High-permeability photovoltaic power distribution area light storage optimal configuration method and device

Abstract

The invention relates to a high-permeability photovoltaic power distribution area optical storage optimization configuration method and device.The method comprises the steps that an optical storage collaborative operation configuration model of a power distribution area is built, the optical storage collaborative operation configuration model comprises a locating and sizing model and an optimization operation model, and the locating and sizing model takes investment economy as a target; the optimization operation model takes the minimum system operation cost, the minimum system light abandoning cost and the minimum system operation safety cost as targets; and solving the optical storage cooperative operation configuration model to obtain an optical storage optimal configuration scheme, and configuring optical storage of the power distribution area according to the optical storage optimal configuration scheme. According to the invention, the effect of energy storage optimal configuration can be achieved.

Description

Technical Field

[0001] This invention relates to the field of renewable energy optimization configuration technology, and in particular to a method and apparatus for optimizing the configuration of photovoltaic and energy storage in a high-penetration photovoltaic distribution area. Background Technology

[0002] The intermittent nature of photovoltaic (PV) power generation and the mismatch between load demand and timing lead to insufficient PV absorption capacity in distribution networks. Furthermore, the randomness of PV power generation and the mismatch with load demand cause node voltage exceedances during periods of high PV power generation. High-proportion low-voltage distributed PV grid connection to distribution substations exacerbates overvoltage problems for users, with severe overvoltages potentially damaging user equipment. Some PV substations even experience voltage exceedances during the day and below the limit at night, severely impacting power quality. The large-scale grid connection of distributed energy sources, such as wind and PV, has led to a continuous increase in the penetration rate of renewable energy based on power electronic converters, resulting in a trend of low inertia and weak system strength in the power system, exacerbating stability issues. Grid-based energy storage systems, due to their advantages in power regulation and power quality assurance, have become an effective means to solve the voltage stability problem in distribution networks. With the grid connection of distributed PV, the power flow direction in the distribution network may change, significantly impacting voltage distribution and line energy consumption. Grid-based energy storage can effectively address the impact of high-proportion renewable energy grid connection on system power flow and voltage regulation.

[0003] Large-scale distributed photovoltaic (PV) grid integration into distribution networks has a significant impact on their operational performance, including power flow and node voltage distribution, particularly on the reliability, security, and economy of the distribution network. Energy storage, as a flexible resource that can participate in the power supply and demand balance of the distribution network, is currently the focus of domestic research, which primarily focuses on control strategies, seeking optimal location and capacity for energy storage—that is, optimal energy storage configuration. Current research on the selection and capacity determination of PV and energy storage in distribution networks does not consider the correlation with the operation of the distribution network. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing the configuration of photovoltaic and energy storage in a high-penetration photovoltaic distribution area, which can achieve the effect of optimizing energy storage configuration.

[0005] The technical solution adopted by this invention to solve its technical problem is: to provide a method for optimizing the configuration of photovoltaic and energy storage in a high-penetration photovoltaic distribution area, comprising the following steps:

[0006] A configuration model for the coordinated operation of photovoltaic and energy storage in a distribution area is constructed. The configuration model includes a site selection and capacity determination model and an optimized operation model. The site selection and capacity determination model aims at investment economics, while the optimized operation model aims at minimizing system operating costs, system curtailment costs, and system operating safety costs.

[0007] The optical-storage collaborative operation configuration model is solved to obtain an optimized configuration scheme for optical-storage, and the optical-storage configuration of the distribution radio area is carried out according to the optimized configuration scheme.

[0008] The objective function of the location and capacity gradation model is expressed as: ,in, This indicates taking the minimum value. This is the sum of the system's annual comprehensive operating costs. The sum of the investment costs for distributed photovoltaic power and grid-connected energy storage is expressed as: , The investment cost of distributed photovoltaic power. The investment cost of grid-type energy storage, For government subsidies for photovoltaic power, The sum of system operation and maintenance costs is expressed as: , For the maintenance costs of distributed photovoltaic power, To reduce the maintenance costs of grid-based energy storage, To reduce network loss in the distribution area, To cover the cost of light curtailment, The cost of electricity is expressed as: , For the first The weight of each scenario The total number of scenes, Electricity prices for main online purchases, for Interactive power of distribution radio stations during a given time period.

[0009] The constraints of the site selection and sizing model include:

[0010] The total grid-connected photovoltaic capacity constraint is expressed as: ,in, This represents the total grid-connected capacity of distributed photovoltaic power. The total active load of the connected system, The ratio of permitted photovoltaic capacity to total load capacity;

[0011] Node installation capacity constraints are expressed as follows: ,in, For nodes In the scene Down Active power generated by distributed photovoltaic systems during specific time periods. For nodes In the scene Down The maximum active power that distributed photovoltaic systems are allowed to generate during a given time period.

[0012] The objective function of the optimized operation model is expressed as: ,in, This indicates taking the minimum value. The system operating cost is expressed as: , The sum of investment costs for distributed photovoltaic power and grid-connected energy storage. The sum of system operation and maintenance costs. The system's cost of light curtailment is expressed as: ,in, For the first The weight of each scenario Total number of scenes A collection of photovoltaic equipment. The curtailment coefficient of photovoltaic power. In the first A scenario At any given time, the maximum possible output of photovoltaic power. In the first A scenario At any given moment, the actual output of photovoltaic power. For a unit of time, The system's operational security cost is expressed as: , Represents the set of busbars. For nodes In the scene Down Actual node voltage values ​​for the time period For nodes In the scene Down The node voltage rating for the time period, This represents the total number of buses in the system.

[0013] The constraints of the optimized operating model include:

[0014] Power flow constraints are expressed as: ,in, and They are nodes Active power and reactive power, and They are nodes and nodes voltage, , and They are nodes and nodes The electrical conductance, susceptance, and voltage phase angle difference between them;

[0015] Branch current constraints are expressed as: ,in, For nodes In the scene Down Current during a given period; This represents the maximum current in the branch.

[0016] Node voltage constraints are expressed as: ,in, For nodes In the scene Down Voltage during the period and They are nodes The minimum and maximum voltages;

[0017] Energy storage battery constraints are expressed as: ,in, For grid-connected energy storage, the state of charge and These represent the minimum and maximum states of charge for grid-type energy storage, respectively.

[0018] When solving the photovoltaic-storage collaborative operation configuration model, a multi-objective particle swarm optimization algorithm is used.

[0019] The process of solving the photovoltaic-storage collaborative operation configuration model using a multi-objective particle swarm optimization algorithm specifically includes:

[0020] Obtain system parameters for the distribution network area and alternative nodes available for distributed photovoltaic access;

[0021] Randomly generate particle velocities and positions, that is, initialize the access location and capacity of distributed photovoltaic energy storage, and use them as input to the optimized operation model;

[0022] Based on the objective function and constraints of the optimized operation model, the distributed photovoltaic and energy storage scheduling operation status for each time period is optimized using a multi-objective particle swarm optimization algorithm and fed back to the site selection and capacity determination model. Based on the feedback of the distributed photovoltaic and energy storage scheduling operation status for each time period, the objective function of the site selection and capacity determination model is calculated to obtain the planning cost and population fitness, and the access location and capacity of distributed photovoltaic and energy storage are optimized and updated.

[0023] Determine whether the preset number of iterations has been reached. If the preset number of iterations has been reached, output the result of the location and capacity model as the planning scheme and the result of the optimized operation model as the operation strategy. If the preset number of iterations has not been reached, repeat the previous step.

[0024] The technical solution adopted by this invention to solve its technical problem is: to provide a high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration device, comprising:

[0025] The module is used to build a configuration model for the coordinated operation of photovoltaic and energy storage in a distribution area. The configuration model includes a site selection and capacity determination model and an optimized operation model. The site selection and capacity determination model aims at investment economy, while the optimized operation model aims at minimizing system operating cost, system curtailment cost, and system operating safety cost.

[0026] The solution execution module is used to solve the optical-storage collaborative operation configuration model to obtain an optimized optical-storage configuration scheme, and configure the optical-storage of the distribution radio area according to the optimized optical-storage configuration scheme.

[0027] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method.

[0028] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-mentioned high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method are implemented.

[0029] Beneficial effects

[0030] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: The present invention uses a multi-objective particle swarm optimization algorithm to optimize the energy storage configuration of the distribution radio station area. The upper layer selects the location and determines the capacity with the goal of minimizing the total cost, while the lower layer comprehensively considers the system economy and voltage deviation. By adjusting the curtailment of solar power and the output of energy storage, multi-objective optimization is achieved, and finally the effect of optimized energy storage configuration is achieved. Attached Figure Description

[0031] Figure 1 This is a flowchart of the high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method according to the first embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the photovoltaic-storage collaborative operation configuration model in the first embodiment of the present invention;

[0033] Figure 3 This is a flowchart of the solution process for the photovoltaic-storage collaborative operation configuration model in the first embodiment of the present invention. Detailed Implementation

[0034] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0035] The first embodiment of the present invention relates to a method for optimizing the configuration of photovoltaic and energy storage in a high-penetration photovoltaic distribution area, such as... Figure 1 As shown, it includes the following steps:

[0036] Step 1: Construct a configuration model for the coordinated operation of optical and energy storage in the distribution radio area.

[0037] The photovoltaic-storage collaborative operation configuration model constructed in this step is as follows: Figure 2 As shown, it includes an upper-level location and capacity model and a lower-level optimization operation model.

[0038] The site selection and capacity determination model aims at investment economics, primarily considering the investment in photovoltaic and energy storage construction as well as the overall operation and maintenance costs of the distribution area, in order to determine the photovoltaic and energy storage access capacity and location. The objective function of the site selection and capacity determination model is expressed as:

[0039] ;

[0040] in, This indicates taking the minimum value. This is the sum of the system's annual comprehensive operating costs. The sum of the investment costs for distributed photovoltaic power and grid-connected energy storage is expressed as: , The investment cost of distributed photovoltaic power. The investment cost of grid-type energy storage, For government subsidies for photovoltaic power, The sum of system operation and maintenance costs is expressed as: , For the maintenance costs of distributed photovoltaic power, To reduce the maintenance costs of grid-based energy storage, To reduce network loss in the distribution area, To cover the cost of light curtailment, The cost of electricity is expressed as: , For the first The weight of each scenario The total number of scenes, Electricity prices for main online purchases, for Interactive power of distribution radio stations during a given time period.

[0041] The constraints of the site selection and sizing model include:

[0042] The total grid-connected photovoltaic capacity constraint is expressed as:

[0043] ;

[0044] in, This represents the total grid-connected capacity of distributed photovoltaic power. The total active load of the connected system, The ratio of permitted photovoltaic capacity to total load capacity;

[0045] Node installation capacity constraints are expressed as follows:

[0046] ;

[0047] in, For nodes In the scene Down Active power generated by distributed photovoltaic systems during specific time periods. For nodes In the scene Down The maximum active power that distributed photovoltaic systems are allowed to generate during a given time period.

[0048] The optimized operation model aims to minimize system operating costs, maximize the proportion of distributed photovoltaic (PV) power consumption, and minimize system operating safety costs. In the PV-storage co-configuration, energy storage can increase PV power consumption. Considering economic efficiency, this implementation converts the PV power consumption rate into a curtailment rate for analysis, i.e., minimizing the curtailment rate instead of maximizing the proportion of distributed PV power consumption. Furthermore, PV grid connection causes voltage waveform changes in distribution transformer areas; therefore, this implementation equivalently minimizes system operating safety costs to minimize distribution transformer area voltage deviation. Thus, the objective function of the optimized operation model is expressed as:

[0049] ;

[0050] in, The system operating cost is expressed as: , The sum of investment costs for distributed photovoltaic power and grid-connected energy storage. The sum of system operation and maintenance costs. The system's cost of light curtailment is expressed as: ,in, For the first The weight of each scenario The total number of scenes, A collection of photovoltaic equipment. The curtailment coefficient of photovoltaic power. In the first A scenario At any given time, the maximum possible output of photovoltaic power. In the first A scenario At any given moment, the actual output of photovoltaic power. For a unit of time, The system's operational security cost is expressed as: , This represents the set of buses, involving all bus nodes in the system. For nodes In the scene Down Actual node voltage values ​​for the time period For nodes In the scene Down The node voltage rating for the time period, This represents the total number of buses in the system.

[0051] The constraints of the optimized operating model include:

[0052] Power flow constraints are expressed as:

[0053] ;

[0054] in, and They are nodes Active power and reactive power, and They are nodes and nodes voltage, , and They are nodes and nodes The electrical conductance, susceptance, and voltage phase angle difference between them;

[0055] Branch current constraints are expressed as:

[0056] ;

[0057] in, For nodes In the scene Down Current during a given period; This represents the maximum current in the branch.

[0058] Node voltage constraints are expressed as:

[0059] ;

[0060] in, For nodes In the scene Down Voltage during the period and They are nodes The minimum and maximum voltages;

[0061] Energy storage battery constraints are expressed as:

[0062] ;

[0063] in, For grid-connected energy storage, the state of charge and These represent the minimum and maximum states of charge for grid-type energy storage, respectively.

[0064] Step 2: Solve the optical-storage collaborative operation configuration model to obtain the optical-storage optimized configuration scheme, and configure the optical-storage of the distribution radio area according to the optical-storage optimized configuration scheme.

[0065] This step employs a multi-objective particle swarm optimization algorithm to solve the photovoltaic-storage collaborative operation configuration model, specifically including:

[0066] Acquisition steps: Obtain system parameters of the distribution network area (raw data such as network parameters, load and irradiance) and alternative nodes available for distributed photovoltaic access;

[0067] Initialization steps: Randomly generate particle velocities and positions, that is, initialize the access location and capacity of distributed photovoltaic energy storage, and use it as input for the lower-level optimized operation model;

[0068] Execution steps: Based on the objective function and constraints of the optimized operation model, the distributed photovoltaic and energy storage scheduling operation status (including its optimized operation cost results) for each time period is optimized using the multi-objective particle swarm optimization algorithm, and fed back to the upper-level site selection and capacity determination model; according to the feedback of the distributed photovoltaic and energy storage scheduling operation status for each time period, the objective function of the site selection and capacity determination model is calculated to obtain the planning cost and population fitness, and the access location and capacity of distributed photovoltaic and energy storage are optimized and updated;

[0069] Judgment steps: Determine whether the preset number of iterations has been reached. If the preset number of iterations has been reached, output the result of the location and capacity model as the planning scheme and the result of the optimized operation model as the operation strategy. If the preset number of iterations has not been reached, repeat the steps.

[0070] It is not difficult to see that the present invention uses a multi-objective particle swarm optimization algorithm to optimize the energy storage configuration of the distribution radio station area. The upper layer selects the site and determines the capacity with the goal of minimizing the total cost, while the lower layer comprehensively considers the system economy and voltage deviation. By adjusting the curtailment of solar power and the output of energy storage, multi-objective optimization is achieved, and the effect of optimized energy storage configuration is finally achieved.

[0071] The second embodiment of the present invention relates to a high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration device, comprising:

[0072] The module is used to build a configuration model for the coordinated operation of photovoltaic and energy storage in a distribution area. The configuration model includes a site selection and capacity determination model and an optimized operation model. The site selection and capacity determination model aims at investment economy, while the optimized operation model aims at minimizing system operating cost, system curtailment cost, and system operating safety cost.

[0073] The solution execution module is used to solve the optical-storage collaborative operation configuration model to obtain an optimized optical-storage configuration scheme, and configure the optical-storage of the distribution radio area according to the optimized optical-storage configuration scheme.

[0074] The objective function of the location and capacity gradation model is expressed as: ,in, This indicates taking the minimum value. This is the sum of the system's annual comprehensive operating costs. The sum of the investment costs for distributed photovoltaic power and grid-connected energy storage is expressed as: , The investment cost of distributed photovoltaic power. The investment cost of grid-type energy storage, For government subsidies for photovoltaic power, The sum of system operation and maintenance costs is expressed as: , For the maintenance costs of distributed photovoltaic power, To reduce the maintenance costs of grid-based energy storage, To reduce network loss in the distribution area, To cover the cost of light curtailment, The cost of electricity is expressed as: , For the first The weight of each scenario The total number of scenes, Electricity prices for main online purchases, for Interactive power of distribution radio stations during a given time period.

[0075] The constraints of the site selection and sizing model include:

[0076] The total grid-connected photovoltaic capacity constraint is expressed as: ,in, This represents the total grid-connected capacity of distributed photovoltaic power. The total active load of the connected system, The ratio of permitted photovoltaic capacity to total load capacity;

[0077] Node installation capacity constraints are expressed as follows: ,in, For nodes In the scene Down Active power generated by distributed photovoltaic systems during specific time periods. For nodes In the scene Down The maximum active power that distributed photovoltaic systems are allowed to generate during a given time period.

[0078] The objective function of the optimized operation model is expressed as: ,in, This indicates taking the minimum value. The system operating cost is expressed as: , The sum of investment costs for distributed photovoltaic power and grid-connected energy storage. The sum of system operation and maintenance costs. The system's cost of light curtailment is expressed as: ,in, For the first The weight of each scenario The total number of scenes, A collection of photovoltaic equipment. The curtailment coefficient of photovoltaic power. In the first A scenario At any given time, the maximum possible output of photovoltaic power. In the first A scenario At any given moment, the actual output of photovoltaic power. For a unit of time, The system's operational security cost is expressed as: , This represents the set of buses, involving all bus nodes in the system. For nodes In the scene Down Actual node voltage values ​​for the time period For nodes In the scene Down The node voltage rating for the time period, This represents the total number of buses in the system.

[0079] The constraints of the optimized operating model include:

[0080] Power flow constraints are expressed as: ,in, and They are nodes Active power and reactive power, and They are nodes and nodes voltage, , and They are nodes and nodes The electrical conductance, susceptance, and voltage phase angle difference between them;

[0081] Branch current constraints are expressed as: ,in, For nodes In the scene Down Current during a given period; This represents the maximum current in the branch.

[0082] Node voltage constraints are expressed as: ,in, For nodes In the scene Down Voltage during the period and They are nodes The minimum and maximum voltages;

[0083] Energy storage battery constraints are expressed as: ,in, For grid-connected energy storage, the state of charge and These represent the minimum and maximum states of charge for grid-type energy storage, respectively.

[0084] The solution execution module uses a multi-objective particle swarm optimization algorithm to solve the photovoltaic-storage collaborative operation configuration model.

[0085] The solution execution module includes:

[0086] The acquisition unit is used to acquire system parameters of the distribution network area and alternative nodes available for distributed photovoltaic access;

[0087] An initialization unit is used to randomly generate particle velocities and positions, that is, to initialize the access location and capacity of distributed photovoltaic energy storage, and to serve as the input of the optimized operation model.

[0088] The execution unit is used to optimize the operation of distributed photovoltaic and energy storage scheduling in each time period based on the objective function and constraints of the optimized operation model using a multi-objective particle swarm optimization algorithm, and feed it back to the site selection and capacity determination model; based on the feedback of the operation of distributed photovoltaic and energy storage scheduling in each time period, the execution unit calculates the objective function of the site selection and capacity determination model, obtains the planning cost and population fitness, and optimizes and updates the access location and capacity of distributed photovoltaic and energy storage.

[0089] The judgment unit is used to determine whether the preset number of iterations has been reached. If the preset number of iterations has been reached, the result of the location and capacity grading model is output as the planning scheme, and the result of the optimized operation model is output as the operation strategy. If the preset number of iterations has not been reached, the process returns to the execution unit.

[0090] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method of the first embodiment.

[0091] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method of the first embodiment.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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 and optical storage) containing computer-usable program code.

[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the configuration of photovoltaic power distribution areas and energy storage in high-penetration photovoltaic power distribution zones, characterized in that, Includes the following steps: A configuration model for the coordinated operation of photovoltaic and energy storage in a distribution area is constructed. The configuration model includes a site selection and capacity determination model and an optimized operation model. The site selection and capacity determination model aims at investment economy, while the optimized operation model aims at minimizing system operating cost, system curtailment cost, and system operating safety cost. The optical-storage collaborative operation configuration model is solved to obtain an optimized configuration scheme for optical-storage, and the optical-storage configuration of the distribution radio area is carried out according to the optimized configuration scheme.

2. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage in accordance with claim 1, characterized in that, The objective function of the location and capacity gradation model is expressed as: ,in, This indicates taking the minimum value. This is the sum of the system's annual comprehensive operating costs. The sum of the investment costs for distributed photovoltaic power and grid-connected energy storage is expressed as: , The investment cost of distributed photovoltaic power. The investment cost of grid-type energy storage, For government subsidies for photovoltaic power, The sum of system operation and maintenance costs is expressed as: , For the maintenance costs of distributed photovoltaic power, To reduce the maintenance costs of grid-based energy storage, To reduce network loss in the distribution area, To cover the cost of light curtailment, The cost of electricity is expressed as: , For the first The weight of each scenario The total number of scenes, Electricity prices for main online purchases, for Interactive power of distribution radio stations during a given time period.

3. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage in accordance with claim 1, characterized in that, The constraints of the site selection and sizing model include: The total grid-connected photovoltaic capacity constraint is expressed as: ,in, This represents the total grid-connected capacity of distributed photovoltaic power. The total active load of the connected system, The ratio of permitted photovoltaic capacity to total load capacity; Node installation capacity constraints are expressed as follows: ,in, For nodes In the scene Down Active power generated by distributed photovoltaic systems during specific time periods. For nodes In the scene Down The maximum active power that distributed photovoltaic systems are allowed to generate during a given time period.

4. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage in accordance with claim 1, characterized in that, The objective function of the optimized operation model is expressed as: ,in, This indicates taking the minimum value. The system operating cost is expressed as: , The sum of investment costs for distributed photovoltaic power and grid-connected energy storage. The sum of system operation and maintenance costs. The system's cost of light curtailment is expressed as: ,in, For the first The weight of each scenario The total number of scenes, A collection of photovoltaic equipment. The curtailment coefficient of photovoltaic power. In the first A scenario At any given time, the maximum possible output of photovoltaic power. In the first A scenario At any given moment, the actual output of photovoltaic power. For a unit of time, The system's operational security cost is expressed as: , Represents the set of busbars. For nodes In the scene Down Actual node voltage values ​​for the time period For nodes In the scene Down The node voltage rating for the time period, This represents the total number of buses in the system.

5. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage according to claim 1, characterized in that, The constraints of the optimized operating model include: Power flow constraints are expressed as: ,in, and They are nodes Active power and reactive power, and They are nodes and nodes voltage, , and They are nodes and nodes The electrical conductance, susceptance, and voltage phase angle difference between them; Branch current constraints are expressed as: ,in, For nodes In the scene Down Current during a given period; This represents the maximum current in the branch. Node voltage constraints are expressed as: ,in, For nodes In the scene Down Voltage during a period of time, and They are nodes The minimum and maximum voltages; Energy storage battery constraints are expressed as: ,in, For grid-type energy storage, the state of charge and These represent the minimum and maximum states of charge for grid-type energy storage, respectively.

6. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage according to claim 1, characterized in that, When solving the photovoltaic-storage collaborative operation configuration model, a multi-objective particle swarm optimization algorithm is used.

7. The method for optimizing the configuration of photovoltaic power distribution areas and energy storage in accordance with claim 6, characterized in that, The process of solving the photovoltaic-storage collaborative operation configuration model using a multi-objective particle swarm optimization algorithm specifically includes: Obtain system parameters for the distribution network area and alternative nodes available for distributed photovoltaic access; Randomly generate particle velocities and positions, that is, initialize the access location and capacity of distributed photovoltaic energy storage, and use them as input to the optimized operation model; Based on the objective function and constraints of the optimized operation model, the distributed photovoltaic and energy storage scheduling operation status for each time period is optimized using a multi-objective particle swarm optimization algorithm and fed back to the site selection and capacity determination model. Based on the feedback of the distributed photovoltaic and energy storage scheduling operation status for each time period, the objective function of the site selection and capacity determination model is calculated to obtain the planning cost and population fitness, and the access location and capacity of distributed photovoltaic and energy storage are optimized and updated. Determine whether the preset number of iterations has been reached. If the preset number of iterations has been reached, output the result of the location and capacity model as the planning scheme and the result of the optimized operation model as the operation strategy. If the preset number of iterations has not been reached, repeat the previous step.

8. A high-penetration photovoltaic distribution area photovoltaic-storage optimized configuration device, characterized in that, include: The module is used to build a configuration model for the coordinated operation of photovoltaic and energy storage in a distribution area. The configuration model includes a site selection and capacity determination model and an optimized operation model. The site selection and capacity determination model aims at investment economy, while the optimized operation model aims at minimizing system operating cost, system curtailment cost, and system operating safety cost. The solution execution module is used to solve the optical-storage collaborative operation configuration model to obtain an optimized optical-storage configuration scheme, and configure the optical-storage of the distribution radio area according to the optimized optical-storage configuration scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-penetration photovoltaic distribution area photovoltaic-storage optimization configuration method as described in any one of claims 1-7.

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