A network-constructing type energy storage black start power supply planning method and system

Abstract

The present application belongs to the field of network-structured energy storage planning, and provides a network-structured energy storage black start power supply planning method and system, generates a candidate start path set; extracts path associated lines, estimates line condition failure probability according to historical line failure records and power failure time conditions, forms a representative power failure scenario set by using a scenario generation model; comprehensively discriminates candidate path availability, network-structured energy storage power energy satisfaction relationship and transient process safety margin, and constructs an index; based on the candidate path, the representative power failure scenario and the planning evaluation index, organizes candidate objects, scenario information, capacity boundaries and planning constraints, forms input data required by the main problem and the scenario sub-problem; iteratively solves the main problem and the scenario sub-problem, and determines a network-structured energy storage planning scheme that meets the black start reliability requirements and has the optimal recovery support capability. The present application can improve the black start success rate and the recovery support capability of the units to be started after a large-scale power failure.

Description

Technical Field

[0001] This invention belongs to the field of grid-based energy storage planning, specifically relating to a grid-based energy storage black-start power supply planning method and system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The risk and difficulty of large-scale power outages caused by extreme weather, cascading failures, and other disturbances increase with the complexity of operating modes. After a major power outage, conventional thermal power units typically cannot start directly due to the loss of external plant power. They require black-start power sources with self-starting, voltage and frequency building, and power transmission capabilities to provide starting power to the units awaiting startup, enabling them to meet startup conditions and further develop usable generating capacity. Grid-based energy storage can establish local voltage and frequency as a voltage source, featuring fast response, flexible deployment, and rapid active and reactive power support, making it a potential candidate for serving as a black-start power source in new power systems.

[0004] However, in order for grid-based energy storage to fully play its role in supporting black start during power outage recovery, it is necessary to plan and configure its location and capacity based on the grid structure, the distribution of generating units to be started, and the conditions of candidate start-up paths.

[0005] Therefore, researching planning methods for grid-based energy storage black start power sources to address the black start needs following large-scale power outages is of great significance for improving the reliability and recovery support capabilities of black start contingency plans after major power outages. However, existing solutions generally assume a given power source, a given path, or a given recovery process, and have not yet formed a systematic plan for the deployment and capacity configuration of grid-based energy storage black start power sources. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a grid-based energy storage black-start power supply planning method and system. This invention can improve the black-start success rate and recovery support capability of units waiting to be started after a large-scale power outage.

[0007] According to some embodiments, the present invention adopts the following technical solution: A method for planning grid-type energy storage black-start power sources includes the following steps: Based on candidate grid-type energy storage nodes, thermal power units to be started and grid topology, a set of candidate start-up paths is generated for each set of grid-type energy storage nodes and thermal power units to be started. Based on the candidate start path set, the associated lines are extracted, the line condition failure probability is estimated based on historical line fault records and power outage conditions, the condition joint distribution of the state of the associated lines is learned using the scenario generation model, the combination of available line states is expanded, and a representative power outage scenario set is formed through scenario reduction. The availability of candidate paths, the power-energy satisfaction relationship of grid-based energy storage, and the safety margin of transient processes are comprehensively judged in representative power outage scenarios. A black start success rate index for energy storage-generator combination is constructed, and a recovery support capability index is constructed based on start-up time, effective power generation capacity after target unit start-up, and distribution dispersion coefficient. Based on candidate paths, representative power outage scenarios, and planning evaluation indicators, candidate objects, scenario information, capacity boundaries, and planning constraints are organized to form the input data required for the main problem and scenario sub-problems. Taking grid-based energy storage planning schemes as the main decision-making object and maximizing recovery support capability as the optimization objective, the sub-problems of path availability, energy storage start-up capability, and comprehensive safety level of transient processes under representative power outage scenarios are used as scenario sub-problems. The black start success rate index and recovery support capability index of candidate energy storage-unit combinations are calculated and fed back, and iterative solutions are performed to determine the grid-based energy storage planning scheme that meets the black start reliability requirements and has the optimal recovery support capability.

[0008] As an alternative implementation, the process of generating a set of candidate startup paths for each set of grid-connected energy storage nodes and thermal power units to be started, based on candidate grid-connected energy storage nodes, thermal power units to be started, and grid topology, includes: for candidate grid-connected energy storage nodes i and thermal power units to be started g Before K Candidate path search method obtains from i to g The set of candidate boot paths, front K The path is searched according to the path priority based on the number of line segments and the length of the line operation.

[0009] As an alternative implementation method, the process of extracting path-related lines based on the candidate startup path set and estimating the line conditional fault probability based on historical line fault records and power outage time conditions includes: for the same group of network-type energy storage nodes i and thermal power units to be started g First extract its front K All routes included in the candidate routes are used to form a set of route-related routes, based on the time of the power outage. τ The conditional information includes several factors such as weather risk level, line load rate, channel environment, equipment status, and historical fault statistics. The conditional fault probability is generated for each line using a conditional fault probability estimation model.

[0010] As an alternative implementation method, the process of using a scenario generation model to learn the conditional joint distribution of the status of the associated lines and expanding the combination of available line statuses, and forming a representative set of power outage scenarios through scenario reduction includes: combining the conditional information at the time of power outage with the conditional fault probability of the associated lines to form a conditional vector; using a conditional variational autoencoder as the scenario generation model to learn the conditional joint distribution of the line status under given risk conditions; the scenario generation model expands the line status samples consistent with historical faults, weather risks, equipment status and load levels through latent variable sampling, which is used to supplement the relevant fault combinations that may occur under the same transmission channel, the same weather or similar operating conditions in the same area; The training objective of the conditional variational autoencoder consists of a line state reconstruction loss term and a latent variable distribution constraint term. The line state reconstruction loss term is used to measure the consistency between the line state combinations generated by the model and the training samples. The latent variable distribution constraint term is used to constrain the latent variable distribution obtained by the encoder to be close to the prior distribution, so that the model can generate new line state combinations through random sampling. Using the availability status of candidate paths as the main feature, typical scenarios are retained through clustering or distance-based representative screening methods, and the probability of deleted scenarios is incorporated into the nearest retained scenario.

[0011] As an alternative implementation method, the process of comprehensively judging the availability of candidate paths, the energy satisfaction relationship of grid-based energy storage, and the safety margin of transient processes under representative power outage scenarios includes: for each energy storage-generator combination, the black start success rate characterizes the probability that grid-based energy storage will successfully start the target thermal power unit via at least one candidate start path under representative power outage scenarios; the recovery support capability characterizes the speed and scale of the target thermal power unit forming effective power generation capacity after successful start-up. Among them, the black start success rate index is jointly determined by three types of conditions: path availability, energy storage start-up capability, and transient process risk. Path availability is obtained based on representative power outage scenarios; energy storage start-up capability is used to determine whether the available energy and power at the time of power outage meet the start-up requirements of the target thermal power unit; transient process risk is used to reflect the safety margin of each black start operation link, including line no-load charging, transformer no-load commissioning, and target unit auxiliary machine start-up. The process of constructing the recovery support capability index includes calculating the average start-up time for a successful black start scenario, constructing a start-up time efficiency coefficient from the average start-up time, using the actual power generation that the target unit can generate within the evaluation time window to represent the effective power generation capacity of the target unit after start-up, using the distribution dispersion coefficient to characterize the supporting role of grid-type energy storage deployment in parallel recovery across multiple regions, and calculating the recovery support capability index based on the start-up time efficiency coefficient, effective power generation capacity, and distribution dispersion coefficient.

[0012] As a further defined implementation method, the black boot success rate indicator is: ; in, π ω Representative scenarios ω The probability, As a condition for successful black start, if there is a candidate path that can successfully start the target thermal power unit in the corresponding scenario, the energy storage-unit combination is considered to have the conditions for successful black start in that scenario. The indicators for restoring support capacity are: ; in, C ref As a normalized baseline value, This is the startup time efficiency coefficient. For effective power generation capacity, The distribution dispersion coefficient represents the strength of the recovery support capacity index, indicating a more robust grid-type energy storage system. i Starting the target thermal power unit g is more conducive to the recovery of subsequent units.

[0013] As an alternative implementation method, the process of organizing candidate objects, scenario information, capacity boundaries, and planning constraints based on candidate paths, representative power outage scenarios, and planning evaluation indicators to form the input data required for the main problem and scenario sub-problems includes: for each group of candidate grid-type energy storage nodes, the following steps are taken: i and thermal power units to be started g The input to the planning problem includes the set of the top K candidate start-up paths, the set of representative power outage scenarios and their probabilities, the calculation relationship of the available state of the candidate paths under scenario ω, the start-up power demand and start-up energy demand of the target thermal power unit, the charging power demand and charging energy demand of the candidate paths, the comprehensive safety level of the transient process, the configurable range of energy storage power capacity and energy capacity, and planning constraint parameters including the black start success rate threshold, the upper limit of the number of sites, and the upper limit of construction resources.

[0014] As an alternative implementation, the main problem aims to maximize recovery support capability, considering constraints on construction resources, the number of sites, and the black start success rate threshold. The scenario sub-problems are used to verify the candidate solutions given by the main problem. For each selected energy storage-generator combination, the sub-problems check the availability of candidate paths, the relationship between energy storage power and energy satisfaction, and the comprehensive safety level of transient processes in a representative power outage scenario set, and update the black start success rate and recovery support capability of the combination. When the black start success rate of a selected energy storage-generator combination is lower than the preset threshold, the scenario sub-problems further determine whether it can reach the success rate threshold through capacity increase. If there is a minimum power capacity and minimum energy capacity required to meet the success rate threshold within the upper limit of capacity, the lower limit constraints of power capacity and energy capacity are fed back to the main problem.

[0015] As an alternative implementation method, the process of iteratively solving to determine the grid-based energy storage planning scheme that meets the black-start reliability requirements and has the optimal recovery support capability includes: the main problem provides the current grid-based energy storage planning scheme based on the formed planning problem input; the scenario subproblem verifies the selected energy storage-unit combination under representative power outage scenarios, and calculates its black-start success rate and recovery support capability; for combinations whose success rate does not meet the standard but can be corrected by capacity increase, feedback is provided on the lower limit of capacity constraint, and for combinations that still cannot meet the standard within the upper limit of capacity, feedback is provided on the exclusion scheme; the main problem is re-optimized under the new capacity cut or exclusion cut constraint, and the final planning scheme is determined among all feasible combinations that meet the black-start success rate threshold with the goal of maximizing the recovery support capability.

[0016] A grid-type energy storage black start power planning system includes: The path generation module is configured to generate a set of candidate startup paths for each set of grid-type energy storage nodes and thermal power units to be started, based on candidate grid-type energy storage nodes, thermal power units to be started and grid topology. The power outage scenario generation and reduction module is configured to extract the associated lines based on the candidate start path set, estimate the line conditional fault probability based on historical line fault records and power outage time conditions, learn the conditional joint distribution of the state of the associated lines using the scenario generation model, expand the combination of available line states, and form a representative power outage scenario set through scenario reduction. The planning and evaluation index construction module is configured to comprehensively judge the availability of candidate paths, the power energy satisfaction relationship of grid-type energy storage, and the safety margin of transient processes under representative power outage scenarios, construct the black start success rate index of energy storage-unit combination, and construct the recovery support capability index based on the start-up time and the effective power generation capacity of the target unit after start-up. The planning model input module is configured to organize candidate objects, scenario information, capacity boundaries and planning constraints based on candidate paths, representative power outage scenarios and planning evaluation indicators, and form the input data required for the main problem and scenario sub-problems. The planning model construction module is configured to take the grid-type energy storage planning scheme as the main problem decision object, maximize the recovery support capability as the optimization objective, and take the path availability, energy storage start-up capability and transient process comprehensive safety level verification under representative power outage scenarios as scenario sub-problems. It calculates and feeds back the black start success rate index and recovery support capability index of candidate energy storage-unit combinations. The decomposition and optimization solution module is configured to perform iterative solutions to determine the optimal grid-type energy storage planning scheme that meets the black-start reliability requirements and has the best recovery support capability.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention innovatively provides a method for constructing outage scenarios based on a candidate start-up path set. The candidate start-up path set is used to extract associated lines, and the probability of line conditional faults is estimated based on historical line fault records and outage conditions. A scenario generation model based on a conditional variational autoencoder is employed to learn the conditional joint distribution of the states of the associated lines, expanding the combinations of available line states. Through scenario reduction, a representative outage scenario set is formed, which can reduce the computational scale of subsequent planning problems while ensuring the coverage of black-start outage scenarios. This invention also innovatively provides a method for constructing planning evaluation indicators. By comprehensively judging the availability of candidate paths, the energy satisfaction relationship of grid-based energy storage, and the safety margin of transient processes under representative outage scenarios, a black-start success rate indicator for energy storage-generator combinations is constructed. Furthermore, a recovery support capability indicator is constructed based on the start-up time and the effective power generation capacity of the target unit after start-up. This achieves reasonable quantification of the start-up reliability and recovery support capability of grid-based energy storage planning schemes, providing an evaluation basis for black-start power source deployment, capacity determination, and unit matching.

[0018] This invention innovatively provides a method for solving grid-based energy storage planning schemes using a master-subproblem decomposition approach. The master problem focuses on the grid-based energy storage planning scheme as the decision-making object, with maximizing recovery support capability as the optimization objective. Constraints are set on construction resources, capacity boundaries, and a black-start success rate threshold. Subproblems are defined as path availability, energy storage startup capability, and comprehensive safety assessment of transient processes under representative power outage scenarios. The black-start success rate and recovery support capability of candidate energy storage-generator combinations are calculated and fed back. For combinations with unsatisfactory success rates, if the threshold can be met within the capacity limit, a capacity constraint is fed back; otherwise, the startup scheme is eliminated. The master problem updates the feasible region accordingly, ultimately determining the grid-based energy storage planning scheme that meets black-start reliability requirements and has optimal recovery support capability. This achieves efficient solution to large-scale planning problems across multiple scenarios and improves the recovery support capability of grid-based energy storage planning schemes while ensuring black-start success rate.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a schematic diagram of the set of the top K candidate start-up paths from a grid-type energy storage system to a thermal power unit to be started, according to one embodiment. Figure 2 This is a schematic diagram illustrating the generation and reduction of a power outage scenario in one embodiment. Figure 3 A flowchart illustrating the decomposition and solution of the main problem and scenario subproblems in a grid-type energy storage planning model for one embodiment; Figure 4 This is a schematic diagram of a grid-type energy storage black-start power supply planning system according to one embodiment. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0026] Example 1 A method for planning grid-type energy storage black-start power sources includes the following steps: Step S1 generates a grid-type energy storage system before the thermal power unit to be started. K Candidate startup path set: Based on candidate grid-type energy storage nodes, thermal power units to be started, and grid topology, using the previous... K The candidate path search method generates a set of candidate start-up paths for each set of grid-type energy storage nodes and thermal power units to be started, forming the object for subsequent scenario construction and scheme evaluation.

[0027] Step S2 constructs a power outage scenario based on the candidate start path set: Based on the candidate start path set obtained in step S1, the associated lines of the path are extracted, and the probability of line conditional faults is estimated based on historical line fault records and power outage conditions; a scenario generation model based on conditional variational autoencoder is used to learn the conditional joint distribution of the state of the associated lines, expand the combination of available line states, and form a representative power outage scenario set through scenario reduction.

[0028] Step S3 constructs planning evaluation indicators based on black start success rate and recovery support capability: comprehensively judges the availability of candidate paths, the power energy satisfaction relationship of grid-type energy storage, and the safety margin of transient processes under representative power outage scenarios, and constructs a black start success rate indicator for energy storage-unit combination; further constructs a recovery support capability indicator based on start-up time, effective power generation capacity after target unit start-up, and distribution dispersion coefficient.

[0029] Step S4 forms the input of the grid-type energy storage black start power planning model: Based on the candidate paths, representative power outage scenarios and planning evaluation indicators obtained from steps S1 to S3, organize the candidate objects, scenario information, capacity boundaries and planning constraints to form the input data required for the main problem and scenario sub-problems, and clarify the planning decision scope, scenario verification conditions and optimization boundaries.

[0030] Step S5: Construction and Solution of the Grid-Based Energy Storage Planning Model Based on Decomposition and Optimization: The grid-based energy storage planning scheme is solved using a master-subproblem decomposition method. The grid-based energy storage planning scheme is the main decision-making object, with maximizing recovery support capacity as the optimization objective. Construction resources, capacity boundaries, and black-start success rate threshold constraints are set. Path availability, energy storage startup capacity, and comprehensive safety verification under representative power outage scenarios are considered as scenario subproblems. The black-start success rate and recovery support capacity of candidate energy storage-unit combinations are calculated and fed back. For combinations with unsatisfactory success rates, if the threshold can be met within the capacity limit, a capacity constraint is fed back; otherwise, the startup scheme is eliminated. The main problem updates the feasible region accordingly, ultimately determining the grid-based energy storage planning scheme that meets the black-start reliability requirements and has the optimal recovery support capacity.

[0031] In step S1, to avoid the difficulty of solving the problem caused by enumerating all recovery paths across the entire network during a black boot, the present invention adopts the following approach: K The idea of ​​generating candidate recovery paths using the shortest path method involves generating a limited number of candidate startup paths for each group of candidate grid-type energy storage nodes and the thermal power units to be started, such as... Figure 1 As shown.

[0032] For candidate grid-type energy storage nodes i And the thermal power unit g to be started, adopting the front K The candidate path search method yields a set of candidate startup paths from i to g: (1) in, This indicates the distance from the grid-type energy storage node i to the thermal power unit g to be started. K A set of candidate startup paths, Indicates the first K Several candidate startup paths. (Previous) KPaths can be searched based on path priority, such as the number of line segments and the length of line operation.

[0033] Therefore, the alternative solutions consist of grid-type energy storage nodes, thermal power units to be started, and candidate start-up paths. Whether a path can be used in a specific power outage scenario is determined jointly by the power outage scenario constructed in step S2 and the planning evaluation indicators formed in step S3.

[0034] In step S2, the previous step obtained in step S1 is described. K After establishing the candidate startup path set, this step constructs a power outage scenario based on the set of lines involved in the candidate paths. For the same set of network-type energy storage nodes... i And the thermal power unit g to be started, first extract its front K All routes included in the candidate routes form a set of associated routes: (2) in, Lp For path p The included set of lines. Subsequent power outage scenario construction will only revolve around... The availability status of the medium-voltage line is expanded; the risk of transformer commissioning and the impact of auxiliary equipment starting are not treated as line fault conditions, but are evaluated in step S3 through transient safety level.

[0035] The probability of a line fault is not directly given by a sample of large-scale power outages across the entire network, but is estimated jointly by historical line faults, trips, defects, maintenance, and the operating environment corresponding to the time of the outage. Let... c τ Given the conditional information at the time of power outage τ, including weather risk level, line load rate, corridor environment, equipment status, and historical fault statistics, the conditional fault probability of line l is expressed as: (3) in, f l (·) indicates a line l A conditional failure probability estimation model is proposed. This model can be obtained from historical failure frequency statistics, weather risk correction models, or data-driven probabilistic models. Its meaning is: when a power outage occurs at a time τ with specific weather and operating conditions, the line... l The possibility that it is not available in the set of associated lines of the candidate startup path.

[0036] After obtaining the probability of line condition failure, a sample of the line's available state is constructed. This sample is then used to analyze the lines in the path-associated line set. l Define the available status of the line. ξ l,ω : ξ l,ω = 1 indicates a line lIn the scene ω Available now. ξ l,ω = 0 indicates that line l is faulty in scenario ω. A vector consisting of the states of all path-related lines. ξ ω express A combination of available line states.

[0037] To address the issues of insufficient real-world large-scale power outage samples and inadequate coverage of related fault combinations, this embodiment employs a conditional variational autoencoder as the scenario generation model. Specifically, it incorporates the conditional information at the time of the power outage... c τ The conditional failure probabilities of the associated routes together form the conditional vector. h i,g,τ This allows the model to learn the conditional joint distribution of line states under given risk conditions. The model generates not the failure probability of a single line, but rather a combination of available states of multiple lines associated with candidate start paths.

[0038] (4) in, G φ For the decoder of conditional variational autoencoder, z is Random latent variables, h i,g,τ Information on the time and conditions of power outage c τ The condition vector is composed of the path-related line conditional failure probability. This generates a combination of available line states. The model expands the line state samples with historical faults, weather risks, equipment status, and load levels through latent variable sampling, supplementing related fault combinations that may occur in the same transmission channel, the same area, or under similar weather or operating conditions.

[0039] The training objective of a conditional variational autoencoder consists of a state reconstruction term and a latent variable distribution constraint term, which can be expressed as: (5) In the formula, the first term is the line state reconstruction loss, which measures the consistency between the line available state combination generated by the model and the training samples, enabling the model to reproduce the line state correlation in historical fault samples and risk correction samples; the second term is the Kullback-Leibler divergence, which is used to constrain the distribution of latent variables obtained by the encoder. q ψ ( z | ξ ω , h i,g,τ Approximate prior distribution p (z This allows the model to generate new combinations of line states through random sampling; β is The tradeoff coefficient is used to adjust the relationship between sample reconstruction accuracy and scene diversity.

[0040] For the candidate paths obtained in step S1 p Its available state in scene ω is determined by the available states of the lines contained in the path, and can be compactly represented as follows: A p,ω = ∏ l∈p ξ l , ω When any line in the path fails, the product is zero, and the path cannot be used to start the target thermal power unit in this scenario; when all lines in the path are available, the path remains available in this scenario.

[0041] After generative expansion, the number of scenarios is usually large. To reduce the scale of subsequent evaluation and optimization, this embodiment reduces the expanded scenarios. Scenario reduction uses the availability status of candidate paths as the main feature, retaining typical scenarios through clustering or distance-based representative screening methods, and incorporating the probability of deleted scenarios into the nearest retained scenario. After reduction, a representative power outage scenario set Ω corresponding to each energy storage-generator combination is obtained. i,g and scene probability π ω The scene generation and reduction are as follows: Figure 2 As shown.

[0042] In step S3, this step is used to form a planning and evaluation index system for energy storage-generator combinations. For each energy storage-generator combination, the black start success rate characterizes the probability that grid-connected energy storage will successfully start the target thermal power unit via at least one candidate start path in a representative power outage scenario; the recovery support capability characterizes the speed and scale at which the target thermal power unit forms effective power generation capacity after being successfully started.

[0043] The success rate of black start is determined by three factors: path availability, energy storage startup capability, and transient process risk. Path availability is derived from representative power outage scenarios in step S2; energy storage startup capability is used to determine whether the available energy and power at the time of power outage meet the startup requirements of the target thermal power unit; and transient process risk reflects the safety margin of black start operations such as line no-load charging, transformer no-load connection, and startup of auxiliary equipment of the target unit.

[0044] First, the available energy for energy storage during a power outage is defined by the battery's state of charge (SOC) ratio and the planned energy capacity: (6) in, The planned energy capacity of grid-type energy storage i The time of the power outage τ The corresponding energy storage SOC ratio, This is the initial energy available for black start at this moment. This energy is used to verify the target thermal power unit's start-up energy and path charging energy.

[0045] For grid-type energy storage i Target thermal power unit g and candidate start-up paths p Scene ω The successful black-start status of a single path is defined as follows: (7) in, H (·) is a discriminant function; it takes the value 1 if the condition inside the parentheses is met, and 0 otherwise. A p,ω This indicates that path p is in the scene. ω The available status is as follows; and The target thermal power units g The starting energy and starting power requirements; and Paths p The charging energy and charging power requirements; For grid-type energy storage i The planned power capacity; To assess the overall safety level of the transient process, M lim The threshold for transient safety is given. Equation (7) means that a path is considered to have started successfully only when the path is available, the energy storage capacity is sufficient, the energy storage power is sufficient, and the transient risk is acceptable.

[0046] Because step S1 retains the previous steps for each energy storage-generator combination. K There are several candidate start-up paths. If at least one candidate path can successfully start the target thermal power unit, the energy storage-unit combination is considered to have the conditions for a successful black start in that scenario. (8) In representative scene set Ω i,g The above is weighted by scenario probability to obtain grid-type energy storage. i Start the target thermal power unit g Black boot success rate: (9) in, π ω for Representative scenes ω The probability of.

[0047] The comprehensive safety level of the transient process is used to transform transient risks in black start operations that are difficult to directly embed into the planning model into evaluation quantities that can be quickly invoked. Referring to the construction ideas of line weight, transformer weight, and auxiliary equipment weight in the unit recovery success rate during the black start phase, this embodiment establishes the safety level of line no-load charging, transformer commissioning, and auxiliary equipment startup support, respectively, and uses a product form to represent the serial relationship of the transient operation chain: (10) In the formula, This indicates the safety level of no-load charging along path p. This indicates the safety level of the transformer being put into operation under no-load conditions in path p. Indicates grid-type energy storage i Start the target unit via path p g The safety level of auxiliary equipment support. If any one of these three factors is too low, the overall transient safety level will be reduced.

[0048] The safety level of line no-load charging is used to characterize the possibility of power frequency overvoltage, self-excitation risk, or grid-type energy storage current impact during candidate path no-load charging. The risk of path no-load charging can be characterized by the path equivalent capacitive reactance. The larger the path equivalent capacitive reactance, the smaller the impact of no-load line charging on the system's reactive power balance and voltage control.

[0049] (11) (12) in, X C,l The equivalent capacitive reactance of line l, For path p The equivalent empty filler impedance, L min This is the minimum threshold set based on the charging safety criteria for unloaded lines. L max Among the candidate paths The maximum value. A value of 0 indicates that the path's no-charge stage does not meet safety requirements; the larger the value, the lower the risk of overvoltage and current surges during no-charge.

[0050] The safety level of transformer energization is used to characterize the impact of inrush current on grid-type energy storage voltage sources when an unloaded transformer is energized in a path. The voltage waveform distortion when an unloaded transformer is energized is related to saturation flux, residual flux, and steady-state flux. For the first transformer in path p... r A transformer, defined as: (13) (14) in, , and Transformers r The saturation flux, residual flux, and steady-state flux. When there are multiple transformers in the path, the input weight of the most unfavorable transformer is used. Characterize the transformer commissioning risk of this route; T max Among the candidate paths The maximum value. The larger the value, the lower the risk of inrush current and voltage distortion when the unloaded transformer is put into operation.

[0051] The safety level of auxiliary equipment startup support is used to characterize whether grid-type energy storage can maintain voltage and frequency within allowable ranges when large auxiliary equipment of a target thermal power unit starts up. Referring to the auxiliary equipment weighting concept, under the condition of satisfying transient voltage and frequency safety constraints, the safety level of grid-type energy storage i via the path is calculated. p Maximum auxiliary capacity allowed to be deployed when supporting target unit g P max i, g,p And compare it with the maximum auxiliary capacity p of the target unit: (15) (16) in, To support the weight of auxiliary machines, G max gaux in the candidate scheme i,g,p The maximum value of gaux. i,g,p A value less than 1 indicates that the auxiliary equipment of the target unit cannot be safely started under voltage and frequency constraints; a value not less than 1 indicates that the auxiliary equipment has the capability to start. The larger the value, the smaller the voltage drop and frequency deviation when the auxiliary machine starts up.

[0052] The three transient safety levels mentioned above can be obtained in advance through offline electromechanical transient simulation, electromagnetic transient simulation, or engineering criteria. The planning model does not directly solve the transient differential equations, but instead uses the transient safety level evaluation results to complete the rapid screening, thus reflecting the difference between black-start power supply planning and conventional energy storage economic planning.

[0053] The recovery support capacity index is used to compare the support effects of different deployment schemes on the recovery of subsequent units and parallel recovery in multiple regions after the target thermal power unit has been started. This index comprehensively considers the start-up time of the target unit, the effective power generation capacity formed by the target unit within the evaluation window, and the spatial support effect of grid-based energy storage deployment on the recovery area coverage. First, the average start-up time is calculated only for the successful black start scenario: (17) in, The comprehensive startup time required for the target thermal power unit g to reach the startup conditions under scenario ω is defined by grid-type energy storage i, including energy storage pressure building, path charging, switching operations, and the target unit's power receiving preparation time. The startup time corresponding to the shortest path in the scenario where the startup is successful.

[0054] The startup time efficiency coefficient is further constructed from the average startup time: (18) in, This represents the allowable startup time window for the target thermal power unit. The larger this coefficient is, the earlier the target thermal power unit reaches the startup conditions; when the average startup time exceeds the allowable time window, this coefficient is set to 0.

[0055] The effective power generation capacity of the target unit after startup can be represented by the actual power generation that the target unit can generate within the evaluation time window. Referring to the concept in recovery capacity evaluation that the power generation capacity generated during the recovery period measures the system recovery speed, the following definition is used: (19) in, The time required for a target thermal power unit to gain startup power and begin to generate effective output. T hor To restore the support capacity assessment window, P g ( t The output curve of the target unit after startup is shown. This integral represents the effective power generation capacity that the target unit can provide for subsequent recovery within the evaluation window.

[0056] To characterize the supporting role of grid-based energy storage deployment in parallel recovery across multiple regions, a deployment dispersion coefficient is further defined: (20) in, For grid-type energy storage i Minimum electrical distance between the power supply and other configured black-start power supplies. d ref This serves as a reference electrical distance for characterizing the reasonable dispersion of black-start power sources. (Regarding grid-type energy storage...) i If it is too close to other black start power supplies, A smaller size indicates limited support for the newly restored area; if it maintains sufficient electrical distance from other black-start power sources, then... A value close to 1 indicates that it is more conducive to forming a decentralized and parallel recovery support capability.

[0057] Based on both startup time and effective power generation capacity, the recovery support capacity index is defined as follows: (twenty one) in, C ref As a normalized baseline value, the maximum effective power generation capacity of the candidate target units within the evaluation time window can be taken. The larger this index is, the stronger the grid-based energy storage capability. i Starting the target thermal power unit g is more conducive to the recovery of subsequent units.

[0058] In step S4, based on steps S1 to S3, the input data for the grid-connected energy storage black-start power planning model is formed. Step S1 provides the set of the top K candidate start-up paths between candidate grid-connected energy storage nodes and the thermal power units to be started; step S2 provides the set of representative outage scenarios and their probabilities related to the candidate paths; step S3 provides the calculation rules for black-start success rate and recovery support capacity. Based on the above results, this step unifies the paths, scenarios, capacity boundaries, and planning constraints into a planning problem input that can be called upon by the subsequent optimization model.

[0059] Specifically, for each group of candidate grid-type energy storage nodes i and thermal power units to be started g The inputs to the planning problem include: the set of the top K candidate start-up paths, the set of representative power outage scenarios and their probabilities, the calculation relationship of the available state of the candidate paths under scenario ω, the start-up power demand and start-up energy demand of the target thermal power unit, the charging power demand and charging energy demand of the candidate paths, the comprehensive safety level of the transient process, the configurable range of energy storage power capacity and energy capacity, and planning constraint parameters such as the black start success rate threshold, the upper limit of the number of sites, and the upper limit of construction resources.

[0060] Through this step, the grid-based energy storage planning problem is defined as follows: given candidate energy storage nodes, target thermal power units, candidate start-up paths, and representative power outage scenarios, determine the configuration location, power capacity, energy capacity, and matching relationship with target thermal power units for grid-based energy storage. This input dataset does not directly provide the final planning scheme, but rather serves as the basis for decomposing and solving the main problem and scenario sub-problems in step S5.

[0061] In step S5, based on the planning problem input formed in step S4, a grid-type energy storage deployment and capacity optimization model under the success rate threshold constraint is established. Since each candidate energy storage-unit combination corresponds to multiple start-up paths and multiple representative power outage scenarios, directly merging all scenarios and solutions would lead to a significant increase in model size. Therefore, this embodiment adopts a main problem-scenario sub-problem decomposition structure.

[0062] The primary problem is responsible for overall planning and decision-making, including selecting which grid-type energy storage nodes to choose, configuring their power and energy capacity, and determining which grid-type energy storage will serve which target thermal power unit. The primary problem aims to maximize recovery support capacity and considers constraints on construction resources, the number of sites, and the black start success rate threshold. (twenty two) (twenty three) (twenty four) , (25) (26) In the formula, z i express Should a grid-type energy storage be configured at node i? x i,g Indicate whether to choose grid-type energy storage i Start the target thermal power unit g, 、 and These are the fixed infrastructure resources, power capacity resources, and energy capacity resource coefficients, respectively. N max and C max These represent the maximum number of sites and the maximum construction resources, respectively. Equation (23) indicates that only when the black start success rate reaches the threshold... P min Only energy storage-generator combinations are allowed to be selected.

[0063] The scenario subproblem is responsible for verifying the candidate solutions given by the main problem. For each selected energy storage-generator combination, the subproblem checks the availability of candidate paths, the relationship between energy storage power and energy satisfaction, and the comprehensive safety level of transient processes in a representative power outage scenario set, and updates the black start success rate of the combination using equations (7) to (9); at the same time, it updates the recovery support capability using equations (17) to (21). When the black start success rate of a selected energy storage-generator combination is lower than the preset threshold, the scenario subproblem further determines whether it can reach the success rate threshold through capacity increase. If there is a minimum power capacity and a minimum energy capacity required to meet the success rate threshold within the upper limit of capacity, the lower limit constraint of capacity is fed back to the main problem: (27) in, and These respectively represent the grid-type energy storage iThe minimum power capacity and minimum energy capacity required for the black start success rate of the target thermal power unit g to reach the threshold. If the energy storage-unit combination is limited by the availability of candidate paths or the overall safety of transient processes, or if the success rate threshold cannot be reached within the capacity limit, then feedback should be sent to the main problem to exclude this start-up scheme. x i,g =0.

[0064] Therefore, as Figure 3 As shown, the decomposition and solution process includes: First, the main problem, based on the planning problem input formed in step S4, provides the current grid-type energy storage planning scheme; second, the scenario sub-problem verifies the selected energy storage-unit combination under representative power outage scenarios, calculating its black start success rate and recovery support capability; third, for combinations whose success rate does not meet the standard but can be corrected by capacity increase, feedback is provided on the lower limit of capacity constraint, and for combinations that still cannot meet the standard within the upper limit of capacity, feedback is provided on the exclusion scheme; finally, the main problem is re-optimized under the new capacity cut or exclusion cut constraint, and the final planning scheme is determined among all feasible combinations that meet the black start success rate threshold, with the goal of maximizing the recovery support capability.

[0065] Using the above methods, the final output includes the location, power capacity, energy capacity, target thermal power unit, recommended start-up path, black start success rate, and recovery support capability indicators for grid-type energy storage configuration.

[0066] Example 2 like Figure 4 As shown, this embodiment provides a grid-type energy storage black-start power planning system, including a candidate path generation module, a power outage scenario generation and reduction module, a planning evaluation index construction module, a planning model input module, and a planning model construction, decomposition, optimization, and solution module.

[0067] forward K Candidate Start-up Path Set Generation Module: Configured to generate the set of candidate grid-type energy storage nodes, thermal power units to be started, and grid topology, using the previous... K The candidate path search method generates a set of candidate start-up paths for each set of grid-type energy storage nodes and thermal power units to be started, forming the object for subsequent scenario construction and scheme evaluation.

[0068] The power outage scenario construction module based on the candidate start path set is configured to extract the associated lines based on the candidate start path set obtained in step S1, estimate the line conditional fault probability based on historical line fault records and power outage conditions, learn the conditional joint distribution of the state of the associated lines using a scenario generation model based on conditional variational autoencoder, expand the combination of available line states, and form a representative power outage scenario set through scenario reduction.

[0069] The planning and evaluation index construction module based on black start success rate and recovery support capability is configured to comprehensively judge the availability of candidate paths, the energy satisfaction relationship of grid-type energy storage power and the safety margin of transient processes under representative power outage scenarios, and construct the black start success rate index of energy storage-unit combination; further, it constructs the recovery support capability index based on the start-up time and the effective power generation capability of the target unit after start-up.

[0070] The input module of the grid-type energy storage black start power planning model is configured to organize candidate objects, scenario information, capacity boundaries and planning constraints based on the candidate paths, representative power outage scenarios and planning evaluation indicators obtained in steps S1 to S3, form the input data required for the main problem and scenario sub-problems, and clarify the planning decision scope, scenario verification conditions and optimization boundaries.

[0071] The module for constructing and solving the grid-based energy storage planning model is configured to solve grid-based energy storage planning schemes using a master-subproblem decomposition method. The main problem is the grid-based energy storage planning scheme, with the optimization objective of maximizing recovery support capacity. Constraints are set on construction resources, capacity boundaries, and a black-start success rate threshold. Subproblems include path availability, energy storage startup capacity, and comprehensive safety verification of transient processes under representative power outage scenarios. The module calculates and feeds back the black-start success rate and recovery support capacity of candidate energy storage-unit combinations. For combinations with unsatisfactory success rates, if the threshold can be met within the capacity limit, a capacity constraint is fed back; otherwise, the startup scheme is eliminated. The main problem updates the feasible region accordingly, ultimately determining the grid-based energy storage planning scheme that meets black-start reliability requirements and has the optimal recovery support capacity.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A planning method for grid-type energy storage black-start power sources, characterized in that, Includes the following steps: Based on candidate grid-type energy storage nodes, thermal power units to be started and grid topology, a set of candidate start-up paths is generated for each set of grid-type energy storage nodes and thermal power units to be started. Based on the candidate start path set, the associated lines are extracted, the line condition failure probability is estimated based on historical line fault records and power outage conditions, the condition joint distribution of the state of the associated lines is learned using the scenario generation model, the combination of available line states is expanded, and a representative power outage scenario set is formed through scenario reduction. The availability of candidate paths, the power-energy satisfaction relationship of grid-based energy storage, and the safety margin of transient processes are comprehensively judged in representative power outage scenarios. A black start success rate index for energy storage-generator combination is constructed, and a recovery support capability index is constructed based on the start-up time and the effective power generation capacity of the target unit after start-up. Based on candidate paths, representative power outage scenarios, and planning evaluation indicators, candidate objects, scenario information, capacity boundaries, and planning constraints are organized to form the input data required for the main problem and scenario sub-problems. The main problem of grid-based energy storage planning is to maximize recovery support capability. The sub-problems are path availability, energy storage start-up capability and comprehensive safety level of transient process under representative power outage scenarios. The black start success rate index and recovery support capability index of candidate energy storage-unit combination are calculated and fed back. The solution is iteratively to determine the grid-based energy storage planning scheme that meets the black start reliability requirements and has the best recovery support capability. The process of comprehensively judging the availability of candidate paths, the energy satisfaction relationship of grid-based energy storage, and the safety margin of transient processes under representative power outage scenarios includes: For each energy storage-generator combination, the black start success rate characterizes the probability that grid-based energy storage will successfully start the target thermal power unit via at least one candidate start path under representative power outage scenarios; the recovery support capability characterizes the speed and scale of the target thermal power unit forming effective power generation capacity after successful start-up. Among them, the black start success rate index is jointly determined by three conditions: path availability, energy storage start-up capability, and transient process risk. Path availability is obtained based on representative power outage scenarios; energy storage start-up capability is used to determine whether the available energy and power at the time of power outage meet the start-up requirements of the target thermal power unit; transient process risk is used to reflect the safety margin of each black start operation link, including line no-load charging, transformer no-load connection, and target unit auxiliary machine start-up. The process of constructing the recovery support capability index includes calculating the average start-up time for a successful black start scenario, constructing a start-up time efficiency coefficient from the average start-up time, using the actual power generation that the target unit can generate within the evaluation time window to represent the effective power generation capacity of the target unit after start-up, using the distribution dispersion coefficient to characterize the supporting role of grid-type energy storage deployment in parallel recovery in multiple regions, and calculating the recovery support capability index based on the start-up time efficiency coefficient, effective power generation capacity, and distribution dispersion coefficient. The black boot success rate metric is: ； in, π ω As a representative scene ω The probability, As a condition for successful black start, if there is a candidate path that can successfully start the target thermal power unit in the corresponding scenario, the energy storage-unit combination is considered to have the conditions for successful black start in that scenario. The indicators for restoring support capacity are: ； in, C ref As a normalized baseline value, This is the startup time efficiency coefficient. For effective power generation capacity, The distribution dispersion coefficient represents the strength of the recovery support capacity index, indicating a more robust grid-type energy storage system. i Starting the target thermal power unit g is more conducive to the recovery of subsequent units; The process of iteratively solving to determine the optimal grid-based energy storage planning scheme that meets the black-start reliability requirements and has the best recovery support capability includes: the main problem provides the current grid-based energy storage planning scheme based on the formed planning problem input; the scenario subproblem verifies the selected energy storage-unit combination under representative power outage scenarios, and calculates its black-start success rate and recovery support capability; for combinations whose success rate does not meet the standard but can be corrected by capacity increase, feedback is provided on the lower limit of capacity constraint, and for combinations that still cannot meet the standard within the upper limit of capacity, feedback is provided on the exclusion scheme; the main problem is re-optimized under the new capacity cut or exclusion cut constraint, and the final planning scheme is determined from all feasible combinations that meet the black-start success rate threshold with the goal of maximizing recovery support capability.

2. The grid-type energy storage black-start power supply planning method as described in claim 1, characterized in that, Based on candidate grid-type energy storage nodes, thermal power units to be started, and grid topology, the process of generating a set of candidate start-up paths for each set of grid-type energy storage nodes and thermal power units to be started includes: for candidate grid-type energy storage nodes i and thermal power units to be started g Before K Candidate path search method obtains from i to g The set of candidate boot paths, front K The path is searched according to the path priority based on the number of line segments and the length of the line operation.

3. The grid-type energy storage black-start power supply planning method as described in claim 1, characterized in that, Based on the candidate startup path set, the process of extracting path-related lines and estimating the line conditional fault probability based on historical line fault records and outage time conditions includes: for the same group of network-type energy storage nodes, the following steps are taken: i and thermal power units to be started g First extract its front K All routes included in the candidate routes are used to form a set of route-related routes, based on the time of the power outage. τ The conditional information includes several factors such as weather risk level, line load rate, channel environment, equipment status, and historical fault statistics. The conditional fault probability is generated for each line using a conditional fault probability estimation model.

4. The grid-type energy storage black-start power supply planning method as described in claim 1, characterized in that, The process of using a scenario generation model to learn the conditional joint distribution of the status of path-related lines, expanding the combination of available line statuses, and forming a representative set of power outage scenarios through scenario reduction includes: combining the conditional information at the time of power outage with the conditional fault probabilities of the path-related lines to form a conditional vector; using a conditional variational autoencoder as the scenario generation model to learn the conditional joint distribution of line status under given risk conditions; the scenario generation model expands the line status samples consistent with historical faults, weather risks, equipment status, and load levels through latent variable sampling to supplement the relevant fault combinations that may occur under the same transmission channel, the same area, or similar operating conditions; The training objective of the conditional variational autoencoder consists of a line state reconstruction loss term and a latent variable distribution constraint term. The line state reconstruction loss term is used to measure the consistency between the line state combinations generated by the model and the training samples. The latent variable distribution constraint term is used to constrain the latent variable distribution obtained by the encoder to be close to the prior distribution, so that the model can generate new line state combinations through random sampling. Using the availability status of candidate paths as the main feature, typical scenarios are retained through clustering or distance-based representative screening methods, and the probability of deleted scenarios is incorporated into the nearest retained scenario.

5. The grid-type energy storage black-start power supply planning method as described in claim 1, characterized in that, Based on candidate paths, representative power outage scenarios, and planning evaluation indicators, the process of organizing candidate objects, scenario information, capacity boundaries, and planning constraints to form the input data required for the main problem and scenario sub-problems includes: for each group of candidate grid-type energy storage nodes ... i and thermal power units to be started g The input to the planning problem includes the set of the top K candidate start-up paths, the set of representative power outage scenarios and their probabilities, the calculation relationship of the available state of the candidate paths under scenario ω, the start-up power demand and start-up energy demand of the target thermal power unit, the charging power demand and charging energy demand of the candidate paths, the comprehensive safety level of the transient process, the configurable range of energy storage power capacity and energy capacity, and planning constraint parameters including the black start success rate threshold, the upper limit of the number of sites, and the upper limit of construction resources.

6. The grid-type energy storage black-start power supply planning method as described in claim 1, characterized in that, The main problem aims to maximize recovery support capability, considering constraints on construction resources, the number of sites, and the black start success rate threshold. The scenario sub-problems are used to verify the candidate solutions given by the main problem. For each selected energy storage-generator combination, the sub-problem checks the availability of candidate paths, the relationship between energy storage power and energy satisfaction, and the comprehensive safety level of transient processes in a representative set of power outage scenarios, and updates the black start success rate and recovery support capability of the combination. When the black start success rate of a selected energy storage-generator combination is lower than the preset threshold, the scenario sub-problem further determines whether it can reach the success rate threshold through capacity increase. If there is a minimum power capacity and minimum energy capacity required to meet the success rate threshold within the upper limit of capacity, the lower limit constraints of power capacity and energy capacity are fed back to the main problem.

7. A grid-type energy storage black-start power supply planning system, using the method described in any one of claims 1-6, characterized in that, include: The path generation module is configured to generate a set of candidate startup paths for each set of grid-type energy storage nodes and thermal power units to be started, based on candidate grid-type energy storage nodes, thermal power units to be started and grid topology. The power outage scenario generation and reduction module is configured to extract the associated lines based on the candidate start path set, estimate the line conditional fault probability based on historical line fault records and power outage time conditions, learn the conditional joint distribution of the state of the associated lines using the scenario generation model, expand the combination of available line states, and form a representative power outage scenario set through scenario reduction. The planning and evaluation index construction module is configured to comprehensively judge the availability of candidate paths, the power energy satisfaction relationship of grid-type energy storage, and the safety margin of transient processes under representative power outage scenarios, construct the black start success rate index of energy storage-unit combination, and construct the recovery support capability index based on the start-up time and the effective power generation capacity of the target unit after start-up. The planning model input module is configured to organize candidate objects, scenario information, capacity boundaries and planning constraints based on candidate paths, representative power outage scenarios and planning evaluation indicators, and form the input data required for the main problem and scenario sub-problems. The planning model construction module is configured to take the grid-type energy storage planning scheme as the main problem decision object, maximize the recovery support capability as the optimization objective, and take the path availability, energy storage start-up capability and transient process comprehensive safety level verification under representative power outage scenarios as scenario sub-problems. It calculates and feeds back the black start success rate index and recovery support capability index of candidate energy storage-unit combinations. The decomposition and optimization solution module is configured to perform iterative solutions to determine the optimal grid-type energy storage planning scheme that meets the black-start reliability requirements and has the best recovery support capability.

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