Optimization Method and Equipment for Black-Start Recovery Path of New Energy Power System with Structure Network-Type Energy Storage

Abstract

The present invention provides an optimization method and device for the black-start recovery path of a new energy power system with a network-forming energy storage. Considering the volatility of the output of new energy units, the method optimizes the black-start modeling of the system after connecting the network-forming energy storage to the bus of the new energy units. The method includes: obtaining the predicted data of the output of new energy units in the new energy power system with a network-forming energy storage, and constructing the black-start network-forming resource constraint conditions and operation constraint conditions considering the network-forming energy storage system; based on the network-forming resource constraint conditions and operation constraint conditions, taking the minimization of user power outage losses as the objective function, constructing an optimization model for the black-start of the new energy power system with a network-forming energy storage; solving the optimization model for the black-start of the new energy power system with the included network-forming energy storage to obtain the black-start recovery path of the power system. The present invention simultaneously considers the black-start characteristics of the network-forming energy storage and the fluctuation characteristics of new energy power generation, and finally realizes the optimization of the black-start recovery path of the power system.

Description

Technical Field

[0001] The present invention belongs to the field of optimization modeling for black start of new - energy power systems, and particularly relates to an optimization method and device for the black - start recovery path of a new - energy power system with grid - forming energy storage. Background Technique

[0002] While the acceleration of the clean and low - carbon energy transition promotes the leapfrog development of clean energies such as wind power and photovoltaic power, it also greatly increases the demand of the power grid for flexible regulation resources. As an emerging power technology, energy storage can decouple power generation and power consumption in time and space, and alleviate the mismatch between new - energy power generation and load. With the increase in the penetration rate of new energy and the decrease in the proportion of traditional units, the new - type new - energy power system will face problems such as internal oscillation and transient over - voltage, which will further lead to the weakening of the system voltage and frequency support capabilities, the decrease of the stability margin, and the increase of the safe - operation risk. Due to its voltage - source characteristics, grid - forming energy storage has the ability to establish frequency and voltage and instantaneous frequency and voltage regulation, and has the potential for black start. It can be used as a black - start resource to participate in the black - start process. It can effectively solve the shortage of local black - start power - source resources and reduce the dependence on traditional energy, thereby optimizing the national energy allocation and improving the economic benefits of energy utilization. Adding new devices such as grid - forming energy storage near new energy to enhance the frequency regulation and voltage control capabilities similar to synchronous generators can comprehensively improve the regulation ability and flexibility of the power system. In summary, it is very necessary to carry out research on the black - start optimization modeling method of a new - energy power system with grid - forming energy storage. Summary of the Invention

[0003] The present invention proposes an optimization method and device for the black - start recovery path of a new - energy power system with grid - forming energy storage. Different from the existing new - energy power - system black - start optimization modeling methods that only consider traditional energy storage, this method takes into account the volatility of the output of new - energy units, and after connecting the grid - forming energy storage to the bus of the new - energy units, conducts black - start optimization modeling on the system.

[0004] The technical solution adopted by the present invention is as follows:

[0005] An optimization method for the black - start recovery path of a new - energy power system with grid - forming energy storage, comprising the following steps:

[0006] S1. Obtain the predicted data of the output of new - energy units in the new - energy power system with grid - forming energy storage. This predicted data is based on the average value of historical data of new - energy units with the same capacity in typical scenarios, and construct the black - start grid - forming resource constraint conditions and operation constraint conditions considering the grid - forming energy - storage system;

[0007] S2. Based on the grid - forming resource constraint conditions and operation constraint conditions, with the minimization of the user's power - outage loss as the objective function, construct a black - start optimization model for the new - energy power system with grid - forming energy storage;

[0008] S3. Solve the black - start optimization model of the new - energy power system with a network - forming energy storage system to obtain the black - start recovery path of the power system.

[0009] Furthermore, the network - forming resource constraint conditions include: the black - start device status constraint, the power balance constraint of the network - forming energy storage device, the remaining power constraint of the network - forming energy storage device, and the remaining power percentage constraint of the network - forming energy storage device; the operation constraint conditions include the power balance constraint.

[0010] Furthermore, the black - start device status constraint is that at least one synchronous generator set or network - forming energy storage device with black - start capacity in each black - start area is in the working state; specifically:

[0011] ,

[0012] where, is the status of the j - th synchronous generator set with black - start capacity in the black - start area at time t, is the charging status of the k - th network - forming energy storage device in the black - start area at time t, is the discharging status of the k - th network - forming energy storage device at time t; is the total number of synchronous generator sets with black - start capacity in the black - start area, is the total number of network - forming energy storage devices in the black - start area.

[0013] Furthermore, the power constraint of the network - forming energy storage device is that within any time interval, the change in the power of the network - forming energy storage device is equal to the sum of charging and discharging within this time interval; specifically:

[0014] ,

[0015] where, represents the power value of the k - th network - forming energy storage device at time t; represents the power value of the k - th network - forming energy storage device at time t - 1; , respectively represent the charging efficiency and discharging efficiency of the k - th network - forming energy storage device; , respectively represent the charging power and discharging power of the k - th network - forming energy storage device at time t; is the time interval for charging and discharging of the network - forming energy storage device, is the total time length of the black - start optimization process.

[0016] Furthermore, the remaining power constraint of the network - forming energy storage device is that the SOC (State of Charge, SOC) of the network - forming energy storage device during the black - start process is within the safe range; specifically:

[0017] ,

[0018] ,

[0019] wherein, represents the maximum power of the k-th network-forming energy storage device, and respectively represent the minimum and maximum percentages of the remaining power of the k-th network-forming energy storage device at time t.

[0020] Furthermore, the constraint on the percentage of the remaining power of the network-forming energy storage device is specifically:

[0021] ,

[0022] ,

[0023] ,

[0024] wherein, and respectively represent the minimum and maximum percentages of the remaining power of the network-forming energy storage device in the s-th stage; represents the state of the i-th load at time t, represents the state of the j-th non-black start unit at time t; is the number of loads, is the number of non-black start units; represents the flag bit for distinguishing the stage where the network-forming energy storage device is located.

[0025] Furthermore, the power balance constraint is: the actual output power of the unit minus the starting power consumed by itself plus the charging and discharging power of the network-forming energy storage is equal to the load power; specifically:

[0026] ,

[0027] wherein, is the actual output power of the j-th unit at time t, is the starting power of the j-th unit at time t; is the power of the i-th load at time t; is the total time length of the black start optimization process; is the total number of units in the new energy power system, is the total number of loads in the new energy power system.

[0028] Furthermore, the objective function of the black start optimization model of the new energy power system with network-forming energy storage is:

[0029] ,

[0030] Among them, z is the user's power outage loss value, is the maximum power of the th load, is the unit power outage loss of the th load.

[0031] An optimization system for the black-start recovery path of a new energy power system with a network-forming energy storage includes:

[0032] A data processing module: used to obtain the predicted data of the output of new energy units in a new energy power system with a network-forming energy storage, and construct the black-start network-forming resource constraints and operation constraints of the network-forming energy storage system;

[0033] A model construction module: used to construct a black-start optimization model of a new energy power system with a network-forming energy storage based on the network-forming resource constraints and operation constraints, with the goal of minimizing the user's power outage loss;

[0034] A model solving module: used to solve the black-start optimization model of the new energy power system with a network-forming energy storage to obtain the black-start recovery path of the power system.

[0035] A computer device, the computer device includes:

[0036] One or more processors;

[0037] A memory for storing one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for optimizing the black-start recovery path of a new energy power system with a network-forming energy storage.

[0039] The beneficial effects of the present invention are:

[0040] Different from the existing black-start optimization modeling methods for new energy power systems that only consider traditional energy storage, the present invention takes into account the volatility of the output of new energy units, and connects the network-forming energy storage to the bus of the new energy unit and then conducts black-start optimization modeling on the system. It focuses on the black-start network-forming resource constraints of the network-forming energy storage system and the operation constraints of the network-forming energy storage system during the black-start process of the power system. Different from ordinary energy storage, the network-forming energy storage is considered in detail in the present invention and its accelerating effect on the power system recovery as a black-start resource is demonstrated. It can be faster than the recovery speed of a new energy power system with ordinary energy storage. The optimization modeling method proposed by the present invention simultaneously considers the black-start characteristics of the network-forming energy storage and the fluctuation characteristics of new energy power generation, and finally realizes the optimization of the black-start recovery path of the power system. Description of the Drawings

[0041] Figure 1 It is a flow chart of an optimization method for the black - start recovery path of a new - energy power system with a grid - forming energy storage provided by an embodiment of the present invention.

[0042] Figure 2 It is a topological graph after zoning of the IEEE 30 - node new - energy power system provided by an embodiment of the present invention.

[0043] Figure 3 It is the predicted data of the output of new - energy units within one day in an embodiment of the present invention.

[0044] Figure 4 It is a graph of the power change during the black - start process of a new - energy power system with a grid - forming energy storage provided by an embodiment of the present invention under Scenario 1.

[0045] Figure 5 It is a graph of the power change during the black - start process of a new - energy power system with a grid - forming energy storage provided by an embodiment of the present invention under Scenario 2.

[0046] Figure 6 It is a graph of the power change during the black - start process of a new - energy power system with a grid - forming energy storage provided by an embodiment of the present invention under Scenario 3. Detailed implementation manners

[0047] Hereinafter, the embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various different forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the content of the present invention. It should be understood that the drawings and embodiments of the present invention are only for illustrative purposes and are not used to limit the protection scope of the present invention.

[0048] As Figure 1 shown, an embodiment of the present invention provides an optimization method for the black - start recovery path of a new - energy power system with a grid - forming energy storage, including:

[0049] An optimization method for the black - start recovery path of a new - energy power system with a grid - forming energy storage includes the following steps:

[0050] S1. Obtain the predicted data of the output of new - energy units in the new - energy power system with a grid - forming energy storage, and construct the black - start grid - forming resource constraints considering the grid - forming energy storage system and the operation constraints of the grid - forming energy storage system during the black - start process of the power system.

[0051] S1.1 During the optimization process of the black - start recovery path of a new - energy power system with network - forming energy storage, at least one synchronous generator set or network - forming energy storage device with black - start capability in each black - start area must be kept in working state at all times. Specifically:

[0052] ,

[0053] where, is the state of the j - th synchronous generator set with black - start capability in the black - start area at time t, is the charging state of the k - th network - forming energy storage device in the black - start area at time t, is the discharging state of the k - th network - forming energy storage device at time is the total number of synchronous generator sets with black - start capability in the black - start area, is the total number of network - forming energy storage devices in the black - start area.

[0054] S1.2 During the optimization process of the black - start recovery path of a new - energy power system with network - forming energy storage, the power - balance constraint of the network - forming energy storage device and the relevant constraints on the ratio of the electricity stored in the network - forming energy storage device to the maximum electricity that can be stored in its fully - charged state need to be considered.

[0055] During any time interval, the change in the electricity of the network - forming energy storage must be equal to the sum of charging and discharging during this time interval. Specifically:

[0056] ,

[0057] where, represents the electricity value of the k - th network - forming energy storage device at time t; represents the electricity value of the k - th network - forming energy storage device at time t - 1; , respectively represent the charging efficiency and discharging efficiency of the k - th network - forming energy storage device; , respectively represent the charging power and discharging power of the k - th network - forming energy storage device at time t; is the time interval for the charging and discharging of the network - forming energy storage device, is the total time length of the black - start optimization process.

[0058] The SOC range of the network - forming energy storage changes dynamically during the black - start process. It is necessary to ensure that it is within a safe range, on the one hand, to support the subsequent stage of black - start, and on the other hand, to leave enough margin as reserve capacity to cope with possible disturbances. Specifically:

[0059] ,

[0060] The above formula represents the initial power of the network-forming energy storage. represents the maximum power of the k-th network-forming energy storage, represents the percentage of the initial power of the network-forming energy storage.

[0061] ,

[0062] ,

[0063] The above formula represents the remaining power constraint of the network-forming energy storage device. Among them, represents the maximum power of the k-th network-forming energy storage device, , respectively represent the minimum and maximum values of the percentage of the remaining power of the k-th network-forming energy storage device at time t.

[0064] ,

[0065] ,

[0066] ,

[0067] The above formula represents the relevant constraints of the percentage of the remaining power of the network-forming energy storage device in different stages. Among them, , respectively represent the minimum and maximum values of the percentage of the remaining power of the network-forming energy storage device in the s-th stage; represents the state of the i-th load at time t, represents the state of the j-th non-black start unit at time t; is the number of loads, is the number of non-black start units; represents the flag bit for distinguishing the stage where the network-forming energy storage device is located, which is divided into 3 stages: in stage 1, neither the load nor the non-black start unit is started; in stage 2, at least one non-black start unit is started and no load is restored; in stage 3, at least one non-black start unit is started and at least one load is restored.

[0068] S1.3 During the black start process of the new energy power system, it is necessary to maintain the active power balance among the charge and discharge power of the network-forming energy storage, the power generation power of the unit, and the load power. The actual output power of the unit minus the start-up power consumed by itself and the sum of the charge and discharge power of the network-forming energy storage is equal to the load power, and the formula is as follows:

[0069] ,

[0070] Among them, is the actual output power of the j-th unit at time t, is the start-up power of the j-th unit at time t; is the power of the i-th load at time t; is the total time length of the black start optimization process; is the total number of units in the new energy power system, is the total number of loads in the new energy power system.

[0071] S2. Based on the above-mentioned network-forming resource constraints and operation constraints, with the goal of minimizing the user power outage loss as the objective function, a black start optimization model for a new energy power system with network-forming energy storage is constructed.

[0072] While ensuring the safety and stability of the new energy power system, the objective function of this model minimizes the user power outage loss value. The formula is as follows:

[0073] ,

[0074] where z is the user power outage loss value, is the maximum power of the -th load, is the unit power outage loss of the -th load, and the function value is related to the power outage time and load type of the -th load.

[0075] S3. Solve the black start optimization model for the new energy power system with network-forming energy storage to obtain the black start recovery path of the power system.

[0076] In a specific embodiment of the present invention, a 200-minute time period of the next day is simulated for black start optimization modeling, with each 5 minutes as a time period for the change of unit and load states. The method of this embodiment is written in Julia software and solved by calling Gurobi. The black start recovery path optimization problem of the new energy power system with network-forming energy storage is expressed as a mixed integer mathematical programming model and solved using a mathematical programming algorithm.

[0077] In this embodiment, the research object is the partitioned IEEE 30-node new energy power system, which is divided into two parts separated by a framework, as shown in Figure 2As shown in the figure. There are 2 thermal power synchronous generators with black-start capabilities, located at nodes 2 and 27 respectively; there are 2 network-forming energy storage devices, located at nodes 13 and 23 respectively; there are 2 non-black-start thermal power units, located at nodes 1 and 22 respectively; the photovoltaic unit is located at node 13, and the wind turbine unit is located at node 23. Among them, in Region 1, there is 1 thermal power synchronous generator with black-start capability located at node 2, and 1 network-forming energy storage device located at node 13; there is 1 non-black-start thermal power unit located at node 1, and there is also 1 photovoltaic unit located at node 13. In Region 2, there is 1 thermal power synchronous generator with black-start capability located at node 27, and 1 network-forming energy storage device located at node 23; there is 1 non-black-start thermal power unit located at node 22, and there is also 1 wind turbine unit located at node 23. Figure 3 It is the typical output curve of the new energy unit within a day (1440 minutes), which is generated from the statistical historical data and is used as the constraint condition for the output upper limit in each time period in the optimization program.

[0078] To prove the applicability of this embodiment in each time period, the following three time periods are considered; steps S1 - S3 are carried out in three different time periods, and the simulation results are as Figures 4 - 6 shown; at the same time, in order to prove that the new energy power system with network-forming energy storage has a shorter black-start recovery time than the new energy power system with ordinary energy storage, a comparative example is set. In the comparative example, the research object is the new energy power system with ordinary energy storage, that is, the network-forming energy storage devices located at nodes 13 and 23 in the new energy power system with network-forming energy storage are replaced with ordinary energy storage of the same capacity size, and then the optimization solution is also carried out in the following three scenarios.

[0079] Scenario 1: The black-start start time is 0:00.

[0080] Scenario 2: The black-start start time is 8:00.

[0081] Scenario 3: The black-start start time is 16:00.

[0082] The regulation results of the new energy power system with network-forming energy storage in the three scenarios are as Figures 4 - 6 shown, and the specific result parameters can be seen in Table 1. It can be seen that the total economic loss in Scenarios 2 - 3 is less than that in Scenario 1 because the time period in Scenario 1 is at night, and the photovoltaic output is almost 0 during this time, which cannot provide power support for the black-start of the new energy power system. The total economic loss and the total power outage loss in Scenario 2 are the least because the photovoltaic unit output at noon is more than that in other scenarios when the start time is noon.

[0083] The result parameters of the new energy power system with ordinary energy storage under three scenarios can be seen in Table 2. Compare the results in Table 1 with those in Table 2. It can be seen that under the same black start starting time, both the total economic loss and the total power outage loss of the new energy power system with network-forming energy storage are less than those of the new energy power system with ordinary energy storage. This shows that the network-forming energy storage is helpful for optimizing the black start recovery path of the new energy power system and can significantly reduce the total economic loss and the total power outage loss. This is because the network-forming energy storage has the black start ability and can make the non-black start units start faster in the initial stage of the black start recovery. While the ordinary energy storage does not have this ability.

[0084] It can be seen from Figures 4 - 6 that the network-forming energy storage outputs power for most of the time during the black start process of the new energy power system, providing power support for the black start process. Due to the volatility of the output of new energy units, the output of the network-forming energy storage located at the same node also keeps changing, making the external output of the node where the new energy unit and the network-forming energy storage are located remain stable, reflecting the role of the network-forming energy storage. The output curve of the unit has an obvious climbing trend with the start of each non-black start unit. The load power curve also rises continuously with the recovery of each load until all loads are restored, and then the load power curve remains a straight line unchanged.

[0085] Table 1 Comparison of the results obtained by the new energy power system with network-forming energy storage under different scenarios

[0086] Case Total economic loss Total power outage loss in electricity Scenario 1 13.431 million yuan 177.9 MWh Scenario 2 13.045 million yuan 172.0 MWh Scenario 3 13.433 million yuan 179.7 MWh

[0087] Table 2 Comparison of the results obtained by the new energy power system with ordinary energy storage under different scenarios

[0088] Case Total economic loss Total power outage loss in electricity Scenario 1 13.938 million yuan 184.7 MWh Scenario 2 13.615 million yuan 177.9 MWh Scenario 3 14.140 million yuan 185.0 MWh

[0089] The optimization modeling method proposed by the present invention takes into account both the black start characteristics of the network-forming energy storage and the fluctuation characteristics of new energy power generation, and finally realizes the optimization of the black start recovery path of the power system.

[0090] Those skilled in the art should understand that the 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 complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0094] The above is only the preferred embodiment of the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present invention. Therefore, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An optimization method for the black-start recovery path of a new energy power system with a network-forming energy storage, characterized in that It includes the following steps: S1. Obtain the predicted data of the output of new energy units in the new energy power system with network-forming energy storage, and construct the black-start network-forming resource constraint conditions and operation constraint conditions considering the network-forming energy storage system; S2. Based on the network-forming resource constraint conditions and operation constraint conditions, construct a black-start optimization model for the new energy power system with network-forming energy storage with the goal of minimizing the user power outage loss; S3. Solve the black-start optimization model for the new energy power system with network-forming energy storage to obtain the black-start recovery path of the power system; The network-forming resource constraint conditions include: black-start equipment status constraint, network-forming energy storage equipment power balance constraint, remaining power constraint of network-forming energy storage equipment, and remaining power percentage constraint of network-forming energy storage equipment; the operation constraint conditions include power balance constraint; The black-start equipment status constraint is that at least one synchronous generator set or network-forming energy storage equipment with black-start capability in each black-start area is in a working state; specifically: ， Among them, is the status of the th synchronous generator unit with black-starting capability in the black-start area at time t, is the charging status of the kth network-forming energy storage device in the black-start area at time t, is the discharging status of the kth network-forming energy storage device at time t; is the total number of synchronous generator units with black-starting capability in the black-start area, is the total number of network-forming energy storage devices in the black-start area; The objective function of the black-start optimization model for the new energy power system with network-forming energy storage is: ， where z is the user's power outage loss value, is the maximum power of the i-th load, is the unit power outage loss of the i-th load.

2. The optimization method for the black start recovery path of the new energy power system with grid-forming energy storage according to claim 1, wherein The network-forming energy storage equipment power balance constraint is that within any time interval, the change in the power of the network-forming energy storage equipment is equal to the sum of charging and discharging within that time interval; specifically: ， Among them, represents the power value of the k-th network-forming energy storage device at time t; represents the power value of the k-th network-forming energy storage device at time t-1; , respectively represent the charging efficiency and discharging efficiency of the k-th network-forming energy storage device; , respectively represent the charging power and discharging power of the k-th network-forming energy storage device at time t; is the time interval for charging and discharging of the network-forming energy storage device, is the total time length of the black start optimization process.

3. The optimization method for the black-start recovery path of a new energy power system with a grid-forming energy storage according to claim 1, wherein, The remaining power constraint of the network-forming energy storage equipment is that the SOC of the network-forming energy storage equipment is within a safe range during the black-start process; specifically: ， ， Among them, represents the maximum power of the k-th grid-forming energy storage device, , respectively represent the minimum and maximum values of the remaining power percentage of the k-th grid-forming energy storage device at time t.

4. The optimization method for the black-start recovery path of the new energy power system with a grid-forming energy storage according to claim 1, wherein, The remaining power percentage constraint of the network-forming energy storage equipment is specifically: ， ， ， Among them, and respectively represent the minimum and maximum values of the remaining power percentage of the network-forming energy storage device in the s-th stage; represents the state of the i-th load at time t, represents the state of the j-th non-black start-up unit at time t; is the number of loads, is the number of non-black start-up units; represents the flag bit for distinguishing the stage where the network-forming energy storage device is located.

5. The optimization method for the black start recovery path of a new energy power system with a grid-forming energy storage according to claim 1, wherein, The power balance constraint is that the actual output power of the unit minus the starting power consumed by itself plus the charging and discharging power of the network-forming energy storage is equal to the load power; specifically: ， Among them, is the actual output power of the j-th unit at time t, is the starting power of the j-th unit at time t; is the load power of the i-th at time t; is the total time length of the black start optimization process; is the total number of units in the new energy power system, is the total number of loads in the new energy power system.

6. A new energy power system black start recovery path optimization system with network-forming energy storage, characterized in that It includes: Data processing module: used to obtain the predicted data of the output of new energy units in the new energy power system with network-forming energy storage, and construct the black-start network-forming resource constraint conditions and operation constraint conditions considering the network-forming energy storage system; Model construction module: used to construct a black-start optimization model for the new energy power system with network-forming energy storage with the goal of minimizing the user power outage loss based on the network-forming resource constraint conditions and operation constraint conditions; Model solving module: used to solve the black-start optimization model for the new energy power system with network-forming energy storage to obtain the black-start recovery path of the power system; The network-forming resource constraint conditions include: black-start equipment status constraint, network-forming energy storage equipment power balance constraint, remaining power constraint of network-forming energy storage equipment, and remaining power percentage constraint of network-forming energy storage equipment; the operation constraint conditions include power balance constraint; The black-start equipment status constraint is that at least one synchronous generator set or network-forming energy storage equipment with black-start capability in each black-start area is in a working state; specifically: ， Among them, is the status of the th synchronous generator set with black start capability in the black start area at time t, is the charging status of the kth network-forming energy storage device in the black start area at time t, is the discharging status of the kth network-forming energy storage device at time t; is the total number of synchronous generator sets with black start capability in the black start area, is the total number of network-forming energy storage devices in the black start area; The objective function of the black-start optimization model for the new energy power system with network-forming energy storage is: ， where z is the user's power outage loss value, is the maximum power of the i-th load, is the unit power outage loss of the i-th load.

7. A computer device, characterized in that, The computer device includes: One or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for optimizing the black start recovery path of the new energy power system with a network-constructing energy storage as described in any one of claims 1 to 5.

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