Method and device for site selection and capacity determination of algorithm electricity collaborative energy storage based on grid-connected green electricity direct connection

Abstract

This invention relates to the field of power system energy storage technology, specifically to a method and device for site selection and capacity determination of computing-powered energy storage based on grid-connected green electricity direct connection. The method includes: acquiring 8760 hours of time-series operational data and line data from a computing center and new energy power plants, setting the connected grid as an infinite power source; using K-means clustering to obtain typical daily scenarios; constructing a parallel computing framework for three energy storage deployment scenarios, with the minimum life-cycle cost as the objective function, combined with constraints such as power balance; and solving the problem using particle swarm optimization and determining the optimal solution through economic comparison. The corresponding system includes modules for annual historical data acquisition, typical daily scenario clustering, parallel optimization framework, optimization problem solving, and solution acquisition, with each module collaboratively executing the above method. This invention fills a technological gap, improves computational efficiency, achieves optimal economic energy storage configuration, ensures power supply reliability, and facilitates the full absorption of green electricity.

Description

Technical Field

[0001] This invention belongs to the field of power system energy storage technology, specifically relating to a method and device for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection. Background Technology

[0002] In the field of power system energy storage, with the coordinated development of the new energy industry and computing infrastructure, the exploration of new energy supply models has become a key focus of technological research. The framework of this new energy supply model achieves direct connection between clean energy power generation units and computing centers through physical lines, while maintaining bidirectional grid connection between the computing center and the public power grid, providing an innovative solution for the coordinated development of computing infrastructure and the power system. Under this model, the supporting energy storage system is a crucial component, requiring two core functions: first, to mitigate the intermittent characteristics of photovoltaic and wind power generation through the timing regulation of energy storage and release; and second, to construct an economically feasible energy supply system for computing centers by leveraging the peak-valley price difference mechanism of the electricity spot market.

[0003] The current mainstream green power consumption model mainly relies on integrated source-grid-load-storage systems and microgrid technology to achieve local power supply. However, this model is limited by the spatial constraints of load sites such as computing centers, resulting in limited consumption capacity. It is only suitable for distributed power sources or small photovoltaic power plants. For large-scale photovoltaic power plants and other new energy power plants with large installed capacity, there is still no effective solution to the problem of full consumption of green power in their area. Green power direct connection technology provides a potential technical path for solving this problem. After the voltage level of large-scale new energy power plants is increased to 110kV or 220kV through a step-up substation, they can be directly connected to the load center through dedicated transmission lines, breaking through the power trading barriers under physical isolation conditions.

[0004] However, the power load of computing centers has significant unique technical characteristics: high power density (>10kW / rack), pronounced load periodicity (peak-to-valley difference exceeding 60%), stringent power supply reliability requirements (average annual power outage duration <5 minutes), clear demand for green energy (renewable energy share ≥50%), and significant potential load volatility (minute-level fluctuations of ±20%). This necessitates not only establishing a reliable connection to the main grid but also configuring optimized energy storage systems to simultaneously meet the demands for high-reliability power supply and optimized power quality.

[0005] Unlike integrated generation-grid-load-storage and microgrid models where energy storage devices can be directly deployed on the load side, green power direct connection systems present a significant electrical distance between renewable energy power plants and load centers. This leads to three technical choices for energy storage configuration: deployment on the renewable energy side, deployment on the load side, or a dual-side collaborative configuration model. Currently, research on optimized energy storage configuration in green power direct connection scenarios is lacking, and there is a lack of unified planning, site selection, and capacity determination methods that can coordinate multiple locations and entities, including renewable energy power plants and computing centers.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] To address the aforementioned technical problems in the existing technology, this invention provides a method and apparatus for site selection and capacity determination of computing-powered energy storage based on grid-connected green electricity direct connection. By utilizing historical data from existing new energy power plants and computing centers, the method solves the problem of high-reliability power supply to computing centers through site selection and capacity determination of energy storage.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] The first aspect is a method for site selection and capacity determination of computing-driven energy storage based on grid-connected green electricity direct connection, including: S1. Obtain the operation data and line data of the computing center and new energy power station that need to be configured for energy storage in the green electricity direct connection scenario according to the set data source, and set the grid to be connected to as an infinite power source; S2. Cluster the runtime data using a clustering method to obtain typical daily scenarios; S3. Construct a parallel computing framework, which corresponds to three energy storage deployment scenarios. Based on the typical daily scenario, set optimization problems for each deployment scenario. The optimization problems include objective functions and constraints. S4. Solve each optimization problem using a preset optimization algorithm to obtain the energy storage configuration results for each deployment scenario; S5. Compare the economic efficiency of the energy storage configuration results to determine the optimal energy storage configuration scheme.

[0010] Furthermore, the operational data includes 8760 hours of time-series operational data for the computing center and 8760 hours of time-series operational data for the new energy power station; The line data includes the proposed connection line data between the computing center and the new energy power station, and the connection line data between the computing center and the external power grid.

[0011] Furthermore, the clustering method is the K-means clustering method, and the running data is annual running data, which is output as a typical daily scene set after clustering processing.

[0012] Furthermore, the three energy storage deployment scenarios are: energy storage is deployed only in new energy power plants, energy storage is deployed only in computing centers, and energy storage is deployed in both new energy power plants and computing centers.

[0013] Furthermore, the objective function is to minimize the life-cycle cost of optimized energy storage configuration, where the life-cycle cost includes construction cost, maintenance cost, and operating cost, satisfying the following:

[0014] in, The annual construction cost, Annual maintenance costs This refers to the annual operating cost.

[0015] Furthermore, the formula for calculating the construction cost is as follows:

[0016] Among them, the formula for calculating the annual value is... Calculate the future value factor of annuity , For interest rates, This refers to the service life; Configure the number of units for a single energy storage capacity; For the construction cost of energy storage, Land costs for energy storage; The formula for calculating the annual operating cost is as follows:

[0017] in, For the annual overhaul cost of energy storage, Annual minor repair costs for energy storage; The formula for calculating the annual maintenance cost is as follows:

[0018] in, The energy consumption rate is typically taken as 2%. For time, the annual cost is calculated using 8760 hours; For electricity price; Energy storage capacity.

[0019] Furthermore, the constraints include equality constraints and inequality constraints. The equality constraints are power balance constraints, and the inequality constraints include voltage constraints, power angle constraints, and constraints on the number of energy storage batteries.

[0020] Furthermore, the formula for the power balance constraint is:

[0021]

[0022] in, , , , , Representing nodes respectively Active output, active load, reactive input, reactive load, and node voltage; , , Representing branch roads The conductivity, susceptance, and phase angle difference, It is a set of nodes.

[0023] Furthermore, the preset optimization algorithm is the particle swarm optimization algorithm; in step S5, the economic comparison is to compare the total annual cost of the entire life cycle under each energy storage deployment scenario, and select the energy storage configuration result with the lowest cost as the best energy storage optimization configuration scheme.

[0024] Secondly, a computing-coordinated energy storage location and capacity determination system based on grid-connected green electricity direct connection includes: The annual historical data acquisition module is used to acquire the operation data and line data of computing centers and new energy power stations that need to be configured with energy storage in the green power direct connection scenario, and to set the grid to be connected as an infinite power source. The typical daily scene clustering module has its data input end connected to the output end of the annual historical data acquisition module. It is used to receive the annual operating data output by the annual historical data acquisition module, and to perform typical daily scene clustering on the annual operating data using a clustering method to obtain typical daily scenes. The parallel optimization framework module has its data input end connected to the output end of the typical daily scene clustering module. It is used to receive the typical daily scene output by the typical daily scene clustering module and construct a parallel computing framework. The parallel computing framework corresponds to three energy storage deployment scenarios. Based on the typical daily scene, optimization problems are set for each deployment scenario. The optimization problem includes an objective function and constraints. The optimization problem-solving module has a data input end connected to the output end of the parallel optimization framework module. It is used to receive the optimization problems under various energy storage deployment scenarios output by the parallel optimization framework module, solve each optimization problem using a preset optimization algorithm, and obtain the energy storage configuration results under each deployment scenario. The scheme acquisition module has its data input end connected to the output end of the optimization problem solving module. It is used to receive the energy storage configuration results under various energy storage deployment scenarios output by the optimization problem solving module, compare the economic efficiency of the energy storage configuration results, and determine the optimal energy storage configuration scheme.

[0025] Compared with existing technologies, the present invention provides a computing-powered energy storage site selection and capacity determination method and device based on grid-connected green electricity direct connection. The method includes: acquiring 8760 hours of time-series operation data and line data from the computing center and new energy power stations, and setting the connected grid as an infinite power source; using K-means clustering to obtain typical daily scenarios; constructing a parallel computing framework for three energy storage deployment scenarios, with the minimum life-cycle cost as the objective function, combined with constraints such as power balance; solving the problem using particle swarm optimization and determining the optimal solution through economic comparison. The corresponding system includes modules for annual historical data acquisition, typical daily scenario clustering, parallel optimization framework, optimization problem solving, and solution acquisition, with each module collaboratively executing the above method. This invention fills a technological gap, improves computing efficiency, achieves optimal economic energy storage configuration, ensures power supply reliability, and facilitates the full consumption of green electricity. Attached Figure Description

[0026] Figure 1 A flowchart of a computing-coordinated energy storage location and capacity determination method based on grid-connected green electricity direct connection provided in an embodiment of the present invention; Figure 2 This is an architecture diagram of a computing-coordinated energy storage location and capacity determination system based on grid-connected green electricity direct connection provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a green electricity direct connection scenario provided by the computing-powered energy storage location and capacity determination calculation method based on grid-connected green electricity direct connection, as provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0028] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0029] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0030] Example 1 See Figure 1 , Figure 1 This is a flowchart of the computing-coordinated energy storage location and capacity determination method based on grid-connected green electricity direct connection proposed in this invention. Specific steps may include: S1. Data Acquisition and Grid Setting: Acquire the operation data and line data of the computing center and new energy power station that need to be configured for energy storage in the green power direct connection scenario according to the set data source, and set the grid to be connected to an infinite power source. Among them, the operation data includes 8760 hours of time-series operation data of the computing center and the new energy power station that need to be configured with energy storage under the green electricity direct connection scenario; the line data includes the proposed connection line data between the computing center and the new energy power station, and the connection line data between the computing center and the external power grid; the power grid setting is to set the connected public power grid as an infinite power source.

[0031] For details, please refer to Figure 3 Based on the set data source, obtain 8760 hours of time-series operation data of the computing center and new energy power station that need to be configured with energy storage in the green power direct connection scenario, as well as the data of the lines to be connected between the two, including 8760 hours of time-series operation data of the computing center. 8760-hour time-series operation data of new energy power stations The two are intended to connect line data and Data connection between the computing center and the external power grid and .

[0032] S2. Cluster the runtime data using a clustering method to obtain typical daily scenarios; Specifically, to reduce the difficulty of solving the optimization problem, the K-means clustering method was used to cluster typical daily scenarios in the aforementioned annual operational data. This clustering process reduced the difficulty of solving the optimization problem and the computational dimensionality, resulting in a set of typical daily scenarios. ; Specifically, clustering yields a set of typical daily scenes. : , , .

[0033] S3. Parallel Computing Framework Construction and Optimization Problem Setting: Construct a parallel computing framework corresponding to three energy storage deployment scenarios. Based on the typical daily scenario, set optimization problems for each deployment scenario. The optimization problems include objective functions and constraints; specifically including: S31. Parallel computing framework construction: Construct a parallel computing framework that includes three energy storage deployment scenarios. The three scenarios are: energy storage is only deployed in new energy power stations, energy storage is only deployed in computing centers, and energy storage is deployed in both new energy power stations and computing centers. S32. Setting the optimization problem: Based on the typical daily scenario, set a corresponding optimization problem for each layout scenario. The optimization problem includes an objective function and constraints. S321. The objective function is to minimize the total life-cycle cost of energy storage optimization configuration. The total life-cycle cost includes construction cost, maintenance cost, and operating cost, satisfying the following:

[0034] in, The annual construction cost, Annual maintenance costs This refers to the annual operating cost.

[0035] The formula for calculating construction costs is:

[0036] Among them, the formula for calculating the annual value is... Calculate the future value factor of annuity , For interest rates, This refers to the service life; Configure the number of units for a single energy storage capacity; For the construction cost of energy storage, Land costs for energy storage; The formula for calculating annual operating costs is:

[0037] in, For the annual overhaul cost of energy storage, Annual minor repair costs for energy storage; The formula for calculating annual operating and maintenance costs is as follows:

[0038] in, The energy consumption rate is typically taken as 2%. For time, the annual cost is calculated using 8760 hours; For electricity price; Energy storage capacity.

[0039] S322. Constraints include equality constraints and inequality constraints; The equality constraint is a power balance constraint, which satisfies:

[0040]

[0041] In the formula: , , , , Representing nodes respectively Active output, active load, reactive input, reactive load, and node voltage; , , Representing branch roads The conductivity, susceptance, and phase angle difference, It is a set of nodes.

[0042] In addition, inequality constraints are variable constraints, including:

[0043]

[0044]

[0045] Output limitations of this type of energy storage at this capacity:

[0046] Constraints on the number of energy storage batteries:

[0047] Due to the limited number of energy storage units that can be configured on the busbar and in the site. This represents the maximum number of busbars and the maximum number that can be connected to the site.

[0048] To facilitate the solution, a parallel computing framework is constructed: energy storage is placed in photovoltaic power plants, computing centers, or both. Three optimization problems are constructed in parallel to further reduce the computational load and improve computational efficiency.

[0049] S4. Solve each optimization problem using a preset optimization algorithm to obtain the energy storage configuration results for each deployment scenario; Specifically, the particle swarm optimization algorithm is used to solve the optimization problems corresponding to the above three energy storage deployment scenarios in parallel, and the energy storage configuration results (including energy storage configuration location, number of configuration units, and energy storage capacity) are obtained for each deployment scenario.

[0050] S5. Compare the economic efficiency of the energy storage configuration results to determine the optimal energy storage configuration scheme.

[0051] Specifically, the energy storage configuration results under three deployment scenarios, and the total life cycle cost of energy storage deployed in new energy power plants. The total lifecycle cost of energy storage configured in the computing center The total life-cycle cost of energy storage when both are configured simultaneously The energy storage configuration with the lowest cost is selected as the optimal energy storage configuration solution.

[0052] Example 2 See Figure 2 , Figure 2This is an architecture diagram of the computing-coordinated energy storage location and capacity determination system based on grid-connected green electricity direct connection proposed in this invention, which may specifically include: M1, Annual Historical Data Acquisition Module, is used to acquire the operation data and line data of computing centers and new energy power stations that need to be configured with energy storage in the green electricity direct connection scenario according to the set data source, and to set the grid to be connected as an infinite power source; Specifically, based on the set data source, the system obtains 8760 hours of time-series operation data of the computing center that needs to be configured with energy storage in the green electricity direct connection scenario, 8760 hours of time-series operation data of the new energy power station, the data of the lines to be connected between the two, and the data of the connection lines between the computing center and the external power grid; at the same time, the grid to be connected is set as an infinite power source, and the obtained operation data, line data and grid setting parameters are transmitted to the typical daily scenario clustering module.

[0053] M2, a typical daily scene clustering module, has its data input end connected to the output end of the annual historical data acquisition module. It is used to receive the annual operating data output by the annual historical data acquisition module and to perform typical daily scene clustering on the operating data using a clustering method to obtain typical daily scenes. Specifically, the annual operation data output by the annual historical data acquisition module is received, and the annual operation data is clustered into typical daily scenarios using the K-means clustering method to obtain a typical daily scenario set, thereby reducing the computational dimensionality. The typical daily scenario set is then transmitted to the parallel optimization framework module.

[0054] M3, a parallel optimization framework module, has its data input end connected to the output end of the typical daily scene clustering module. It is used to receive the typical daily scene output by the typical daily scene clustering module and construct a parallel computing framework. The parallel computing framework corresponds to three energy storage deployment scenarios. Based on the typical daily scene, it sets optimization problems for each deployment scenario. The optimization problem includes an objective function and constraints. Specifically, the typical daily scenario set output by the typical daily scenario clustering module is received, and a parallel computing framework is constructed that includes three energy storage deployment scenarios (energy storage is only deployed in new energy power stations, energy storage is only deployed in computing centers, and energy storage is deployed in both new energy power stations and computing centers). Based on the typical daily scenario set, a corresponding optimization problem is set for each deployment scenario. The optimization problem includes an objective function and constraints (the objective function is to minimize the total life cycle cost of energy storage, and the constraints include power balance constraints, voltage constraints, power angle constraints, and constraints on the number of energy storage batteries). The optimization problems corresponding to the three deployment scenarios are then transmitted to the optimization problem solving module.

[0055] M4, the optimization problem solving module, has its data input end connected to the output end of the parallel optimization framework module. It is used to receive the optimization problems under each energy storage deployment scenario output by the parallel optimization framework module, and solve each optimization problem using a preset optimization algorithm to obtain the energy storage configuration results under each deployment scenario. Specifically, the module receives three optimization problems output by the parallel optimization framework module, uses the particle swarm optimization algorithm to solve each optimization problem in parallel, obtains the energy storage configuration results for each energy storage deployment scenario, and transmits the energy storage configuration results for each scenario to the scheme acquisition module.

[0056] M5, the scheme acquisition module, has its data input end connected to the output end of the optimization problem solving module. It is used to receive the energy storage configuration results under various energy storage deployment scenarios output by the optimization problem solving module, compare the economic efficiency of the energy storage configuration results, and determine the best scheme for energy storage optimization configuration.

[0057] Specifically, the system receives three energy storage configuration results from the optimization problem solving module, performs an economic comparison of the total annual cost over the entire life cycle corresponding to each result, and finally obtains and outputs the optimal energy storage configuration scheme.

[0058] Example 3 Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores program code for a computing-powered energy storage location and capacity determination calculation method based on grid-connected green electricity direct connection. The program code includes instructions for executing the computing-powered energy storage location and capacity determination calculation method based on grid-connected green electricity direct connection as described in Embodiment 1 or any possible implementation thereof.

[0059] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0060] Example 4 Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor; The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute the computational power storage location and capacity calculation method based on grid-connected green electricity direct connection in Embodiment 4 or any possible implementation thereof.

[0061] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0062] In summary, the present invention has the following advantages: 1. Provides a dedicated calculation method for the site selection and capacity determination of energy storage systems in green electricity direct connection scenarios, solving the problem of optimizing the configuration of energy storage in multiple locations (new energy side, load side, dual-side collaboration) when there is a significant electrical distance between new energy power stations and computing centers; 2. Reduce the difficulty and computational dimensionality of solving energy storage optimization configuration problems by using K-means clustering to process annual time-series operating data, avoiding the complexity and redundancy of direct calculation of massive data; 3. Significantly reduce the computational load of optimization problems. By using a parallel computing framework for three energy storage deployment scenarios, the computational efficiency of energy storage configuration schemes is greatly improved. 4. Achieve optimal life-cycle cost for energy storage configuration by comprehensively considering the objective function of construction, maintenance, and operation costs to ensure the economic feasibility of the energy storage system; 5. Ensure the reliability and power quality of the power supply to the computing center, and adapt to the power needs of the computing center with high power density, strong load fluctuation and high power supply reliability through constraints such as power balance, voltage and power angle. 6. Facilitate the full absorption of green electricity from large-capacity renewable energy power plants, break through the absorption capacity limitations of traditional integrated source-grid-load-storage and microgrid technologies, and promote the large-scale application of green electricity direct connection mode.

[0063] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for selecting and sizing energy storage based on grid-connected green electricity direct connection and algorithmic electricity collaboration, characterized in that, include: S1. Obtain the operation data and line data of the computing center and new energy power station that need to be configured for energy storage in the green electricity direct connection scenario according to the set data source, and set the grid to be connected to as an infinite power source; S2. Cluster the runtime data using a clustering method to obtain typical daily scenarios; S3. Construct a parallel computing framework, which corresponds to three energy storage deployment scenarios. Based on the typical daily scenario, set optimization problems for each deployment scenario. The optimization problems include objective functions and constraints. S4. Solve each optimization problem using a preset optimization algorithm to obtain the energy storage configuration results for each deployment scenario; S5. Compare the economic efficiency of the energy storage configuration results to determine the optimal energy storage configuration scheme.

2. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection as described in claim 1, characterized in that, The operational data includes 8760 hours of time-series operational data for the computing center and 8760 hours of time-series operational data for the new energy power stations; The line data includes the proposed connection line data between the computing center and the new energy power station, and the connection line data between the computing center and the external power grid.

3. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection as described in claim 1, characterized in that, The clustering method is K-means clustering, and the running data is annual running data. After clustering, a set of typical daily scenes is output.

4. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection as described in claim 1, characterized in that, The three energy storage deployment scenarios are: energy storage is deployed only in new energy power plants, energy storage is deployed only in computing centers, and energy storage is deployed in both new energy power plants and computing centers.

5. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection according to claim 1, characterized in that, The objective function is to minimize the life-cycle cost of optimized energy storage configuration. The life-cycle cost includes construction cost, maintenance cost, and operating cost, and satisfies the following: in, The annual construction cost, Annual maintenance costs This refers to the annual operating cost.

6. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection according to claim 5, characterized in that, The formula for calculating the construction cost is as follows: Among them, the formula for calculating the annual value is... Calculate the future value factor of annuity , For interest rates, This refers to the service life; Configure the number of units for a single energy storage capacity; For the construction cost of energy storage, Land costs for energy storage; The formula for calculating the annual operating cost is as follows: in, For the annual overhaul cost of energy storage, Annual minor repair costs for energy storage; The formula for calculating the annual maintenance cost is as follows: in, The energy consumption rate is typically taken as 2%. For time, the annual cost is calculated using 8760 hours; For electricity price; Energy storage capacity.

7. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection according to claim 1, characterized in that, The constraints include equality constraints and inequality constraints. The equality constraints are power balance constraints, and the inequality constraints include voltage constraints, power angle constraints, and constraints on the number of energy storage batteries.

8. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection according to claim 7, characterized in that, The formula for the power balance constraint is: in, , , , , Representing nodes respectively Active output, active load, reactive input, reactive load, and node voltage; , , Representing branches The conductivity, susceptance, and phase angle difference, It is a set of nodes.

9. The method for site selection and capacity determination of computing-coordinated energy storage based on grid-connected green electricity direct connection according to claim 1, characterized in that, The preset optimization algorithm is the particle swarm optimization algorithm; in step S5, the economic comparison is to compare the total annual cost of the entire life cycle under each energy storage deployment scenario, and select the energy storage configuration with the lowest cost as the best energy storage optimization configuration scheme.

10. A computing-coordinated energy storage location and capacity determination system based on grid-connected green electricity direct connection, characterized in that, include: The annual historical data acquisition module is used to acquire the operation data and line data of computing centers and new energy power stations that need to be configured with energy storage in the green power direct connection scenario, and to set the grid to be connected as an infinite power source. The typical daily scene clustering module has its data input end connected to the output end of the annual historical data acquisition module. It is used to receive the annual operating data output by the annual historical data acquisition module, and to perform typical daily scene clustering on the annual operating data using a clustering method to obtain typical daily scenes. The parallel optimization framework module has its data input end connected to the output end of the typical daily scene clustering module. It is used to receive the typical daily scene output by the typical daily scene clustering module and construct a parallel computing framework. The parallel computing framework corresponds to three energy storage deployment scenarios. Based on the typical daily scene, optimization problems are set for each deployment scenario. The optimization problem includes an objective function and constraints. The optimization problem-solving module has a data input end connected to the output end of the parallel optimization framework module. It is used to receive the optimization problems under various energy storage deployment scenarios output by the parallel optimization framework module, solve each optimization problem using a preset optimization algorithm, and obtain the energy storage configuration results under each deployment scenario. The scheme acquisition module has its data input end connected to the output end of the optimization problem solving module. It is used to receive the energy storage configuration results under various energy storage deployment scenarios output by the optimization problem solving module, compare the economic efficiency of the energy storage configuration results, and determine the optimal energy storage configuration scheme.

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