Optimal configuration method of light storage direct flexible capacity considering whole life cycle carbon emission

Abstract

This invention discloses a method for optimizing the capacity configuration of photovoltaic-storage-DC-flexible systems, taking into account carbon emissions throughout the entire lifecycle, and relates to the fields of distributed energy planning and building energy conservation. The method includes: a data acquisition module for acquiring building load demand, meteorological data, and dynamic carbon emission factors of the power grid; a refined carbon accounting module for constructing a lifecycle carbon emission model integrating the implicit carbon of equipment and the dynamic carbon of system operation; a system operation modeling module for establishing models of photovoltaic output, energy storage charging and discharging, DC network losses, and flexible load response; and a multi-objective optimization configuration module, which uses the minimization of the comprehensive economic cost and total carbon emissions throughout the system's lifecycle as joint optimization objectives, and solves for the optimal installed capacity of photovoltaics and energy storage through an optimization algorithm. This invention overcomes the limitations of traditional configurations that rely on static carbon factors and a single economic orientation, fully leveraging the dynamic carbon reduction potential of DC-flexible characteristics, and achieving global optimization of the economics and real low-carbon benefits of building microgrids.

Description

Technical Field

[0001] This invention relates to the field of building energy systems and microgrid optimization technology, and in particular to a method for optimizing the configuration of photovoltaic, energy storage, direct current and flexible capacity taking into account carbon emissions throughout the entire life cycle. Background Technology

[0002] As the proportion of building energy consumption continues to rise, the pressure to reduce carbon emissions in the building sector is increasing. Traditional building energy systems mostly rely on AC power supply, which presents problems such as multiple AC / DC conversion stages and high transmission losses when connecting distributed renewable energy sources and DC loads. New microgrid systems, centered on photovoltaics, energy storage, DC power distribution, and flexible loads, have become key to breaking this deadlock. However, how to scientifically configure the capacity of each device within the system to balance economic benefits and low-carbon goals in actual operation remains a pressing technical challenge.

[0003] Existing microgrid capacity configuration methods mostly focus on economic efficiency or carbon emissions during operation, which have the following limitations: (1) They lack a full life cycle assessment and often ignore the implicit carbon emissions of photovoltaic, energy storage and other equipment during the manufacturing and recycling stages; (2) They do not fully explore the adjustment potential of flexible loads, especially ignoring the dual attributes of electric vehicles as both mobile loads and distributed energy storage; (3) They ignore the time-series dynamic characteristics of grid carbon emission factors with power structure and time-sharing scheduling, making it difficult to guide accurate low-carbon responses; (4) The capacity configuration planning and the underlying operation scheduling are not sufficiently coupled, making it difficult for the scheme to achieve global optimization. Therefore, it is urgent to propose an integrated optimization configuration method that takes into account both comprehensive economic efficiency and full life cycle carbon emissions. Summary of the Invention

[0004] In response to the problems in the existing technology,

[0005] To address the technical problems in the background of this application, the present invention provides the following technical solution: a photovoltaic-storage-flexible capacity optimization configuration method considering full lifecycle carbon emissions, which constructs a multi-objective optimization framework by integrating dynamic carbon emission signals, flexible load response mechanisms, and a full lifecycle carbon accounting model, including the following steps:

[0006] Step 1: Obtain multi-dimensional time-series basic data, including the operating environment and historical data of the target building microgrid. The basic data includes the building's historical load sequence, meteorological forecast data, and the dynamic time-series carbon emission factor of the external power grid to which it is connected.

[0007] Step 2: Construct basic physical equipment operation models. Based on the meteorological forecast data and equipment parameters, construct photovoltaic module output prediction models, energy storage system charging and discharging operation models, and DC distribution network power transmission loss models.

[0008] Step 3: Construct a flexible load response model guided by carbon signals, extract the adjustable load characteristics within the building, establish a flexible load response mechanism guided by the dynamic time-series carbon emission factors, and quantify the adjustment power of flexible load reduction / transfer during high carbon emission periods and increase during low carbon emission periods.

[0009] Step 4: Construct a carbon emission accounting model for equipment, and use the full life cycle assessment method to calculate the carbon footprint of photovoltaic modules, energy storage batteries and related converter equipment in the production, transportation, installation and scrapping recycling stages, and convert it into equivalent annual carbon emission.

[0010] Step 5: Construct a dynamic operation carbon emission accounting model. Combine the real-time power flow of the physical equipment operation model and the flexible load response model to calculate the dynamic operation carbon emissions generated by the dynamic time-series carbon emission factor when the microgrid interacts with the external power grid during each scheduling period.

[0011] Step 6: Construct a comprehensive economic cost model for the system, calculate the full life-cycle economics of the microgrid system, and calculate the equivalent annual investment cost of photovoltaic and energy storage equipment, the annual operation and maintenance cost of the system, and the annual net electricity purchase cost of interaction with the external power grid.

[0012] Step 7: Set system operation boundary constraints and establish a set of constraints to ensure the safe and stable operation of the microgrid. The constraints include at least: real-time power balance constraints of the DC bus, safety boundary and charge / discharge rate constraints of the energy storage system's state of charge, and maximum interactive power constraints of the inter-grid tie lines.

[0013] Step 8: Perform multi-objective capacity optimization solution. The joint optimization objectives are to minimize the comprehensive economic cost and the total carbon emissions accumulated from implicit carbon emissions and dynamic operating carbon emissions. Under the condition of satisfying the operating boundary constraints, the multi-objective intelligent optimization algorithm is input for iterative optimization and outputs the optimal capacity configuration scheme of photovoltaic and energy storage.

[0014] Furthermore, step 1 involves obtaining multidimensional time-series basic data, including historical load sequences of buildings. Meteorological forecast data, including irradiance With temperature and the dynamic time-series carbon emission factor of the external power grid to which it is connected. .

[0015] Furthermore, step 2 involves constructing a basic physical equipment operation model, including:

[0016] Light force output model:

[0017] ;

[0018] in, This refers to the output power under standard test conditions. Indicates the light intensity under standard test conditions; The ambient temperature is denoted as T, which represents the standard test conditions; T is the actual surface temperature of the photovoltaic cell array.

[0019] The formula for updating the state of charge (SOC) of energy storage is:

[0020] Charging process:

[0021] ;

[0022] Discharge process:

[0023] ;

[0024] in, , These represent the charging and discharging power of the energy storage battery, in kW; , These represent the charge and discharge efficiencies of the energy storage battery.

[0025] DC distribution network loss model:

[0026] .

[0027] Furthermore, the flexible load response model guided by the carbon signal in step 3:

[0028] The flexible load includes the charging load of electric vehicle clusters. Building temperature control load , its in Total regulating power during the time period satisfy:

[0029] ;

[0030] In carbon emission factors Under guidance, the following response mechanism is established: When When the load is high, reduce or shift the load; when When the load is low, increase the load.

[0031] Furthermore, step 4 employs a life-cycle assessment method to calculate the implicit carbon emissions of the equipment:

[0032] ;

[0033] in,

[0034] Photovoltaic system ;

[0035] Energy storage system ;

[0036] Convert it to an annual average: , This refers to the system's lifespan.

[0037] Furthermore, in step 5, by combining the real-time power flow of the physical equipment operation model and the flexible load response model, a dynamic operation carbon emission accounting model is constructed based on the dynamic operation carbon emission amount generated by the dynamic time-series carbon emission factor when the microgrid interacts with the external power grid during each scheduling period.

[0038] ;

[0039] in, This refers to the power exchanged with the power grid.

[0040] Furthermore, step 6 involves constructing a comprehensive economic cost model for the system, calculating the full life-cycle economics of the microgrid system, and determining the equivalent annual investment cost including photovoltaic and energy storage equipment. Annual system operation and maintenance costs and annual net electricity purchase costs for interaction with external power grids :

[0041] ;

[0042] Among them, investment costs Operation and maintenance costs Electricity purchase cost .

[0043] Furthermore, step 7 sets system operation boundary constraints to establish a set of constraints that ensure the safe and stable operation of the microgrid. These constraints include at least the following:

[0044] Power balance constraints:

[0045] ;

[0046] Energy storage constraints:

[0047] ;

[0048] Connection constraints:

[0049] .

[0050] Furthermore, in step 8, a multi-objective capacity optimization solution is performed to construct a bi-objective optimization model:

[0051] .

[0052] Furthermore, a multi-objective intelligent optimization algorithm is employed to solve the constructed multi-objective optimization model. First, the capacity decision variables are encoded, and the population is initialized within constraints. Second, the fitness of individuals is calculated using a time-series execution strategy to evaluate their economic and low-carbon performance. Subsequently, multi-generational iterative optimization is performed through an algorithmic evolution mechanism, and a Pareto optimal solution set is output after convergence. Finally, a multi-attribute decision-making method is introduced to comprehensively evaluate this solution set, selecting the globally optimal configuration scheme that balances economic benefits and carbon reduction goals.

[0053] The technical solution provided by this invention includes a method for optimizing the capacity configuration of a photovoltaic-storage-DC-flexible building microgrid that takes into account carbon emissions throughout its entire life cycle. By introducing a full life cycle assessment mechanism for equipment, it breaks through the limitations of traditional single-operation-period accounting, making carbon emission assessment more systematic and comprehensive. It uses time-series dynamic carbon signals to accurately guide flexible load response, effectively improving the system's low-carbon operation capability. Relying on the photovoltaic-storage DC power distribution architecture, it significantly reduces the energy loss caused by multi-stage AC / DC conversion. It achieves a deep integration and multi-objective synergistic optimization of the system's comprehensive economic cost and full life cycle low-carbon benefits. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart provided for an embodiment of the present invention; Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0058] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms unless the context clearly indicates otherwise.

[0059] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0060] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0061] This invention provides a method for optimizing the capacity configuration of a photovoltaic-storage-direct-drive-flexible building microgrid that takes into account carbon emissions throughout its entire life cycle, such as... Figure 1 As shown, this optimized configuration method specifically includes the following steps:

[0062] Step 1: Obtain multidimensional time-series basic data, including historical load sequences. Meteorological forecast data, including irradiance With temperature and the dynamic time-series carbon emission factor of the external power grid to which it is connected. Specifically, it includes the following:

[0063] Obtain historical load data from the building side. The historical load data is a sequence of electricity consumption collected at preset time intervals of 15 minutes, denoted as:

[0064] ;

[0065] in, This indicates the number of time steps within the scheduling period.

[0066] Secondly, meteorological forecast data is acquired, which includes environmental variables related to photovoltaic power generation, specifically: solar irradiance series. Unit: W / m²; Ambient temperature series Unit: ℃;

[0067] The meteorological data can be obtained through meteorological forecasting models or historical meteorological databases, and maintains the same time resolution as the time series.

[0068] In this embodiment of the invention, the dynamic time-series carbon emission factor of the external power grid is obtained and denoted as: ;

[0069] in, It represents the carbon emission intensity corresponding to a unit of electricity, with the unit being kgCO2 / kWh. It varies over time and is used to reflect the carbon emission level of power grid supply at different time periods.

[0070] The historical building load data, meteorological forecast data, and dynamic time-series carbon emission factors are aligned and preprocessed to a unified time scale, including missing value imputation, outlier removal, and normalization, to form a unified multi-dimensional time-series input dataset. This provides data support for subsequent photovoltaic power output prediction, load modeling, and optimization solutions.

[0071] Step 2: Construct basic physical equipment operation models. Based on the meteorological forecast data and equipment parameters, construct photovoltaic module output prediction models, energy storage system charging and discharging operation models, and DC distribution network power transmission loss models.

[0072] The photovoltaic power generation model is equivalent to the corresponding circuit, with current... ,Voltage These represent the output current and output voltage of the photovoltaic cell, respectively. The energy conversion expression is:

[0073] ;

[0074] ;

[0075] Light intensity (G) is the most direct factor affecting the power output of photovoltaic (PV) cells. Temperature (T) also affects the power generation efficiency of PV cells because the parameters used to calculate power may differ at different temperatures, thus requiring corrections.

[0076] ;

[0077] ;

[0078] In this embodiment of the invention, the actual output power P of the photovoltaic power output model in step 2 is... PV The actual output power of a photovoltaic power generation system can be obtained by comparing and estimating the actual power generation under actual operating conditions, ambient temperature, and irradiance with the irradiance under standard test conditions. The calculation formula is as follows:

[0079]

[0080] in, This refers to the output power under standard test conditions. Indicates the light intensity under standard test conditions; T represents the ambient temperature under standard test conditions; T represents the actual surface temperature of the photovoltaic cell array.

[0081] The formula for updating the state of charge (SOC) of energy storage is:

[0082] Charging process:

[0083] ;

[0084] Discharge process:

[0085] ;

[0086] in, , These represent the charging and discharging power of the energy storage battery, in kW; , These represent the charge and discharge efficiencies of the energy storage battery.

[0087] DC distribution network loss model:

[0088] .

[0089] Step 3: Construct a flexible load response model guided by carbon signals, extract the adjustable load characteristics within the building, establish a flexible load response mechanism guided by the dynamic time-series carbon emission factors, and quantify the adjustment power of flexible load reduction / transfer during high carbon emission periods and increase during low carbon emission periods.

[0090] The flexible load includes the charging load of electric vehicle clusters. Building temperature control load , its in Total regulating power during the time period satisfy:

[0091] ;

[0092] In this embodiment of the invention, a dynamic time-series carbon emission factor from the external power grid is introduced. A load response mechanism based on carbon signals will be constructed. Specifically:

[0093] when Higher than the preset threshold When the current period is determined to be a high-carbon emission period, strategies for reducing or shifting flexible loads are implemented to ensure their power meets the following requirements:

[0094] ;

[0095] when Below the preset threshold When the current period is determined to be a high-carbon emission period, strategies for reducing or shifting flexible loads are implemented to ensure their power meets the following requirements:

[0096] ;

[0097] in, The baseline load level; This refers to the amount of load reduction or transfer. This represents the increase in load.

[0098] To ensure user comfort and equipment operation constraints, the flexible load also meets the following constraints:

[0099] Adjustment range constraints:

[0100] ;

[0101] Load recovery constraints:

[0102] .

[0103] Step 4: Construct a model for calculating the implicit carbon emissions of equipment, used to quantify the carbon emissions generated by key equipment in a photovoltaic-storage-direct-drive-flexible building microgrid throughout its entire life cycle. The model is based on a life cycle assessment method, uniformly calculating the carbon emissions of equipment during the manufacturing, transportation, installation, and end-of-life recycling stages, and converting them into equivalent annual average carbon emissions. The specific process is as follows:

[0104] The main equipment in the system is categorized, including at least photovoltaic modules, energy storage batteries, and related power electronic converters. Their corresponding life-cycle carbon emissions are expressed as follows: , , .

[0105] The total implicit carbon emissions of the system then satisfy:

[0106] ;

[0107] In this embodiment of the invention, the carbon emissions of each device are calculated using its unit capacity carbon emission coefficient and capacity parameters, specifically as follows:

[0108] The implicit carbon emissions of photovoltaic systems are:

[0109] ;

[0110] The carbon emissions inherent in energy storage systems are:

[0111] ;

[0112] The implicit carbon emissions from power electronic converter equipment are:

[0113] ;

[0114] in, For the installed capacity of photovoltaic systems; This refers to the rated capacity of the energy storage system. For converter equipment capacity; , , These are the carbon emission coefficients per unit capacity of the corresponding equipment.

[0115] In this embodiment of the invention, to achieve a unified comparison with carbon emissions during the operation phase, the implicit carbon emissions throughout the entire life cycle are converted into an annual average value, and the calculation formula is as follows:

[0116] ;

[0117] This represents the system's average annual implicit carbon emissions. This refers to the system's lifespan.

[0118] The above modeling method enables a quantitative assessment of the implicit carbon emissions of photovoltaic-storage-flexible building microgrid equipment, and provides a basis for carbon emission constraints and objective functions for subsequent multi-objective optimization.

[0119] Step 5: Construct a dynamic operation carbon emission accounting model. Combine the real-time power flow of the physical equipment operation model and the flexible load response model to calculate the dynamic operation carbon emissions generated by the dynamic time-series carbon emission factor when the microgrid interacts with the external power grid during each scheduling period.

[0120] Based on the photovoltaic output model, energy storage operation model, and flexible load response model, the microgrid's performance at time [time] is obtained. The power balance relationship is determined, and the interaction power between the power grid and the external power grid is calculated, denoted as . ,when When, it means the microgrid purchases electricity from the external grid; when When this occurs, it indicates that the microgrid is transmitting power to the external power grid.

[0121] In this embodiment of the invention, a dynamic time-series carbon emission factor is introduced. This is used to characterize the carbon emission intensity per unit of electricity and to establish a dynamic carbon emission calculation model:

[0122] ;

[0123] in, This represents the total carbon emissions of the system during the scheduling cycle; This refers to the power exchanged with the power grid.

[0124] In this embodiment of the invention, the dynamic carbon emission model is coupled with the flexible load response model, enabling load adjustment behavior to directly influence... The changes in carbon emissions can reduce electricity demand during periods of high carbon emissions and increase electricity load during periods of low carbon emissions, thereby achieving a temporal optimized distribution of carbon emissions.

[0125] Step 6: Construct a comprehensive economic cost model for the system, calculate the full life-cycle economics of the microgrid system, and calculate the equivalent annual investment cost including photovoltaic and energy storage equipment. Annual system operation and maintenance costs and annual net electricity purchase costs for interaction with external power grids :

[0126] ;

[0127] The equivalent annual investment cost is calculated based on the initial investment cost of the equipment and an annualized discount rate. The formula is as follows:

[0128] ;

[0129] in, , , These are the initial investment costs for the photovoltaic system, energy storage system, and converter equipment, respectively. The discount rate;

[0130] The initial investment cost of each piece of equipment is related to its capacity:

[0131] ;

[0132] ;

[0133] in, , This refers to the investment cost per unit capacity.

[0134] The annual operating and maintenance cost of the system can be estimated based on equipment capacity or investment ratio, as follows:

[0135] ;

[0136] Or equivalently represented as:

[0137] ;

[0138] Based on the power interaction between the microgrid and the external power grid, a power purchase cost model is constructed:

[0139] ;

[0140] In some implementations, only the cost of electricity purchase can be considered while the revenue from grid connection is ignored. In this case:

[0141] ;

[0142] The above-mentioned comprehensive economic cost model, combined with the aforementioned carbon emission model, together constitute the objective function of the multi-objective optimization model, enabling the system to achieve synergistic optimization of economy and low carbon emissions during capacity configuration.

[0143] Step 7: Set system operation boundary constraints and establish a set of constraints to ensure the safe and stable operation of the microgrid. The constraints include at least: real-time power balance constraints of the DC bus, safety boundary and charge / discharge rate constraints of the energy storage system's state of charge, and maximum interactive power constraints of the inter-grid tie lines.

[0144] DC bus power balance constraints, at any scheduling time The system must satisfy the law of conservation of energy, that is:

[0145] ;

[0146] in: Photovoltaic power output; The charging and discharging power of the energy storage system; For power exchange with the external power grid; This represents the total building load.

[0147] Energy storage system state of charge constraints: To ensure the safe operation of the energy storage system, its state of charge must meet the following constraints:

[0148] ;

[0149] in, For a moment The state of charge; , Do not specify the minimum and maximum allowed states of charge.

[0150] Energy storage charging and discharging power and rate constraints: The charging and discharging power of the energy storage system at any given time must meet the rated capacity limit of the equipment.

[0151] ;

[0152] In this embodiment of the invention, to limit the charge / discharge rate, the following must be satisfied:

[0153] ;

[0154] in, , These are the maximum charging and discharging power, respectively; This refers to the energy storage ratio factor. This refers to the rated capacity of the energy storage system.

[0155] Energy storage operation continuity constraint: The state of charge of the energy storage system must satisfy the energy balance relationship as it changes over time.

[0156] ;

[0157] Power exchange constraints between the microgrid and the grid: To ensure the security of the connection with the external power grid, the power exchange between the microgrid and the grid must meet the following requirements:

[0158] .

[0159] Step 8: Perform multi-objective capacity optimization. The joint optimization objectives are minimizing the overall economic cost and minimizing the total carbon emissions (sum of implicit and dynamic operating carbon emissions). Under the condition of satisfying the operating boundary constraints, perform multi-objective capacity optimization, construct a dual-objective optimization model, input it into a multi-objective intelligent optimization algorithm for iterative optimization, and output the optimal capacity configuration scheme for photovoltaics and energy storage. The specific process is as follows:

[0160] (1) Optimize the definition of decision variables, and select key capacity parameters of the system as optimization decision variables, including: , .

[0161] (2) Construct a dual-objective optimization model and establish a multi-objective optimization model with economic efficiency and low carbon emissions as the objectives. The objective function is expressed as:

[0162] ;

[0163] (3) Embedding of constraints: The operational boundary constraints established in step 7 are embedded into the optimization model, including power balance constraints, energy storage state of charge and power constraints, grid interaction power constraints, etc., so as to construct a complete capacity optimization problem.

[0164] (4) Multi-objective optimization solution mechanism: The above model is solved by using a multi-objective intelligent optimization algorithm.

[0165] (5) Pareto optimal solution set construction: Through a multi-objective optimization process, a set of non-dominant capacity configuration schemes are obtained to form a Pareto optimal solution set, which is used to characterize the trade-off between economic efficiency and carbon emissions.

[0166] (6) Determining the optimal configuration scheme: Based on the Pareto solution set, a decision method is used to comprehensively evaluate each candidate scheme and determine the final capacity configuration scheme.

Claims

1. A method for optimal configuration of optical storage direct flexible capacity considering life cycle carbon emissions, characterized in that, Includes the following steps: Step 1: Obtain multi-dimensional time-series basic data, including the operating environment and historical data of the target building microgrid. The basic data includes the building's historical load sequence, meteorological forecast data, and the dynamic time-series carbon emission factor of the external power grid to which it is connected. Step 2: Construct basic physical equipment operation models. Based on the meteorological forecast data and equipment parameters, construct photovoltaic module output prediction models, energy storage system charging and discharging operation models, and DC distribution network power transmission loss models. Step 3: Construct a flexible load response model guided by carbon signals, extract the adjustable load characteristics within the building, establish a flexible load response mechanism guided by the dynamic time-series carbon emission factors, and quantify the adjustment power of flexible load reduction / transfer during high carbon emission periods and increase during low carbon emission periods. Step 4: Construct a carbon emission accounting model for equipment, and use the full life cycle assessment method to calculate the carbon footprint of photovoltaic modules, energy storage batteries and related converter equipment in the production, transportation, installation and scrapping recycling stages, and convert it into equivalent annual carbon emission. Step 5: Construct a dynamic operation carbon emission accounting model. Combine the real-time power flow of the physical equipment operation model and the flexible load response model to calculate the dynamic operation carbon emissions generated by the dynamic time-series carbon emission factor when the microgrid interacts with the external power grid during each scheduling period. Step 6: Construct a comprehensive economic cost model for the system, calculate the full life-cycle economics of the microgrid system, and calculate the equivalent annual investment cost of photovoltaic and energy storage equipment, the annual operation and maintenance cost of the system, and the annual net electricity purchase cost of interaction with the external power grid. Step 7: Set system operation boundary constraints and establish a set of constraints to ensure the safe and stable operation of the microgrid. The constraints include at least: real-time power balance constraints of the DC bus, safety boundary and charge / discharge rate constraints of the energy storage system's state of charge, and maximum interactive power constraints of the inter-grid tie lines. Step 8: Perform multi-objective capacity optimization solution. The joint optimization objectives are to minimize the comprehensive economic cost and the total carbon emissions accumulated from implicit carbon emissions and dynamic operating carbon emissions. Under the condition of satisfying the operating boundary constraints, the multi-objective intelligent optimization algorithm is input for iterative optimization and outputs the optimal capacity configuration scheme of photovoltaic and energy storage.

2. The method of claim 1, wherein, The step 1 obtains multi-dimensional time sequence basic data, including historical load sequence , weather forecast data, and dynamic time sequence carbon emission factor of the accessed external power grid .

3. The method of claim 1, wherein, Step 2, which involves constructing a basic physical equipment operation model, includes: Light force output model: ； in, This refers to the output power under standard test conditions. Indicates the light intensity under standard test conditions; The ambient temperature is denoted as T, which represents the standard test conditions; T is the actual surface temperature of the photovoltaic cell array. Energy storage system model: ； ； in, , These represent the charging and discharging power of the energy storage battery, in kW; , These represent the charge and discharge efficiencies of the energy storage battery. DC distribution network loss model: 。 4. The method of claim 1, wherein, The flexible load response model guided by the dynamic time-series carbon emission factor in step 3 includes the following construction and scheduling logic: The flexible load includes electric vehicle cluster charging load and building temperature control load, which in total regulation power of the period satisfies: 。 5. The method of claim 1, wherein, In step 4, a model for calculating the implicit carbon emissions of equipment is constructed using a life-cycle assessment method. 。 6. The method of claim 1, wherein, In step 5, by combining the real-time power flow of the physical equipment operation model and the flexible load response model, the dynamic operating carbon emissions generated by the dynamic time-series carbon emission factor are calculated when the microgrid interacts with the external power grid during each scheduling period. This constructs a dynamic operating carbon emission accounting model, expressed as: ； wherein, is the power exchanged with the grid.

7. The method of claim 1, wherein, In step 6, a comprehensive economic cost model of the system is constructed to calculate the full life-cycle economics of the microgrid system and to calculate the equivalent annual investment cost including photovoltaic and energy storage equipment. Annual system operation and maintenance costs and annual net electricity purchase costs for interaction with external power grids : 。 8. The method of claim 1, wherein, In step 7, system operation boundary constraints are set to establish a set of constraints that ensure the safe and stable operation of the microgrid. These constraints include at least the following: Power balance constraints: ； Energy storage constraints: ； Connection constraints: 。 9. The method of claim 1, wherein, In step 8, a multi-objective capacity optimization solution is performed to construct a dual-objective optimization model: 。

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