Low-carbon power distribution and utilization optical storage system capacity configuration method, device and equipment, readable storage medium and program product

Abstract

The invention relates to a low-carbon power distribution and utilization optical storage system capacity configuration method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a topological structure of power distribution and utilization and system parameters of the power distribution and utilization, and generating source load data in a target day based on the topological structure and the system parameters; based on the source load data in the target day, establishing an economic model and a carbon emission model of the optical storage system; based on an economical model and a carbon emission model, an objective function is determined, and the optimization objective of the objective function is to minimize the leveling electric carbon cost, the average annual leveling net kilowatt-hour electric carbon cost and the penalty cost of the voltage problem; establishing a capacity optimization constraint of the optical storage system; according to a capacity optimization constraint and the objective function, constructing a capacity optimization model of the optical storage system; and solving the capacity optimization model to obtain the capacity configuration of the optical storage system. By adopting the method, the capacity and the position of the energy storage configuration of the optical storage system can be comprehensively optimized.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution and distributed energy system optimization, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for capacity configuration of a low-carbon power distribution photovoltaic storage system. Background Art

[0002] At present, large-scale distributed photovoltaic grid connection and its consumption face many challenges, and the problem of abandoned light is particularly prominent. In this situation, energy storage technology used to store energy has been developed. Energy storage has the characteristics of achieving energy transfer and balancing the supply and demand relationship of the system and fast response speed. It can significantly enhance the controllability of new energy such as wind power and photovoltaics, and achieve peak load regulation of the power grid and suppress power fluctuations. Therefore, how to reasonably configure energy storage capacity is an effective way to promote the local consumption of distributed photovoltaics.

[0003] There are few existing studies that use the actual cost per unit of electricity as an energy storage cost indicator, and most of them only stay at the economic level. As the problem of carbon emissions becomes increasingly prominent, more new measurement indicators should be highlighted to cover both the economic and social benefit levels. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for capacity configuration of a low-carbon distributed photovoltaic storage system that can more comprehensively optimize the capacity and location of energy storage configuration in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for configuring the capacity of a low-carbon electricity-light-storage system, including:

[0006] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0008] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0009] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0010] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving 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 is obtained, and the source and load data is input into the capacity optimization model to solve and 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 by a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system.

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

[0014] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

[0015] In one embodiment, the economic model of the solar-storage system is established based on the source-load data of the target day, including:

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

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

[0018] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0020] In a second aspect, the present application also provides a low-carbon electricity-photovoltaic storage system capacity configuration device, comprising:

[0021] An acquisition module, used to acquire a topological structure of power distribution and a system parameter of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameter;

[0022] Establishing a module for establishing an economic model and a carbon emission model of the photovoltaic storage system based on the source-load data on the target day;

[0023] A first determination module is used to determine the levelized electricity-carbon cost, the average annual levelized net kilowatt-hour electricity-carbon cost and the penalty cost of voltage problems of the photovoltaic storage system based on the economic model and the carbon emission model;

[0024] A second determination module is used to determine an objective function based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0025] The optimization module is used to establish capacity optimization constraints of the photovoltaic storage system; construct a capacity optimization model of the photovoltaic storage system according to 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] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0029] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0030] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0031] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0033] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0035] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0036] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0037] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0041] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0042] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0043] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

[0044] In the above-mentioned low-carbon power distribution photovoltaic storage system capacity configuration method, the topological structure of power distribution and the system parameters of the power distribution are obtained, and the source and load data on the target day are generated based on the topological structure and the system parameters; based on the source and load data on the target day, an economic model and a carbon emission model of the photovoltaic storage system are established; based on the economic model and the carbon emission model, the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem of the photovoltaic storage system are determined; based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem, an objective function is determined, and the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem; a capacity optimization constraint of the photovoltaic storage system is established; according to the capacity optimization constraint and the objective function, a capacity optimization model of the photovoltaic storage system is constructed; the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic storage system. While satisfying the capacity optimization constraints, the capacity configuration plan is determined by minimizing the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem, which can more comprehensively optimize the capacity and location of the energy storage configuration of the photovoltaic storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1A schematic diagram of a flow chart of a method for configuring the capacity of a low-carbon electricity-photovoltaic storage system in one embodiment;

[0047] Figure 2 A detailed flow chart of a method for configuring the capacity of a low-carbon electricity-photovoltaic storage system in one embodiment;

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

[0049] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In one embodiment, Figure 1 As shown, a method for configuring the capacity of a low-carbon electricity-light storage system is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

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

[0053] Among them, the power distribution topology refers to the connection relationship and layout method between various components in the power distribution. It describes the physical architecture of the power distribution and is an abstract representation of the overall structure of the power distribution. The power distribution system parameters are a general term for various parameters that describe the electrical and physical characteristics of each component and the overall system in the power distribution. Daily source-load data refers to the output data of the power source and the power consumption data of the load in the power distribution within a day, reflecting the changes in the power supply and load demand of the power distribution at different times of the day.

[0054] For example, firstly, the topological structure and relevant system parameters of a certain area are extracted according to the power distribution. Based on the topological structure and system parameters, a multi-dimensional random extraction technology is used to generate a large-scale source-load scenario. A step-by-step elimination strategy is adopted to eliminate those scenarios that do not meet the requirements or have high repetitiveness according to the preset screening criteria. After multiple rounds of screening, the source-load data for the target day is obtained.

[0055] Step 104: Establish an economic model and a carbon emission model of the photovoltaic storage system based on the source-load data on the target day.

[0056] Among them, the economic model is used to determine the economic cost, 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 of electricity of the photovoltaic storage system, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of voltage problems.

[0058] Among them, the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of voltage problems are used to analyze and balance the relationship between the economic cost and carbon emissions of the photovoltaic storage system.

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

[0060] Among them, minimizing the objective function can achieve the optimal solution to carbon emission problems and various economic cost problems.

[0061] Step 110, establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving 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 smooth operation of the system, improve the performance of the equipment, and achieve economic and environmental protection.

[0063] Exemplarily, a capacity optimization constraint of the photovoltaic storage system is established, and a capacity optimization model of the photovoltaic storage system is constructed based on the capacity optimization constraint and the objective function; when the photovoltaic storage system needs to be set up, 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 above-mentioned low-carbon power distribution photovoltaic storage system capacity configuration method, the topological structure of power distribution and the system parameters of the power distribution are obtained, and the source and load data on the target day are generated based on the topological structure and the system parameters; based on the source and load data on the target day, an economic model and a carbon emission model of the photovoltaic storage system are established; based on the economic model and the carbon emission model, the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem of the photovoltaic storage system are determined; based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem, an objective function is determined, and the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem; a capacity optimization constraint of the photovoltaic storage system is established; according to the capacity optimization constraint and the objective function, a capacity optimization model of the photovoltaic storage system is constructed; the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic storage system. While satisfying the capacity optimization constraints, the capacity configuration plan is determined by minimizing the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity and the penalty cost of the voltage problem, 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 is obtained, input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is solved; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system.

[0067] Optionally, the source-load data includes power supply data and load data; the power supply data includes: power generation data in different scenarios, power generation data in different scenarios, power generation efficiency data in different scenarios, and power supply operation status data in different scenarios; the load data includes: load power data in different scenarios, load curve data in different scenarios, load characteristic data in different scenarios, and load classification data in different scenarios, etc. The scenarios can be industrial scenarios, commercial scenarios, and scenarios in different time periods; industrial scenarios include: load data in heavy industry, light industry, etc.; commercial scenarios include: load data in retail, catering, entertainment, etc.; time periods include: peak load, valley load, and flat load scenarios.

[0068] Exemplarily, in multiple scenarios where the source and load of the photovoltaic storage system are deterministic, the deterministic source and load data of the photovoltaic storage system are obtained, input into a capacity optimization model, the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic storage system, and the photovoltaic storage system is set according to the capacity configuration; in multiple scenarios where the source and load of the photovoltaic storage system are uncertain, the capacity optimization model is solved by a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system, and the photovoltaic storage system is set according to the capacity configuration.

[0069] In this embodiment, it is determined whether the source load of the photovoltaic storage system to be set is determined to determine the calculation method to be adopted, so that the photovoltaic storage system capacity configuration that is more suitable for the actual local situation can be obtained.

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

[0071] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

[0072] Exemplarily, 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, calculate the objective function value in each scenario; among which, 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 ​​under each scenario, and update the decision vector based on the weighted average; 4. , based on the decision vector, update the weight vector in each scenario to obtain the current capacity configuration of the solar storage system; 5. , based on the decision vector and the weighted average, determine the current error value; 6. If When the algorithm reaches the optimal solution, it will return to step 1, otherwise it will terminate. Determine whether the convergence condition is met; if not, it will return to step 1; if it is met, it will stop iterating and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system. Where i represents the number of iterations, which is used to track the iteration process of the algorithm to ensure that the algorithm gradually approaches the optimal solution, c is the cost coefficient vector with a length of n, and the decision vector x (where ), is a feasible solution in scenario s. We use the subscript s to emphasize that the specific problem features will depend on the actual observed scenarios, and the set S represents the set of possible scenarios. is the decision vector of length n for the ith iteration, and the associated cost coefficient vector , is the objective function, is the penalty factor, is the mean of the i-th iteration of length n, for each scene ,use represents the probability of occurrence, The weight vector for the i-th iteration, represents the error value of the i-th iteration, is the given termination threshold, which is used as the convergence condition.

[0073] In this embodiment, through multiple iterations of optimization, an optimized capacity configuration of the photovoltaic storage system can be obtained for configuring the capacity of the photovoltaic storage system.

[0074] In an exemplary embodiment, the economic model of the solar-storage system is established based on the source-load data of the target day, including:

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

[0076] For example, based on the source-load data on the target day, the economic cost of the photovoltaic storage system is determined. The economic cost includes investment cost, operation cost, recovery cost, energy storage electricity price difference income, energy storage subsidy income, photovoltaic electricity price difference income and distribution line loss cost. Based on the economic cost, an economic model of the photovoltaic storage system is established. The formula is:

[0077] (1)

[0078] (2)

[0079] (3)

[0080] (4)

[0081] (5)

[0082] (6)

[0083] (7)

[0084] (8)

[0085] (9)

[0086] (10)

[0087] (11)

[0088] in, is the total investment cost of the photovoltaic storage equipment, is the energy storage investment cost, is the photovoltaic investment cost, is the total operating cost of photovoltaic energy storage, is the energy storage operating cost, is the photovoltaic operation cost, is the photovoltaic energy storage recovery cost, in formula (4) is the daily return of energy storage arbitrage, in formula (5) is the annual return of energy storage arbitrage, in formula (6) is the daily income of energy storage subsidy, in formula (7) is the total income during the subsidy period, in formula (8) is the daily income from the photovoltaic power price difference, in formula (9) is the annual income of the photovoltaic power price difference, in formula (10): is the positive benefit of reducing carbon emissions from the power grid, in formula (11) is the line loss cost of the power grid, , , , and It is the summed benefits in multiple scenarios. is a binary variable, i.e., whether energy storage is configured at this node; The capacity factor refers to the ratio of the actual power generation of a power generation device within a certain period of time to its theoretical maximum power generation. It is used to evaluate the utilization rate of the energy storage system. is the full life cycle of energy storage, i.e. the floating charge life, in years; where r is the annual interest rate; It is expressed as the configured energy storage capacity; is the unit energy storage capacity cost; The whole life cycle of photovoltaic power generation, in years; Indicates the rated capacity of photovoltaic power, which is the configured photovoltaic capacity; is the unit photovoltaic capacity cost; is the operation coefficient of energy storage in scenario s, is the operation coefficient of photovoltaic under scenario s, is the recovery factor of the photovoltaic energy storage device, is the number of days the energy storage system operates in a year, is the time function of the electricity price, and In the scene And the time is The discharge active power of the energy storage and the charging power of the energy storage to the grid are: and Respectively in the scene Node i and time is The discharge state variable / charge state variable of the energy storage, is the probability size under the operating costs of photovoltaic energy storage equipment and line loss costs, and under scenario s, is the subsidy coefficient for energy storage, The time interval is 1h, is the number of days the photovoltaic system operates in a year, The number of days in a year. represents the carbon trading coefficient, represents the carbon emission factor, For the original power distribution in the scene Node i and time is The active power under After configuring photovoltaic energy storage for power distribution, Node i and time is The active power under is the line loss factor under scenario s, is the branch current from node i to node j at a certain time t under scenario s, To match the electrical branch ij resistance.

[0089] In this embodiment, by establishing an economic model of 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, the step of establishing a carbon emission model of a solar-storage system based on the source-load data on the target day includes:

[0091] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0093] (12)

[0094] (13)

[0095] (14)

[0096] (15)

[0097] (16)

[0098] (17)

[0099] in, Represented as the entire life cycle of the photovoltaic energy storage power station Total emissions, Represents the initial equipment production stage of photovoltaic energy storage power station Emissions, Indicates the number of types of materials required for the production of photovoltaic energy storage power station equipment, The weight of the i-th material required to produce photovoltaic energy storage power station equipment, The carbon emission coefficient of the i-th material required to produce photovoltaic energy storage power station equipment, Represented as the material transportation stage of the photovoltaic energy storage power station Emissions, Indicates the number of types of materials required for the transportation phase of the photovoltaic energy storage power station. is the weight of the i-th material required for the transportation of photovoltaic energy storage power station equipment, is the carbon emission coefficient of the i-th material required in the transportation stage of photovoltaic energy storage power station equipment, is the distance of the i-th material required for the transportation phase of the photovoltaic energy storage power station equipment, Represents the construction phase of the photovoltaic energy storage power station Emissions, Indicates the number of types of materials required in the construction phase of a photovoltaic energy storage power station, is the weight of the i-th material required for the construction of photovoltaic energy storage power station equipment, is the carbon emission coefficient of the i-th material required in the construction phase of photovoltaic energy storage power station equipment, Production process for the construction phase of photovoltaic energy storage power station equipment Emissions, It is expressed as the photovoltaic energy storage power station in the operation stage Emissions, The carbon emissions generated by the production of failed materials during the operation of photovoltaic energy storage power station equipment. The carbon emissions generated during the transportation of failed materials during the operation phase of photovoltaic energy storage power station equipment. Indicates the number of types of materials required for the operation phase of a photovoltaic energy storage power station. is the mass of the i-th material required for the operation of the photovoltaic energy storage power station equipment, is the carbon emission factor of the i-th material required during the operation phase of the photovoltaic energy storage power station equipment, Represents the decommissioning phase of a photovoltaic energy storage power station Emissions, Indicates the number of material types in the decommissioning phase of a photovoltaic energy storage power station, is the mass of the i-th material in the decommissioning stage of photovoltaic energy storage power station equipment, is the carbon emission factor of the i-th material in the decommissioning stage of photovoltaic energy storage power station equipment. Formula (13) is the carbon emission calculation model for the production stage of photovoltaic energy storage equipment, formula (14) is the carbon emission calculation model for the material transportation stage of photovoltaic energy storage equipment, formula (15) is the carbon emission calculation model for the construction stage of photovoltaic energy storage equipment, formula (16) is the carbon emission calculation model for the operation stage of photovoltaic energy storage equipment power station, and formula (17) is the carbon emission calculation model for the decommissioning stage of photovoltaic energy storage equipment power station.

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

[0101] In an exemplary embodiment, based on the economic model and the carbon emission model, the levelized carbon cost of electricity of the photovoltaic storage system, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem are determined, and based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, the objective function is determined, including: based on the economic model and the carbon emission model, the levelized carbon cost of electricity of the photovoltaic storage system, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem are determined; based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, the objective function is determined, and the formula is as follows:

[0102] (18)

[0103] (19)

[0104] (20)

[0105] (twenty one)

[0106] (twenty two)

[0107] (twenty three)

[0108] (twenty four)

[0109] in, represents the levelized carbon cost of electricity, Represents the total cost of the photovoltaic energy storage system during its entire life cycle, Represents the total carbon emissions of the photovoltaic energy storage system throughout its entire life cycle. represents the average operating life, represents the carbon unit price of carbon emissions, represents the penalty cost of not solving the voltage problem, are the weight coefficients assigned to the multi-objective functions. is the optimization objective function. Formula (19) represents the average annual levelized carbon cost of the photovoltaic energy storage system, formula (20) represents the unit carbon price of the photovoltaic energy storage system during its entire life cycle, formula (21) represents the annual income of the photovoltaic energy storage system, formula (22) is the average annual levelized net kilowatt-hour carbon cost of the photovoltaic energy storage system during its life cycle, formula (23) is the penalty cost of the voltage problem, and formula (24) is the objective function. The objective function weight coefficient is assigned by using a combination of quantitative and qualitative multi-criteria decision making to minimize the objective function value. By minimizing the objective function, the voltage distribution can be improved, the average annual levelized net kilowatt-hour carbon cost of the photovoltaic energy storage system can be minimized, and the unit carbon price of the photovoltaic energy storage system during its life cycle can be minimized.

[0110] In this embodiment, by minimizing the objective function, the voltage distribution is improved, the average annual levelized net kilowatt-hour electricity carbon cost of the photovoltaic storage system is minimized, and the unit electricity carbon price during the life cycle of the photovoltaic storage system is minimized.

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

[0112] Exemplarily, capacity optimization constraints include: power system flow constraints, energy storage related operation constraints, energy storage life constraints based on discharge depth, energy storage related strategy constraints, photovoltaic output constraints and carbon emission constraints. 1. The power system flow constraint formula is:

[0113] (25)

[0114] (26)

[0115] (27)

[0116] (28)

[0117] (29)

[0118] (30)

[0119] (31)

[0120] in is the first-stage decision variable, i.e., a 0-1 variable ( DG (Distributed Generation) is not configured. Configure DG when Indicates in the scene And the time is Next Node Active output / reactive output of traditional generators; Indicates in the scene And the time is Next, if at node If photovoltaic power is configured at the node, it is the active power output / reactive power output of photovoltaic power. Energy storage is configured at Indicates in the scene And the time is The discharge active / reactive power of the energy storage, Indicates in the scene And the time is Charging active / reactive power of energy storage; Indicates in the scene And the time is Next Node The active power / reactive power that the load needs to consume; Indicates the resistance / reactance on the branch line; It is expressed as the total power flowing from node j to downstream node k at a certain time t under scenario s (there may be more than one downstream branch connected to the node, so the summation is required); is the total power flowing from node i to downstream node j at a certain time t under scenario s (there may be more than one downstream branch connected to the node, so the summation is required); is the branch power loss from node i to node j at a certain time t under scenario s; is the branch current from node i to node j at a certain time t under scenario s; , is the charging and discharging state of the energy storage at node j at a certain time t under scenario s. is the voltage amplitude of node i in scenario s; is the active power / reactive power transmitted from node i to node j in scenario s, and B is the number of branch sets; It is expressed as the lower / upper bound of the voltage amplitude of node i under scenario s; Expressed as the lower / upper bound of the active output of the traditional generator at node i, Expressed as the lower / upper bound of the reactive power output of the conventional generator at node i; It is expressed as 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 node 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. Equation (31) is the branch current constraint. The configuration variables of the active / reactive 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 as , the purpose of which is that if the node If DG is configured, photovoltaic and energy storage are configured together. 2. The formula for energy storage related operation constraints is:

[0121] (32)

[0122] (33)

[0123] (34)

[0124] (35)

[0125] (36)

[0126] (37)

[0127] (38)

[0128] (39)

[0129] (40)

[0130] in, / Indicates the active capacity / reactive capacity configured by the energy storage, Indicates the upper limit of active capacity / reactive capacity configured by the energy storage. Indicates the upper limit of the capacity configured by photovoltaic. Indicates the tangent value of the power factor angle, Indicates the upper limit of the number of DGs configured. is the charging power of the energy storage, is the discharge power of the energy storage, and the maximum values ​​of the charging power and the discharge power of the energy storage are , Indicates the current state of charge. Indicates the state of charge at the last moment. Indicates the charge and discharge efficiency, is the time interval, i.e. 1h; and is a binary 0-1 variable.

[0131] and When the energy storage is in charging state, and When and When , the energy storage is in an idle state; Indicates the maximum / minimum value of the charge state at node i at the current time t; Indicates the remaining power at the end of the energy storage cycle. Represents the remaining power at the beginning of the energy storage cycle. Formulas (32)-(34) are the capacity constraints for the specific configuration of energy storage and photovoltaics. Formula (32) represents the capacity range of energy storage and photovoltaics. Formula (33) indicates that the reactive output of energy storage can be obtained from the active output of energy storage. Formula (34) ensures that the active capacity is equal to the reactive capacity. Formula (35) represents the constraint on the upper limit of the number of energy storages. Formula (36) The upper and lower bounds of energy storage charging and discharging power, Formula (37) The energy storage charging and discharging state constraint, Formula (38) The energy continuity constraint of energy storage, Formula (39) The energy storage charge state constraint, and Formula (40) The energy storage periodic balance constraint. 3. The energy storage life constraint formula based on discharge depth is:

[0132] (41)

[0133] (42)

[0134] in, represents the energy storage discharge state at node i at time t-1, It is a constant obtained by fitting. Its value may vary for different types of batteries. Usually, battery manufacturers will provide relevant parameters. Indicates the number of cycles converted to an equivalent 100% discharge depth; Represents the floating charge 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 discharge depth model constraint of energy storage and takes into account 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 discharge depth 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 indicates that the battery energy storage can work normally until the maximum daily equivalent cycle number corresponding to the floating charge life is reached. The significance of the above inequality constraint is to consider the limit of the equivalent full cycle number of energy storage charge and discharge based on the life model of the discharge depth to extend the working time of energy storage. 4. The formula for energy storage related strategy constraints is:

[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, formula (43) ensures that energy storage charging takes precedence over flowing to the next node, that is, the excess photovoltaic output is connected to the grid; formula (44) can ensure that energy storage is charged first. When the photovoltaic output value is not much different from the load demand value, is the charging power of energy storage. When the photovoltaic output value is much greater than the load demand value, is the upper limit of the energy storage charging power; the result obtained in formula (45) is the increased power flowing to the next node, that is, the photovoltaic output satisfies the load and energy storage. When the load demand is greater than the photovoltaic output, formula (46) ensures that the energy storage discharge takes precedence over the purchase of electricity, and this purchase of electricity is from the user's perspective; formula (47) can ensure that the energy storage is discharged first. When the photovoltaic output value is not much different from the load demand value, is the discharge power of energy storage. When the photovoltaic output value is much greater than the load demand value, is the upper limit of the energy storage discharge power; formula (48) is the amount of electricity purchased from the upper-level power grid. By controlling the priority of energy storage charging and discharging, energy storage charging and discharging takes precedence over excess photovoltaic output grid connection and power purchase. At the same time, based on the discharge depth model, the number of equivalent full cycles of energy storage charging and discharging is limited to extend the working life of energy storage, making the energy storage charging and discharging strategy relatively more flexible. The above energy storage related strategies are the overall strategy framework, and the power purchase part of energy storage is one of the specific implementation details under this framework. The above defines the basic operating principles of the energy storage system under different circumstances. The following power purchase part specifically describes how the energy storage system purchases electricity from the power grid under the guidance of these principles to maintain its normal operation. Charging of energy storage There are two ways. One way is from the excess output of photovoltaic after meeting the load, that is, , the other way is from energy storage to purchase electricity from the grid. Therefore, when calculating the energy storage charging cost, it is necessary to distinguish between the photovoltaic charging part and the part of purchasing electricity from the grid. Analysis of the power purchase part of the grid: When the power generation capacity of the photovoltaic power station exceeds the immediate load demand, first ensure that the load demand is fully met. Subsequently, the remaining photovoltaic output is directly directed to the energy storage system on the grid side for charging. This process not only realizes the effective use of electric energy, but also avoids the waste of electric energy. It is worth noting that at this stage, the charging of the energy storage system is completely dependent on the excess output of the photovoltaic power station, without additional cost. In order to minimize the power purchase cost of the energy storage system, when the photovoltaic output is sufficient to meet the energy storage charging demand, the energy storage system does not purchase electricity from the grid. On the contrary, when the load demand exceeds the power generation capacity of the photovoltaic power station, the photovoltaic output cannot meet the full load demand. At this time, the electricity of the photovoltaic power station is no longer used to charge the energy storage system, but gives priority to meeting the basic needs of the load. When the power reserve in the energy storage system is not enough to supplement the load demand, the system will automatically purchase electricity from the upper grid to ensure that the load demand is continuously met. In this scenario, the energy storage system needs to purchase electricity from the grid to maintain its discharge capacity, thereby ensuring the stability and reliability of power supply. Status is At this time, the electricity price is at the normal price, and the energy storage purchases electricity from the power grid; secondly, the electricity price is at the off-peak price, and the energy storage purchases electricity from the power grid; the formula is as follows:

[0142] (49)

[0143] (50)

[0144] (51)

[0145] (52)

[0146] (53)

[0147] in, is a binary variable. In formula (49), when the remaining state of charge of the energy storage is low, in formula (50), when the electricity price is the normal price, and in formula (52), when the electricity price is the valley price, let the binary variables be 1. By adding constraints (51) and (53), the charging state variables of the energy storage are controlled. 5. The formula for photovoltaic output constraint is:

[0148] (54)

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

[0150] (55)

[0151] in, Indicates that power distribution is in the scene And the time is Next Node Active power output of traditional generators before configuring the photovoltaic energy storage system; Indicates that power distribution is in the scene And the time is Next Node The active power output of the traditional generator after the photovoltaic energy storage system is configured; Formula (55) represents the indicator constraint that the carbon emissions generated by this part of the power distribution must be reduced by at least 5%.

[0152] In an exemplary embodiment, Figure 2As shown, a low-carbon capacity configuration method for a photovoltaic storage system for power distribution is firstly to extract the topological structure and obtain relevant system parameters according to the power distribution in a certain area. Based on the topological structure and system parameters, a multi-dimensional random extraction technology is used to generate a large-scale source-load scenario. A step-by-step elimination strategy is adopted, and those scenarios that do not meet the requirements or have high repetitiveness are eliminated round by round according to the preset screening criteria. After multiple rounds of screening, the source-load data within the target day are obtained. Based on the source-load data within the target day, the economic cost of the photovoltaic storage system is determined, and the economic cost includes at least one of the investment cost, operation cost, recovery cost, energy storage electricity price difference income, energy storage subsidy income, photovoltaic electricity price difference income and power distribution line loss cost; an economic model of the photovoltaic storage system is established based on the economic cost; based on the source-load data within the target day, the carbon emissions of the photovoltaic storage system in the establishment stage are determined, and the establishment stage includes at least one of the photovoltaic storage equipment production stage, photovoltaic storage material transportation stage, photovoltaic storage construction stage, photovoltaic storage power station operation stage, and photovoltaic storage power station decommissioning stage; a carbon emissions model is established based on the carbon emissions of the photovoltaic storage system in the establishment stage. Based on the economic model and the carbon emission model, the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem of the photovoltaic storage system are determined; based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, the objective function is determined. Capacity optimization constraints for the photovoltaic storage system are established, and the capacity optimization constraints include at least one of: power system flow constraints, energy storage-related operation constraints, energy storage life constraints based on discharge depth, energy storage-related strategy constraints, photovoltaic output constraints, and carbon emission constraints. According to the capacity optimization constraints and the objective function, a capacity optimization model for the photovoltaic storage system is constructed; when it is necessary to set up a photovoltaic storage system, the capacity optimization model is used to determine the capacity configuration of the photovoltaic storage system, and then the photovoltaic storage system is set up according to the capacity configuration. When the photovoltaic storage system is in a multi-scenario situation with deterministic source and load, the deterministic source and load data of the photovoltaic storage system is obtained, input into a capacity optimization model, and the capacity optimization model is solved to obtain the capacity configuration of the photovoltaic storage system, and the photovoltaic storage system is set according to the capacity configuration; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a multi-stage optimization iteration technology, and 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 in each scenario is calculated; based on the objective function in each scenario, a weighted average is determined, and a decision vector is updated based on the weighted average; based on the decision vector, a weight vector of each scenario is updated; and the current capacity configuration of the photovoltaic storage system is obtained.For the i-th iteration, the capacity configuration of the photovoltaic storage system obtained in the previous iteration is obtained, 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, the objective function value in each scenario is calculated; the weighted average of the objective function values ​​in each scenario is calculated, and based on the weighted average, the decision vector is updated; based on the decision vector, the weight vector in 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 so, the iteration is stopped, and the capacity configuration of the current photovoltaic storage system is used as the optimized capacity configuration of the photovoltaic storage system; the photovoltaic storage system is set according to the capacity configuration.

[0153] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0154] In an exemplary embodiment, Figure 3 As shown, a low-carbon electricity-photovoltaic storage system capacity configuration device 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] An acquisition module, used to acquire a topological structure of power distribution and a system parameter of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameter;

[0156] Establishing a module for establishing an economic model and a carbon emission model of the photovoltaic storage system based on the source-load data on the target day;

[0157] A first determination module is used to determine the levelized electricity-carbon cost, the average annual levelized net kilowatt-hour electricity-carbon cost and the penalty cost of voltage problems of the photovoltaic storage system based on the economic model and the carbon emission model;

[0158] A second determination module is used to determine an objective function based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0159] The optimization module is used to establish capacity optimization constraints of the photovoltaic storage system; construct a capacity optimization model of the photovoltaic storage system according to 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 an 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 is obtained, input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is solved; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system.

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

[0163] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

[0164] In an exemplary embodiment, the establishing module is further used to:

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

[0166] In an exemplary embodiment, the establishing module is further used to:

[0167] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0169] Each module in the above-mentioned low-carbon electric-photovoltaic storage system capacity configuration device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0170] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the topological structure of power distribution and the system parameters of power distribution. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a low-carbon power distribution photovoltaic storage system capacity configuration method is implemented.

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

[0172] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0173] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0175] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0176] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0177] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

[0178] In one embodiment, when the processor executes the computer program, the processor further implements 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 is obtained, input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is solved; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system.

[0180] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0181] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

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

[0184] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0185] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0187] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0188] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0190] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0191] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0192] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving 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, the following steps are also implemented:

[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 is obtained, input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is solved; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a 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, the following steps are also implemented:

[0196] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

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

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

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

[0200] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0202] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0203] Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters;

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

[0205] Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems;

[0206] Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem;

[0207] Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving 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, the following steps are also implemented:

[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 is obtained, input into the capacity optimization model, and the capacity configuration of the photovoltaic storage system is solved; when the photovoltaic storage system is in a multi-scenario situation with uncertain source and load, the capacity optimization model is solved by a 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, the following steps are also implemented:

[0211] Initialize the capacity optimization model to obtain the initial capacity configuration of the photovoltaic storage system; during the iteration process, obtain the capacity configuration of the photovoltaic storage system obtained in the previous iteration; wherein, 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, calculate the objective function value in each scenario; calculate the weighted average of the objective function values ​​in each scenario, and update the decision vector based on the weighted average; based on the decision vector, update the weight vector in 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 so, stop the iteration and use the current capacity configuration of the photovoltaic storage system as the optimized capacity configuration of the photovoltaic storage system.

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

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

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

[0215] Based on the source-load data on the target day, the carbon emissions during the establishment phase of the photovoltaic storage system are determined, and the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase and the photovoltaic storage power station decommissioning phase; a carbon emissions model is established based on the carbon emissions during the establishment phase of the photovoltaic storage system.

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

[0217] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0218] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A low-carbon method for configuring the capacity of an electric-photovoltaic storage system, characterized in that: The method comprises: Acquire the topological structure of power distribution and the system parameters of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameters; Based on the source-load data on the target day, an economic model and a carbon emission model of the photovoltaic storage system are established; Based on the economic model and the carbon emission model, determine the levelized electricity carbon cost of the photovoltaic storage system, the average annual levelized net kilowatt-hour electricity carbon cost and the penalty cost of voltage problems; Based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, determining an objective function, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem; Establishing capacity optimization constraints of the photovoltaic storage system; constructing a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; solving the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

2. The method according to claim 1, characterized in that The step of 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 source and load determinism, 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; When the photovoltaic storage system is in a multi-scenario situation with source and load uncertainty, the capacity optimization model is solved by a 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 method of solving the capacity optimization model by using a multi-stage optimization iteration technology to obtain the capacity configuration of the photovoltaic storage system includes: Initializing the capacity optimization model to obtain an 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; wherein, 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, calculating the objective function value in each scenario; Calculating a weighted average of the objective function values ​​in each of the scenarios, and updating the decision vector based on the weighted average; Based on the decision vector, the weight vectors in each scenario are updated to obtain the current capacity configuration of the photovoltaic storage system; Determine a current error value based on the decision vector and the weighted average value; determine whether a convergence condition is met based on the error value; If satisfied, the iteration is stopped, and the capacity configuration of the current photovoltaic storage system is used as the optimized capacity configuration of the photovoltaic storage system.

4. The method according to claim 1, characterized in that: The economic model of the photovoltaic storage system is established based on the source-load data of the target day, including: Based on the source-load data on the target day, determine the economic cost of the photovoltaic storage system, wherein the economic cost includes at least one of investment cost, operation cost, recovery cost, energy storage electricity price difference income, energy storage subsidy income, photovoltaic electricity price difference income and distribution line loss cost; An economic model of the photovoltaic storage system is established based on the economic cost.

5. The method according to claim 1, characterized in that The step of establishing a carbon emission model of the photovoltaic storage system based on the source-load data on the target day includes: Based on the source-load data on the target day, determine the carbon emissions during the establishment phase of the photovoltaic storage system, where the establishment phase includes at least one of the photovoltaic storage equipment production phase, the photovoltaic storage material transportation phase, the photovoltaic storage construction phase, the photovoltaic storage power station operation phase, and the 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.

6. The method according to claim 1, characterized in that The capacity optimization constraints of the photovoltaic storage system include: at least one of: power system flow constraints, energy storage related operation constraints, energy storage life constraints based on discharge depth, energy storage related strategy constraints, photovoltaic output constraints and carbon emission constraints.

7. A low-carbon electric-photovoltaic storage system capacity configuration device, characterized in that: The device comprises: An acquisition module, used to acquire a topological structure of power distribution and a system parameter of the power distribution, and generate source-load data within a target day based on the topological structure and the system parameter; Establishing a module for establishing an economic model and a carbon emission model of the photovoltaic storage system based on the source-load data on the target day; A first determination module is used to determine the levelized electricity-carbon cost, the average annual levelized net kilowatt-hour electricity-carbon cost and the penalty cost of voltage problems of the photovoltaic storage system based on the economic model and the carbon emission model; A second determination module is used to determine an objective function based on the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem, wherein the optimization goal of the objective function is to minimize the levelized carbon cost of electricity, the average annual levelized net kilowatt-hour carbon cost of electricity, and the penalty cost of the voltage problem; The optimization module is used to establish capacity optimization constraints of the photovoltaic storage system; construct a capacity optimization model of the photovoltaic storage system according to the capacity optimization constraints and the objective function; and solve the capacity optimization model to obtain the capacity configuration of the photovoltaic storage system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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