Optical storage direct-flexible system multi-target capacity optimization method considering economy and carbon emission

Abstract

The invention provides an optical storage direct flexible system multi-target capacity optimization method considering economy and carbon emission, and the method comprises the steps: completing the building of mathematical models of a photovoltaic power generation model and an energy storage battery model, and completing the building of a load model through a probability model according to park data; the method comprises the following steps: acquiring typical annual hourly solar irradiance and environment temperature of a target site optical storage direct-flexible system, hourly electrical load data of a building or a park and a charge state of an energy storage battery, and calculating power output of photovoltaic equipment through data of the photovoltaic equipment; and determining constraint conditions of the system, carrying out iterative operation on the optical storage and charging system based on a load tracking management strategy and by adopting an intensity Pareto algorithm so as to obtain a capacity configuration result with optimal comprehensive performance of the optical storage and charging system, and drawing various power curves of a typical day of the system. According to the method, a multi-target capacity configuration method of economy and carbon emission can be optimized cooperatively, and economic and low-carbon coordinated development of the optical storage direct-flexible system is realized.

Description

A Multi-Objective Capacity Optimization Method for Photovoltaic-Storage-Directional-Flexible Systems Considering Economic Efficiency and Carbon Emissions Technical Field

[0001] This invention relates to the field of photovoltaic-storage-direct-drive-flexible simulation technology, specifically to a multi-objective capacity optimization method for photovoltaic-storage-direct-drive-flexible systems that considers economic efficiency and carbon emissions. Background Technology

[0002] With the advancement of global energy transition and dual-carbon goals, distributed renewable energy, represented by photovoltaics (PV), has developed rapidly. To address the intermittency and volatility of PV power generation and improve its local absorption rate and power supply reliability, PV-storage-DC-flexible technology has emerged. In PV-storage-DC-flexible systems, the capacity configuration of core equipment such as PV modules, energy storage batteries, and AC / DC converters is a prerequisite for determining the system's technical and economic performance and environmental benefits. A well-designed capacity configuration scheme can maximize the utilization of PV resources, reduce the system's total life-cycle cost, and provide crucial support for low-carbon or even zero-carbon operation for end-users such as buildings and industrial parks.

[0003] Currently, existing technologies have explored certain approaches to the capacity configuration of photovoltaic energy storage systems. However, most of these methods have limitations. Some configuration methods primarily focus on a single economic indicator as the optimization objective, failing to incorporate carbon emissions as a key quantitative indicator, leading to a deviation between the configuration scheme and the goal of green and low-carbon development. Other methods typically separate economic and carbon emission objectives for single-objective optimization. This approach struggles to reveal the inherent trade-off between economic costs and carbon reduction effects, and cannot provide decision-makers with a Pareto-optimal solution set covering different preferences, thus limiting the universality and optimality of the configuration scheme. 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 multi-objective capacity optimization method for photovoltaic-storage-DC-flexible systems considering both economic efficiency and carbon emissions. This method constructs a mathematical model incorporating both economic and carbon emission objectives, and employs a multi-objective intelligent optimization algorithm to solve it, ultimately obtaining a set of optimal capacity configuration schemes. The method includes the following steps:

[0006] Step 1: System Model Establishment. Complete the mathematical model establishment of the photovoltaic power generation model and the energy storage battery model, and complete the load model establishment through the probability model based on the park data.

[0007] Step 2: Obtain typical annual hourly solar irradiance, ambient temperature, and hourly electricity load data (P) of the target location's photovoltaic-storage-direct current-flexible system. loadThe power output P of the photovoltaic device is calculated using the state of charge (SOC) of the energy storage battery and the data from the photovoltaic device. PV ;

[0008] Step 3: Establish a multi-objective optimization model, selecting the system's annualized cost and carbon emissions as the indicators. Both optimization objectives employ minimization options; that is, the smaller the objective value, the better the corresponding system performance.

[0009] Step 4: Determine the system constraints, including system power balance constraints, energy storage unit capacity constraints, energy storage unit charging and discharging power constraints, photovoltaic capacity constraints, and DC side capacity constraints;

[0010] Step 5: Calculate the fitness function based on the strength Pareto algorithm, including standardization of multiple optimization objectives and calculation of the fitness function;

[0011] Step 6: Data input. Input photovoltaic output power and electrical load data to determine the maximum executable range of each component of the system.

[0012] Step 7: Assumptions: To ensure the energy storage unit simulates a typical daily operation, the charge of the energy storage unit should be equal at the initial time t=0 and the final time t=T. A margin is maintained for the initial charge.

[0013] Step 8: Load tracking management strategy. Considering both off-grid and grid-connected operating conditions, a load tracking management strategy is adopted to ensure a stable power supply to users. Based on the load variation pattern, the power and energy status of the power system layer and the energy storage system layer in the microgrid system are simultaneously regulated.

[0014] Step 9: Perform iterative operations on the photovoltaic storage and charging system based on the intensity Pareto algorithm to solve the problem iteratively, thereby obtaining the capacity configuration result with the best overall performance of the photovoltaic storage and charging system, and plotting the power curves of the system on a typical day;

[0015] Furthermore, the actual output power P of the photovoltaic power generation model in step 1 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:

[0016]

[0017] 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.

[0018] Furthermore, the state of charge (SOC) of the energy storage device, modeled using an energy storage battery, is used as the state variable. The SOC of the battery represents the proportion of the current remaining battery capacity to the total available capacity, defined as:

[0019] ;

[0020] Its dynamic changes follow the ampere-hour integration method and are discretized based on this. It can be rewritten in the following form according to electric power and energy efficiency factor:

[0021] ;

[0022] in, , The charging and discharging power of energy storage, , For energy storage charging and discharging efficiency, This refers to the rated energy capacity of the energy storage battery.

[0023] Furthermore, the system annualized cost target in step 3 satisfies:

[0024] ;

[0025] Carbon emission targets, satisfying:

[0026] ;

[0027] Furthermore, the constraints in step 4 are as follows;

[0028] System power balance constraints:

[0029] ;

[0030] Energy storage unit capacity constraints:

[0031] ;

[0032] Energy storage unit charge / discharge power constraints:

[0033] ;

[0034] ;

[0035] ;

[0036] Photovoltaic capacity constraints:

[0037] .

[0038] Furthermore, step 5, the fitness function calculation based on the intensity Pareto algorithm, mainly includes the standardization of multiple optimization objectives and the calculation of the fitness function; the standardization of multiple optimization objectives includes the standardization of economic objectives and carbon emission objectives, and their formulas are as follows:

[0039] ;

[0040] ;

[0041] in, and They are respectively The maximum and minimum values ​​exist; and They are respectively The maximum and minimum values ​​that exist.

[0042] Furthermore, in step 6, the photovoltaic output power and electrical load data are input to determine the maximum executable range of each component of the system.

[0043] Furthermore, in step 7, conditional assumptions are made. To ensure that the energy storage unit can well simulate a general daily operation process, the energy storage unit's charge should be equal at the initial time t=0 and the final time t=T. A certain margin needs to be left in the initial charge. In the load tracking management strategy, based on the electricity price period and power deficit situation, it is further divided into multiple operating modes A1 to A4 and B1, B2, C1, and C2. Specifically: Mode B2 is when the total system power generation is equal to or greater than the electricity load, and the electricity price is in a valley period; surplus electricity is used or valley electricity is purchased to charge the energy storage. Mode A3 is when the total system power generation is less than the electricity load, and the electricity price is in a peak or normal period; the energy storage discharges to supply power, and there is no power interaction with the grid.

[0044] Furthermore, step 8 considers that the system in this paper needs to take into account both off-grid and grid-connected operating conditions. To ensure a stable supply of electricity to users, a load tracking management strategy is adopted. Based on the load variation pattern, the power and energy status of both the power system layer and the energy storage system layer in the microgrid system are simultaneously regulated.

[0045] Furthermore, when considering grid-connected operating conditions:

[0046] when At that time, the total power generation of the system is greater than the power of the electrical load. The power generation inside the system can meet the demand of the electrical load, and there is a certain amount of surplus electricity available for energy storage charging or grid connection sales.

[0047] when At this time, the total power generation of the system is equal to the power of the electrical load, the power generation within the system can meet the electrical load demand, and there is no surplus power.

[0048] when and When the total power generation of the system is less than the power of the load and the electricity price is in the normal or off-peak period, the power generation within the system cannot meet the demand of the load. Since the electricity price is in the normal or off-peak period, it is advisable to purchase electricity to make up for the load shortfall.

[0049] when and At that time, the total power generation of the system is less than the power of the load and the electricity price is at its peak. The power generation within the system cannot meet the demand of the load. Since the electricity price is at its peak, purchasing electricity is not considered.

[0050] Furthermore, the iterative operation process based on the Intensity Pareto Algorithm (SPEA2) in step 9 is as follows: First, the photovoltaic output at each moment is calculated based on actual weather data; second, based on the system power generation and SOC status, the total power of the electrical load and the energy storage charging and discharging power within the system are determined in conjunction with the load tracking management strategy; for normal operating conditions, the SPEA2 algorithm is used to perform population initialization, fitness allocation, environment selection, tournament selection, crossover and mutation operations, taking into account the two objectives of economic benefits and carbon emissions and the energy storage economic operation penalty factor; finally, after the cyclic iteration is completed, the optimal power allocation scheme is selected from the Archive set.

[0051] The technical solution provided by this invention includes a method for multi-objective capacity optimization of a photovoltaic-storage-direct-current-flexible microgrid that considers economic efficiency and carbon emissions. This method determines the power allocation strategy for each part of the photovoltaic-storage-direct-current-flexible microgrid, thereby configuring recommended photovoltaic capacity, energy storage capacity, and converter capacity. This improves the accuracy of energy storage capacity configuration and simultaneously enhances renewable energy utilization and system efficiency. Furthermore, the capacity configuration method of this invention makes the differences in energy storage capacity configuration under optimization objectives more intuitive, which is beneficial for users to guide the selection of optimization objectives based on the energy storage capacity configuration results, thus addressing the technical deficiencies in existing photovoltaic-storage-direct-current-flexible microgrid capacity configuration methods. Attached Figure Description

[0052] 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.

[0053] Figure 1 is a flowchart of a multi-objective capacity optimization method for a direct-drive flexible photovoltaic-storage system provided in an embodiment of the present invention.

[0054] Figure 2 is a flowchart of the strength Pareto optimization algorithm provided in an embodiment of the present invention. Detailed Implementation

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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)."

[0060] This invention provides a multi-objective capacity optimization method for photovoltaic-storage-DC-flexible systems that considers economic efficiency and carbon emissions, as shown in Figures 1 and 2. The optimization configuration method specifically includes the following steps:

[0061] Step 1: System Model Establishment. Complete the mathematical models for the photovoltaic power generation model and the energy storage battery model, and establish the load model using a probability model based on park data. 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:

[0062] ;

[0063] ;

[0064] 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.

[0065] ;

[0066] ;

[0067] In this embodiment of the invention, the actual output power P of the photovoltaic power generation model in step 1 PV The actual output power of a photovoltaic power generation system can be estimated by comparing and using the actual power generation under real-world operating conditions, ambient temperature, and light intensity with the light intensity under standard test conditions. The calculation formula is as follows:

[0068]

[0069] 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.

[0070] Step 2: Obtain typical annual hourly solar irradiance, ambient temperature, and hourly electricity load data (P) of the target location's photovoltaic-storage-direct current-flexible system. load The power output P of the photovoltaic device is calculated using the state of charge (SOC) of the energy storage battery and the data from the photovoltaic device. PV .

[0071] Based on the above analysis, the power output calculation model for a photovoltaic array can be obtained:

[0072]

[0073] Among them, G ref The light intensity under standard conditions is taken as 1000 W / m. 2 ;T ref The temperature under standard conditions is taken as 25℃; This is the power derating factor, used to calculate the losses caused by dust and stains on the photovoltaic panel surface, and is set to 0.9. Under standard conditions (solar irradiance 1000W / m²)2 The rated output power of the photovoltaic cell at an ambient temperature of 25℃, in W; k is the power temperature coefficient.

[0074] In this embodiment of the invention, the energy storage device in step 2 is modeled using an energy storage battery, with its state of charge (SOC) used as the state variable. The SOC of the battery represents the proportion of the current remaining battery capacity to the total available capacity, defined as:

[0075] ;

[0076] Its dynamic changes follow the ampere-hour integration method and are discretized based on this. It can be rewritten in the following form according to electric power and energy efficiency factor:

[0077] ;

[0078] in, , The charging and discharging power of energy storage, , For energy storage charging and discharging efficiency, This refers to the rated energy capacity of the energy storage battery.

[0079] Step 3: Establish a multi-objective optimization model, selecting the system's annualized cost and carbon emissions as the indicators. Both optimization objectives employ minimization options; that is, the smaller the objective value, the better the corresponding system performance. Specifically,

[0080] When economic efficiency is the system optimization objective, the annualized cost satisfies:

[0081] ;

[0082] in, For system investment and operation and maintenance costs, This represents the system's net electricity purchase cost.

[0083] When the system's carbon emissions are the optimization objective, the following conditions must be met:

[0084] ;

[0085] in, The annual net carbon emissions of the system in the scenario satisfy:

[0086] .

[0087] The electricity generated by a photovoltaic-storage-DC-flexible system primarily comes from the upstream power grid and the system's own photovoltaic power. Considering only operational aspects, the electricity provided by photovoltaic power is "green electricity," with no carbon emissions. However, the carbon emissions from electricity provided by the upstream power grid are mainly determined by the proportion of fossil fuel power generation. Therefore, a carbon emission factor from the upstream power grid is introduced. This refers to the carbon emissions per kilowatt-hour, used to calculate the carbon emissions during the operation of the "photovoltaic-storage-DC-flexible" power supply system.

[0088] Step 4: Determine the system constraints, mainly including system power balance constraints, energy storage unit capacity constraints, energy storage unit charge / discharge power constraints, photovoltaic capacity constraints, and DC-side capacity constraints. Specifically,

[0089] System power balance constraints:

[0090] ;

[0091] Energy storage unit capacity constraints:

[0092] ;

[0093] Energy storage unit charge / discharge power constraints:

[0094] ;

[0095] ;

[0096] ;

[0097] Photovoltaic capacity constraints:

[0098] .

[0099] Step 5: Calculate the fitness function based on the intensity Pareto algorithm. This mainly includes standardization of multiple optimization objectives and calculation of the fitness function;

[0100] Due to economic objectives Since units exist, the following standardization process is performed:

[0101] ;

[0102] in, and They are respectively The possible maximum and minimum values.

[0103] carbon emission targets The following standardization process shall be performed:

[0104] ;

[0105] in, and They are respectively The possible maximum and minimum values.

[0106] After performing the above standardization process on the two optimization objectives, , The smaller the value, the better the configuration result.

[0107] Step 6: Data Input. Input the photovoltaic output power and electrical load data to determine the maximum operable range of each component of the system;

[0108] Step 7, Assumptions. To ensure the energy storage unit accurately simulates a typical daily operation, the charge of the energy storage unit should be equal at the initial time t=0 and the final time t=T. A certain margin needs to be allowed in the initial charge.

[0109] By acquiring the charging and discharging current at each moment, integrating the current, and comparing it with the energy storage battery capacity, we can obtain the SOC value at that moment by adding or subtracting it from the initial SOC. The method is simple and easy to implement, and the expression is as follows:

[0110] ;

[0111] SOC value at the next moment:

[0112] ;

[0113] ;

[0114] 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.

[0115] Step 8: Load Tracking Management Strategy. Considering that the system in this paper needs to handle both off-grid and grid-connected operating conditions, a load tracking management strategy is adopted to ensure a stable power supply to users. Based on the load variation patterns, the power system layer and energy storage system layer of the microgrid are simultaneously regulated in terms of power and energy status. Specifically,

[0116] when At that time, the system's internal power generation can meet the electrical load demand, and there is a certain amount of surplus electricity available for energy storage charging or grid connection sales.

[0117] First, determine the state of charge of the energy storage battery. When excess electricity can only be sold through grid connection, the system enters C1 operating mode (total power generation exceeds electrical load, energy storage batteries reach maximum SOC, and excess electricity is sold to the grid through competitive bidding); when When excess electricity is used, it first charges the energy storage battery. If there is still a surplus after the energy storage battery is fully charged, the surplus electricity is then sold to the grid, and the system enters the C2 operating mode (total power generation is greater than the load, excess electricity is used to charge the energy storage battery, and the remaining electricity is sold to the grid through bidding). If the excess electricity just fully charges the energy storage battery, the system enters the B1 operating mode (total power generation is greater than the load, excess electricity is used to charge the energy storage battery, and there is no power interaction with the grid). If the excess electricity is insufficient to fully charge the energy storage battery, the system considers purchasing electricity from the grid for energy storage. When the electricity price is in off-peak hours, the system enters the B2 operating mode (total power generation equals the load, off-peak electricity is purchased to charge the energy storage battery). Conversely, when the electricity price is in normal or peak hours, the system enters the B1 operating mode.

[0118] when At that time, the system's internal power generation can meet the electrical load demand, and there is no surplus power.

[0119] First, determine the state of charge of the energy storage battery. If energy storage cannot continue, the system enters A1 operating mode (total power generation equals electrical load, with no power interaction with energy storage or the grid); when When considering purchasing electricity from the grid for energy storage, the system enters B2 operating mode when the electricity price is in a low-price period; conversely, when the electricity price is in a normal or peak period, the system enters A1 operating mode.

[0120] when and When the system's internal power generation cannot meet the electricity load demand, and since the electricity price is in normal or off-peak hours, purchasing electricity can be considered to make up for the electricity load shortfall.

[0121] First, determine the electricity price for that time period. Based on the time period in which the electricity price falls, implement the corresponding optimization strategy. Off-peak period: When... When energy storage cannot continue and the purchased electricity is only used to make up for the power deficit of the electrolyzer, the system enters the A2 operating mode (total power generation is less than the electrical load, no power interaction with energy storage, and power purchase to make up for the power deficit of the electrical load); when During off-peak hours, the energy storage battery can be charged, and the purchased electricity serves two purposes: to meet the power shortage of the electrolyzer and to store energy during off-peak periods. During normal periods: when... When the energy storage battery can no longer discharge and power needs to be purchased to make up for the power deficit of the electrolyzer, the system enters the A2 working mode; when If the energy storage output can fully compensate for the power deficit of the electrolyzer, the system enters the A3 working mode (total power generation is less than the electrical load, energy storage discharges to supply the electrical load, and there is no power interaction with the grid); otherwise, additional electricity needs to be purchased, and the system enters the A4 working mode (total power generation is less than the electrical load, energy storage discharges to supply the electrical load, and electricity is purchased to make up for the power deficit of the electrical load).

[0122] Step 9: Iteratively solve the photovoltaic-storage-charging system based on the intensity Pareto algorithm. First, calculate the photovoltaic output at each moment based on actual weather data. Second, determine the total power of the electrical load and the energy storage charging and discharging power within the system based on the system's power generation and SOC status, combined with the load tracking management strategy. For normal operating conditions, combining the two objectives of economic benefits and carbon emissions with the energy storage economic operation penalty factor, use the SPEA2 algorithm for population initialization, array definition, determination of optimization objectives, construction of optimization strategies, fitness allocation, tournament selection, crossover and mutation operations. Finally, after the loop iteration ends, select the optimal power allocation scheme from the Archive set.

[0123] Furthermore, an optimization strategy is constructed. First, based on actual weather data, the photovoltaic output at each moment is calculated. Second, based on the system's power generation and SOC status, a logical supplement to the intensity Pareto algorithm is formed by matching special operating conditions such as insufficient or sufficient photovoltaic power generation, to determine the total power of the electrical load and the charging and discharging power of energy storage within the system. Then, for normal operating conditions, the intensity Pareto algorithm is used to solve the problem by combining the two objectives of economic benefits and carbon emissions with the energy storage economic operation penalty factor. Finally, after the cyclic iteration is completed, the optimal power allocation scheme is selected.

[0124] This invention provides an optimized configuration method for photovoltaic-storage-DC-flexible microgrids. Based on two optimization objectives—economic efficiency and carbon emission reduction—it determines the power allocation strategy for each component of the photovoltaic-storage-DC-flexible microgrid, thereby configuring the optimal photovoltaic capacity, converter capacity, and energy storage capacity to improve renewable energy utilization and system efficiency. Furthermore, the capacity configuration method of this invention enables users to select optimization objectives based on the capacity configuration results.

Claims

1. A multi-objective capacity optimization method for photovoltaic-storage-DC-flexible systems considering economic efficiency and carbon emissions, characterized in that, Includes the following steps: Step 1: System Model Establishment. Establish mathematical models for the photovoltaic power generation and energy storage battery models, and build a load model based on park data using a probability model. Step 2: Obtain annual hourly solar irradiance, ambient temperature, and hourly electricity load data (P) of the photovoltaic-storage-DC-flexible system at the target location. load Based on the state of charge (SOC) of the energy storage battery model, the power output P of the photovoltaic device is calculated using photovoltaic device data. PV Step 3: Establish a multi-objective optimization model, selecting the system's annualized cost and carbon emissions as the indicators; both optimization objectives adopt the minimization option, meaning the smaller the optimization objective value, the better the corresponding system performance; Step 4: Determine the system constraints, including system power balance constraints, energy storage unit capacity constraints, energy storage unit charging and discharging power constraints, photovoltaic capacity constraints, and DC side capacity constraints; Step 5: Calculate the fitness function based on the intensity Pareto algorithm, including standardization processing of multiple optimization objectives and calculation of the fitness function; Step 6: Data input, inputting photovoltaic output power and electrical load data to determine the maximum executable range of each component of the system; Step 7: Condition assumptions, To ensure that the energy storage unit simulates a typical daily operation, the energy storage unit's charge should be equal at the initial time t=0 and the final time t=T; the initial charge should have a margin; Step 8: Load tracking management strategy, considering both off-grid and grid-connected operating conditions, adopts a load tracking management strategy to ensure a stable supply of electricity to users; based on the load variation pattern, the power and energy status of the power system layer and the energy storage system layer in the microgrid system are simultaneously regulated; Step 9: Iterative operation is performed on the photovoltaic-storage-charging system based on the intensity Pareto algorithm to obtain the optimal capacity configuration result for the photovoltaic-storage-charging system's comprehensive performance, and the power curves of various components on a typical day of the system are plotted.

2. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, Actual output power of photovoltaic power generation system The calculation formula is as follows: ;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.

3. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, Using the state of charge (SOC) of the energy storage battery model as the state variable, its discretization calculation formula is as follows: ； ;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.

4. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, The calculation formula for the multi-objective optimization model in step 3 is as follows: System annualized cost target satisfy: ;in, For system investment and operation and maintenance costs, The system's net electricity purchase cost; system carbon emissions are satisfied as the target: ;in, The annual net carbon emissions of the system in the scenario satisfy: 。 5. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, The constraints in step 4 include: system power balance constraints: Energy storage unit capacity constraints: Energy storage unit charging and discharging power constraints: ； ； Photovoltaic capacity constraints: 。 6. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, The standardization process for multiple optimization objectives in step 5 includes standardization of economic objectives and carbon emission objectives, with the following formulas: ； ;in, and They are respectively The maximum and minimum values ​​exist; and They are respectively The maximum and minimum values ​​that exist.

7. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, The load tracking management strategy in step 8 includes: when the total power generation of the system is greater than the power load. At that time, if the energy storage battery is not fully charged Excess electricity is prioritized for charging the energy storage battery, and the remaining electricity after full charging is sold to the grid; if the energy storage battery is already full, excess electricity is directly sold to the grid; when the total power generation of the system equals the power of the electrical load... If the electricity price is during off-peak hours and the energy storage is not fully charged, electricity will be purchased from the grid to charge the energy storage; when the total power generation of the system is less than the power load... When: If the electricity price is in normal or off-peak hours, consider purchasing electricity to make up for the load shortfall; if the electricity price is in peak hours, prioritize energy storage discharge to meet the load demand; if energy storage discharge is insufficient to meet the load, then purchase electricity to make up the shortfall.

8. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 7, characterized in that, The load tracking management strategy is further divided into multiple working modes, A1 to A4 and B1, B2, C1, and C2, based on the electricity price period and power deficit. Mode B2 is when the total power generation of the system is equal to or greater than the electricity load and the electricity price is in the off-peak period. It utilizes surplus electricity or purchases off-peak electricity to charge the energy storage. Mode A3 is when the total power generation of the system is less than the electricity load and the electricity price is in the peak or normal period. It is powered by the energy storage discharging and does not interact with the grid.

9. The multi-objective capacity optimization method for a photovoltaic-storage-DC-flexible system considering economic efficiency and carbon emissions as described in claim 1, characterized in that, The iterative operation process based on the Intensity Pareto Algorithm (SPEA2) in step 9 is as follows: First, the photovoltaic output at each moment is calculated based on actual weather data; second, based on the system power generation and SOC status, the total power of the electrical load and the energy storage charging and discharging power in the system are determined in conjunction with the load tracking management strategy. For normal operating conditions, the SPEA2 algorithm is used to perform population initialization, fitness allocation, environment selection, tournament selection, crossover and mutation operations, taking into account the two objectives of economic benefits and carbon emissions and the energy storage economic operation penalty factor. Finally, after the loop iteration is completed, the optimal power allocation scheme is selected from the Archive.

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