Black-start recovery path optimization method and device for new energy power system containing network construction type energy storage

Abstract

The invention provides a method and equipment for optimizing a black-start recovery path of a new energy power system containing network construction type energy storage, and the method considers the fluctuation of the output of a new energy unit, and carries out the black-start optimization modeling of the system after the network construction type energy storage is connected to a bus of the new energy unit. The method comprises the steps of obtaining prediction data of new energy unit output in a new energy power system containing network construction type energy storage, and constructing a black-start network construction resource constraint condition and an operation constraint condition considering the network construction type energy storage system; based on the network construction resource constraint condition and the operation constraint condition, taking minimization of user power failure loss as a target function, and constructing a new energy power system black-start optimization model containing network construction type energy storage; and solving the new energy power system black-start optimization model containing the network construction type energy storage to obtain a power system black-start recovery path. According to the method, the black-start characteristic of network construction type energy storage and the fluctuation characteristic of new energy power generation are considered at the same time, and finally optimization of the black-start recovery path of the power system is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of optimization modeling of black start of new energy power system, and in particular to a method and device for optimizing black start recovery path of new energy power system containing grid-type energy storage. Background Art

[0002] While the transformation of clean and low-carbon energy accelerates the leapfrog development of clean energy such as wind power and photovoltaics, it also greatly increases the demand for flexible adjustment resources in the power grid. As an emerging power technology, energy storage can achieve the decoupling of 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 energy power system will face problems such as internal oscillation and transient overvoltage, which will lead to the weakening of the system voltage and frequency support capacity, the decrease of stability margin, and the increase of safe operation risk. Due to its voltage source characteristics, grid-type energy storage has the ability to build frequency and voltage and instantaneous frequency and voltage regulation, and has black start potential, which 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 resources and reduce dependence on traditional energy, thereby optimizing the national energy allocation and improving the economic benefits of energy utilization. Adding new equipment such as grid-type energy storage with frequency regulation and voltage control capabilities similar to synchronous generators near new energy can comprehensively improve the regulation capability and flexibility of the power system. In summary, it is very necessary to carry out research on the black start optimization modeling method of new energy power system containing grid-type energy storage. Summary of the invention

[0003] The present invention proposes a black start recovery path optimization method and device for a new energy power system containing grid-type energy storage. Different from the existing black start optimization modeling method for a new energy power system that only considers traditional energy storage, this method takes into account the volatility of the output of new energy units, connects the grid-type energy storage to the busbar of the new energy unit, and performs black start optimization modeling on the system.

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

[0005] A method for optimizing a black start recovery path of a new energy power system including grid-connected energy storage comprises the following steps:

[0006] S1. Obtain forecast data on the output of new energy units in a new energy power system containing grid-type energy storage. The forecast data is based on the average historical data of new energy units of the same capacity in typical scenarios, and constructs black start grid-type energy storage system-considered grid-type energy storage system resource constraints and operation constraints;

[0007] S2. Based on the network resource constraints and operation constraints, a black start optimization model for a new energy power system including network energy storage is constructed with minimization of user power outage losses as the objective function;

[0008] S3. Solve the black start optimization model of the new energy power system containing grid-connected energy storage to obtain the black start recovery path of the power system.

[0009] Furthermore, the networking resource constraints include: black start device status constraints, networking energy storage device power balance constraints, networking energy storage device remaining power constraints and networking energy storage device remaining power percentage constraints; the operation constraints include power balance constraints.

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

[0011] ,

[0012] in, is the state of the jth synchronous generator set with black start capability in the black start area at time t, is the charging state of the kth grid-connected energy storage device in the black start area at time t, is the discharge state of the kth grid-type 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 grid-connected energy storage devices in the black start area.

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

[0014] ,

[0015] in, Represents the power value of the kth grid-connected energy storage device at time t; Indicates the power value of the kth grid-connected energy storage device at time t-1; , They represent the charging efficiency and discharging efficiency of the kth grid-type energy storage device respectively; , They represent the charging power and discharging power of the kth grid-connected energy storage device at time t respectively; is the time interval between charging and discharging of the grid-type energy storage device, The total time length for the black start optimization process.

[0016] Furthermore, the remaining power of the grid-type energy storage device is constrained as follows: during the black start process, the SOC (State of Charge, SOC) of the grid-type energy storage device is within a safe range; specifically:

[0017] ,

[0018] ,

[0019] in, represents the maximum value of the kth grid-connected energy storage device, , They respectively represent the minimum and maximum percentages of the remaining power of the kth grid-connected energy storage device at time t.

[0020] Furthermore, the remaining power percentage constraint of the grid-type energy storage device is specifically:

[0021] ,

[0022] ,

[0023] ,

[0024] in, , They represent the minimum and maximum percentages of the remaining power of the grid-connected energy storage device in the sth stage respectively; represents the state of the i-th load at time t, Indicates the state of the jth non-black start unit at time t; is the load quantity, is the number of non-black start units; A flag that indicates the stage of the grid-connected energy storage device.

[0025] Furthermore, the power balance constraint is: the sum of the actual output power of the unit minus the startup power consumed by itself and the charging and discharging power of the grid-type energy storage is equal to the load power; specifically:

[0026] ,

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

[0028] Furthermore, the objective function of the black start optimization model of the new energy power system containing grid-connected energy storage is:

[0029] ,

[0030] Among them, z is the power outage loss value of the user, For the The maximum power of a load, For the Unit power outage loss of a load.

[0031] A black start recovery path optimization system for a new energy power system including grid-connected energy storage, comprising:

[0032] Data processing module: used to obtain the forecast data of the output of new energy units in the new energy power system containing grid-type energy storage, and to construct the black start grid resource constraints and operation constraints considering the grid-type energy storage system;

[0033] Model building module: used to build a black start optimization model of a new energy power system including grid-based energy storage based on the grid-based resource constraints and operation constraints, with minimizing the power outage losses of users as the objective function;

[0034] Model solving module: used to solve the black start optimization model of the new energy power system containing grid-connected energy storage, and obtain the black start recovery path of the power system.

[0035] A computer device, comprising:

[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 black start recovery path optimization method for the new energy power system containing grid-connected energy storage.

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

[0040] Unlike 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, connects the grid-type energy storage to the busbar of the new energy units, and performs black start optimization modeling on the system. The focus is on the black start networking resource constraints of the grid-type energy storage system, as well as the operating constraints of the grid-type energy storage system during the black start of the power system. Grid-type energy storage is different from ordinary energy storage. The present invention considers in detail and demonstrates its accelerating effect on power system recovery as a black start resource, which can recover faster than a new energy power system containing ordinary energy storage. The optimization modeling method proposed in the present invention simultaneously considers the black start characteristics of the grid-type energy storage and the volatility characteristics of new energy power generation, and ultimately achieves the optimization of the black start recovery path of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0043] Figure 3 It is the forecast data of the output of the new energy unit within one day in the embodiment of the present invention.

[0044] Figure 4 It is a power change diagram of the black start process of the new energy power system with grid-connected energy storage under scenario 1 provided in an embodiment of the present invention.

[0045] Figure 5 It is a power change diagram of the black start process of the new energy power system with grid-connected energy storage under scenario 2 provided in an embodiment of the present invention.

[0046] Figure 6 It is a power change diagram of the black start process of the new energy power system with grid-connected energy storage under scenario 3 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of 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 intended to limit the scope of protection of the present invention.

[0048] like Figure 1 As shown, one embodiment of the present invention provides a method for optimizing a black start recovery path of a new energy power system including a grid-connected energy storage, comprising:

[0049] A method for optimizing a black start recovery path of a new energy power system including grid-connected energy storage comprises the following steps:

[0050] S1. Obtain the forecast data of the output of new energy units in the new energy power system containing grid-type energy storage, and construct the black start grid resource constraints considering the grid-type energy storage system and the operation constraints of the grid-type energy storage system during the black start of the power system.

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

[0052] ,

[0053] in, is the state of the jth synchronous generator set with black start capability in the black start area at time t, is the charging state of the kth grid-connected energy storage device in the black start area at time t, for The discharge state of the kth grid-type energy storage device at the moment; is the total number of synchronous generator sets with black start capability in the black start area, is the total number of grid-connected energy storage devices in the black start area.

[0054] S1.2 In the process of optimizing the black start recovery path of a new energy power system containing grid-connected energy storage, it is necessary to consider the power balance constraints of the grid-connected energy storage equipment, as well as the constraints related to the ratio of the power stored in the grid-connected energy storage equipment to the maximum power that can be stored when it is fully charged.

[0055] In any time interval, the change in the amount of electricity in the grid-type energy storage must be equal to the sum of the charging and discharging in the time interval, specifically:

[0056] ,

[0057] in, Represents the power value of the kth grid-connected energy storage device at time t; Indicates the power value of the kth grid-connected energy storage device at time t-1; , They represent the charging efficiency and discharging efficiency of the kth grid-type energy storage device respectively; , They represent the charging power and discharging power of the kth grid-connected energy storage device at time t respectively; is the time interval between charging and discharging of the grid-type energy storage device, The total time length for the black start optimization process.

[0058] The SOC range of grid-connected energy storage changes dynamically during the black start process, and it is necessary to ensure that it is within the safe range, on the one hand to support the black start in the subsequent stages, and on the other hand to leave enough margin as backup capacity to deal with possible disturbances. Specifically:

[0059] ,

[0060] The above formula represents the initial capacity of grid-type energy storage. represents the maximum value of the kth grid-type energy storage, Indicates the initial power percentage of the grid-type energy storage.

[0061] ,

[0062] ,

[0063] The above formula represents the remaining power constraint of the grid-connected energy storage device. represents the maximum value of the kth grid-connected energy storage device, , They respectively represent the minimum and maximum percentages of the remaining power of the kth grid-connected energy storage device at time t.

[0064] ,

[0065] ,

[0066] ,

[0067] The above formula represents the relevant constraints on the remaining power percentage of grid-connected energy storage devices at different stages. , They represent the minimum and maximum percentages of the remaining power of the grid-connected energy storage device in the sth stage respectively; represents the state of the i-th load at time t, Indicates the state of the jth non-black start unit at time t; is the load quantity, is the number of non-black start units; The flag that indicates the stage of the grid-connected energy storage device is divided into three stages: Stage 1: neither the load nor the non-black start unit is started; Stage 2: at least one non-black start unit is started and no load is restored; 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 of the new energy power system, it is necessary to maintain the active balance between the charging and discharging power of the grid-type energy storage, the generating power of the unit and the load power. The actual output power of the unit minus the sum of the self-consumed starting power and the charging and discharging power of the grid-type energy storage is equal to the load power, and the formula is as follows:

[0069] ,

[0070] in, is the actual output power of the jth unit at time t, is the starting power of the jth unit at time t; is the i-th load power at time t; The total time length of the black start optimization process; is the total number of units in the renewable energy power system, is the total number of loads in the renewable energy power system.

[0071] S2. Based on the grid resource constraints and operation constraints, a black start optimization model for a new energy power system including grid-type energy storage is constructed with minimizing user power outage losses as the objective function.

[0072] The objective function of this model is to minimize the power outage loss value of users while ensuring the safety and stability of the new energy power system. The formula is as follows:

[0073] ,

[0074] Among them, z is the power outage loss value of the user, For the The maximum power of a load, For the The unit power outage loss of the load is The power outage time of a load is related to the load type.

[0075] S3. Solve the black start optimization model of the new energy power system containing grid-connected 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 in the future day is simulated for black start optimization modeling, with every 5 minutes as a unit and load state change time period. The method is written in Julia software in the embodiment of the present invention, and Gurobi is called for solution. The black start recovery path optimization problem of a new energy power system containing grid-type energy storage is expressed as a mixed integer mathematical programming model, and a mathematical programming algorithm is used for solution.

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

[0078] In order to prove the applicability of this embodiment in various time periods, the following three time periods are considered; steps S1-S3 are performed in three different time periods, and the simulation results are as follows: Figure 4-6 As shown; at the same time, in order to prove that the black start recovery time of the new energy power system with grid-type energy storage is shorter than that of 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 grid-type energy storage devices located at nodes 13 and 23 in the new energy power system with grid-type energy storage are replaced with ordinary energy storage of the same capacity, and then the optimization solution is also performed in the following three scenarios.

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

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

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

[0082] The control results of the new energy power system with grid-connected energy storage in three scenarios are as follows: Figure 4-6 The specific result parameters can be seen in Table 1. It can be seen that the total economic loss of scenario 2-3 is less than that of scenario 1. This is because the time period of scenario 1 is at night, during which the photovoltaic output is almost 0, and it is impossible to provide power support for the black start of the new energy power system. The total economic loss and total power outage loss of scenario 2 are the least, because the starting time is noon and the photovoltaic unit output is more than other scenarios.

[0083] The result parameters of the new energy power system with ordinary energy storage in three scenarios can be seen in Table 2. Compare the results of Table 1 with Table 2. It can be seen that at the same black start start time, the total economic loss and total power outage loss of the new energy power system with grid-type energy storage are less than those of the new energy power system with ordinary energy storage. This shows that grid-type 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 total power outage loss. This is because grid-type energy storage has black start capability, which can enable non-black start units to start faster in the early stage of black start recovery. Ordinary energy storage does not have this capability.

[0084] from Figure 4-6 It can be seen that the grid-type energy storage is output 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 the new energy unit, the output of the grid-type energy storage located at the same node is also constantly changing, so that the node where the new energy unit and the grid-type energy storage are located remains stable, reflecting the role of the grid-type energy storage. The unit output curve has an obvious climbing trend with the start of each non-black start unit. The load power curve also continues to rise with the recovery of each load until all loads are restored, and the load power curve remains a straight line.

[0085] Table 1 Comparison of results obtained in different scenarios for new energy power systems with grid-connected energy storage

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

[0087] Table 2 Comparison of results obtained in different scenarios for new energy power systems with common energy storage

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

[0089] The optimization modeling method proposed in the present invention takes into account the black start characteristics of grid-connected energy storage and the fluctuation characteristics of renewable energy power generation, and ultimately achieves the optimization of the black start recovery path of the power system.

[0090] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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 flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] The above is only a preferred embodiment of the present invention. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above disclosed methods and technical contents without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. 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 falls within the scope of protection of the technical solution of the present invention.

Claims

1. A method for optimizing black start recovery path of a new energy power system containing grid-connected energy storage, characterized in that: The following steps are involved: S1. Obtain forecast data on the output of new energy units in a new energy power system containing grid-type energy storage, and construct black start grid-type energy storage system-considering resource constraints and operation constraints; S2. Based on the network resource constraints and operation constraints, a black start optimization model for a new energy power system including network energy storage is constructed with minimization of user power outage losses as the objective function; S3. Solve the black start optimization model of the new energy power system containing grid-connected energy storage to obtain the black start recovery path of the power system.

2. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 1 is characterized in that: The networking resource constraints include: black start device status constraints, networking energy storage device power balance constraints, networking energy storage device remaining power constraints and networking energy storage device remaining power percentage constraints; the operation constraints include power balance constraints.

3. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 2 is characterized in that: The black start equipment status constraint is: in each black start area, at least one synchronous generator set or grid-connected energy storage device with black start capability is in working state; specifically: ， in, is the first The status of synchronous generator sets with black start capability, is the charging state of the kth grid-connected energy storage device in the black start area at time t, is the discharge state of the kth grid-type 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 grid-connected energy storage devices in the black start area.

4. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 2 is characterized in that: The power balance constraint of the grid-type energy storage device is: in any time interval, the power change of the grid-type energy storage device is equal to the sum of charging and discharging in the time interval; specifically: ， in, Represents the power value of the kth grid-connected energy storage device at time t; Indicates the power value of the kth grid-connected energy storage device at time t-1; , They represent the charging efficiency and discharging efficiency of the kth grid-type energy storage device respectively; , They represent the charging power and discharging power of the kth grid-connected energy storage device at time t respectively; is the time interval between charging and discharging of the grid-type energy storage device, The total time length for the black start optimization process.

5. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 2 is characterized in that: The remaining power constraint of the grid-type energy storage device is: the SOC of the grid-type energy storage device is within the safe range during the black start process; specifically: ， ， in, represents the maximum value of the kth grid-connected energy storage device, , They respectively represent the minimum and maximum percentages of the remaining power of the kth grid-connected energy storage device at time t.

6. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 2 is characterized in that: The remaining power percentage constraint of the grid-type energy storage device is specifically: ， ， ， in, , They represent the minimum and maximum percentages of the remaining power of the grid-connected energy storage device in the sth stage respectively; represents the state of the i-th load at time t, Indicates the state of the jth non-black start unit at time t; is the load quantity, is the number of non-black start units; A flag that indicates the stage of the grid-connected energy storage device.

7. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 2 is characterized in that: The power balance constraint is: the sum of the actual output power of the unit minus the startup power consumed by itself and the charging and discharging power of the grid-type energy storage is equal to the load power; specifically: ， in, is the actual output power of the jth unit at time t, is the starting power of the jth unit at time t; is the i-th load power at time t; The total time length of the black start optimization process; is the total number of units in the renewable energy power system, is the total number of loads in the renewable energy power system.

8. The black start recovery path optimization method of a new energy power system containing grid-connected energy storage according to claim 1 is characterized in that: The objective function of the black start optimization model of the new energy power system with grid-connected energy storage is: , where z is the power outage loss value for the user, is the maximum power of the ith load, is the unit power outage loss of the ith load.

9. A black start recovery path optimization system for a new energy power system with grid-connected energy storage, characterized in that: include: Data processing module: used to obtain the forecast data of the output of new energy units in the new energy power system containing grid-type energy storage, and to construct the black start grid-type energy storage system with consideration of the grid-type energy storage system's grid-type energy storage system's resource constraints and operation constraints; Model building module: used to build a black start optimization model of a new energy power system including grid-based energy storage based on the grid-based resource constraints and operation constraints, with minimizing the power outage losses of users as the objective function; Model solving module: used to solve the black start optimization model of the new energy power system containing grid-connected energy storage, and obtain the black start recovery path of the power system.

10. A computer device, characterized in that: The computer device comprises: 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 black start recovery path optimization method for a new energy power system containing grid-connected energy storage as described in any one of claims 1-8.

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