Method for drawing fine production and discharge map of retired new energy device based on wind and light prediction

Abstract

This invention discloses a method for drawing a refined production and disposal map of decommissioned renewable energy devices based on wind and solar forecasting. Belonging to the field of renewable energy resource recycling and forecasting technology, this invention first constructs a multi-source fusion database containing high-resolution wind and solar resource data, power plant information, socio-economic data, and device attribute data. Then, it constructs a two-level forecasting model—macro-guided and micro-site selection—to predict the refined spatial layout and installed capacity distribution of newly added wind and solar power plants in future years. Next, it constructs a refined production and disposal calculation model including age-based decommissioning functions, technology iteration elimination functions, and random failure functions to calculate the production and disposal volume of decommissioned devices in each grid unit at different time points. Finally, it uses geographic information systems and spatiotemporal data visualization technology to generate a multi-dimensional spatiotemporal map of production and disposal. This invention can achieve refined and visualized forecasting of the production and disposal of decommissioned devices at the national scale, providing an intuitive decision-making basis for recycling system planning and resource scheduling.

Description

Technical Field

[0001] This invention belongs to the field of new energy resource recycling and prediction technology, specifically involving a method for drawing a refined production and disposal map of retired new energy devices based on wind and solar forecasting. Background Technology

[0002] Guided by the "dual carbon" goals, my country's new energy industry is developing on a large scale, with numerous photovoltaic and wind power stations being built. This is accompanied by an approaching wave of decommissioning of new energy devices (photovoltaic modules, wind turbine blades, etc.). The efficient recycling and reuse of decommissioned new energy devices is crucial for ensuring resource security and reducing environmental burden. Achieving this goal requires accurately understanding the production and disposal trends and spatiotemporal distribution of decommissioned devices.

[0003] Currently, the evaluation technology for retired new energy devices mainly suffers from the following status and shortcomings: (1) Limited scope of existing assessments: Existing technologies mostly focus on the statistics of retired devices in a single power station or a local area, such as making small-scale calculations on the amount of retired photovoltaic power stations built in a certain province. There is a lack of a systematic assessment framework covering the whole country. Such local assessments cannot reflect the overall distribution characteristics and changing trends of retired new energy devices across the country, and are difficult to support the planning and construction of a nationwide recycling system.

[0004] (2) Incomplete consideration of influencing factors: Existing prediction methods mostly estimate the amount of decommissioning based on single factors such as the construction time of the power station and the service life of the devices, without fully integrating key influencing factors such as wind and solar resource endowment, regional GDP development level, energy demand intensity, and wind-solar complementary development model. In reality, these factors jointly determine the construction scale and spatial layout of wind and solar power stations, which in turn directly affect the amount and spatiotemporal distribution of decommissioned devices, resulting in a large deviation in the existing prediction results.

[0005] (3) Lack of visualization and dynamic prediction capabilities: Existing technologies mostly output the prediction results of decommissioning volume in the form of numerical statistics, without forming a visualized and refined production and scheduling map, and cannot intuitively present the differences in the distribution of decommissioned devices in different regions and at different times; at the same time, they lack dynamic update and trend projection functions, making it difficult to adapt to the changes in decommissioning trends brought about by future adjustments to the construction planning of wind and solar power stations, and unable to provide intuitive and accurate decision support for the layout of recycling points and resource scheduling.

[0006] Therefore, there is an urgent need for a method that can integrate multi-source dynamic data to achieve refined and visualized prediction of retired new energy devices in terms of both production and spatial emissions, so as to support the scientific layout and policy formulation of the recycling industry. Summary of the Invention

[0007] This invention provides a method for creating a refined production and disposal map of decommissioned renewable energy devices based on wind and solar resource forecasting. This method overcomes the shortcomings of existing technologies, such as coarse prediction granularity, incomplete consideration of dynamic factors, and insufficient characterization of the production and disposal process. The method aims to achieve refined and dynamic prediction of the production and disposal volume, timing, and spatial distribution of decommissioned devices nationwide and in key regions by integrating wind and solar resource forecasting, socio-economic drivers, technological iteration, and equipment loss models. It also generates a visualized spatiotemporal map of production and disposal, providing a decision-making basis for constructing an accurate and efficient system for the recycling and utilization of decommissioned devices.

[0008] The technical solution for implementing the present invention is as follows: A method for drawing a refined production and layout map of decommissioned renewable energy devices based on wind and solar forecasting includes the following steps: Step S1: Construct a multi-source fusion basic database, which includes at least: high-resolution gridded wind and solar resource data, information on existing and planned wind and solar power stations, socio-economic data, and attribute data of new energy devices; Step S2: Construct a two-level prediction model of macro-guidance and micro-site selection to predict the refined spatial layout and installed capacity distribution of newly added wind and solar power plants in each future year; Step S3: Based on the output of Step S2, construct a refined calculation model for the production and disposal volume of decommissioned devices, and calculate the production and disposal volume of decommissioned devices for each grid cell at different time points; the model includes: Age-based retirement function based on Weibull distribution, Technological iterative elimination function based on logistic functions And a stochastic failure function based on the baseline failure rate and the space environment risk coefficient; Step S4: Using geographic information system and spatiotemporal data visualization technology, generate a multi-dimensional spatiotemporal map of production and output data calculated in step S3. The map includes at least a spatiotemporal distribution map of production and output intensity, a cumulative evolution map of production and output, and a schematic diagram of production and output paths.

[0009] Furthermore, in step S1, the target dataset is corrected using machine learning methods based on measured meteorological elements from wind and solar power stations or wind and solar measurement stations to obtain high-resolution gridded wind and solar resource data. The meteorological elements include wind speed and total irradiance.

[0010] Furthermore, the two-level prediction model includes: S2.1: Based on regional economic development goals, energy policy constraints, carbon emission pathways and electricity demand, use system dynamics models or machine learning algorithms to predict the macro-target for annual new installed capacity in each target region during the planning period; S2.2: Using geographic grids as the basic unit, a multi-factor comprehensive evaluation model is constructed. Each grid is scored for its site selection suitability. The newly added installed capacity in the macro-objectives is preferentially allocated to grids with high scores, generating a dataset of spatial layout and capacity distribution of newly added wind and solar power stations for each future year. Furthermore, in step S2.2, the evaluation dimensions of the multi-factor comprehensive evaluation model include: resource endowment, construction conditions, grid connection conditions, economic orientation, and wind and solar power complementarity characteristics.

[0011] Further, in step S3, the formula for calculating the production and discharge volume is:

[0012] in, Let t represent the production output of type j devices in grid i in year t. To correspond to the installed capacity, Based on the year of commissioning T i,j The retirement age function, where t represents the year. For the technology iteration and elimination function, This is a random fault function.

[0013] Furthermore, the age-retirement function is described using the Weibull cumulative distribution function:

[0014] in, Let L be the age-based retirement function, L be the device's service life, β be the retirement event distribution characteristic parameter, and η be the characteristic lifetime parameter. It represents exponentiation with base e.

[0015] Furthermore, the technology iteration elimination function is described using a logistic function:

[0016] Let α be the production and disposal rate of type j devices in year t, which is a technology iteration and elimination function. tech The maximum annual additional elimination rate, This represents exponential operations to the base e, k is the iteration rate parameter, and t represents the year. 0,j The critical year or the year when new technologies for type j devices comprehensively surpass old technologies in terms of cost-effectiveness.

[0017] Furthermore, the random fault function is:

[0018] in, This represents the instantaneous annual probability that a device of type j located in network i will be decommissioned due to a random failure in year t. The baseline failure rate; The age factor represents the change in failure rate as the device's service life L increases. This represents the space environment risk coefficient.

[0019] Furthermore, the formula for calculating the space environment risk coefficient Γ(i) is as follows:

[0020] WS i max The maximum wind speed at which grid i's history caused structural damage to wind turbine blades or tearing of photovoltaic panels; HI i Let i be the humidity index of grid i; S i For grid i, the hail level or dust storm frequency; Norm() is the normalization function. w1, w2, and w3 represent the weights of each environmental risk factor.

[0021] Furthermore, in step S4, the spatiotemporal distribution map of production and discharge intensity uses a heat map or a graded color map to show the production and discharge density in different periods and regions; the cumulative evolution map of production and discharge dynamically shows the spatiotemporal accumulation process of the total production and discharge; and the schematic diagram of production and discharge path, combined with the logistics network, shows the possible flow of production and discharge hotspots to the recycling and processing center.

[0022] Beneficial effects: 1. This invention integrates high-resolution gridded wind and solar resources, power plant information, socio-economic data and other sources to construct a systematic evaluation framework covering the whole country, breaking through the limitations of existing local statistics and providing a reliable basis for the macro-planning of the recycling system.

[0023] 2. This invention pioneers a two-level model of "regional macro-target prediction + grid-based micro-site selection", which integrates resource endowment, construction conditions, grid connection conditions, economic orientation and wind-solar complementary characteristics into a unified score, making the prediction of future power plant spatial layout closer to the actual decision-making logic.

[0024] 3. This invention abandons the traditional black-box extrapolation and adopts three interpretable functions coupled together for calculation: age retirement (Weibull distribution), technology iteration (logistic function), and random failure (baseline failure rate × environmental risk), making the prediction results more accurate and traceable.

[0025] 4. The random fault function of this invention introduces environmental factors such as wind speed, humidity index, hail / dust storm and their differentiated weights (configured separately for photovoltaic and wind turbines), which can quantify the additional decommissioning risk of devices in different geographical areas and improve the local resolution of production and failure prediction.

[0026] 5. This invention utilizes GIS technology to output heat maps of production and waste discharge intensity, dynamic maps of cumulative evolution, and schematic diagrams of logistics paths, intuitively displaying the production and waste discharge density, evolution trends, and recycling flow in different periods and regions, providing a visualization tool for the layout of recycling outlets and resource scheduling.

[0027] 6. This invention establishes a regular update mechanism for models and maps, which can incorporate the latest power plant planning, technological iterations, environmental data, etc., to realize dynamic rolling simulation of production and emission forecasts, adapt to future policy and market changes, and overcome the shortcomings of existing technologies that lack dynamic update capabilities. Attached Figure Description

[0028] Figure 1 This is a flowchart of a method for drawing a refined production and layout map of decommissioned new energy devices based on wind and solar forecasting, according to the present invention.

[0029] Figure 2 This is a schematic diagram illustrating the collaborative operation of the refined calculation model for the production and discharge volume of retired devices in this invention. Detailed Implementation

[0030] This invention provides a method for drawing a refined production and disposal map of decommissioned renewable energy devices based on wind and solar forecasting, such as... Figure 1 As shown, the specific steps include: Step S1: Multi-source data acquisition and fusion processing Collect and integrate the following multi-source databases: Wind and solar resource data: refers to high-resolution gridded wind and solar resource data obtained by using machine learning methods to correct the target dataset using measured meteorological elements from wind and solar power plants or wind and solar measurement stations.

[0031] The target dataset refers to the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5 Reanalysis dataset (ERA5), which contains high-resolution gridded solar radiation and wind speed data with a resolution of 30km × 30km. Using measured meteorological elements (wind speed, total irradiance) from wind and solar power plants or wind and solar measurement stations as the data benchmark, machine learning methods are employed to correct the ERA5 data, resulting in high-resolution gridded wind and solar resource data.

[0032] Power station information data: refers to the geographical location, installed capacity, commissioning year, and technology type of existing and planned wind and solar power stations.

[0033] Socioeconomic data: refers to regional GDP and forecast data, electricity demand growth, energy policy intensity index, distribution of power grid facilities, etc.

[0034] Device attribute data: including average design life, failure rate curves, material composition, and technology iteration cycle of different types of photovoltaic modules and wind turbine blades.

[0035] Spatial registration, format standardization, missing value imputation, and normalization are performed on the above data to construct a spatiotemporally consistent basic database.

[0036] Step S2: Refined Prediction of Installed Capacity and Spatial Distribution of Wind and Solar Power Stations Based on the database from step S1, a two-level prediction model of "macro-guidance - micro-location" is constructed: S2.1 Regional Macro-construction Scale Forecast: Analyze the relationship between regional economic development goals (GDP growth rate), energy policy constraints, carbon emission peaking paths and electricity demand forecasts. Use system dynamics models or machine learning algorithms (such as gradient boosting trees (XGBoost / LightGBM) - a powerful tool for nonlinear feature fitting) to predict the macro-targets for new photovoltaic and wind power installed capacity in each target region (such as provinces and cities) during the planning period (such as until 2050).

[0037] S2.2 Grid-based Site Selection and Capacity Spatial Allocation: Under macro-level objective constraints, a multi-factor comprehensive evaluation model for power plant site selection is constructed. The Analytic Hierarchy Process (AHP) is employed, using geographic grids as the basic unit. Each grid is quantitatively scored across multiple dimensions, including resource endowment (theoretical power generation), construction conditions (land use type, slope, ecological sensitivity), grid connection conditions (distance from the grid), economic orientation (proximity to high GDP areas or load centers), and system friendliness (complementary characteristics of wind and solar power output to improve absorption). Based on the scores, the annual new installed capacity predicted in step S2.1 is preferentially allocated to grid areas with high suitability, thereby generating a refined spatial layout and capacity distribution dataset for new wind and solar power plants in future years.

[0038] Step S3: Construction of a refined calculation model for the production and output of retired devices For each power plant (or grid cell) predicted in Step 2, the installed capacity data is used to refine the calculation of its decommissioning output at different time points by introducing the device lifetime distribution function, technology obsolescence model, and fault statistics model. The collaborative workflow of the refined calculation model for decommissioned device output is as follows: Figure 2 As shown.

[0039] Further considering factors such as regional recycling infrastructure layout and logistics costs, a spatiotemporal emission allocation model is established to determine the most likely time and initial spatial node for the generating devices to enter the recycling process. The basic formula for calculating production and emissions is as follows:

[0040] Among them, P i,j,t C represents the production output of type j devices in grid i in year t. i,j To correspond to the installed capacity, f age Based on the year of commissioning T i,j The age-retirement function, f tech f is the technology iteration and elimination function. fail This is a random fault function.

[0041] Furthermore, the age-related retirement function is described by the Weibull cumulative distribution function, simulating the process of a batch of homogeneous devices reaching the end of their lifespan due to natural aging and performance degradation. This process does not involve all devices failing on the same day, but rather following a certain probability distribution around their average lifespan. This function describes the cumulative probability of a device that has served for L years retiring due to age.

[0042]

[0043]

[0044] Where L represents the service duration of the device (in years), t is the current year, and T i,j The year in which device type j in grid i was put into operation; β is the distribution characteristic parameter of the retirement event.

[0045] β < 1: High early failure rate (indicating defective batches). β=1: The failure rate is constant, degenerating into an exponential distribution; β > 1 (normal case): failure rate increases over time, which is consistent with the wear and tear aging process; for photovoltaic modules, β is usually between 3 and 5; η is a characteristic life parameter, which is directly related to the "average life" or "rated life".

[0046] Furthermore, the technology iteration obsolescence function is described by a logistic function, simulating the rate at which older technologies are proactively replaced by the market due to the significant advantages of new-generation technologies in terms of economics (e.g., levelized cost of electricity) or performance. This function describes the annual obsolescence rate of type j devices due to technological iteration in year t.

[0047] Where, α tech The maximum annual additional obsolescence rate that may result from technological iteration is typically 0.01 to 0.1. k is the iteration speed parameter, which reflects the speed at which new technologies penetrate the market; t0,j The new technology for type j devices comprehensively surpasses the old technology in terms of cost-effectiveness in the "critical year" or "year of mandatory policy switch". Furthermore, the random failure function, based on a model describing the baseline failure rate and the space environment risk coefficient, simulates unplanned decommissioning caused by external random shocks (extreme weather, accidents) or internal accidental defects. Its probability of occurrence is related to the severity of the environment in which the device operates and the device's own "age." This function describes the annual instantaneous probability of a type j device located in network i decommissioning due to random failure in year t.

[0048]

[0049] Where, λ 0,j The baseline failure rate represents the inherent failure rate of type j devices over their lifespan under standard operating conditions, typically ranging from 0.1% to 0.5% per year. γ age (L) is the age-related factor, representing the change in failure rate with service life L.

[0050]

[0051] n is a positive coefficient, which can be used for linear regression analysis based on historical operation and maintenance data; Γ(i) is the space environment risk coefficient, which is usually greater than 1 and is calculated based on the environment coefficient of grid i.

[0052] WS i max The maximum wind speed at which grid i's history caused structural damage to wind turbine blades or tearing of photovoltaic panels; HI i The damp heat index of grid i (high temperature and high humidity accelerate material aging and electrical failure). S i For grid i, the hail level or dust storm frequency; Norm() is a normalization function that normalizes the above environmental indicators to the range of [0,1]. w1, w2, and w3 represent the weights of various environmental risk factors, which can be determined based on failure statistics and expert experience. For photovoltaic modules, w1, w2, and w3 are 0.1, 0.6, and 0.5, respectively. "Damp heat" is the most significant environmental stress leading to performance degradation and failure of photovoltaic modules. It can cause yellowing and delamination of the encapsulation material (EVA), accelerate corrosion of the cells and solder ribbons, and induce potential-induced degradation (PID). Physical impact damage caused by hail, strong sandstorms, etc., is the main instantaneous cause of module glass breakage. Although the frequency of occurrence may be lower than that of damp heat, the destructive power of a single event is strong, while the impact of wind speed is relatively low. For wind turbine blades, w1, w2, and w3 are 0.8, 0.3, and 0.2, respectively. Extreme wind speeds exceeding the design rating (such as shear speeds above the cutoff speed), turbulence, and wind shear will cause the blades to bear huge alternating stresses, which are the main external factors leading to composite material fatigue, accelerated leading edge erosion, and even structural fracture (blade breakage). Humid and hot environments will affect the matrix properties of composite materials (glass fiber / epoxy resin), which may reduce their stiffness and may exacerbate the propagation of microcracks, while extreme weather such as hail has a relatively small impact.

[0053] Step S4: Generation and Visualization of Spatiotemporal Production and Output Maps Using Geographic Information System (GIS) and spatiotemporal data visualization technology, the refined production and output data (including production and output volume, time, and spatial location) obtained in step 3 are integrated. A multi-dimensional production and output map is then created, including: Spatiotemporal distribution map of production and discharge intensity: The production and discharge density in different periods and regions is displayed using a heat map or graded color scheme.

[0054] Cumulative Evolution Chart of Production and Output: Dynamically displays the spatiotemporal accumulation process of total production and output.

[0055] Production and discharge route diagram: Combined with the logistics network, it shows the possible flow from major production and discharge hotspots to recycling and processing centers.

[0056] The generated refined production and distribution maps will be output as digital layers, interactive maps, or static thematic maps. A regular update mechanism for the model and maps will be established, incorporating the latest data to continuously update the prediction results and maps.

[0057] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for drawing a refined production and layout map of decommissioned new energy devices based on wind and solar forecasting, characterized in that, Includes the following steps: Step S1: Construct a multi-source fusion basic database, which includes at least: high-resolution gridded wind and solar resource data, information on existing and planned wind and solar power stations, socio-economic data, and attribute data of new energy devices; Step S2: Construct a two-level prediction model of macro-guidance and micro-site selection to predict the refined spatial layout and installed capacity distribution of newly added wind and solar power plants in each future year; Step S3: Based on the output of Step S2, construct a refined calculation model for the production and disposal volume of decommissioned devices, and calculate the production and disposal volume of decommissioned devices for each grid cell at different time points; the model includes: Age-based retirement function based on Weibull distribution, Technological iterative elimination function based on logistic functions And a stochastic failure function based on the baseline failure rate and the space environment risk coefficient; Step S4: Using geographic information system and spatiotemporal data visualization technology, generate a multi-dimensional spatiotemporal map of production and output data calculated in step S3. The map includes at least a spatiotemporal distribution map of production and output intensity, a cumulative evolution map of production and output, and a schematic diagram of production and output paths.

2. The method according to claim 1, characterized in that, In step S1, the target dataset is corrected using machine learning methods based on measured meteorological elements from wind and solar power stations or wind and solar measurement stations to obtain high-resolution gridded wind and solar resource data. The meteorological elements include wind speed and total irradiance.

3. The method according to claim 1, characterized in that, The two-level prediction model includes: S2.1: Based on regional economic development goals, energy policy constraints, carbon emission pathways and electricity demand, use system dynamics models or machine learning algorithms to predict the macro-target for annual new installed capacity in each target region during the planning period; S2.2: Using geographic grids as the basic unit, a multi-factor comprehensive evaluation model is constructed. Each grid is scored for its site selection suitability, and the newly added installed capacity in the macro objectives is preferentially allocated to grids with high scores. This generates a dataset of the spatial layout and capacity distribution of newly added wind and solar power stations for each future year.

4. The method according to claim 3, characterized in that, In step S2.2, the evaluation dimensions of the multi-factor comprehensive evaluation model include: resource endowment, construction conditions, grid connection conditions, economic orientation, and wind and solar power complementarity characteristics.

5. The method according to claim 1, characterized in that, In step S3, the formula for calculating the production and discharge volume is: in, Let t represent the production output of type j devices in grid i in year t. To correspond to the installed capacity, Based on the year of commissioning T i,j The retirement age function, where t represents the year. For the technology iteration and elimination function, This is a random fault function.

6. The method according to claim 1 or 5, characterized in that, The age-based retirement function is described using the Weibull cumulative distribution function: in, Let L be the age-based retirement function, L be the device's service life, β be the retirement event distribution characteristic parameter, and η be the characteristic lifetime parameter. It represents exponentiation with base e.

7. The method according to claim 1 or 5, characterized in that, The technology iteration elimination function is described using a logistic function: Let α be the production and disposal rate of type j devices in year t, which is a technology iteration and elimination function. tech The maximum annual additional elimination rate, This represents exponential operations to the base e, k is the iteration rate parameter, and t represents the year. 0,j The critical year or the year when new technologies for type j devices comprehensively surpass old technologies in terms of cost-effectiveness.

8. The method according to claim 1 or 5, characterized in that, The random fault function is: in, This represents the instantaneous annual probability that a device of type j located in network i will be decommissioned due to a random failure in year t. The baseline failure rate; The age factor represents the change in failure rate as the device's service life L increases. This represents the space environment risk coefficient.

9. The method according to claim 8, characterized in that, The formula for calculating the space environment risk coefficient Γ(i) is as follows: WS i max The maximum wind speed at which grid i's history caused structural damage to wind turbine blades or tearing of photovoltaic panels; HI i Let i be the humidity index of grid i; S i For grid i, the hail level or dust storm frequency; Norm() is the normalization function. w1, w2, and w3 represent the weights of each environmental risk factor.

10. The method according to claim 1, characterized in that, The spatiotemporal distribution map of production and discharge intensity mentioned in step S4 uses a heat map or a graded color map to show the production and discharge density in different periods and regions; the cumulative evolution map of production and discharge dynamically shows the spatiotemporal accumulation process of the total production and discharge; the schematic diagram of production and discharge path combined with the logistics network shows the possible flow direction from production and discharge hotspot areas to the recycling and processing center.

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