energy or energy storage device under the optimal black start path. By incorporating the reconstructed power grid topology and the path optimization result into the evaluation, the end-to-end quantitative black start capability can be obtained, which provides a scientific basis for power grid operation management and operation decision, and provides an operable reference for black start scheme optimization. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of a black start capability evaluation method for grid-constructed new energy and energy storage devices. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the application more clear, the technical scheme of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. The embodiments described in the application are only a part of the embodiments of the application, not all the embodiments. Based on the spirit of the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0021] As an embodiment of the present application, the specific embodiment of the black start capability evaluation method of grid-forming new energy and energy storage devices is disclosed. Referring to Figure 1 , Figure 1 The flowchart of the black start capability evaluation method of grid-forming new energy and energy storage devices.

[0022] Step 1: Obtain the real-time running state of the power grid, judge the fault position and state of the power grid; and reconstruct the power grid topology according to the fault area.

[0023] 1.1: Deploy edge devices at each power grid node to monitor the running state of the power grid equipment and collect the running data of the power grid node; Deploy edge devices at each power grid node to collect parameter data at a collection frequency of 1 second, including current, voltage, power, and frequency under different loads. The power grid nodes include power generation nodes (new energy and energy storage devices), intermediate nodes (substations and distribution stations), load nodes, etc.

[0024] According to the real-time collected voltage and current data, it is judged whether abnormal conditions such as voltage and current drop, voltage loss, etc. occur, and when the voltage or current exceeds the corresponding threshold, the fault detection is triggered. The threshold setting method of current and voltage is as follows: Classify the collected historical parameter data according to the load period, including low load period (less than 40% of rated power), medium load period (40%-60% of rated power), high load period (60%-80% of rated power), and high load period (more than 80% of rated power).

[0025] On the edge device, the statistical values of the calculation node parameter data of each load period are calculated, including the mean and standard deviation. For each load period, the fault detection trigger threshold of current, voltage, and frequency For , represents the mean, represents the standard deviation; is a multiple set according to the requirements of the load period. For example, in the low load period, because the load is low, the stability of the power grid is strong, It can be set to 0.5; in the high load period, the load fluctuation range of the power grid is large, It can be set to 2 or 3.

[0026] When the real-time data of the power grid exceeds the upper and lower limits of the fault trigger threshold , the fault detection is triggered immediately, and the fault detection includes voltage mutation, current mutation, and frequency anomaly.

[0027] As an optional mode of the embodiment, for the power grid node triggering the fault early warning, the instantaneous change value of the voltage and the current is calculated, and the instantaneous change value is the difference between the current voltage / current value and the voltage / current value before a sampling interval: ; wherein, and are the instantaneous change values of the voltage and the current respectively, and are the current voltage value and the current current value respectively; is the sampling interval.

[0028] When the instantaneous change value exceeds the fault detection threshold value , it is confirmed that the fault occurs in the power grid node. The fault detection threshold value ; is the upper limit of the fault detection triggering threshold value ; is the offset, which is set as the product of the current voltage standard deviation corresponding to the load period and the offset weight; the offset weight is the ratio of the voltage and current instantaneous change value to the sampling interval.

[0029] In the embodiment, is the preliminary fault detection threshold value, which is set to be relatively loose, and can quickly respond to the mutation of the current and voltage. However, because the power grid may have a short-term fluctuation or interference in some cases (for example, equipment switching or power fluctuation), the parameter value exceeding does not mean that a real fault occurs. In order to ensure that the power grid node fault detection does not trigger unnecessary fault response due to short-term fluctuation or error, an offset is added in the embodiment to set the fault confirmation standard more strictly, thereby reducing false positives.

[0030] After the edge device completes the fault detection, it can be confirmed that the specific power grid node where the edge device is located has a fault, and the fault position of the power grid is also confirmed.

[0031] 1.2: Constructing a power grid topology map, and reconstructing the power grid topology according to the node fault condition.

[0032] 1.2.1: Obtaining the position coordinates, device types, connection lines and attributes (such as impedance, voltage level, etc.) of all nodes of the power grid according to the GIS system; The power grid topology map is constructed by using the undirected graph structure, and the nodes in the graph represent power devices (such as transformer substations, distribution stations, new energy devices, energy storage devices and load nodes, etc.), and the edges represent the connection lines. Each edge is assigned a weight, and the influencing factors of the weight include line impedance, line capacity, line length, voltage level and recovery cost, etc. The smaller the edge weight is, the more preferred the line is.

[0033] For node and , the edge weight is expressed as: ; wherein is the impedance amplitude of the line, is the line capacity, is the line length, is the voltage level, represents the recovery cost of the line (related to repair cost and repair time); , , , and are weights, which are adjusted according to the target of the power grid topology reconfiguration in this embodiment; for example, when fast recovery is the priority target, the weights of the recovery cost and the path length are increased, and the line with low cost and easy recovery is preferentially selected for connection.

[0034] 1.2.2; for the fault node determined in step 1, its position in the topology structure is kept in the power grid topology graph, but a fault state flag is set, indicating that the node is currently unavailable; in addition, the edges connected to the fault node are also updated in state and marked as disconnected; the topology structure is updated.

[0035] As an optional step of this embodiment, based on the updated power grid topology graph, the steady-state simulation is performed by using the AC power flow calculation method to verify that the updated power grid topology can operate normally.

[0036] Step 2: Evaluate the recovery priority of the load node and the output capacity of the new energy and energy storage device.

[0037] 2.1: Based on the recovery demand of the load node in the power grid, different load nodes are given priority.

[0038] For each load node in the updated power grid topology graph, according to its recovery urgency, power demand level, and recovery difficulty, the priority score is calculated in the following way: ; In the formula, is the priority score of the load node , , and are the recovery urgency, power demand level, and recovery difficulty of the load node , , and The weighting coefficients for urgency, power demand level, and ease of recovery can be set and adjusted according to the actual situation.

[0039] Furthermore, the recovery urgency is graded and scored according to the load type and the importance of the service object. An example is given in Table 1 of this embodiment.

[0040] Table 1. Examples of Emergency Score for Recovery

[0041] The power demand level is represented by the proportion of the node's maximum load power, and the calculation method is as follows: ; In the formula, For load nodes Maximum power demand, This represents the maximum single-node power demand among all load nodes.

[0042] The ease of recovery is determined by considering the recovery time, equipment status, and backup power availability; the calculation method is as follows: ; In the formula, The standardized score for the time required for recovery is the ratio of the estimated recovery time of a load node to the longest tolerable outage time. The equipment status score is obtained based on the diagnostic results of the load node, and the value is [0.10]. The worse the equipment status, the higher the score. The availability of backup power is quantified by the available backup power capacity of the load node. If there is no available backup power nearby, the availability of backup power is 0. , and They are respectively , and The weighting coefficients can be set and adjusted according to actual conditions.

[0043] 2.2: Calculate the real-time output capacity of new energy sources and energy storage devices in the updated power grid topology.

[0044] For wind turbines, photovoltaic arrays, and energy storage systems, real-time data on operating status and environmental conditions are collected and preprocessed. Table 2 provides some examples of the collected data.

[0045] Table 2 Examples of collected data

[0046] The maximum available output capacity of each wind and solar node is calculated by combining physical and empirical models. Specifically: 2.2.1: For wind power, the instantaneous available power is calculated by the wind speed-power conversion model based on the power curve and meteorological parameter correction, and dynamic constraints are introduced for real-time correction. It is expressed as: ; where, is the real-time wind power instantaneous available power, which represents the maximum active power that the wind turbine can output under the current meteorological conditions; is the wind turbine power curve function, provided by the wind turbine manufacturer, and the function output is the rated power ratio at wind speed ; is the air density correction coefficient, , and are the current air density and the standard air density, respectively; is the wind direction deviation correction coefficient, , is the angle between the current wind direction and the main shaft direction of the wind turbine.

[0047] is the dynamic constraint correction coefficient, which is the minimum limit composed of multiple grid operating state sub-constraints; the dynamic constraint correction coefficient is expressed as: ; where, , , , and are the voltage constraint, line capacity constraint, frequency stability constraint, fault isolation constraint and energy storage coordination constraint, respectively.

[0048] It should be noted that the selection of sub-constraints can be adaptively adjusted according to the actual situation of the grid.

[0049] 2.2.2: For photovoltaic, the theoretical output is calculated according to , and then corrected by the component temperature. Where is the real-time solar radiation intensity (unit: W / m2), is the photovoltaic component efficiency, is the total area of the photovoltaic component; the way to correct the theoretical output according to the component temperature is: ; where, is the corrected photovoltaic output, is the temperature coefficient, and are the reference temperature and the current actual component temperature, respectively.

[0050] 2.2.3: For the energy storage system, the real-time actual discharge power that the energy storage system can provide is calculated according to the real-time state of charge, the rated capacity and the maximum charge and discharge power; it is represented as: ; wherein, is the real-time actual discharge power, is the rated maximum discharge power of the energy storage system; and is the current state of charge and the minimum allowable state of charge threshold; is the discharge time window, and is the capacity and discharge efficiency of the energy storage device.

[0051] For the discharge time window, the traditional way is to set a value marked by the manufacturer or a preset fixed time length, but during the black start stage of power grid outage, the energy storage system faces the challenges of large load fluctuation, complex operating environment and variable equipment state. The fixed discharge time window may not be able to meet the demand for continuous power supply when the load demand surges or the state of the energy storage system decreases. Therefore, the embodiment dynamically adjusts according to the state of the energy storage system, the load demand and the state of the power grid to avoid resource waste or power shortage caused by the fixed time window. The specific steps are as follows: At the moment of power restoration, the real-time state of charge SOC, health condition SOH, battery temperature and ambient temperature of the energy storage device are obtained; The historical load and environmental parameters are used to predict the load demand at the moment of power restoration using a time series model; it is assumed that the moment of power restoration is , the load demand at this moment is , the historical load data of the previous moments are used as the prediction basis; the process is represented as: ; wherein, is the time series prediction model selected in the embodiment.

[0052] As an optional way of the embodiment, the time series prediction model can select a gated recurrent unit or other neural network prediction method.

[0053] The minimum safe SOC threshold is set, the lower limit of the safe SOC is; and the temperature safety threshold is , and are the lower limit of the safe temperature and the upper limit of the safe temperature respectively, which are set according to the battery material.

[0054] It is assumed that the maximum discharge power of the energy storage device is , possibly in an amount , the theoretical maximum discharge time window is ; wherein, , is the rated capacity of the energy storage device.

[0055] Combined with load forecasting and security constraints, a weighted fusion dynamic correction discharge time window is adopted; denoted as: ; wherein, is the corrected discharge time window, is a temperature safety function that reduces the discharge time when the temperature is close to the threshold value; , and are weight coefficients, satisfying , which are adjusted according to the phased needs of the black start stage. For example, in the initial stage of black start, the load power fluctuates violently, so the value of needs to be increased.

[0056] 3. According to the load node recovery priority, the real-time output capacity of new energy and energy storage devices, the power grid topology graph is converted into a weighted graph, and the edges and nodes in the weighted graph are corrected.

[0057] 3.1: In the weighted graph, the nodes include load nodes , power source nodes (new energy, energy storage), and intermediate nodes (substations, distribution stations); For load nodes, according to the recovery priority , a weight is assigned, denoted as ; is a small constant to prevent zero.

[0058] For power source nodes, according to the real-time output capacity , a weight is assigned, denoted as ; wherein, includes the corrected wind power instantaneous available power, the corrected photovoltaic output, and the actual discharge power that the energy storage system can provide.

[0059] For intermediate nodes, according to the path cost , a weight is assigned, denoted as ; wherein, the path cost is the comprehensive calculation value of the main transformer input cost, time cost, voltage overrun cost, and power shortage cost of the intermediate node.

[0060] 3.2: Keep the edge weights of the weighted graph consistent with the edge weights of the power grid topology graph; introduce a node weight correction on the edge weights of the power grid topology graph. The specific implementation is: The restoration priority of the load node, the real-time output capacity of the power source node, and the path cost of the intermediate node are all normalized to [0, 1]; the endpoint comprehensive value is calculated by a correction function, which is expressed as: ; wherein, is the endpoint comprehensive value between the load node and the power source node , is the restoration priority normalization value of the load node , is the real-time output capacity normalization value of the power source node , is the adjustment factor for balancing the "supply capacity" and "load value".

[0061] The edge weight of the weighted graph is then modified as ; wherein, is the modified edge weight between the load node and the power source node , is the original edge weight in the power grid topology graph between the load node and the power source node , is the influence intensity, which measures the sensitivity of the node characteristics to the edge weight correction.

[0062] 3.3: According to the node weights and the modified edge weights in the weighted graph, the minimum restoration cost path is searched as the best black start path through a shortest path search algorithm.

[0063] As an optional way of this step, a shortest path search algorithm such as Dijkstra's shortest path algorithm can be selected to solve the best black start path according to the weighted graph.

[0064] In the power grid topology graph under the condition of conventional power restoration, the influencing factors of edge weights include line impedance, line capacity, line length, voltage level, and restoration cost. However, in the scenario of distribution network black start, the path selection depends not only on the line conditions but also on the values of the path endpoints (i.e., the load nodes and the power source nodes). For example, high-priority loads are worth restoring as soon as possible, and nodes with more available power can drive more load restoration.

[0065] The embodiment introduces node weight correction in the edge weight definition of the weighted graph, reduces the path search cost of high-value nodes, and makes the path to high-priority load or large power supply more preferred in the search, that is, pursues the "cost-effective" of black start recovery.

[0066] 4. According to the best black start path, the black start strategy is executed in the simulation environment, and the black start capability of the new energy and energy storage device is evaluated according to the recovery condition evaluation.

[0067] As an embodiment of the present step, the start-stop sequence of the new energy (wind power, photovoltaic) and the energy storage device is determined according to the best black start path. The power threshold and start condition required for starting each device are determined to ensure that the starting process meets the safety and stability requirements of the power system.

[0068] As an embodiment of the present step, multiple simulations are performed, the start-stop instructions are sent by the control system, the output power of each device is adjusted, and the power grid load is gradually recovered until stability is reached. During the black start process, the time required for starting the new energy and energy storage device, the climbing process, the synchronization characteristics, etc. are monitored in real time.

[0069] 4.1: During the multiple simulation black start process, the edge device records the operating data of each new energy and energy storage device in real time, including but not limited to actual output power curve, voltage offset, frequency offset, power margin, and start success status (i.e., whether it is successfully connected to the grid).

[0070] After multiple simulations, for each new energy and energy storage device, calculate the single evaluation index, including average start time, average available start power, voltage and frequency support capability, and influence intensity coefficient.

[0071] Further, the average start time is ; is the start time of the th simulation, ; represents the time point when the new energy or energy storage device receives the start command, represents the time point when the output power reaches the stable grid connection level; is the number of simulations.

[0072] Further, the average available start power is ; is the available start power of the th simulation, .

[0073] Further, the voltage and frequency support capability is ; wherein is the voltage support capability, , The average value after summing the maximum voltage deviations of all single simulations; For frequency support capability, , The average value after summing the maximum frequency deviations of all single simulations; and The weighting coefficients for voltage support capability and frequency support capability satisfy... .

[0074] Furthermore, the influence intensity coefficient is i.e., power generation node The mean value of the strength coefficient is affected in multiple simulations; the first The influence intensity coefficient of the second simulation is ; in, This represents the power change during the black start process. ; For nodes In the The power received during the second simulation of the start command. They are nodes In the The simulation stabilized the output power; In the optimal recovery path, excluding nodes In addition, the sum of power changes of other new energy / energy storage nodes.

[0075] As an optional approach in this step, the average startup success rate of new energy / energy storage nodes across all simulations can also be calculated, which is the ratio of the number of successful grid connection attempts to the total simulation coefficients.

[0076] 4.2: Based on the individual evaluation indicators obtained in step 4.1, calculate the comprehensive score of the black start capability for each new energy / energy storage device, expressed as: ; in, For nodes Overall score for black start capability; , , and These serve as reference values ​​for each individual indicator, designed according to the system objectives. , , and Let be the weighting coefficients of each term in the formula, satisfying .

[0077] 4.3: Based on the comprehensive score of the black start capability of each new energy / energy storage device, it is classified into different levels. Table 3 shows an example of level classification based on thresholds.

[0078] Table 3 Grade division example

[0079] As an optional embodiment of the present application, the comprehensive score grade provides a quantitative black start capability evaluation for each new energy / energy storage device, which can quickly identify high-capability and low-impact key devices as starting or main nodes in the next black start path selection, while assisting in determining the auxiliary position of medium-grade devices, avoiding low-grade bottleneck devices from undertaking critical tasks, thereby optimizing the path sequence, improving overall reliability, and reducing the search space, and accelerating path planning decisions.

[0080] According to the power grid topology changes when the power grid is in fault outage, and according to the demand differences of load nodes, the present application finds the best black start path. The best black start path can verify the starting capability index of each new energy or energy storage device under the actual system coordination condition, not only investigating the average starting time, the maximum available starting power, and the voltage and frequency support capability of the device itself, but also revealing the mutual influence and power coupling effect between devices, identifying potential bottlenecks and key nodes in the path, so that the black start capability evaluation is more comprehensive, reliable, and close to actual operation. At the same time, the evaluation results can provide a scientific basis for device grade division, starting sequence optimization, auxiliary energy storage configuration, and system-level black start strategy, and provide a reference for formulating a high-reliability black start scheme and improving the rapid recovery capability of microgrids or regional power grids.

[0081] As an embodiment of the present application, a black start capability evaluation system for network-type new energy and energy storage devices is disclosed, which executes the specific implementation manner of the black start capability evaluation method embodiment. The system comprises: a topology reconstruction module; for detecting the fault nodes of the power grid according to the real-time operation state of the power grid, using a double-layer fault judgment method in the load period; and updating the connection relationship of the nodes of the power grid according to the fault nodes, to obtain a reconstructed power grid topology map; a recovery capability evaluation module; for calculating the recovery priority score of each load node in the reconstructed power grid topology, and calculating the real-time output capability of each new energy and energy storage device; a black start path search module; for converting the reconstructed power grid topology map into a weighted graph, to correct the edge weight and node weight of the weighted graph based on the path cost of the recovery priority, the output capability, and the intermediate nodes; and searching for an optimal black start path based on the corrected weighted graph; a black start capability evaluation module; for deploying the black start path in a simulation environment to perform black start simulation; recording the operation data of each new energy and energy storage device in each simulation process; and evaluating the black start capability of a single new energy / energy storage device according to the operation data.

[0082] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for evaluating the black-start capability of grid-type new energy and energy storage devices, characterized in that, include: Based on the real-time operating status of the power grid, a two-layer fault judgment method is adopted during different load periods to detect faulty nodes in the power grid; and the connection relationship of power grid nodes is updated according to the faulty nodes to obtain a reconstructed power grid topology. In reconstructing the power grid topology, the recovery priority score of each load node is calculated, and the real-time output capacity of each new energy source and energy storage device is calculated. The reconstructed power grid topology is transformed into a weighted graph to restore priority, output capacity, and path cost of intermediate nodes, and the edge weights and node weights of the weighted graph are corrected; based on the corrected weighted graph, the optimal black start path is searched. Deploy the black boot path in the simulation environment to perform black boot simulation; Record the operating data of each new energy source and energy storage device during each simulation; evaluate the black start capability of a single new energy source / energy storage device based on the operating data.

2. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, The dual-layer fault detection method includes: For each power grid node, preliminary detection of operating parameters is performed during different load periods; when the preliminary detection results exceed the trigger threshold, fault detection is triggered. For a power grid node that triggers fault detection, calculate the instantaneous change value of the operating parameters; if the instantaneous change value exceeds the fault detection threshold, the power grid node is determined to be a fault node. The fault detection threshold is the sum of the upper limit of the trigger threshold and the offset. The offset is the product of the standard deviation of the operating parameters during the load period corresponding to the fault detection and the offset weight. The offset weight is the ratio of the instantaneous change value of the parameter to the interval used.

3. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, The steps for obtaining the reconstructed power grid topology map include: Obtain the location, equipment type, connection lines, and line attributes of all nodes in the power grid, and construct the power grid topology using an undirected graph structure; The edge weights of the power grid topology diagram are the weighted sum of multiple factors, including line impedance, line capacity, line length, voltage level, and restoration cost; wherein, the line capacity and voltage level are taken as reciprocals in the weighted calculation. A fault status marker is set for the faulty node, and the edge connected to the faulty node is marked as disconnected. In the power grid topology diagram, the topology connection relationship is updated according to the fault status marker and disconnection marker to obtain the reconstructed power grid topology diagram.

4. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, The recovery priority of the load node is calculated as a weighted sum of the recovery urgency, power demand level, and recovery difficulty of the load node. The urgency of recovery is assigned according to load type; the power demand level is expressed as the ratio of the maximum power demand of a load node to the maximum single-node power demand among all load nodes. The recovery difficulty is the baseline value of 10 minus the weighted sum of the three deduction items. The deductions include the recovery time, equipment status score, and available backup power capacity of the load node; the recovery time is the ratio of the estimated recovery time of the load node to the maximum tolerable outage time.

5. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, Calculate the real-time output capacity of new energy sources and energy storage devices in the reconstructed power grid topology; including: For wind power devices in new energy sources, an instantaneous available power is calculated using a wind speed-power conversion model based on power curves and meteorological parameters, and grid operation state constraints are introduced for real-time correction. For photovoltaic devices in new energy sources, the theoretical output is calculated based on the light intensity and then corrected according to the module temperature. For energy storage devices, the actual discharge power that the energy storage system can provide in real time is calculated based on the real-time state of charge, rated capacity and maximum charge and discharge power, and is expressed as the minimum power constraint after discharge efficiency correction. The minimum power constraint is the minimum between the maximum allowable discharge power and the available power determined by the state of charge. The available power determined by the state of charge is calculated as the product of the rated capacity and a fraction. The numerator of the fraction is the current available capacity, and the denominator is the discharge time window.

6. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 5, characterized in that, The discharge time window is dynamically corrected by combining load forecasting and safety constraints; the corrected discharge time window is a weighted sum of the theoretical maximum discharge time window, the energy-load ratio, and the temperature safety function. The energy load ratio is the ratio of the available energy of the energy storage device to the predicted load.

7. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, The weighted graph includes load nodes, power generation nodes, and intermediate nodes; The weight of a load node is represented as the reciprocal of its recovery priority; the weight of a generator node is represented as the reciprocal of its real-time output capability; and the weight of an intermediate node is represented as the reciprocal of its path cost. For the edges between load nodes and power generation nodes, the edge weights are adjusted by the comprehensive value of the endpoints; based on the node weights in the weighted graph and the adjusted edge weights, the shortest path is searched as the optimal black start path.

8. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 7, characterized in that, The step of correcting edge weights based on endpoint comprehensive value includes: For the edge between the load node and the power generation node, the endpoint comprehensive value is calculated as the product of the node balancing term and the path cost suppression term. The node balancing term is a weighted sum of the normalized value of recovery priority and the normalized value of real-time output capability; The path cost suppression term is 1 minus the path cost normalization value; The corrected edge weight is the ratio of the original edge weight in the power grid topology diagram to the comprehensive value of the endpoint; during the calculation, the comprehensive value of the endpoint is adjusted by the influence intensity.

9. The method for evaluating the black-start capability of grid-type new energy and energy storage devices according to claim 1, characterized in that, During multiple simulated black start processes, the operation data of new energy and energy storage devices are recorded in real time; for each new energy and energy storage device, individual evaluation indicators are calculated, including average start-up time, average available start-up power, voltage and frequency support capability, and influence intensity coefficient. Based on the individual evaluation indicators and their reference values, calculate the comprehensive score of the black start capability of each new energy / energy storage device under the optimal black start path.

10. A black-start capability assessment system for grid-type new energy and energy storage devices, operating the black-start capability assessment method as described in any one of claims 1-9, characterized in that, The system includes: The topology reconfiguration module is used to detect faulty nodes in the power grid based on the real-time operating status of the power grid and the two-layer fault judgment method according to the load period; and to update the connection relationship of power grid nodes according to the faulty nodes to obtain the reconfigured power grid topology map. Recovery capability assessment module; used to calculate the recovery priority score of each load node and the real-time output capability of each new energy source and energy storage device in the reconfiguration of the power grid topology; The black start path search module is used to convert the reconstructed power grid topology into a weighted graph to restore priority, output capacity, and path cost of intermediate nodes, and to correct the edge weights and node weights of the weighted graph; based on the corrected weighted graph, it searches for the optimal black start path. The black-start capability assessment module is used to deploy the black-start path in the simulation environment to perform black-start simulation; record the operating data of each new energy source and energy storage device during each simulation; and evaluate the black-start capability of a single new energy source / energy storage device based on the operating data.

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