Photovoltaic power station carbon emission reduction evaluation method and device, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of power system carbon emission measurement, and particularly discloses a photovoltaic power station carbon emission reduction evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: calibrating the land area which can be used for laying a photovoltaic energy storage system in a target region; calculating the optimal laying inclination angle of the photovoltaic energy storage system in each charging station, and calculating the generated energy data through photovoltaic power generation in the target area; a photovoltaic energy storage integrated station operation scheduling optimization model for optimizing the energy consumption state parameters of each charging station is established, and the energy consumption state parameters of all the charging stations in the target area are updated in cooperation with the power generation capacity data; calculating carbon emission indexes of all charging stations included in the target area through the updated energy consumption state parameters, and evaluating the carbon emission reduction potential of constructing a photovoltaic energy storage charging station cluster in the target area according to the carbon emission indexes and historical data; the method has the following advantages: accurate evaluation of carbon emission of the charging station cluster and quantification of emission reduction potential are realized.

Description

Technical Field

[0001] This invention relates to the field of carbon emission metering technology in power systems, and more specifically, to methods, devices, electronic equipment, and storage media for assessing carbon emission reduction in photovoltaic power plants. Background Technology

[0002] Electric vehicle charging stations, as a critical infrastructure, have become an important support for alleviating driving anxiety among electric vehicle users and promoting the rapid development of the electric vehicle market. Electric vehicles, driven by electricity, significantly reduce carbon emissions during their operation compared to traditional internal combustion engine vehicles; therefore, their charging systems have positive environmental benefits.

[0003] However, with the large-scale promotion of electric vehicles and charging stations, their dependence on the power system is increasing, bringing new environmental burdens. In particular, the additional electricity load caused by charging often requires the public power grid to allocate more power supply. In the current power grid structure, traditional high-carbon emission power sources still account for a large proportion. Therefore, this additional electricity load will indirectly increase the carbon emissions of the public power grid, forming a carbon emission "transfer" problem. That is, although carbon emissions in the transportation sector are reduced, new carbon emission growth is created on the power supply side.

[0004] To address this issue, clean energy sources such as photovoltaic (PV) power generation are considered a priority. Constructing clean energy supply pathways that combine PV with energy storage systems is expected to effectively reduce grid carbon emissions from charging loads. However, introducing renewable energy sources like PV into charging systems increases system complexity, involving the coordinated operation of multiple stages such as power generation, energy storage, and charging. This results in a broader carbon emission boundary and more diverse carbon emission flow paths, making existing carbon emission accounting methods insufficient to meet the needs for refined, dynamic, and accurate carbon emission measurement across the entire lifecycle of this integrated system. Summary of the Invention

[0005] The present invention aims to provide a method, device, electronic equipment and storage medium for assessing carbon emission reduction of photovoltaic power plants, so as to solve or improve the problem that the existing carbon emission accounting methods are difficult to meet the accurate measurement requirements of carbon emissions in multiple stages and multiple paths throughout the entire life cycle of photovoltaic-energy storage-charging integrated systems.

[0006] In view of this, the first aspect of the present invention is to provide a method for assessing carbon emission reduction in photovoltaic power plants.

[0007] A second aspect of the present invention is to provide an apparatus.

[0008] A third aspect of the present invention is to provide an electronic device.

[0009] A fourth aspect of the present invention is to provide a computer-readable storage medium.

[0010] The first aspect of the present invention provides a method for assessing carbon emission reduction of photovoltaic power plants, comprising the following steps: marking the land area available for laying photovoltaic energy storage systems within a target area using satellite imagery of all charging stations; calculating the optimal laying tilt angle of the photovoltaic energy storage system within each charging station based on geographical information and solar irradiance data, and calculating the power generation data of the target area through photovoltaic power generation using the land area and the optimal laying tilt angle; establishing an integrated photovoltaic energy storage station operation scheduling optimization model that optimizes the energy consumption state parameters of each charging station, considering multiple alternative operational objectives for each charging station; updating the energy consumption state parameters of all charging stations within the target area using the integrated photovoltaic energy storage station operation scheduling optimization model and the power generation data; calculating the carbon emission index of the target area including all charging stations using the updated energy consumption state parameters; and assessing the carbon emission reduction potential of constructing a photovoltaic energy storage charging station cluster in the target area based on the carbon emission index and historical data.

[0011] In any of the above technical solutions, the step of establishing an integrated photovoltaic energy storage station operation scheduling optimization model for optimizing the energy consumption state parameters of each charging station includes: acquiring charging demand data of the electric vehicle queue in the current charging station; constructing multiple operation mode optimization models that consider the operation objectives according to the selectable operation objectives of the current charging station; and constructing an integrated photovoltaic energy storage station operation scheduling optimization model for each charging station through all the operation mode optimization models.

[0012] In any of the above technical solutions, the construction of multiple operation mode optimization models considering the available operation objectives of the charging station includes: taking the satisfaction of electric vehicle charging as the operation objective of the charging station, constructing a baseline mode mathematical model that considers the cost factors of the photovoltaic energy storage system and the public grid jointly supplying electric vehicle charging; taking the optimal economic benefit as the operation objective of the charging station, constructing an economically optimal mode mathematical model that considers the power output of the photovoltaic energy storage system and the time-of-use electricity price of the public grid; and taking the maximum utilization of the power output of the photovoltaic energy storage system as the operation objective of the charging station, constructing a photovoltaic self-consumption mode optimization model.

[0013] In any of the above technical solutions, the scenarios in which the charging station selects its operating target include: Scenario 1, within the same target area, all the charging stations select the same and / or different operating targets; Scenario 2, within the same simulated time period, the same charging station selects the same and / or different operating targets.

[0014] In any of the above technical solutions, the energy consumption status parameters are updated through the following steps: The photovoltaic energy storage integrated station operation scheduling optimization model includes all operation mode optimization models, which use the power generation data and the charging demand data to simulate all charging stations within the target area to obtain the amount of electricity obtained by each charging station from the public grid, the amount of electricity supplied to electric vehicles, and the amount of electricity consumed by the photovoltaic system within a simulation period; The amount of electricity obtained from the public grid, the amount of electricity supplied to electric vehicles, and the amount of electricity consumed by the photovoltaic system are packaged into the energy consumption status parameters in units of simulation time.

[0015] In any of the above technical solutions, the step of calculating the carbon emission index of the target area including all charging stations using the updated energy consumption state parameters includes: calculating the carbon emission increment of the electricity generated by the public grid through the electricity obtained from the public grid; calculating the carbon emission reduction generated by the corresponding public grid electricity through the electricity self-consumed by the photovoltaic system; calculating the carbon emission reduction generated by electric vehicles replacing fuel vehicles through the electricity provided to electric vehicles; and calculating the carbon emission index by weighting and summing all the carbon emission increments and carbon emission reductions according to the carbon emission discount rate.

[0016] In any of the above technical solutions, the calculation of the carbon emission index also includes the carbon emission increment generated by the charging station providing electricity to electric vehicles and the carbon emission increment generated by deploying the photovoltaic energy storage system in the charging station.

[0017] A second aspect of the present invention provides an apparatus comprising: a satellite image calibration module for acquiring geographic information and available land area occupied by all charging stations within a target area; a photovoltaic power generation analysis module for calculating the optimal installation tilt angle of each charging station based on the geographic information and solar irradiance data, and for combining the optimal installation tilt angle with the land area to estimate and acquire the power generation data of the photovoltaic energy storage system; an operation scheduling optimization module for establishing and solving the operation scheduling optimization model of the integrated photovoltaic energy storage and charging station, and updating the energy consumption status parameters of each charging station; and an emission reduction potential assessment module for calculating the carbon emission index of the target area including all charging stations based on the updated energy consumption status parameters, combined with the power generation data and the energy usage data of the public power grid; and for assessing the carbon emission reduction potential of constructing a photovoltaic energy storage and charging station cluster in the target area by comparing the carbon emission index with the historical data.

[0018] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described above.

[0019] A fourth aspect of the present invention provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0020] The beneficial effects of this invention compared to the prior art are as follows: Based on a thorough consideration of the various operational objectives that different charging stations may face (such as maximizing economic benefits, prioritizing green emission reduction, or accommodating user needs), an optimization model for the operation and scheduling of integrated photovoltaic and energy storage stations was established. This model can dynamically update energy consumption status parameters for each charging station by comprehensively considering photovoltaic output forecasting, energy storage system capacity characteristics, electric vehicle charging demand, and the power status of the public grid. Through this multi-objective scheduling, it ensures flexible strategy switching under different time periods or business demands, and also significantly improves the utilization rate of photovoltaic output and the synergistic capabilities of energy storage.

[0021] With the comprehensive integration of photovoltaic, energy storage, and electric vehicle loads into a single system, carbon emission measurement becomes more complex and involves more steps. To address this complexity, this invention presents a systematic approach spanning the entire process, from satellite image calibration and power generation prediction to operational scheduling and carbon emission calculation. By progressively updating energy flow data at each stage of "power generation—energy storage—consumption," it can more comprehensively and accurately reflect actual carbon emissions during operation, overcoming the limitations of traditional methods that only provide partial or static assessments.

[0022] This approach not only focuses on the operation of individual charging stations but also integrates data on the power generation, energy storage configuration, and electricity load of multiple charging stations to comprehensively assess the carbon emission indicators of the entire target area. By comparing this data with historical data, the emission reduction benefits of large-scale deployment of photovoltaic energy storage charging station clusters can be clearly presented, providing a reliable basis for regional clean energy planning or city-level low-carbon transportation strategies.

[0023] Additional aspects and advantages of embodiments of the invention will become apparent in the following description or may be learned by practice of embodiments of the invention. Attached Figure Description

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This invention provides a flowchart for assessing the carbon emission reduction potential of regional photovoltaic energy storage and charging integrated power station clusters throughout their entire lifecycle. Figure 3 This invention provides an assessment of the annual photovoltaic power generation potential of a regional photovoltaic energy storage and charging integrated power station. Figure 4This is a simulation result of the regional photovoltaic energy storage and charging integrated power station production of the present invention; Figure 5 This invention presents the results of an assessment of the carbon emission reduction potential of regional photovoltaic energy storage and charging integrated power stations throughout their entire lifecycle. Figure 6 This is a block diagram of the device structure logic of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0025] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0027] Please see Figures 1-7 The following describes some embodiments of the photovoltaic power plant carbon emission reduction assessment method, apparatus, electronic equipment and storage medium.

[0028] The first aspect of this invention provides a method for assessing carbon emission reduction in photovoltaic power plants. In some embodiments of this invention, such as... Figures 1-5 As shown, the method includes the following steps: S101 uses satellite imagery to determine the land area within the target region that can be used to install photovoltaic energy storage systems, covering all areas occupied by charging stations.

[0029] Here, considering the dispersed and non-fixed locations of charging stations, making manual measurement difficult and time-consuming, this invention uses satellite imagery to measure the land area within each charging station suitable for installing photovoltaic energy storage systems. This land area considers the maximum possible photovoltaic area across all land within the charging station, not just the parking space area, to maximize the charging station's renewable energy integration capability. i For each charging station, the maximum land area suitable for installing a photovoltaic energy storage system is denoted as [missing information]. .

[0030] As mentioned above, the distribution of charging stations in the areas involved in this invention is characterized by dispersion and large scale. The specific location, shape, land area, and surrounding available space of each charging station are all different. Manually measuring the land area occupied by each charging station is not only cumbersome, time-consuming, and costly, but also easily affected by subjective factors or terrain complexity, resulting in inconsistent and inaccurate measurement results. To solve these problems, this invention introduces satellite image analysis technology to accurately pinpoint the land area suitable for installing photovoltaic energy storage systems. In this way, large amounts of geospatial data can be processed automatically or semi-automatically in a short period, enabling large-scale assessment of photovoltaic deployment potential across a region.

[0031] By statistically analyzing and calibrating the land area within the target region where charging stations can accommodate photovoltaic energy storage systems, we can not only quickly identify suitable locations for building or expanding photovoltaic energy storage systems, but also conduct multi-dimensional analysis based on subsequent information such as solar irradiance, terrain, and building obstruction. In other words, this provides a clear spatial reference for the selection and capacity design of subsequent photovoltaic energy storage systems, maximizing the proportion of renewable energy in the energy supply structure of the target region.

[0032] For example, in urban centers, charging stations are often located around commercial buildings or multi-story parking garages, where ground space is limited but rooftop and parking shed spaces are relatively abundant. Satellite imagery can capture the shape and area distribution of the building's rooftop structure, and combined with 3D modeling data, suitable areas for installing photovoltaic energy storage systems can be precisely identified. In this case, solar panels can often be installed on rooftops or carports by adding a support structure, significantly increasing power generation without occupying additional urban ground space.

[0033] S102 calculates the optimal installation tilt angle of the photovoltaic energy storage system in each charging station based on geographical information and solar irradiance data, and calculates the power generation data of photovoltaic power generation in the target area based on land area and optimal installation tilt angle.

[0034] Here, solar irradiance conditions vary across different regions, latitudes, and seasons. To improve the power generation efficiency of photovoltaic (PV) modules in a photovoltaic energy storage system, it is typically necessary to finely adjust the tilt angle and orientation (e.g., facing due south, with a tilt angle of a certain degree) of the PV panels. Determining the optimal installation tilt angle requires considering multiple dimensions, including geographical location (latitude and longitude, altitude), seasonal sunshine duration, and the energy output performance of the PV panels throughout the year, to ensure that the PV energy storage system achieves optimal PV energy returns over a longer period. In step S101, the land area available for installing the PV energy storage system within the range of each charging station has been obtained. In this step, this information needs to be combined with the calculated optimal installation tilt angle, comprehensively considering factors such as the land occupied by the supporting structure, the spacing between modules, and the impact of shading, to obtain the number of PV modules that can be deployed at each charging station under the optimal installation tilt angle and its actual coverage area. By integrating the solar energy resource assessment model and the optimal installation tilt angle design results, the photovoltaic power generation at different time scales such as daily average, monthly, and annual can be predicted, providing crucial energy supply data for subsequent operation scheduling optimization and carbon emission calculation.

[0035] As described above, by comprehensively integrating geographical information, solar irradiance data, system losses, and layout design, a scientific and effective decision-making foundation is laid for the subsequent operation of photovoltaic energy storage systems. This process aims to maximize solar energy utilization within existing space conditions while also considering technical feasibility and economic costs. Ultimately, this results in a comprehensive, detailed, and actionable assessment of photovoltaic power generation potential, providing strong quantitative evidence for large-scale deployment of photovoltaic energy storage systems and subsequent scheduling optimization and carbon emission calculations.

[0036] Step S102 specifically includes: Step 2.1: Calculate the optimal installation tilt angle of the photovoltaic panels in the photovoltaic energy storage system of the charging station based on the latitude and longitude location information of the charging station, using the following empirical formula:

[0037] In the formula, The latitude of the location of the charging station; The optimal tilt angle for installing photovoltaic panels.

[0038] Step 2.2: Calculate the solar hour angle As shown in the following formula:

[0039] In the formula, To fill in true solar time.

[0040] Step 2.3: Calculate the solar altitude angle The solar altitude angle is the angle between the incident sunlight and the horizontal plane, calculated as follows:

[0041] In the formula, The solar altitude angle; It is the solar declination angle, and ;in, The total number of days in a year; It refers to the number of days counted from the first day of each year.

[0042] Step 2.4: Calculate the solar azimuth angle and the angle of incidence of the sun , are the angles between the projection of the incident sunlight onto the horizontal plane and the meridian direction, and the angle between the incident sunlight and the surface of the photovoltaic panel, respectively, calculated as follows:

[0043]

[0044] Step 2.5: Calculate the direct radiation, scattered radiation, and reflected radiation intensity on the tilted surface of the photovoltaic panel. Solar radiation on the photovoltaic panel plane is the sum of total horizontal radiation, direct radiation, and surface scattering. The direct radiation on the tilted surface is calculated using the following formulas. ID b Scattered radiation Id b and reflected radiation IR b strength:

[0045]

[0046]

[0047] In the formula, The intensity of direct solar radiation on the inclined surface; The intensity of solar scattered radiation on the inclined surface; The intensity of ground-reflected radiation obtained on the inclined surface; The surface reflectance of the photovoltaic panel is taken as an empirical value of 0.2.

[0048] The total solar radiation on the tilted surface of the photovoltaic panel is:

[0049] Step 2.6: Assess the annual power generation capacity of each charging station using the following formula:

[0050] In the formula, For calculating the time; Let t be the power output of the photovoltaic panel. For inverter efficiency; Photovoltaic panel conversion efficiency; The formula for calculating the radiation area of ​​a photovoltaic panel is as follows: ; Let t be the total solar radiation on the tilted surface of the photovoltaic panel.

[0051] S103. Considering multiple operational objectives for each charging station, establish an optimization model for the operation and scheduling of photovoltaic energy storage integrated stations to optimize the energy consumption status parameters of each charging station. Update the energy consumption status parameters of all charging stations in the target area using the photovoltaic energy storage integrated station operation and scheduling optimization model and the power generation data.

[0052] Here, after integrating a charging station with photovoltaic panels and a certain proportion of energy storage to form a photovoltaic-storage-charging integrated system, the output coordination of various units within the system, including photovoltaics, energy storage, and electric vehicles, will greatly affect the energy production and consumption of the entire system. Therefore, it is necessary to consider multiple operating conditions and assess the carbon emission reduction potential under different operating conditions. In this invention, three operating modes are proposed, namely the baseline mode, the economically optimal mode, and the photovoltaic self-consumption mode.

[0053] As mentioned above, based on multiple operational objectives for each charging station, a photovoltaic-energy storage integrated station operation scheduling optimization model was established, which integrates photovoltaic power generation, energy storage system, and electric vehicle load factors. The model is then used in conjunction with the available power generation data to dynamically update the energy consumption status parameters of all charging stations within the target area. In this process, by closely integrating photovoltaic and energy storage and combining them with the electricity demand of electric vehicles, the system forms a highly coupled overall architecture of "power generation-energy storage-electricity consumption". It is necessary to seek the optimal or near-optimal operation strategy under different objective orientations, so as to maximize economic and environmental benefits while taking into account the impact on the public power grid load. After introducing photovoltaic (PV) and energy storage, charging stations no longer solely rely on grid power. Instead, they can flexibly adjust the use of PV power generation based on the actual operation and maintenance needs of the station. For example, PV power can be prioritized for vehicle charging, used for energy storage system charging, or sold to the grid during specific periods. These choices significantly impact the overall system's carbon emissions and economic benefits. Therefore, this invention addresses the differentiated needs of different operators or managers by providing three representative operating modes: First, the baseline operating mode, often used for comparison with more complex subsequent scheduling strategies, measures the system's basic emission reduction effect through the simplest energy flow control method; second, the economically optimal operating mode, focusing on maximizing operational revenue through the synergy of energy storage and PV under market incentive mechanisms such as peak-valley pricing or electricity sales profits, while considering economic indicators such as equipment costs, daily operation and maintenance, and electricity sales revenue; and third, the PV self-consumption operating mode, which maximizes operational revenue through the synergy of energy storage and PV under market incentive mechanisms such as peak-valley pricing or electricity sales profits, while also considering economic indicators such as equipment costs, daily operation and maintenance, and electricity sales revenue; and fourth, the PV self-consumption operating mode, which maximizes the utilization of PV power by... Utilizing local photovoltaic power generation and reducing reliance on the external power grid can further reduce carbon emissions and fundamentally decrease the use of high-carbon power sources. However, a trade-off needs to be struck between economic benefits, energy storage configuration requirements, and daily dispatch flexibility. Based on this, a comprehensive approach is taken, considering multiple factors such as real-time photovoltaic output forecasting, energy storage charging and discharging timing, electric vehicle charging demand response, and grid-side power supply characteristics. This allows each charging station to iteratively solve and obtain corresponding dispatch plans under three operating modes, thus providing comprehensive decision support for managers. Finally, the model outputs updated energy consumption state parameters, including the charge level of the energy storage system, power distribution, electric vehicle queuing charging plans, and strategies for purchasing or selling electricity to the grid at different times. This ensures comprehensive control over carbon emissions and operational benefits within the region, and the dispatch results can be applied to subsequent overall carbon reduction potential assessments.

[0054] Specifically, the steps for establishing an optimized operation scheduling model for photovoltaic-energy storage integrated stations that optimizes the energy consumption state parameters of each charging station include: Obtain the charging demand data of the electric vehicle queue in the current charging station.

[0055] Based on the available business objectives of the current charging stations, multiple operation mode optimization models that take into account the business objectives are constructed.

[0056] An optimization model for the operation and scheduling of photovoltaic energy storage integrated stations is constructed for each charging station by optimizing all operating modes.

[0057] To address the specific description above, it is first necessary to obtain the charging demand data of the electric vehicle queue in the current charging station. This includes not only basic information such as the remaining battery power, expected charging time, maximum charging power, and vehicle queue order of each electric vehicle, but also to classify their charging preferences according to vehicle type, usage scenario, and owner needs. This is so that the difference between emergency charging and general charging needs can be accurately distinguished in subsequent optimization calculations.

[0058] After obtaining complete charging demand data, multiple different operation mode optimization models will be constructed based on the available business objectives of the current charging stations. Specifically, these include a profit model with maximizing economic benefits as the primary objective, a green model with optimal carbon emission reduction or maximizing photovoltaic self-generation as the primary objective, and a compromise model that balances costs and emission reduction while meeting specific policy or grid constraints. Each model will be designed in detail for charging power scheduling, energy storage system charging and discharging strategies, and energy exchange methods with the grid. The optimal solution under different business objectives will be obtained through corresponding algorithms or solvers (such as linear programming, nonlinear programming, or mixed integer programming).

[0059] Finally, by integrating all the above-mentioned optimization models for different operating modes, this invention combines their respective advantages and specificities to construct an integrated photovoltaic and energy storage station operation scheduling optimization model suitable for each charging station. This model not only considers the power generation sequence of the photovoltaic system and the capacity constraints of the energy storage system, but also performs refined load allocation and timing management for the queuing and charging needs of electric vehicles. Furthermore, it incorporates all these elements and the weights and priorities of various business objectives into a unified decision-making framework, thereby achieving comprehensive optimization and real-time adjustment of photovoltaic power generation, energy storage charging and discharging, electric vehicle charging, and energy exchange with the public power grid. This lays the foundation for flexible switching or mixed application of different modes in subsequent actual operation, and further supports the accurate assessment and continuous improvement of overall carbon emission levels and economic benefit indicators.

[0060] As can be seen from the above, the established photovoltaic energy storage integrated station operation scheduling optimization model can not only closely match the actual operation needs, but also calculate the corresponding carbon emission data and economic benefit indicators under different modes, providing detailed data support for subsequent carbon emission reduction potential assessment and long-term business decision-making.

[0061] Specifically, based on the available operational objectives of the charging stations, several operational mode optimization models considering these objectives are constructed, including: To achieve the operational goal of charging stations, a baseline mathematical model is constructed that considers the cost factors of both photovoltaic energy storage systems and the public power grid jointly supplying electric vehicle charging.

[0062] Taking optimal economic returns as the operational objective of charging stations, a mathematical model for the economically optimal model is constructed, taking into account the power output of photovoltaic energy storage systems and the time-of-use electricity price of the public grid.

[0063] The goal of charging stations is to maximize the electricity output of photovoltaic energy storage systems, and an optimization model for photovoltaic self-consumption mode is constructed.

[0064] Regarding the specific description above, when constructing multiple operational mode optimization models that consider business objectives, this invention first abstracts different types of business objectives into several quantifiable mathematical models. This allows for a more direct discovery of the characteristics and advantages / disadvantages of each model in the subsequent solution and comparison stages. Specifically, the baseline model, the economically optimal model, and the photovoltaic self-consumption model each have their own emphasis in terms of core objectives, constraints, variable settings, and practical application scenarios. However, they are all based on the coupling relationship between the photovoltaic energy storage system and the electric vehicle charging load. By rationally scheduling the electrical energy supplied by the public power grid and the energy generated by the photovoltaic system, different business objectives can be met and evaluated.

[0065] This paper proposes a baseline mathematical model that takes meeting the charging needs of electric vehicles as the operational objective of charging stations, and considers the cost factors of jointly supplying electric vehicle charging by photovoltaic energy storage systems and the public power grid. Under this model, it is primarily assumed that the operator does not impose strong economic or environmental demands, but rather considers "ensuring that all arriving vehicles complete their expected charging within the specified time" as the core constraint or objective. Other indicators (such as electricity price differences and carbon emissions) only play a supporting role in decision-making.

[0066] Taking optimal economic returns as the operational objective of charging stations, a mathematical model is constructed to construct an economically optimal model that considers the power output of photovoltaic energy storage systems and the time-of-use pricing of the public grid. In this model, the operator focuses more on how to achieve optimal profit or cost through flexible scheduling in a volatile electricity market and charging demand environment.

[0067] The operational objective of charging stations is to maximize the electricity output of photovoltaic energy storage systems, and an optimization model for photovoltaic self-consumption mode is constructed. This model focuses on maximizing the utilization rate of local renewable energy, striving to consume all photovoltaic power generation locally, and minimizing the purchase of electricity from external grids, in order to achieve the goal of the lowest carbon emissions or the highest proportion of green energy.

[0068] As described above, by constructing three major categories of mathematical models—the baseline model, the economically optimal model, and the photovoltaic self-consumption model—this invention can output optimal or near-optimal time-series scheduling plans for the same region, the same group of photovoltaic energy storage devices, and the same batch of electric vehicle charging demands under each model. Furthermore, by comparing the results of different models horizontally, the economic return rate, carbon emission level, photovoltaic utilization rate, and impact on grid operation can be quantified. Based on this, managers or operators can choose the most suitable operating strategy or make compromises according to their own objectives (such as break-even operation, profit maximization, or low-carbon strategy). Subsequently, the scheduling details output by these models can be used to form a guiding operation manual to assist the on-site system in dynamically adjusting under different seasons or electricity market environments. This method, combining "objective diversification" with "refined mathematical modeling," ensures the efficient utilization of the deep coupling between photovoltaic energy storage and electric vehicle charging, and provides solid technical support for the overall optimization and green transformation of the regional energy system.

[0069] Specifically, the baseline model represents the most basic operating condition of the photovoltaic-storage-charging system, which is the easiest to implement in production. It does not require adjusting the charging power of electric vehicles, only needing to meet basic supply-demand balance and economic efficiency. Its objective function is as follows:

[0070] In the formula: For time intervals; To represent the time step; The time period covered by the entire scheduling optimization; The objective function for the baseline operating mode; The costs of providing charging services for electric vehicles, the operation and maintenance costs of energy storage modules (BS) in photovoltaic and photovoltaic energy storage systems, and the costs / benefits of energy interaction with the public grid are respectively. These represent the actual charging power of the electric vehicle, the actual output power of the photovoltaic system, the charging and discharging power of the energy storage system, and the power exchange with the public grid. The constraints of the baseline model optimization include the power balance of the photovoltaic-energy storage-charging system, and the relevant constraints of the energy storage and photovoltaic systems, as follows:

[0071] In the formula: The projected charging demand for the charging station; For the energy storage charge / discharge state, 0-1 control variables are used; This represents the maximum charging and discharging power of the energy storage. To improve the charging and discharging efficiency of energy storage; and These represent the maximum and minimum values ​​of the state of charge of the energy storage system; For energy storage capacity; This represents the maximum power output of the photovoltaic system.

[0072] Specifically, the mathematical model for the economically optimal model is as follows: Under an economic objective, the charging station achieves the overall optimal economic goal by controlling the power of the electric vehicle fleet to match the output of the photovoltaic system and the adjustment of time-of-use electricity prices. The objective function is as follows:

[0073] In the formula, The objective function is the economically optimal operating mode. The constraints are basically the same as the baseline; the second term in the relevant constraint equations needs to be changed to: ,in, Adjustable charging power ratio for electric vehicles.

[0074] Specifically, the photovoltaic self-consumption mode optimization model aims to make charging stations more environmentally friendly by consuming more photovoltaic power while reducing the use of non-clean energy and carbon emissions. The objective function is:

[0075] In the formula: Let t be the amount of photovoltaic power consumed by the charging station at time t; To calculate the time slot; Let be the objective function for the photovoltaic self-consumption mode. The constraints of the photovoltaic self-consumption mode optimization model are consistent with those of the economically optimal model.

[0076] In any of the above embodiments, the scenarios in which the charging station selects its operating objective include: Scenario 1: Within the same target area, all charging stations select the same and / or different operating objectives to set different user needs for charging stations in different locations.

[0077] Scenario 2: During the same simulation period, the same charging station selects the same and / or different operating objectives to set up different user needs at different times for the same charging station.

[0078] In this embodiment, ...

[0079] Specifically, energy consumption status parameters are updated through the following steps: The photovoltaic-energy storage integrated station operation scheduling optimization model, encompassing all operation modes, uses generated electricity data and charging demand data to simulate all charging stations within the target area. This simulates the electricity each charging station receives from the public grid, the electricity supplied to electric vehicles, and the electricity consumed by the photovoltaic system over a simulated period. Under the optimization models of three operation modes, the energy production and consumption of charging stations in the region are simulated for 8760 hours annually, and the electricity received from the public grid, the electricity supplied to electric vehicles, and the electricity consumed by the photovoltaic system are statistically analyzed under different operation modes.

[0080] Using simulation time as the unit, the electricity obtained from the public power grid, the electricity provided to electric vehicles, and the electricity consumed by the photovoltaic system are packaged into energy consumption state parameters.

[0081] In response to the above specific descriptions, and in order to better adapt to the differentiated operational needs and complex user behaviors faced by different charging stations under different temporal and spatial conditions, two scenarios for charging stations to select business objectives are proposed, so as to flexibly match the multi-mode optimization model with specific operational practices.

[0082] Regarding the description of scenario one, within the same target area, all charging stations may choose the same or different operating objectives. This is because, within a large area, the geographical location, surrounding traffic flow, user types, and travel habits of each charging station often vary greatly, resulting in different applicability and priorities of the same set of operating objectives or optimization strategies in different locations. For example, charging stations located in downtown commercial districts may serve a large number of electric vehicle users who are rushing by every day. For these users, charging time or waiting time is crucial. Therefore, such stations may prefer to choose the "economically optimal mode" or the "baseline operation mode" to ensure both operational revenue and user experience. In the suburbs of the same city, some charging stations, which have a larger area to install photovoltaic arrays and energy storage systems and mainly serve vehicles that stay for long periods of time (such as commuter vehicles or logistics fleets), are more suitable for the "photovoltaic self-consumption mode" to maximize the local utilization rate of renewable energy while taking into account environmental protection and stable power supply. Furthermore, some charging stations located in important transportation hubs or highway service areas need to meet the fast charging needs of a large number of cross-regional electric vehicles. In actual operation, they may need to balance certain economic benefits with prioritizing the reliability of fast charging services. Therefore, they will make comprehensive trade-offs among the "baseline mode," "economically optimal mode," and "photovoltaic self-consumption mode," and may even make time-of-day adjustments based on daytime and nighttime traffic characteristics. By allowing different charging stations within the same area to choose the same or different operating objectives, this invention achieves a good match with diversified social needs, and also enables the baseline model, economic model and photovoltaic self-consumption model to reflect their respective advantages on a larger scale, and form a flexible division of labor and collaboration at the overall regional level.

[0083] Regarding the description of Scenario 2, within the same simulated time period, the same charging station can select the same or different operating objectives at different times, thereby more finely adapting to the user demand structure and external conditions at different times. In actual operation, the traffic flow of a charging station is not constant. The vehicle charging demand during the morning and evening peak hours and the midday off-peak hours differ greatly, and the peak hours corresponding to photovoltaic power generation do not always coincide with the concentrated charging hours of electric vehicles; in addition, peak and off-peak electricity prices are usually divided into several-hour intervals, resulting in a certain periodic variation in the cost of electricity purchase at different times. Therefore, within the same simulation timeframe, a charging station might choose a "photovoltaic self-consumption mode" during the daytime when there is high sunshine, primarily supplying photovoltaic power to vehicles with relatively long dwell times, and storing as much surplus energy as possible during the energy storage system's off-peak hours to provide low-carbon electricity for vehicles charging at the station during the night when there is no sunshine. However, once the peak electricity consumption period arrives in the evening or nighttime, in order to obtain more economic benefits, the operator might immediately switch to the "economically optimal mode," using the previously stored electricity in the energy storage to sell electricity to the grid during peak electricity prices or to meet high-priced charging demands, thereby earning additional revenue. Alternatively, if traffic drops sharply late at night, and the station only needs to meet sporadic charging demands and wants to maintain low operating costs at night, it can revert to the "baseline operation mode," using only the necessary power to charge a small number of vehicles, while prioritizing the safety and lifespan of the energy storage batteries. By introducing a mechanism for switching operational objectives at different times within the same charging station, the scheduling optimization method of this invention can continuously change the charging strategy on a daily, weekly, or longer time scale, making the most of real-time photovoltaic power generation status, energy storage capacity, public grid electricity price, and vehicle queuing demand to achieve dynamic balance scheduling of diversified objectives.

[0084] S104 calculates the carbon emission index of the target area including all charging stations using the updated energy consumption status parameters, and assesses the carbon emission reduction potential of building a photovoltaic energy storage charging station cluster in the target area based on the carbon emission index and historical data.

[0085] Here, by comprehensively analyzing the updated energy consumption status parameters, the carbon emission indicators of the target area, including all charging stations, are accurately calculated. Based on this, and combined with historical operating data, the overall carbon emission reduction potential that can be achieved by building a photovoltaic energy storage charging station cluster in the target area is evaluated. This process not only requires the complete integration of the output results of previous steps (such as photovoltaic power generation forecasting, energy storage dispatching strategies, electric vehicle charging demand allocation, and public grid electricity consumption dynamics), but also fully considers the differences in energy consumption and emission characteristics under different periods and dispatching modes, so as to quantify and compare the current emission status and future emission reduction space at the entire regional scale.

[0086] As described above, it is possible to quantify, at a regional scale, the absolute amount of carbon emissions that can be reduced by implementing the construction of photovoltaic energy storage charging station clusters and operating them using appropriate scheduling modes, compared to a scenario with no modifications or only a small amount of photovoltaic input, as well as the proportion of carbon emission reduction in the overall transportation sector or power system. This comprehensive assessment result is crucial for government departments or energy regulatory agencies: it provides investors or operating companies with observable and predictable environmental benefit data, helping them make rational choices between economic and ecological benefits.

[0087] Step S104 specifically includes: Step 4.1: Calculation method for carbon emissions during the production and recycling stages of photovoltaic systems. Carbon dioxide emissions from the construction of photovoltaic systems include two categories: indirect carbon emissions and direct carbon emissions. Indirect carbon emissions refer to the conversion of electrical energy consumption of production equipment during the photovoltaic system industry chain into corresponding emissions. Emissions, here mainly refer to the carbon emissions generated by the electrical energy consumed in processes such as polysilicon reduction, cutting, module packaging, and system integration and installation, which can be expressed as:

[0088] In the formula, This refers to the indirect carbon emissions per unit power during photovoltaic production. This refers to the electricity consumption per unit of power during photovoltaic production. This is a carbon emission factor for the local power grid.

[0089] Direct emissions refer to the amount of carbon dioxide emitted directly in the photovoltaic industry, primarily from the direct carbon emissions during the silicon reduction process from silica sand. Each kWp of photovoltaic power requires 12.5 kg of silicon, corresponding to a direct carbon emission of 18.4 kg / kWp. Therefore, the carbon emissions per unit power photovoltaic unit during construction are:

[0090] In the formula, Carbon emissions generated during the manufacturing process of photovoltaic modules; Carbon emissions generated during the investment process of photovoltaic modules; For inverter efficiency; The power generation efficiency of photovoltaic modules; This refers to the installation area of ​​the photovoltaic modules.

[0091] Carbon emissions during the recycling phase mainly come from the carbon emissions generated during the component recycling and dismantling processes.

[0092] In the formula, Carbon emissions generated during the photovoltaic module recycling process; a The carbon emission coefficient for effective recycling of photovoltaic modules.

[0093] Step 4.2: Carbon emission calculation method for integrated photovoltaic-storage-charging station operation. The carbon emissions from the integrated photovoltaic-storage-charging station during operation include: carbon emissions from using public grid electricity, carbon reductions from using clean photovoltaic power, and carbon reductions from providing electricity to electric vehicles to replace fuel. The carbon emissions from using public grid electricity are calculated as follows:

[0094] In the formula, The total carbon emissions generated from using electricity from the public power grid; Let t be the power used by the public grid.

[0095] The carbon emission reduction generated by using clean photovoltaic electricity is the carbon emission reduction resulting from replacing public grid electricity with photovoltaic electricity, which is recorded as a negative number and calculated as follows:

[0096] In the formula, The total amount of carbon emissions reduced by using clean photovoltaic power; The photovoltaic output at time t.

[0097] The carbon emission reduction of electric vehicles replacing gasoline vehicles is mainly calculated based on the average electricity consumption per 100 kilometers of electric vehicles in the local electric vehicle market.

[0098] In the formula, The energy consumption per 100 kilometers for electric vehicle type k can be found on the China Industrialization Information Platform. The market share of electric vehicle models in category k; This represents the average power consumption per 100 kilometers. This is a collection of electric vehicle models.

[0099] Calculate the average carbon emissions per 100 kilometers a car travels, with gasoline having a carbon emission coefficient of 3.04 kg. -eq / kg, which is 2.20kg -eq / L (92# gasoline):

[0100] In the formula, The fuel consumption per 100 kilometers for the kth type of vehicle can be found on the China Industrialization Information Platform. This represents the average carbon emissions per 100 kilometers.

[0101] Step 4.3: Calculation of annual values ​​of carbon emissions throughout the entire life cycle. The annual values ​​of carbon emissions throughout the entire life cycle are calculated using the annual value method based on investment costs.

[0102] In the formula, y represents the carbon emission discount rate; y represents the operating years of the integrated photovoltaic-storage-charging station. The annual carbon emissions of the integrated photovoltaic, energy storage, and charging station throughout its entire life cycle; This represents the total carbon emission reduction and substitution amount for the integrated photovoltaic, energy storage, and charging station.

[0103] Specifically, the steps for calculating carbon emission indicators for the target area, including all charging stations, using updated energy consumption state parameters include: The carbon emission increment generated by electricity from the public power grid is calculated by measuring the amount of electricity obtained from the public power grid.

[0104] The carbon emission reduction generated by the electricity consumed by the photovoltaic system is calculated based on the corresponding public grid electricity.

[0105] The carbon emission reduction resulting from electric vehicles replacing gasoline-powered vehicles is calculated by the amount of electricity supplied to them.

[0106] The carbon emission index is calculated by weighting and summing all carbon emission increases and decreases based on the carbon emission discount rate.

[0107] Regarding the specific description above, the carbon emission increment of electricity generated by the public grid is calculated based on the amount of electricity obtained from the public grid. During operation and scheduling, each charging station inevitably needs to draw electricity from the public grid at certain times because its photovoltaic and energy storage systems cannot provide completely independent power 24 / 7. To quantify the carbon emissions caused by this electricity consumption, this invention multiplies the amount of electricity obtained from the grid in each time period by the corresponding emission factor (generally provided by the local government or grid company, and may vary depending on the power generation structure, peak electricity consumption, or seasonality) to obtain the carbon emission increment on the public grid side. For example, if a charging station uses 100 kWh of grid electricity during peak hours, and the proportion of thermal power units is relatively high at this time, resulting in a grid emission factor of 0.8 kg... If the energy consumption is / kWh, then the increase in emissions from the charging station during this period is 80 kg. If the proportion of renewable energy in the power grid increases during off-peak hours at night, the emission factor decreases to 0.5 kg. If the emissions increase is per kWh, then for the same amount of electricity consumed, the emissions increase is only 50 kg. By dynamically summing these time-period emissions, it is possible to reveal in detail the emissions impact of purchasing electricity from the public grid at different points in time.

[0108] The carbon emission reduction from the public grid is calculated by measuring the amount of electricity self-consumed by the photovoltaic (PV) system. When the electricity generated by the PV system is directly utilized by the load inside the charging station or by the electric vehicles connected to it, the dependence on the public grid is reduced, thus achieving carbon emission reduction compared to traditional fossil fuel power supply. In other words, without this PV electricity, the charging station would have to buy more electricity from the grid, resulting in additional emissions. Therefore, this invention also multiplies the self-consumed PV electricity by the grid emission factor corresponding to that time period to obtain the emission offset value formed by PV power generation against grid electricity purchases. For example, if a charging station has strong sunlight and high PV output during midday, providing 200 kWh of electricity to the vehicles inside the parking lot, and the grid emission factor is 0.7 kg, then... / kWh, then this portion of self-consumption by the photovoltaic system is 140 kg. The emission reduction will be even greater if the grid emission factor is higher or the photovoltaic output is greater during that period.

[0109] The carbon emission reduction resulting from replacing gasoline-powered vehicles with electric vehicles is calculated by providing them with electricity. The promotion of electric vehicles aims to reduce fossil fuel consumption and emissions in the transportation sector; therefore, when electric vehicles are powered by electricity, they generate a carbon emission reduction benefit compared to traditional gasoline-powered vehicles. To quantify this benefit, this invention assesses the additional carbon emissions that would have been incurred if a gasoline-powered vehicle had been used instead of an electric vehicle, based on the actual amount of electricity the vehicle receives at the charging station and the fuel consumption and emission factors of a gasoline-powered vehicle under the same driving conditions. "Emissions." For example, if a charging station provides 1000 kWh of charging for a vehicle on a certain day, the corresponding driving distance can be estimated to be 5000 kilometers; if the fuel consumption per kilometer is... Emissions are approximately 0.18 kg. Therefore, the total weight reduction from using electric vehicles to replace fuel vehicles was 900 kg. Of course, for a more accurate assessment, the differences in fuel consumption factors of vehicle types (cars, logistics vehicles, buses, etc.) and the specific regional fuel emission standards and driving conditions can be considered to make the alternative emissions more consistent with the actual situation.

[0110] The carbon emission index is calculated by weighting and summing all incremental and reduced carbon emissions based on the carbon emission discount rate. The carbon emission discount rate is a factor used to measure the value of emissions and reductions over time, typically considering factors such as anticipated increases in future carbon costs or carbon taxes, stronger climate policies, and technological advancements leading to lower emission reduction costs. In the accounting system of this invention, different discount coefficients can be assigned to emissions and reductions for different time periods or categories. By comparing their actual values ​​on the same time benchmark, a more realistic emission assessment result is obtained. For example, if carbon prices are expected to rise in the next few years, the costs or social impacts corresponding to emissions during peak periods will receive greater weight, and the emission reduction significance of photovoltaics and energy storage during these periods will be more significant; conversely, in regions where carbon trading or policy subsidies are not available in the short term, the discount rate may be set lower. By discounting and weighting multiple results such as incremental emissions from the public power grid, emission reduction from photovoltaic self-consumption, and emission reduction from electric vehicle substitution, this invention can calculate a comprehensive carbon emission index, thereby providing objective and quantitative data for subsequent energy conservation and emission reduction decisions, carbon quota allocation, and even regional energy planning.

[0111] In any of the above embodiments, the calculation of carbon emission indicators also includes the carbon emission increment generated by the charging station providing electricity to electric vehicles and the carbon emission increment generated by deploying photovoltaic energy storage systems in the charging station.

[0112] In this embodiment, the additional emission factors that may arise during the construction and operation of the entire photovoltaic-storage-charging system are quantified in detail. First, regarding the carbon emission increment generated by the charging station providing electricity to electric vehicles, it is necessary to recognize that although the operation of the vehicle itself no longer directly consumes fuel and emits exhaust gases, the electricity absorbed by the electric vehicle from the charging pile may still undergo several high-carbon emission stages before reaching the vehicle. Specifically, even with the introduction of photovoltaic power generation and energy storage systems, a certain proportion of the electricity will still come from the public power grid, and coal-fired power, gas-fired power, or other fossil fuel units, which account for varying proportions of the grid's energy composition, will contribute considerable greenhouse gas emissions. Therefore, when a charging station purchases a large amount of electricity from the grid during peak load or periods of insufficient photovoltaic power generation, and then supplies the electricity to several electric vehicles through its internal power distribution facilities, this process itself will lead to additional indirect emissions. To ensure accuracy, this invention combines time-of-use pricing or load curves to differentiate between daytime and nighttime grid emission factors and the real-time proportion of thermal power and renewable energy sources. This allows the amount of electricity delivered to vehicles via charging stations in each time period to be multiplied by the corresponding emission factor and summed to obtain the "carbon emission increment derived from providing electricity to electric vehicles." When necessary, various loss coefficients for vehicles, charging piles, and even in-station delivery routes are corrected to make this data as close as possible to the actual operating conditions.

[0113] While deploying photovoltaic energy storage systems within charging stations can generate clean electricity during the usage phase, the initial construction, installation, and subsequent operation and maintenance inevitably lead to increased carbon emissions. Therefore, when conducting emission assessments over the entire life cycle or a longer period, this invention incorporates emission factors from the manufacturing, transportation, installation, maintenance, and eventual disposal and recycling of photovoltaic modules and energy storage batteries into a comprehensive consideration, thereby calculating a "carbon emission contribution value" corresponding to a unit capacity or unit of power generation. In actual calculations, if the scale of photovoltaic and energy storage is relatively large, the corresponding emissions during the manufacturing and construction phases will be more substantial, requiring amortization over a certain number of years or total power generation to the operation phase.

[0114] The carbon emission reduction assessment method for photovoltaic power plants provided by this invention first uses satellite imagery to determine the land area suitable for photovoltaic installation at each charging station. Combining this with the latitude and longitude location information of each charging station, the optimal tilt angle for photovoltaic panel installation is calculated. Then, the technical potential of the photovoltaic system at the charging stations in the region is assessed using solar irradiance data. Subsequently, a full life-cycle carbon emission assessment system for photovoltaic and energy storage systems is constructed. Different operation and scheduling models for integrated photovoltaic-energy storage-charging power plants are established to calculate the annual electricity consumption from the public grid and the electricity absorbed by the photovoltaic system at each charging station. Based on the grid carbon emission factor, and according to the principle that photovoltaic substitution leads to emission reduction while grid electricity consumption leads to emission increase, the emission reduction potential is calculated. Furthermore, the carbon emission reduction contribution of providing electricity to electric vehicles to replace gasoline vehicles is also calculated. This invention can enhance the role of integrated photovoltaic-energy storage-charging power plants in reducing carbon emissions and promoting sustainable development, contributing to the achievement of energy transition and dual-carbon goals.

[0115] A second aspect of the present invention provides an apparatus 2. In some embodiments of the present invention, such as... Figure 6 As shown, the device 2 includes: The satellite image calibration module 201 is used to obtain the geographical information of all charging stations in the target area and the available land area.

[0116] The photovoltaic power generation analysis module 202 is used to calculate the optimal laying tilt angle for each charging station based on the geographical information and the solar irradiance data, and to combine the optimal laying tilt angle with the land area to estimate and obtain the power generation data of the photovoltaic energy storage system.

[0117] The operation scheduling optimization module 203 is used to establish and solve the operation scheduling optimization model of the photovoltaic energy storage charging integrated station, and update the energy consumption status parameters of each charging station.

[0118] The emission reduction potential assessment module 204 is used to calculate the carbon emission index of the target area including all charging stations based on the updated energy consumption status parameters, combined with the power generation data and the energy use data of the public power grid; and to assess the carbon emission reduction potential of building a photovoltaic energy storage charging station cluster in the target area by comparing the carbon emission index with the historical data.

[0119] The device provided by this invention utilizes a satellite image calibration module to quickly and accurately locate the geographical information of each charging station and assess the usable land area, laying the foundation for large-scale deployment of photovoltaic systems. The photovoltaic power generation analysis module, through optimal tilt angle calculation and solar irradiance prediction, helps formulate the optimal photovoltaic layout scheme and accurately estimate power generation. Furthermore, the operation scheduling optimization module combines the characteristics of the energy storage system and the charging demand of electric vehicles to dynamically solve multi-objective energy scheduling strategies, maximizing the utilization of renewable energy and reducing dependence on the public power grid. The emission reduction potential assessment module integrates updated energy consumption data with historical benchmarks for carbon emission analysis, not only quantifying the environmental performance of each charging station under different operating models but also providing objective evidence for regional carbon emission reduction decisions, thereby achieving multiple benefits in reducing operating costs, improving energy efficiency, and alleviating environmental pressure.

[0120] An embodiment of the third aspect of the present invention provides an electronic device. In some embodiments of the present invention, such as... Figure 7 As shown, an electronic device is provided, which may include: a desktop computer, a laptop, a handheld computer, and a cloud server, etc. The electronic device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that... Figure 7 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or different components.

[0121] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0122] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 3. The memory 302 can also include both internal and external storage units of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0123] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium. In some embodiments of the present invention, a computer-readable storage medium is provided that, when executed by processor 301, implements the steps of the above-described method. Therefore, the computer-readable storage medium provided in the fourth aspect of the present invention has all the technical effects of the above-described steps, which will not be repeated here.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0126] In the embodiments provided in this disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0127] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0128] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for assessing carbon emission reduction in photovoltaic power plants, characterized in that, Includes the following steps: Satellite imagery was used to determine the land area within the target region that could be used to install photovoltaic energy storage systems, covering all areas occupied by charging stations. The optimal installation angle of the photovoltaic energy storage system in each charging station is calculated based on geographical information and solar irradiance data, and the amount of electricity that can be generated by photovoltaic power generation in the target area is calculated based on the land area and the optimal installation angle. Considering multiple operational objectives for each of the charging stations, an optimization model for the operation and scheduling of photovoltaic energy storage integrated stations is established to optimize the energy consumption status parameters of each charging station. The energy consumption status parameters of all charging stations in the target area are updated through the optimization model for the operation and scheduling of photovoltaic energy storage integrated stations and the power generation data. The carbon emission index of the target area, including all charging stations, is calculated using updated energy consumption status parameters. Based on the carbon emission index and historical data, the carbon emission reduction potential of building a photovoltaic energy storage charging station cluster in the target area is assessed.

2. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 1, characterized in that, The steps of establishing an optimized photovoltaic energy storage integrated station operation scheduling model that optimizes the energy consumption state parameters of each charging station include: Obtain the charging demand data of the electric vehicle queue in the current charging station; Based on the available business objectives of the charging station, multiple operation mode optimization models that take into account the business objectives are constructed respectively; An operation scheduling optimization model for each of the aforementioned charging stations is constructed using all the operation mode optimization models.

3. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 2, characterized in that, The aforementioned construction of multiple operation mode optimization models considering the available operation objectives of the charging station includes: Taking the satisfaction of electric vehicle charging as the operational objective of the charging station, a baseline model mathematical model is constructed that considers the cost factors of the joint supply of electric vehicle charging by the photovoltaic energy storage system and the public power grid. Taking the optimal economic benefit as the operating objective of the charging station, a mathematical model of the optimal economic model is constructed, taking into account the power output of the photovoltaic energy storage system and the time-of-use electricity price of the public grid. The operation objective of the charging station is to maximize the power output of the photovoltaic energy storage system, and an optimization model for the photovoltaic self-consumption mode is constructed.

4. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 3, characterized in that, The circumstances under which the charging station selects its operating target include: Scenario 1: Within the same target area, all the charging stations have selected the same and / or different operating objectives; Scenario 2: During the same simulation period, the operating objectives selected by the same charging station are the same and / or different.

5. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 2, characterized in that, The energy consumption status parameters are updated through the following steps: The photovoltaic energy storage integrated station operation scheduling optimization model includes all operation mode optimization models, which use the power generation data and the charging demand data to simulate all charging stations in the target area, so as to obtain the amount of electricity obtained by each charging station from the public grid, the amount of electricity provided to electric vehicles, and the amount of electricity consumed by the photovoltaic system within a certain simulation period. Using simulated time as the unit, the electricity obtained from the public power grid, the electricity provided to electric vehicles, and the electricity consumed by the photovoltaic system are packaged into the energy consumption state parameters.

6. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 5, characterized in that, The step of calculating the carbon emission index of the target area including all charging stations using the updated energy consumption state parameters includes: The carbon emission increment generated by the electricity obtained from the public power grid is calculated. The carbon emission reduction generated by the electricity consumed by the photovoltaic system is calculated based on the corresponding public grid electricity. The carbon emission reduction resulting from replacing gasoline-powered vehicles with electric vehicles is calculated by the amount of electricity supplied to them. The carbon emission index is calculated by weighting and summing all the carbon emission increments and reductions based on the carbon emission discount rate.

7. The method for assessing carbon emission reduction in photovoltaic power plants according to claim 6, characterized in that, The calculation of the carbon emission index also includes the carbon emission increment generated by the charging station providing electricity to electric vehicles and the carbon emission increment generated by deploying the photovoltaic energy storage system in the charging station.

8. An apparatus for implementing the carbon emission reduction assessment method for photovoltaic power plants as described in any one of claims 1-7, characterized in that, include: The satellite image calibration module is used to obtain the geographical information of all charging stations within the target area and the available land area. The photovoltaic power generation analysis module is used to calculate the optimal laying tilt angle for each charging station based on the geographical information and the solar irradiance data, and to combine the optimal laying tilt angle with the land area to estimate and obtain the power generation data of the photovoltaic energy storage system. The operation scheduling optimization module is used to establish and solve the operation scheduling optimization model of the photovoltaic energy storage charging integrated station, and update the energy consumption status parameters of each charging station. The emission reduction potential assessment module is used to calculate the carbon emission index of the target area including all charging stations based on the updated energy consumption status parameters, combined with the power generation data and the energy use data of the public power grid; by comparing the carbon emission index with the historical data, the carbon emission reduction potential of building a photovoltaic energy storage charging station cluster in the target area is assessed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic power plant carbon emission reduction assessment method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic power plant carbon emission reduction assessment method as described in any one of claims 1 to 7.

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