Low-carbon matching power and light storage system capacity configuration method, device, equipment, readable storage medium and program product

Abstract

The application relates to a low-carbon capacity configuration method and device of a power matching optical storage system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a topology structure of power matching and system parameters of power matching, generating target daily source load data based on the topology structure and the system parameters; establishing an economic model and a carbon emission model of the optical storage system based on the target daily source load data; determining a target function based on the economic model and the carbon emission model, wherein the optimization target of the target function is to minimize the flatized electric carbon cost, the average annual flatized net kilowatt-hour electric carbon cost and the penalty cost of the voltage problem; establishing a capacity optimization constraint of the optical storage system; constructing a capacity optimization model of the optical storage system according to the capacity optimization constraint and the target function; and solving the capacity optimization model to obtain the capacity configuration of the optical storage system. The method can comprehensively optimize the capacity and position of the energy storage configuration of the optical storage system.

Description

Technical Field

[0001] This application relates to the field of power distribution and distributed energy system optimization technology, and in particular to a low-carbon power distribution photovoltaic energy storage system capacity configuration method, apparatus, computer equipment, computer-readable storage medium and computer program product. Background Technology

[0002] Currently, large-scale distributed photovoltaic (PV) grid connection and its consumption face numerous challenges, with curtailment being particularly prominent. Under these circumstances, energy storage technology has been developed. Energy storage possesses characteristics such as enabling energy transfer, balancing system supply and demand, and rapid response, significantly enhancing the controllability of new energy sources like wind and solar power, and enabling grid peak shaving and power fluctuation suppression. Therefore, rationally allocating energy storage capacity is an effective way to promote the local consumption of distributed PV power.

[0003] Existing research has limited use of the actual cost per unit of electricity as an indicator of energy storage cost, and it only focuses on the economic aspects. With the increasing prominence of carbon emissions, more new metrics should be developed to cover both economic and social benefits. Summary of the Invention

[0004] Therefore, it is necessary to provide a low-carbon energy storage system capacity configuration method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can more comprehensively optimize the capacity and location of energy storage configuration, addressing the aforementioned technical problems.

[0005] In a first aspect, this application provides a low-carbon method for configuring the capacity of a photovoltaic-storage system, comprising:

[0006] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0007] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0008] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0009] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0010] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0011] In one embodiment, solving the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system includes:

[0012] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, and the source and load data are input into the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved through multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0013] In one embodiment, the step of solving the capacity optimization model using a multi-stage optimization iteration technique to obtain the capacity configuration of the photovoltaic-storage system includes:

[0014] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0015] In one embodiment, establishing an economic model for the photovoltaic-storage system based on the target intraday source-load data includes:

[0016] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0017] In one embodiment, establishing a carbon emission model for the photovoltaic-storage system based on the target intraday source-load data includes:

[0018] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0019] In one embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0020] Secondly, this application also provides a low-carbon capacity configuration device for an energy-powered photovoltaic-storage system, comprising:

[0021] The acquisition module is used to acquire the topology of power distribution and the system parameters of the power distribution, and generate source-load data for the target day based on the topology and the system parameters;

[0022] A module is established to build an economic model and a carbon emission model for the photovoltaic-storage system based on the target intraday source-load data;

[0023] The first determining module is used to determine the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues of the photovoltaic-storage system based on the economic model and the carbon emission model.

[0024] The second determining module is used to determine an objective function based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0025] An optimization module is used to establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; and solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0026] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0027] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0028] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0029] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0030] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0031] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0032] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0033] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0034] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0035] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0036] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0037] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0038] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0039] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0040] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0041] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0042] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0043] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0044] In the aforementioned low-carbon power distribution and energy storage system capacity configuration method, the following steps are taken: First, the topology and system parameters of the power distribution system are obtained. Based on the topology and system parameters, target intraday source-load data is generated. Second, based on the target intraday source-load data, an economic model and a carbon emission model for the power distribution and energy storage system are established. Third, based on the economic model and the carbon emission model, the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues are determined. Fourth, based on the LCOE, the average annual net LCOE, and the penalty cost for voltage issues, an objective function is determined, with the optimization objective of minimizing the LCOE, the average annual net LCOE, and the penalty cost for voltage issues. Fifth, capacity optimization constraints for the power distribution and energy storage system are established. Sixth, based on the capacity optimization constraints and the objective function, a capacity optimization model for the power distribution and energy storage system is constructed. Finally, the capacity optimization model is solved to obtain the capacity configuration of the power distribution and energy storage system. Under the constraint of capacity optimization, the capacity configuration scheme is determined by minimizing the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of voltage problems, which can more comprehensively optimize the capacity and location of the energy storage configuration of the photovoltaic-storage system. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1This is a flowchart illustrating a low-carbon capacity configuration method for an energy-photovoltaic-storage system in one embodiment.

[0047] Figure 2 A detailed flowchart of a low-carbon energy storage system capacity configuration method in one embodiment;

[0048] Figure 3 This is a structural block diagram of a low-carbon energy storage system capacity configuration device in another embodiment;

[0049] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] In one embodiment, such as Figure 1 As shown, a low-carbon capacity configuration method for a photovoltaic-storage system is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0052] Step 102: Obtain the topology of power distribution and the system parameters of the power distribution, and generate source-load data for the target day based on the topology and the system parameters.

[0053] Among them, the power distribution topology refers to the connection relationships and layout of various components in power distribution. It describes the physical architecture of power distribution and is an abstract representation of the overall structure of power distribution. Power distribution system parameters are a collective term for various parameters describing the electrical and physical characteristics of individual components and the overall system. Intraday source-load data refers to the power output data of the power source and the power consumption data of the load in power distribution within a one-day time frame, reflecting the changes in power supply and load demand at different times of the day.

[0054] For example, the topology and relevant system parameters of a region's power distribution are first extracted. Based on the topology and system parameters, a large-scale source-load scenario is generated using multi-dimensional random sampling techniques. A stepwise elimination strategy is adopted, and scenarios that do not meet the requirements or have high repetition are eliminated round by round according to preset screening criteria. After multiple rounds of screening, the source-load data for the target day is obtained.

[0055] Step 104: Based on the target intraday source-load data, establish an economic model and a carbon emission model for the photovoltaic-storage system.

[0056] Among them, the economic model is used to determine the economic costs, and the carbon emission model is used to determine the carbon emission situation.

[0057] Step 106: Based on the economic model and the carbon emission model, determine the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage issues of the photovoltaic-storage system.

[0058] Among them, the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues are used to analyze and balance the relationship between the economic costs and carbon emissions of photovoltaic-storage systems.

[0059] Step 108: Based on the levelized carbon cost, the average annual net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, determine the objective function. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0060] Minimizing the objective function can lead to optimal solutions for carbon emission issues and various economic cost issues.

[0061] Step 110: Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0062] Among them, the capacity optimization constraints of the photovoltaic-storage system are used to ensure the stable operation of the system, improve the performance of the equipment, and achieve economic and environmental protection.

[0063] For example, capacity optimization constraints for the photovoltaic-storage system are established, and a capacity optimization model for the photovoltaic-storage system is constructed based on the capacity optimization constraints and the objective function. When it is necessary to set up the photovoltaic-storage system, the capacity optimization model is solved to determine the capacity configuration of the photovoltaic-storage system, and then the photovoltaic-storage system is set up according to the capacity configuration.

[0064] In the aforementioned low-carbon power distribution and energy storage system capacity configuration method, the following steps are taken: First, the topology and system parameters of the power distribution system are obtained. Based on the topology and system parameters, target intraday source-load data is generated. Second, based on the target intraday source-load data, an economic model and a carbon emission model for the power distribution and energy storage system are established. Third, based on the economic model and the carbon emission model, the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues are determined. Fourth, based on the LCOE, the average annual net LCOE, and the penalty cost for voltage issues, an objective function is determined, with the optimization objective of minimizing the LCOE, the average annual net LCOE, and the penalty cost for voltage issues. Fifth, capacity optimization constraints for the power distribution and energy storage system are established. Sixth, based on the capacity optimization constraints and the objective function, a capacity optimization model for the power distribution and energy storage system is constructed. Finally, the capacity optimization model is solved to obtain the capacity configuration of the power distribution and energy storage system. Under the constraint of capacity optimization, the capacity configuration scheme is determined by minimizing the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of voltage problems, which can more comprehensively optimize the capacity and location of the energy storage configuration of the photovoltaic-storage system.

[0065] In an exemplary embodiment, solving the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system includes:

[0066] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, input into the capacity optimization model, and the capacity configuration of the photovoltaic-storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0067] Optionally, source-load data includes power supply data and load data. Power supply data includes: power generation data, power generation quantity data, power generation efficiency data, and power supply operating status data under different scenarios. Load data includes: load power data, load curve data, load characteristic data, and load classification data under different scenarios. Scenarios can be industrial, commercial, or time-period scenarios. Industrial scenarios include load data for heavy industry, light industry, etc.; commercial scenarios include load data for retail, catering, entertainment, etc.; time periods include: peak load, off-peak load, and flat load scenarios.

[0068] For example, in a multi-scenario situation where the source and load of the photovoltaic-storage system are deterministic, deterministic source and load data of the photovoltaic-storage system are obtained, input into a capacity optimization model, and the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic-storage system. The photovoltaic-storage system is then configured according to the capacity configuration. In a multi-scenario situation where the source and load of the photovoltaic-storage system are uncertain, a multi-stage optimization iteration technique is used to solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system. The photovoltaic-storage system is then configured according to the capacity configuration.

[0069] In this embodiment, the calculation method to be used is determined by whether the source load of the required optical storage system is determined, which can result in an optical storage system capacity configuration that is more suitable for the local conditions.

[0070] In an exemplary embodiment, the step of solving the capacity optimization model using a multi-stage optimization iteration technique to obtain the capacity configuration of the photovoltaic-storage system includes:

[0071] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0072] For example, the capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic storage system; the iterative process includes: 1. A new iteration; 2. , Based on the capacity configuration and objective function of the photovoltaic-storage system obtained in the previous iteration, the objective function values ​​for each scenario are calculated; where, for the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration; 3. Calculate the weighted average of the objective function values ​​for each scenario, and update the decision vector based on the weighted average; 4. Based on the decision vector, the weight vectors for each scenario are updated to obtain the current capacity configuration of the optical storage system; 5. Based on the decision vector and the weighted average, determine the current error value; 6. If If the condition is met, return to step 1; otherwise, terminate. Check if the convergence condition is met; if not, return to step 1; if met, stop iterating and use the current capacity configuration of the photovoltaic-storage system as the optimized capacity configuration. Here, i represents the iteration number, used to track the algorithm's iteration process and ensure the algorithm gradually approaches the optimal solution, c is a cost coefficient vector of length n, and the decision vector x (where...) ), This is a feasible solution in scenario s. We use the subscript s to emphasize that specific problem characteristics will depend on the actual observed scenario, and the set S represents the set of possible scenarios. It is the decision vector of length n in the i-th iteration, and the associated cost coefficient vector. , Let be the objective function. As a penalty factor, It is the mean of the i-th iteration of length n, for each scenario. ,use Indicates the probability of occurrence. The weight vector for the i-th iteration. Let represent the error value of the i-th iteration. The given termination threshold is used as a condition for determining convergence.

[0073] In this embodiment, through multiple iterations of optimization, an optimized capacity configuration of the optical storage system can be obtained, which can then be used to configure the capacity of the optical storage system.

[0074] In an exemplary embodiment, establishing an economic model for the photovoltaic-storage system based on the target intraday source-load data includes:

[0075] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0076] For example, based on the source-load data within the target day, the economic cost of the photovoltaic-storage system is determined. This economic cost includes investment cost, operating cost, recovery cost, revenue from energy storage electricity price difference, revenue from energy storage subsidies, revenue from photovoltaic electricity price difference, and distribution line loss cost. An economic model for the photovoltaic-storage system is then established based on this economic cost, using the following formula:

[0077] (1)

[0078] (2)

[0079] (3)

[0080] (4)

[0081] (5)

[0082] (6)

[0083] (7)

[0084] (8)

[0085] (9)

[0086] (10)

[0087] (11)

[0088] in, This represents the total investment cost of the photovoltaic and energy storage equipment. For energy storage investment costs, For photovoltaic investment costs, The total operating cost of photovoltaic energy storage, For energy storage operating costs, For photovoltaic operating costs, For the cost of photovoltaic energy storage recovery, in equation (4) For the daily profit of energy storage arbitrage, in equation (5) For the annual return of energy storage arbitrage, in equation (6) For the daily revenue of energy storage subsidies, in equation (7) For the total revenue over the subsidy period, in equation (8) For the daily revenue from the photovoltaic electricity price difference, in equation (9) The annual return for the photovoltaic electricity price difference is given in equation (10). For the positive benefits of reduced carbon emissions from the power grid, in equation (11) For the cost of power grid line losses, its , , , and This refers to the summation yield across multiple scenarios. This is a binary variable, indicating whether energy storage should be configured at this node; The capacity factor is the ratio of the actual power generation of a power generation device to its theoretical maximum power generation within a certain period of time. It is used to evaluate the utilization rate of an energy storage system. The term refers to the entire lifecycle of energy storage, also known as the float lifecycle, expressed in years; where r is the annual interest rate. This indicates the configured energy storage capacity; Cost per unit of energy storage capacity; The entire lifecycle of photovoltaics is measured in years. This indicates the rated capacity of the photovoltaic system, which is the configured photovoltaic capacity. Cost per unit of photovoltaic capacity; The operating coefficient of energy storage in scenario s, Let be the operating coefficient of photovoltaics in scenario s. The recovery coefficient of photovoltaic energy storage equipment. This represents the number of days the energy storage system operates within a year. It is a time function of electricity price. and In the scene respectively And at any time The active power of energy storage discharge and the charging power of energy storage to the grid. and They represent the scenes respectively. Node i and time is Discharge state variables / charge state variables of energy storage Given the operating costs of photovoltaic energy storage equipment and line loss, and considering the probability of such a scenario (s), This is the subsidy coefficient for energy storage. The time interval is 1 hour. This refers to the number of days the photovoltaic system operates within a year. It refers to the number of days in a year. Indicates the carbon trading coefficient. Indicates carbon emission factor, For the original power distribution in the scenario Node i and time is The active power below, After configuring photovoltaic energy storage for power distribution in the scenario Node i and time is The active power below, Let be the line loss factor in scenario s. Let be the branch current from node i to node j at a certain time t in scenario s. The resistor is used for the electrical branch ij.

[0089] In this embodiment, by establishing an economic model for the photovoltaic-storage system, the economic cost can be reduced as much as possible when determining the capacity configuration of the photovoltaic-storage system.

[0090] In an exemplary embodiment, establishing a carbon emission model for the photovoltaic-storage system based on the target intraday source-load data includes:

[0091] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0092] For example, based on the source-load data within the target day, the carbon emissions during the establishment phase of the photovoltaic-storage system are determined. The establishment phase includes the production phase of photovoltaic-storage equipment, the transportation phase of photovoltaic-storage materials, the construction phase of photovoltaic-storage, the operation phase of the photovoltaic-storage power station, and the decommissioning phase of the photovoltaic-storage power station. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system, using the following formula:

[0093] (12)

[0094] (13)

[0095] (14)

[0096] (15)

[0097] (16)

[0098] (17)

[0099] in, This represents the entire lifecycle of a photovoltaic energy storage power station. Total emissions This indicates the initial equipment production stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required for the production of photovoltaic energy storage power station equipment. The weight of the i-th material required for the production of photovoltaic energy storage power station equipment. The carbon emission coefficient of the i-th material required for the production of photovoltaic energy storage power station equipment. This refers to the material transportation stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the transportation phase of a photovoltaic energy storage power station. Let be the weight of the i-th material required during the transportation phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the transportation phase of photovoltaic energy storage power station equipment. Let be the distance of the i-th type of material required during the transportation phase of the photovoltaic energy storage power station equipment. This indicates the construction phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the construction phase of a photovoltaic energy storage power station. Let represent the weight of the i-th material required during the construction phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the construction phase of a photovoltaic energy storage power station. Production process during the construction phase of photovoltaic energy storage power station equipment Emissions This indicates the photovoltaic energy storage power station during the operation phase. Emissions This refers to the carbon emissions generated from the production of materials that fail during the operation of photovoltaic energy storage power station equipment. This refers to the carbon emissions generated during the transportation of failed materials from photovoltaic energy storage power station equipment during operation. This indicates the types and quantities of materials required during the operation of a photovoltaic energy storage power station. Let the mass of the i-th material required for the operation of the photovoltaic energy storage power station equipment be denoted as . Let i be the carbon emission factor of the i-th material required during the operation of photovoltaic energy storage power station equipment. This indicates the decommissioning phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials used during the decommissioning phase of a photovoltaic energy storage power station. For the quality of the i-th material during the decommissioning phase of photovoltaic energy storage power station equipment, Let i be the carbon emission factor of the i-th material during the decommissioning stage of photovoltaic energy storage power station equipment. Equation (13) is the carbon emission calculation model for the production stage of photovoltaic energy storage equipment, Equation (14) is the carbon emission calculation model for the material transportation stage of photovoltaic energy storage equipment, Equation (15) is the carbon emission calculation model for the construction stage of photovoltaic energy storage equipment, Equation (16) is the carbon emission calculation model for the operation stage of photovoltaic energy storage power station, and Equation (17) is the carbon emission calculation model for the decommissioning stage of photovoltaic energy storage power station.

[0100] In this embodiment, by establishing a carbon emission model for the photovoltaic-storage system, carbon emissions can be reduced as much as possible when determining the capacity configuration of the photovoltaic-storage system.

[0101] In an exemplary embodiment, based on an economic model and a carbon emission model, the levelized carbon cost (LCOE), average annual net LCOE, and penalty cost for voltage issues of the photovoltaic-storage system are determined. Based on these LCOE, average annual net LCOE, and penalty cost for voltage issues, an objective function is determined, including: determining the LCOE, average annual net LCOE, and penalty cost for voltage issues of the photovoltaic-storage system based on the economic model and the carbon emission model; and determining the objective function based on these LCOE, average annual net LCOE, and penalty cost for voltage issues, as shown in the following formula:

[0102] (18)

[0103] (19)

[0104] (20)

[0105] (twenty one)

[0106] (twenty two)

[0107] (twenty three)

[0108] (twenty four)

[0109] in, Indicates the levelized cost of electricity (LCOE). This represents the total cost of a photovoltaic energy storage system over its entire lifespan. This indicates the total carbon emissions of a photovoltaic energy storage system throughout its entire lifespan. Indicates the average number of years of operation. The carbon price, representing carbon emissions. This indicates the penalty cost for not resolving the voltage problem. The weight coefficients assigned to the multi-objective functions respectively. To optimize the objective function, Equation (19) represents the average annual levelized carbon cost of the photovoltaic energy storage system, Equation (20) represents the unit carbon price of the photovoltaic energy storage system throughout its entire life cycle, Equation (21) represents the annual revenue of the photovoltaic energy storage system, Equation (22) represents the average annual levelized net kilowatt-hour carbon cost of the photovoltaic energy storage system during its life cycle, Equation (23) represents the penalty cost for voltage issues, and Equation (24) represents the objective function. A multi-criteria decision-making approach combining quantitative and qualitative methods is used to assign weight coefficients to the objective function, minimizing its value. Minimizing the objective function achieves improvements in voltage distribution, minimizes the average annual levelized net kilowatt-hour carbon cost of the photovoltaic energy storage system, and minimizes the unit carbon price of the photovoltaic energy storage system during its life cycle.

[0110] In this embodiment, by minimizing the objective function, the following are achieved: improving voltage distribution, minimizing the average annual net kilowatt-hour carbon cost of the photovoltaic-storage system, and minimizing the unit carbon price during the life cycle of the photovoltaic-storage system.

[0111] In an exemplary embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0112] For example, capacity optimization constraints include: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints. 1. The formula for power system flow constraints is:

[0113] (25)

[0114] (26)

[0115] (27)

[0116] (28)

[0117] (29)

[0118] (30)

[0119] (31)

[0120] in These are the first-stage decision variables, i.e., 0-1 variables ( DG (Distributed Generation) was not configured at that time. (Configure DG at the time). Indicates in the scene And at any time Next node The active power output / reactive power output of a traditional generator; Indicates in the scene And at any time Below, if at node If photovoltaic (PV) systems are configured at a location, then the output is the active power / reactive power of the PV system; if at a node... Energy storage is installed there. Indicates in the scene And at any time The active / reactive power of the energy storage discharge. Indicates in the scene And at any time The active / reactive power of the energy storage charging; Indicates in the scene And at any time Next node Active power / reactive power required to operate at the load; This indicates the resistance / reactance of the branch line; This represents the total power flowing from node j to downstream node k at a certain time t in scenario s (there may be more than one downstream branch connected to a node, so summation is required). Let be the total power flowing from node i to downstream node j at a certain time t in scenario s (there may be more than one downstream branch connected to a node, so summation is required). Let be the branch power loss from node i to node j at a certain time t in scenario s; Let be the branch current from node i to node j at a certain time t in scenario s; , Let represent the charging and discharging state of the energy stored at node j at a certain time t in scenario s. Let be the voltage amplitude of node i in scenario s; Let B be the active power / reactive power transmitted between node i and node j in scenario s, and let B be the number of branch sets. This is represented as the lower / upper bound of the voltage amplitude at node i in scenario s; This is represented as the lower / upper bound of the active power output of the conventional generator at node i. This is represented as the lower / upper bound of the reactive power output of the conventional generator at node i; This represents the maximum value constraint of the branch current from node i to node j under scenario s. Equations (25)-(31) represent the power flow constraints of the power system based on multiple time scales, equations (25) and (26) represent the active / reactive power balance constraints at node j, equation (27) is the voltage drop constraint, equation (28) is the convex relaxation constraint of the branch power flow, equation (29) is the node voltage amplitude constraint, equation (30) is the generator active / reactive power constraint, and equation (31) is the branch current constraint. The configuration variables of the active / reactive power output of photovoltaics and the configuration variables of the discharge active / reactive power and charging active / reactive power of energy storage are set to the same value. Its purpose is to ensure that if the node If distributed generation (DG) is configured, then photovoltaic (PV) and energy storage are configured together. 2. The formula for the operational constraints related to energy storage is:

[0121] (32)

[0122] (33)

[0123] (34)

[0124] (35)

[0125] (36)

[0126] (37)

[0127] (38)

[0128] (39)

[0129] (40)

[0130] in, / This indicates the active / reactive capacity configured for energy storage. This indicates the maximum active power capacity / maximum reactive power capacity configured for energy storage. This indicates the upper limit of the photovoltaic capacity. This represents the tangent of the power factor angle. This indicates the maximum number of DGs that can be configured. The charging power for energy storage, The maximum values ​​of the energy storage discharge power, energy storage charging power, and energy storage discharge power are all [missing information]. , This indicates the current state of charge. This indicates the state of charge at the previous moment. Indicates charge / discharge efficiency. This is the time interval, i.e., 1 hour; and For binary 0-1 variables, when

[0131] and At this time, the energy storage is in a charging state. and At this time, the stored energy is in a discharge state. and At this time, the energy storage is in an idle state; This represents the maximum / minimum value of the state of charge at node i at the current time t; This indicates the remaining electricity at the end of the energy storage cycle. This represents the remaining electricity at the beginning of the energy storage cycle. Equations (32)-(34) are the capacity constraints for specific configurations of energy storage and photovoltaics. Equation (32) represents the capacity range of energy storage and photovoltaics. Equation (33) represents that the reactive power output of energy storage can be obtained from the active power output of energy storage. Equation (34) ensures that the active capacity and reactive capacity are equal. Equation (35) represents the constraint on the upper limit of the number of energy storage units. Equation (36) is the upper and lower bound constraints of energy storage charging and discharging power. Equation (37) is the constraint on the charging and discharging state of energy storage. Equation (38) is the constraint on the energy continuity of energy storage. Equation (39) is the constraint on the state of charge of energy storage. Equation (40) is the constraint on the beginning and end balance of the energy storage cycle. 3. The energy storage lifetime constraint formula based on the depth of discharge is:

[0132] (41)

[0133] (42)

[0134] in, This represents the energy storage discharge state at node i at time t-1. It is a constant obtained by fitting, and its value will be different for different types of batteries. Usually, battery manufacturers will provide relevant parameters. This represents the number of cycles equivalent to 100% depth of discharge. denoted as the float life of energy storage; N is the number of cycles. Equations (41) and (42) represent the life model of energy storage, which is based on the depth of discharge model constraint and considers the limit of the equivalent full cycle number of charge and discharge of energy storage. In equation (41), if the energy storage starts charging at time t, the equivalent full cycle number is calculated according to the depth of discharge at time t-1. The left side of the inequality is the calculation of the total equivalent full cycle number in a typical day, and the right side of the inequality represents the maximum daily equivalent cycle number corresponding to ensure that the battery energy storage can work normally until the float life is reached. The significance of the above inequality constraint is that, based on the depth of discharge life model, the limitation of the equivalent full cycle number of charge and discharge of energy storage is considered to extend the working time of energy storage. 4. The formulas for the energy storage related strategy constraints are:

[0135] (43)

[0136] (44)

[0137] (45)

[0138] (46)

[0139] (47)

[0140] (48)

[0141] The above are the constraints of energy storage-related strategies. When the photovoltaic output is greater than the load demand, equation (43) ensures that energy storage charging takes priority over the flow to the next node, that is, the excess photovoltaic output is connected to the grid; equation (44) can ensure that energy storage is charged first, when the difference between the photovoltaic output value and the load demand value is not large. The charging power for energy storage is required when the photovoltaic output is much greater than the load demand. The upper limit of the charging power of energy storage is given by equation (45); the power obtained in equation (45) is the increased power flowing to the next node, that is, the photovoltaic output meets the load and energy storage. When the load demand is greater than the photovoltaic output, equation (46) ensures that the energy storage discharge takes priority over the purchase of electricity, which is from the user's perspective; equation (47) can ensure that the energy storage discharges first, when the difference between the photovoltaic output value and the load demand value is not large. The discharge power of energy storage is such that when the photovoltaic output is much greater than the load demand, The upper limit of the discharge power of the energy storage is given by equation (48); the amount of electricity purchased from the load in the upper-level grid is obtained by equation (48). By controlling the priority of energy storage charging and discharging, the energy storage charging and discharging takes precedence over the grid connection and purchase of excess photovoltaic power. At the same time, the equivalent full cycle number of energy storage charging and discharging is limited based on the depth of discharge model to extend the working life of the energy storage, thereby making the energy storage charging and discharging strategy more flexible. The above-mentioned energy storage related strategies are the overall strategy framework, while the energy storage electricity purchase part is one of the specific implementation details under this framework. The above defines the basic operating principles of the energy storage system under different conditions. The following electricity purchase part specifically describes how the energy storage system purchases electricity from the grid to maintain its normal operation under the guidance of these principles. Energy storage charging There are two methods. One method is the excess output from photovoltaic power after meeting the load, i.e. Another approach is to purchase electricity from the grid for energy storage. Therefore, when calculating the charging cost of energy storage, it's crucial to distinguish between the photovoltaic (PV) charging portion and the portion purchased from the grid. Regarding the grid-purchased electricity portion: When the PV power plant's generating capacity exceeds immediate load demand, the first priority is to ensure the load demand is fully met. Subsequently, the remaining PV output is directly directed to the grid-side energy storage system for charging. This process not only achieves efficient use of electricity but also avoids waste. It's worth noting that at this stage, the energy storage system's charging relies entirely on the excess output of the PV power plant, requiring no additional cost. To minimize the energy storage system's electricity purchase cost, when PV output is sufficient to meet the energy storage charging demand, the energy storage system does not purchase electricity from the grid. Conversely, when load demand exceeds the PV power plant's generating capacity, PV output cannot meet the full load demand. In this case, the PV power plant's electricity is no longer used for charging the energy storage system but is prioritized to meet the basic load demand. When the energy reserves in the energy storage system are insufficient to supplement load demand, the system will automatically purchase electricity from the upstream grid to ensure the continuous meeting of load demand. In this scenario, energy storage systems need to purchase electricity from the grid to maintain their discharge capacity, thereby ensuring the stability and reliability of the power supply. Energy storage power purchase strategies include: one is energy storage... The state is Furthermore, the electricity price is either at its normal level, and the energy storage purchases electricity from the grid; or the electricity price is at its off-peak level, and the energy storage purchases electricity from the grid. The formula is as follows:

[0142] (49)

[0143] (50)

[0144] (51)

[0145] (52)

[0146] (53)

[0147] in, For binary variables, in equation (49) when the remaining charge state of the energy storage is low, in equation (50) when the electricity price is the normal price, and in equation (52) when the electricity price is the off-peak price, let all their binary variables be 1, and control the charging state variables of the energy storage by adding constraints (51) and (53). 5. The formula for photovoltaic output constraint is:

[0148] (54)

[0149] in, This represents the upper limit of photovoltaic active / reactive power at node i. Equation (54) represents the upper and lower bound constraints of photovoltaic active and reactive power. 6. The formula for carbon emission constraints is:

[0150] (55)

[0151] in, Indicates power distribution in the scenario And at any time Next node The active power output of traditional generators before the installation of a photovoltaic energy storage integrated system; Indicates power distribution in the scenario And at any time Next node The active power output of the traditional generator after the photovoltaic energy storage system is configured; Equation (55) represents the target constraint that the carbon emissions generated by the power distribution of this part should be reduced by at least 5%.

[0152] In one exemplary embodiment, such as Figure 2As shown, a low-carbon power distribution photovoltaic-storage system capacity configuration method first extracts the topology and obtains relevant system parameters based on the power distribution of a certain region. Based on the topology and system parameters, a large-scale source-load scenario is generated using multi-dimensional random sampling technology. A stepwise elimination strategy is adopted, and scenarios that do not meet the requirements or have high repetition are eliminated round by round according to preset screening criteria. After multiple rounds of screening, the target day's source-load data is obtained. Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and power distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost. Based on the target day's source-load data, the carbon emissions during the photovoltaic-storage system establishment phase are determined. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the photovoltaic-storage system establishment phase. Based on the economic model and the carbon emission model, the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues of the photovoltaic-storage system are determined. Based on these LCOE, the average annual net LCOE, and the penalty cost for voltage issues, an objective function is determined. Capacity optimization constraints for the photovoltaic-storage system are established, including at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints. Based on the capacity optimization constraints and the objective function, a capacity optimization model for the photovoltaic-storage system is constructed. When a photovoltaic-storage system needs to be set up, the capacity optimization model is used to determine the capacity configuration of the system, and then the system is set up according to the capacity configuration. In the case of a multi-scenario photovoltaic (PV) storage system with deterministic source and load, deterministic source and load data of the PV storage system are acquired, input into a capacity optimization model, and the capacity optimization model is solved to obtain the capacity configuration of the PV storage system. The PV storage system is then configured according to the capacity configuration. In the case of a multi-scenario photovoltaic (PV) storage system with uncertain source and load, the capacity optimization model is solved using a multi-stage optimization iteration technique. For the first iteration, the parameters of the capacity optimization model are initialized. Based on the parameters and the objective function, the objective function value of the capacity optimization model under each scenario is calculated. Based on the objective function under each scenario, a weighted average value is determined, and the decision vector is updated based on the weighted average value. The weight vector of each scenario is updated based on the decision vector, resulting in the current capacity configuration of the PV storage system.For the i-th iteration, obtain the capacity configuration of the photovoltaic-storage system obtained in the previous iteration, where i is greater than or equal to 2; based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, calculate the objective function value for each scenario; calculate the weighted average of the objective function values ​​for each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector for each scenario to obtain the current capacity configuration of the photovoltaic-storage system; based on the decision vector and the weighted average, determine the current error value; based on the error value, determine whether the convergence condition is met; if it is met, stop the iteration, and use the current capacity configuration of the photovoltaic-storage system as the optimized capacity configuration of the photovoltaic-storage system; configure the photovoltaic-storage system according to the capacity configuration.

[0153] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0154] In one exemplary embodiment, such as Figure 3 As shown, a low-carbon capacity configuration device for a photovoltaic-storage system is provided, comprising: an acquisition module 301, an establishment module 302, a first determination module 303, a second determination module 304, and an optimization module 305, wherein:

[0155] The acquisition module is used to acquire the topology of power distribution and the system parameters of the power distribution, and generate source-load data for the target day based on the topology and the system parameters;

[0156] A module is established to build an economic model and a carbon emission model for the photovoltaic-storage system based on the target intraday source-load data;

[0157] The first determining module is used to determine the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues of the photovoltaic-storage system based on the economic model and the carbon emission model.

[0158] The second determining module is used to determine an objective function based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0159] An optimization module is used to establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; and solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0160] In one exemplary embodiment, the optimization module is further configured to:

[0161] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, input into the capacity optimization model, and the capacity configuration of the photovoltaic-storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0162] In one exemplary embodiment, the optimization module is further configured to:

[0163] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0164] In one exemplary embodiment, the establishment module is further configured to:

[0165] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0166] In one exemplary embodiment, the establishment module is further configured to:

[0167] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0168] In one embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0169] Each module in the aforementioned low-carbon power-photovoltaic-storage system capacity configuration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0170] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores the power distribution topology and system parameters. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a low-carbon power distribution photovoltaic energy storage system capacity configuration method.

[0171] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0172] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0173] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0174] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0175] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0176] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0177] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0178] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0179] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, input into the capacity optimization model, and the capacity configuration of the photovoltaic-storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0180] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0181] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0182] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0183] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0184] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0185] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0186] In one embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0187] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0188] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0189] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0190] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0191] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0192] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0193] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0194] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, input into the capacity optimization model, and the capacity configuration of the photovoltaic-storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0195] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0196] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0197] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0198] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0199] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0200] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0201] In one embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0202] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0203] Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters;

[0204] Based on the target intraday source-load data, an economic model and a carbon emission model for the photovoltaic-storage system are established.

[0205] Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined.

[0206] Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem.

[0207] Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system.

[0208] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0209] When the photovoltaic-storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic-storage system are acquired, input into the capacity optimization model, and the capacity configuration of the photovoltaic-storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

[0210] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0211] The capacity optimization model is initialized to obtain the initial capacity configuration of the photovoltaic-storage system. During the iteration process, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is obtained. For the first iteration, the capacity configuration of the photovoltaic-storage system obtained in the previous iteration is the initial capacity configuration. Based on the capacity configuration of the photovoltaic-storage system obtained in the previous iteration and the objective function, the objective function value under each scenario is calculated. The weighted average of the objective function values ​​under each scenario is calculated, and the decision vector is updated based on the weighted average. Based on the decision vector, the weight vector under each scenario is updated to obtain the current capacity configuration of the photovoltaic-storage system. Based on the decision vector and the weighted average, the current error value is determined. Based on the error value, it is determined whether the convergence condition is met. If it is met, the iteration is stopped, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

[0212] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0213] Based on the target day's source-load data, the economic cost of the photovoltaic-storage system is determined. The economic cost includes at least one of the following: investment cost, operating cost, recovery cost, energy storage electricity price difference revenue, energy storage subsidy revenue, photovoltaic electricity price difference revenue, and distribution line loss cost. An economic model of the photovoltaic-storage system is established based on the economic cost.

[0214] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0215] Based on the target day's source-load data, determine the carbon emissions during the establishment phase of the photovoltaic-storage system. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system.

[0216] In one embodiment, the capacity optimization constraints of the photovoltaic-storage system include at least one of the following: power system flow constraints, energy storage-related operational constraints, energy storage lifetime constraints based on depth of discharge, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints.

[0217] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0218] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0219] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A low-carbon capacity configuration method for an energy-photovoltaic-storage system, characterized in that, The method includes: Obtain the topology of power distribution and the system parameters of the power distribution, and generate target intraday source-load data based on the topology and the system parameters; Based on the source-load data within the target day, the economic cost of the photovoltaic-storage system is determined. This economic cost includes: energy storage investment cost, photovoltaic investment cost, energy storage operation cost, photovoltaic operation cost, photovoltaic energy storage recovery cost, annual return from energy storage arbitrage, total return within the subsidy period, annual return from photovoltaic electricity price difference, positive benefits from reduced carbon emissions to the grid, and grid line loss costs. An economic model for the photovoltaic-storage system is established based on these economic costs. Based on the source-load data within the target day, the carbon emissions during the establishment phase of the photovoltaic-storage system are determined. This establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system. The formula is as follows: （12） （13） （14） （15） （16） （17） in, Represented as the entire lifecycle of a photovoltaic energy storage power station Total emissions; Equation (13) is the carbon emission calculation model for the production stage of photovoltaic energy storage equipment. This indicates the initial equipment production stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required for the production of photovoltaic energy storage power station equipment. The weight of the i-th material required for the production of photovoltaic energy storage power station equipment. The carbon emission coefficient of the i-th material required for the production of photovoltaic energy storage power station equipment; Equation (14) is the carbon emission calculation model for the material transportation stage of photovoltaic energy storage equipment. This refers to the material transportation stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the transportation phase of a photovoltaic energy storage power station. Let be the weight of the i-th material required during the transportation phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the transportation phase of photovoltaic energy storage power station equipment. Let represent the distance of the i-th type of material required during the transportation phase of the photovoltaic energy storage power station equipment; Equation (15) is the carbon emission calculation model for the construction phase of the photovoltaic energy storage equipment. This indicates the construction phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the construction phase of a photovoltaic energy storage power station. Let represent the weight of the i-th material required during the construction phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the construction phase of a photovoltaic energy storage power station. Production process during the construction phase of photovoltaic energy storage power station equipment Emissions; Equation (16) is the carbon emission calculation model for photovoltaic energy storage equipment power station during operation. This indicates the photovoltaic energy storage power station during the operation phase. Emissions This refers to the carbon emissions generated from the production of materials that fail during the operation of photovoltaic energy storage power station equipment. This refers to the carbon emissions generated during the transportation of failed materials from photovoltaic energy storage power station equipment during operation. This indicates the types and quantities of materials required during the operation of a photovoltaic energy storage power station. Let the mass of the i-th material be the mass required for the operation of the photovoltaic energy storage power station equipment. Let be the carbon emission factor of the i-th material required during the operation phase of the photovoltaic energy storage power station equipment; Equation (17) is the carbon emission calculation model for the decommissioning phase of the photovoltaic energy storage power station equipment. This indicates the decommissioning phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials used during the decommissioning phase of a photovoltaic energy storage power station. For the quality of the i-th material during the decommissioning phase of photovoltaic energy storage power station equipment, The carbon emission factor for the i-th material during the decommissioning phase of photovoltaic energy storage power station equipment; Based on the economic model and the carbon emission model, the levelized carbon cost, average annual net kilowatt-hour carbon cost, and penalty cost for voltage problems of the photovoltaic-storage system are determined. Based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem, an objective function is determined. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem. Establish capacity optimization constraints for the photovoltaic-storage system; construct a capacity optimization model for the photovoltaic-storage system based on the capacity optimization constraints and the objective function; solve the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system; the capacity optimization constraints include: carbon emission constraints and energy storage lifetime constraints based on the depth of discharge, and the formula for the carbon emission constraints is: in, Indicates power distribution in the scenario And at any time Next node The active power output of traditional generators before the installation of photovoltaic and energy storage systems; Indicates power distribution in the scenario And at any time Next node The active power output of a traditional generator after the installation of a photovoltaic and energy storage system; the carbon emission constraint formula indicates a constraint that the carbon emissions generated by this part of the power distribution should be reduced by at least 5%; the energy storage lifetime constraint formula based on the depth of discharge is: （41） （42） in, and For binary 0-1 variables, when and At this time, the energy storage is in a charging state. and At this time, the stored energy is in a discharge state. and At this time, the energy storage is in an idle state. These are constants obtained through fitting; This represents the number of cycles equivalent to 100% depth of discharge. This represents the float charge lifetime of the energy storage; N is the number of cycles.

2. The method according to claim 1, characterized in that, Solving the capacity optimization model to obtain the capacity configuration of the photovoltaic-storage system includes: When the photovoltaic storage system is in a multi-scenario situation with deterministic source and load, the source and load data of the photovoltaic storage system are obtained, the source and load data are input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is obtained by solving the model. When the photovoltaic-storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic-storage system.

3. The method according to claim 2, characterized in that, The process of solving the capacity optimization model using multi-stage optimization iteration techniques to obtain the capacity configuration of the photovoltaic-storage system includes: Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic-storage system; During the iteration process, the capacity configuration of the optical storage system obtained in the previous iteration is obtained; wherein, for the first iteration, the capacity configuration of the optical storage system obtained in the previous iteration is the initial capacity configuration; Based on the capacity configuration of the optical storage system obtained in the previous iteration and the objective function, the objective function value is calculated for each scenario. Calculate the weighted average of the objective function values ​​for each scenario, and update the decision vector based on the weighted average. Based on the decision vector, the weight vectors for each scenario are updated to obtain the current capacity configuration of the optical storage system. Based on the decision vector and the weighted average, determine the current error value; based on the error value, determine whether the convergence condition is met; If the conditions are met, the iteration stops, and the current capacity configuration of the photovoltaic-storage system is taken as the optimized capacity configuration of the photovoltaic-storage system.

4. The method according to claim 1, characterized in that, The capacity optimization constraints of the photovoltaic-storage system also include at least one of the following: power system power flow constraints, energy storage-related operational constraints, energy storage-related strategy constraints, and photovoltaic output constraints.

5. A low-carbon capacity configuration device for an electro-optical-storage system, characterized in that, The device includes: The acquisition module is used to acquire the topology of power distribution and the system parameters of the power distribution, and generate source-load data for the target day based on the topology and the system parameters; A module is established to determine the economic cost of the photovoltaic-storage system based on the source-load data within the target day. The economic cost includes: energy storage investment cost, photovoltaic investment cost, energy storage operation cost, photovoltaic operation cost, photovoltaic energy storage recovery cost, annual return from energy storage arbitrage, total return within the subsidy period, annual return from photovoltaic electricity price difference, positive benefits from reduced carbon emissions to the grid, and grid line loss costs. An economic model of the photovoltaic-storage system is established based on these economic costs. Based on the source-load data within the target day, the carbon emissions during the establishment phase of the photovoltaic-storage system are determined. The establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system. The formula is as follows: （12） （13） （14） （15） （16） （17） Based on the target day's source-load data, the carbon emissions during the establishment phase of the photovoltaic-storage system are determined. This establishment phase includes at least one of the following: photovoltaic-storage equipment production phase, photovoltaic-storage material transportation phase, photovoltaic-storage construction phase, photovoltaic-storage power station operation phase, and photovoltaic-storage power station decommissioning phase. A carbon emission model is established based on the carbon emissions during the establishment phase of the photovoltaic-storage system; the formula is as follows: （12） （13） （14） （15） （16） （17） in, Represented as the entire lifecycle of a photovoltaic energy storage power station Total emissions; Equation (13) is the carbon emission calculation model for the production stage of photovoltaic energy storage equipment. This indicates the initial equipment production stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required for the production of photovoltaic energy storage power station equipment. The weight of the i-th material required for the production of photovoltaic energy storage power station equipment. The carbon emission coefficient of the i-th material required for the production of photovoltaic energy storage power station equipment; Equation (14) is the carbon emission calculation model for the material transportation stage of photovoltaic energy storage equipment. This refers to the material transportation stage of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the transportation phase of a photovoltaic energy storage power station. Let be the weight of the i-th material required during the transportation phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the transportation phase of photovoltaic energy storage power station equipment. Let represent the distance of the i-th type of material required during the transportation phase of the photovoltaic energy storage power station equipment; Equation (15) is the carbon emission calculation model for the construction phase of the photovoltaic energy storage equipment. This indicates the construction phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials required during the construction phase of a photovoltaic energy storage power station. Let represent the weight of the i-th material required during the construction phase of the photovoltaic energy storage power station equipment. Let be the carbon emission coefficient of the i-th material required during the construction phase of a photovoltaic energy storage power station. Production process during the construction phase of photovoltaic energy storage power station equipment Emissions; Equation (16) is the carbon emission calculation model for photovoltaic energy storage equipment power station during operation. This indicates the photovoltaic energy storage power station during the operation phase. Emissions This refers to the carbon emissions generated from the production of materials that fail during the operation of photovoltaic energy storage power station equipment. This refers to the carbon emissions generated during the transportation of failed materials from photovoltaic energy storage power station equipment during operation. This indicates the types and quantities of materials required during the operation of a photovoltaic energy storage power station. Let the mass of the i-th material be the mass required for the operation of the photovoltaic energy storage power station equipment. Let be the carbon emission factor of the i-th material required during the operation phase of the photovoltaic energy storage power station equipment; Equation (17) is the carbon emission calculation model for the decommissioning phase of the photovoltaic energy storage power station equipment. This indicates the decommissioning phase of a photovoltaic energy storage power station. Emissions This indicates the types and quantities of materials used during the decommissioning phase of a photovoltaic energy storage power station. For the quality of the i-th material during the decommissioning phase of photovoltaic energy storage power station equipment, The carbon emission factor for the i-th material during the decommissioning phase of photovoltaic energy storage power station equipment; The first determining module is used to determine the levelized cost of electricity (LCOE), the average annual net LCOE, and the penalty cost for voltage issues of the photovoltaic-storage system based on the economic model and the carbon emission model. The second determining module is used to determine an objective function based on the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem. The optimization objective of the objective function is to minimize the levelized carbon cost, the average annual levelized net kilowatt-hour carbon cost, and the penalty cost of the voltage problem. An optimization module is used to establish capacity optimization constraints for the photovoltaic-storage system; based on the capacity optimization constraints and the objective function, a capacity optimization model for the photovoltaic-storage system is constructed; the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic-storage system; the capacity optimization constraints include: carbon emission constraints and energy storage lifetime constraints based on the depth of discharge, and the formula for the carbon emission constraints is: in, Indicates power distribution in the scenario And at any time Next node The active power output of traditional generators before the installation of photovoltaic and energy storage systems; Indicates power distribution in the scenario And at any time Next node The active power output of a traditional generator after the installation of a photovoltaic and energy storage system; the carbon emission constraint formula indicates a constraint that the carbon emissions generated by this part of the power distribution should be reduced by at least 5%; the energy storage lifetime constraint formula based on the depth of discharge is: （41） （42） in, and For binary 0-1 variables, when and At this time, the energy storage is in a charging state. and At this time, the stored energy is in a discharge state. and At this time, the energy storage is in an idle state. These are constants obtained through fitting; This represents the number of cycles equivalent to 100% depth of discharge. This represents the float charge lifetime of the energy storage; N is the number of cycles.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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