Method and computer program product for determining a transportation scheme for decommissioned photovoltaic modules

Abstract

Embodiments of the present application provide a method and a computer program product for determining a transportation scheme of decommissioned photovoltaic modules. The spatio-temporal distribution data of the decommissioned amount of the decommissioned photovoltaic modules in a target region can be predicted by using the basic information of the photovoltaic power stations in the target region. According to the spatio-temporal distribution data, the positions of a plurality of transfer stations in the target region are selected. In combination with the geographic position information of the photovoltaic power stations, the transfer stations and photovoltaic module recycling points, and the road network data, a path set of all possible paths for transporting the decommissioned photovoltaic modules is constructed. Then, based on the spatio-temporal distribution data and the attribute information of each path in the path set, an optimization model is constructed, in which the transportation amount of the decommissioned photovoltaic modules of each path in the path set is taken as a decision variable, and the net income of the recycling of the decommissioned photovoltaic modules is taken as an optimization target. By solving the optimization model, an optimized transportation scheme can be obtained. The transportation scheme determined by the present application can improve the transportation efficiency and save transportation resources.

Description

Technical Field

[0001] This application relates to the field of photovoltaic module recycling technology, and more specifically, to a method and computer program product for determining a transportation plan for retired photovoltaic modules. Background Technology

[0002] Photovoltaic power generation, as a clean and sustainable energy form, has been widely applied and rapidly developed. However, after a certain number of years of operation, photovoltaic modules will be decommissioned due to aging, damage, and other reasons. The recycling of decommissioned photovoltaic modules is not only related to environmental protection but also a crucial link in resource recycling. Given the widespread distribution of photovoltaic power plants, adopting a reasonable and efficient transportation scheme during the recycling of decommissioned photovoltaic modules can improve transportation efficiency and conserve resources. However, current technologies often lack systematic planning and optimization for the transportation of decommissioned photovoltaic modules, resulting in transportation schemes that are often unreasonable and inefficient, leading to excessively long overall transportation distances, low transportation efficiency, and waste of transportation resources. Therefore, it is necessary to provide a more reasonable transportation scheme for the recycling of decommissioned photovoltaic modules to avoid resource waste, adapt to large-scale recycling of decommissioned photovoltaic modules, and achieve rational and optimal resource allocation. Summary of the Invention

[0003] In view of this, this application provides a method and computer program product for determining a transportation plan for decommissioned photovoltaic modules.

[0004] According to a first aspect of this application, a method for determining a transportation plan for decommissioned photovoltaic modules is provided, the method comprising:

[0005] Based on the basic information of multiple photovoltaic power plants within the target area, the spatiotemporal distribution data of the decommissioned photovoltaic modules within the target area are determined; wherein, the basic information is related to the decommissioning time and / or decommissioning quantity of the photovoltaic modules;

[0006] Based on the spatiotemporal distribution data, multiple transfer stations are set up within the target area, and the geographical location information of each transfer station is determined;

[0007] Based on the geographical location information of the multiple photovoltaic power stations, the geographical location information of the multiple transfer stations, the geographical location information of the multiple photovoltaic module recycling points in the target area, and the road network data in the target area, a path set is constructed, and the attribute information of each path in the path set is determined; wherein, the paths in the path set include the path between any photovoltaic power station and any transfer station, and the path between any transfer station and any photovoltaic module recycling point;

[0008] An optimization model is constructed based on the spatiotemporal distribution data and the attribute information of each path in the path set. The decision variables of the optimization model include at least the transportation volume of retired photovoltaic modules for each path in the path set. The optimization objective of the optimization model includes at least maximizing the net revenue from the recovery of the retired photovoltaic modules. The net revenue is determined based on the revenue generated from the recovery of the retired photovoltaic modules and the recovery cost of the retired photovoltaic modules. The recovery cost includes at least the transportation cost of the retired photovoltaic modules.

[0009] Solve the optimization model to determine the transportation volume, and determine the transportation plan for the retired photovoltaic modules based on the transportation volume.

[0010] According to a second aspect of this application, a computer program product is provided, the computer program product comprising a computer program that, when executed, implements the method mentioned in the first aspect above.

[0011] According to a third aspect of this application, an electronic device is provided, the electronic device including a processor, a memory, and a computer program stored in the memory that is executable by the processor, wherein the processor executes the computer program to implement the method mentioned in the first aspect above.

[0012] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed, implements the method mentioned in the first aspect above.

[0013] By applying the solution provided in this application, the spatiotemporal distribution data of the amount of decommissioned photovoltaic modules in the target area can be predicted using the basic information of photovoltaic power plants in the target area. Based on the spatiotemporal distribution data of the decommissioned amount, the locations of multiple transfer stations are selected in the target area. Combining the geographical location information of photovoltaic power plants, transfer stations, and photovoltaic module recycling points with road network data, a set of all possible routes that can be used to transport decommissioned photovoltaic modules is constructed, and the attribute information of each route in each set is determined. Then, based on the spatiotemporal distribution data and the attribute information of each route, an optimization model can be constructed with the transportation volume of decommissioned photovoltaic modules on each route in the set as the decision variable and maximizing the net benefit of decommissioned photovoltaic module recycling as the optimization objective. By solving the optimization model, the optimized transportation route map and the transportation volume corresponding to each transportation route can be obtained. In this embodiment of the application, when selecting a transfer station, the selection is based on the spatiotemporal distribution data of the predicted decommissioning volume, which fully considers the dynamic changes in the distribution density of decommissioned photovoltaic modules, making the determined transfer station more reasonable. Furthermore, by constructing an optimization model to optimize the transportation path and the transportation volume corresponding to each transportation path, the allocation relationship between the transfer station, photovoltaic power station, and photovoltaic module recycling point can be optimized to minimize the overall transportation distance, save transportation resources, and improve transportation efficiency.

[0014] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating an application scenario of one embodiment of this application.

[0017] Figure 2 This is a flowchart illustrating a method for determining a transportation plan for retired photovoltaic modules according to one embodiment of this application.

[0018] Figure 3 This is a schematic diagram illustrating the allocation of multiple photovoltaic power plants to a transfer station according to one embodiment of this application.

[0019] Figure 4 This is a schematic diagram illustrating the transportation scheme in the process of determining the recycling of decommissioned photovoltaic modules according to one embodiment of this application.

[0020] Figure 5 This is a schematic diagram of the logical structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] With my country's installed photovoltaic (PV) power plant capacity exceeding 1000GW, the first batch of large-scale installed modules has entered the retirement phase. It is predicted that by 2030, the annual retirement capacity of PV modules in China will surge to 30GW, corresponding to a weight of over 1.5 million tons, thus creating a recycling market expected to reach hundreds of billions of yuan. However, the recycling and reuse of retired PV modules faces numerous challenges. For example, due to the wide distribution of PV power plants and the large volume of retired PV modules, inappropriate transportation plans not only lead to low transportation efficiency and significant waste of transportation resources, but also cause substantial environmental pollution.

[0023] However, in related technologies, transportation schemes for retired photovoltaic (PV) modules often lack systematic planning and optimization. For example, some transportation schemes do not consider the establishment of transfer stations, directly transporting retired PV modules from PV power plants to PV module recycling points. This transportation method is often inefficient, resulting in excessively long overall transportation distances and serious waste of transportation resources. Moreover, while this transportation scheme may be suitable for small-scale retired PV modules, it is extremely inefficient for large-scale retired PV modules. Some transportation schemes, although considering the establishment of transfer stations, have relatively simple logic for setting up transfer stations. For example, they use a fixed radius algorithm to determine transfer stations, failing to fully consider the dynamic changes in the distribution density of retired PV modules, and lacking systematic planning for the management and scheduling of transfer stations. The allocation relationship between PV power plants and transfer stations is not reasonably determined, resulting in an unreasonable final transportation route, low transportation efficiency, waste of transportation resources, and significant environmental pollution.

[0024] Therefore, in the process of recycling retired photovoltaic modules, it is necessary to provide a more efficient transportation solution to avoid wasting resources.

[0025] Based on this, this application provides a method for determining a transportation plan for decommissioned photovoltaic (PV) modules. It can utilize basic information about PV power plants within a target area to predict the spatiotemporal distribution data of the decommissioned PV modules within that area. Based on this data, multiple transfer stations are selected within the target area. Combining the geographical locations of PV power plants, transfer stations, and PV module recycling points with road network data, a set of all possible routes for transporting decommissioned PV modules is constructed. The attribute information of each route in the set is determined. Then, based on this spatiotemporal distribution data and the attribute information of each route, an optimization model is constructed, using the transportation volume of decommissioned PV modules along each route in the set as the decision variable and maximizing the net benefit of decommissioned PV module recycling as the optimization objective. By solving this optimization model, the optimized transportation route map and the transportation volume corresponding to each transportation route can be obtained. In this embodiment of the application, when selecting a transfer station, the selection is based on the spatiotemporal distribution data of the predicted decommissioning volume, which fully considers the dynamic changes in the distribution density of decommissioned photovoltaic modules, making the determined transfer station more reasonable. Furthermore, by constructing an optimization model to optimize the transportation path and the transportation volume corresponding to each transportation path, the allocation relationship between the transfer station, photovoltaic power station, and photovoltaic module recycling point can be optimized to minimize the overall transportation distance, save transportation resources, and improve transportation efficiency.

[0026] The method for determining the transportation plan of decommissioned photovoltaic modules provided in this application embodiment can be executed by various electronic devices, such as mobile phones, tablets, laptops, desktop computers, cloud servers, etc., and this application embodiment does not impose any restrictions.

[0027] In some exemplary embodiments, the method can be executed by specialized decommissioned photovoltaic module transportation planning software (e.g., an app), such as... Figure 1 As shown, users only need to select the target area in the pre-built map, and the software can automatically plan the optimal transportation plan based on the relevant information of photovoltaic power stations in the target area (location distribution, type, installation time of photovoltaic power stations, etc.), road network information (such as road network map, road distance, weight limit information, speed limit information, etc.) and return it to the user.

[0028] like Figure 2 As shown, the method may include the following steps:

[0029] S202. Based on the basic information of multiple photovoltaic power stations within the target area, determine the spatiotemporal distribution data of the decommissioned photovoltaic modules within the target area; wherein, the basic information is related to the decommissioning time and / or decommissioning quantity of the photovoltaic modules;

[0030] In step S202, basic information of multiple photovoltaic power stations within the target area can be obtained. The target area can be any area where photovoltaic modules need to be planned for retirement. For example, the target area can be a country, a province, a city, etc. This application embodiment does not impose any restrictions.

[0031] The basic information for each photovoltaic (PV) power station can be various types of information related to the prediction of PV module retirement. For example, it can be information used to determine the retirement time and quantity of PV modules. For instance, basic information can include the type of PV modules in the power station; for example, PV module types can be monocrystalline silicon, polycrystalline silicon, thin-film, or bifacial modules. Different types of retired PV modules have different lifespans, meaning the type of PV module affects the retirement time. Furthermore, different types of PV modules have different densities, which affects the quantity of retired PV modules, i.e., the weight of the retired PV modules. Basic information can also include the latitude and longitude information of the PV power station. Since different latitudes and longitudes correspond to different climates, and climate has a significant impact on the lifespan of PV modules, the retirement time of PV modules can be predicted by combining latitude and longitude information. Basic information can also include the installation time, installed capacity, theoretical lifespan, and other information that may affect the retirement time or quantity of PV modules; this application's embodiments do not impose limitations on this.

[0032] The amount of photovoltaic modules that have been decommissioned can be measured by various indicators such as quantity and weight, and this application does not impose any restrictions on these indicators.

[0033] S204. Based on the spatiotemporal distribution data, set up multiple transfer stations within the target area and determine the geographical location information of each transfer station;

[0034] In step S204, multiple transfer stations can be selected within the target area based on the spatiotemporal distribution data of retired photovoltaic modules, and the geographical location information of each transfer station can be determined. Since the spatiotemporal distribution data of retired photovoltaic modules reflects the expected quantity and distribution of retired photovoltaic modules generated by each photovoltaic power station within a specific time and space range, this data can reflect the geographical distribution and temporal variation characteristics of retired photovoltaic modules. Therefore, transfer stations selected based on this data can adapt to the distribution characteristics of retired photovoltaic modules in different regions, exhibiting good adaptability and flexibility, and making the layout of transfer stations more rational.

[0035] S206. Based on the geographical location information of the multiple photovoltaic power stations, the geographical location information of the multiple transfer stations, the geographical location information of the multiple photovoltaic module recycling points in the target area, and the road network data in the target area, construct a path set and determine the attribute information of each path in the path set; wherein, the paths in the path set include the path between any photovoltaic power station and any transfer station, and the path between any transfer station and any photovoltaic module recycling point;

[0036] In step S206, after determining the geographical location information of multiple transfer stations, the geographical location information of multiple photovoltaic module recycling points within the target area can be obtained. Considering that the transportation route of retired photovoltaic modules is: photovoltaic power station - transfer station - photovoltaic module recycling point, a path set can be constructed based on the geographical location information of multiple photovoltaic power stations, multiple transfer stations, multiple photovoltaic module recycling points within the target area, and road network data within the target area. The paths in the path set include all possible paths between multiple photovoltaic power stations and multiple transfer stations, and all possible paths between multiple transfer stations and multiple photovoltaic module recycling points. For example, a road between any photovoltaic power station and any transfer station can be determined based on road network data, and this can be used as a path in the path set. Alternatively, a road between any transfer station and any photovoltaic module recycling point can be determined based on road network data, and this can also be used as a path in the path set.

[0037] After constructing the path set, the attribute information of each path in the path set can be determined based on the road network data. The attribute information can be various types of information such as path distance, weight limit information, speed limit information, and traffic restriction information; this embodiment of the application does not impose any limitations.

[0038] S208. Construct an optimization model based on the spatiotemporal distribution data and the attribute information of each path in the path set, wherein the decision variables of the optimization model include at least the transportation volume of retired photovoltaic modules for each path in the path set, and the optimization objective of the optimization model includes at least maximizing the net revenue from the recovery of the retired photovoltaic modules, wherein the net revenue is determined based on the revenue generated from the recovery of the retired photovoltaic modules and the recovery cost of the retired photovoltaic modules, and the recovery cost includes at least the transportation cost of the retired photovoltaic modules;

[0039] In step S208, an optimization model can be constructed based on the spatiotemporal distribution data of the retired photovoltaic modules and the path attribute information of each path in the aforementioned path set. The decision variables of the optimization model include at least the transportation volume of retired photovoltaic modules for each path in the path set, i.e., the transportation volume of retired photovoltaic modules required for each path. The optimization objective of the optimization model includes at least maximizing the net revenue from recovering the retired photovoltaic modules, whereby the net revenue is determined based on the revenue generated from recovering the retired photovoltaic modules and the recovery cost of the retired photovoltaic modules. The recovery cost includes at least the transportation cost incurred in transporting the retired photovoltaic modules.

[0040] By constructing an optimization model that uses the transportation volume of each route as a decision variable and maximizes the net revenue from the recovery of the retired photovoltaic module as the optimization objective, the transportation volume of each route can be optimized, that is, a transportation scheme with lower transportation costs and higher recovery revenue can be determined.

[0041] Of course, when constructing the optimization model, in addition to the decision variables and optimization objectives mentioned above, other decision variables and optimization models can be added based on actual needs. The specific settings can be based on actual needs, and this application embodiment does not impose any restrictions.

[0042] S210. Solve the optimization model to determine the transportation volume, and determine the transportation plan for the retired photovoltaic modules based on the transportation volume.

[0043] In step S210, the optimization model can be solved to determine the transport volume on each path, and a transport plan for retired photovoltaic modules can be determined based on the transport volume. For example, the optimal transport path can be determined, that is, the optimal correspondence between photovoltaic power plants and transfer stations, and the correspondence between transfer stations and photovoltaic module recycling points can be determined, resulting in a reasonable transport path distribution map. Furthermore, the transport volume on each transport path can be optimized to minimize transport costs, save resources consumed in transporting retired photovoltaic modules, and thus maximize the net revenue from the recycling of photovoltaic modules.

[0044] In some embodiments, the decision variables of the optimization model may further include the configuration scheme of transportation vehicles used on each path in the path set, wherein the configuration scheme includes at least the type and quantity of transportation vehicles. The optimization objective of the optimization model also includes minimizing the total carbon emissions generated during the transportation of the decommissioned photovoltaic modules, wherein the total carbon emissions are determined based on the unit carbon emissions corresponding to different types of transportation vehicles. Considering the different attribute information of different paths, such as distance, speed limits, weight limits, and road conditions, appropriate transportation vehicle types can be selected based on the attribute information of each path; for example, different models of trucks can be selected. Therefore, the configuration scheme of transportation vehicles on each path can be used as a decision variable to optimize the configuration scheme of transportation vehicles on each path to obtain the transportation scheme with the lowest cost and highest efficiency.

[0045] Furthermore, when determining transportation plans for retired photovoltaic modules, related technologies typically only consider revenue and neglect environmental impact. To balance environmental impact, when constructing optimization models, minimizing the total carbon emissions generated during the transportation of the retired photovoltaic modules can be used as the optimization objective, thereby reducing environmental pollution during transportation. The total carbon emissions can be determined based on the transportation volume, distance, type of transportation used, and unit carbon emissions of that type of transportation for each route.

[0046] By optimizing the configuration of transportation vehicles and the selection of routes, transportation costs can be reduced, resource consumption can be decreased, and environmental pollution during transportation can be reduced by minimizing carbon emissions.

[0047] In some embodiments, the decision variables of the optimization model also include the amount of decommissioned photovoltaic modules collected at each photovoltaic power station and the amount of decommissioned photovoltaic modules recovered at each photovoltaic module recycling point. The recycling cost of the decommissioned photovoltaic modules also includes one or more of the following: collection cost and disposal cost. The collection cost is determined based on the type and quantity of decommissioned photovoltaic modules collected, and the disposal cost is determined based on the disposal capacity of each photovoltaic module recycling point and the amount of decommissioned photovoltaic modules recovered at that point. Considering that different photovoltaic module recycling points have different disposal capacities for decommissioned photovoltaic modules—for example, the number and type of decommissioned photovoltaic modules that can be disposed of, and the revenue generated from disposing of decommissioned photovoltaic modules, are all different—and that the amount of decommissioned photovoltaic modules collected in a single instance can be dynamically adjusted for each photovoltaic power station, in order to maximize the net revenue from recycling decommissioned photovoltaic modules, the amount of decommissioned photovoltaic modules collected at each photovoltaic power station and the amount of decommissioned photovoltaic modules recovered at each photovoltaic module recycling point can also be used as decision variables when constructing the optimization model. When determining the recycling cost, the collection cost of collecting decommissioned photovoltaic modules, the transportation cost of transporting decommissioned photovoltaic modules, and the disposal cost of disposing of photovoltaic modules can all be considered simultaneously. Optimizing the collection volume of decommissioned photovoltaic (PV) modules at each PV power plant can improve collection efficiency and reduce resource waste. By considering the disposal capacity and the volume of decommissioned PV modules collected at each recycling point, the disposal process can be optimized, improving efficiency. Furthermore, by combining transportation, collection, and processing costs to optimize the collection volume, the volume of decommissioned PV modules collected at each recycling point, and transportation routes, a more rational collection, transportation, and disposal plan can be provided, saving resource consumption during the recycling process, reducing costs, and improving recycling efficiency.

[0048] In some embodiments, the optimization model can be solved based on one or more of the following constraints:

[0049] (1) The amount of retired photovoltaic modules collected from any photovoltaic power station shall not exceed the amount of retired photovoltaic modules from that photovoltaic power station.

[0050] (2) The amount of retired photovoltaic modules recycled at any photovoltaic module recycling point shall not exceed the total amount of photovoltaic modules disposed of at that recycling point. This constraint ensures that the amount of modules received by each recycling point will not exceed its processing capacity, which can avoid the recycling points from operating under overload, ensure the smooth and efficient recycling process, help optimize resource allocation, prevent the overload of processing facilities, and improve the overall efficiency of recycling and processing.

[0051] (3) The total transport capacity of the transport vehicles configured for any one of the routes in the set of routes is not less than the transport volume corresponding to that route. This constraint ensures that the transport vehicles configured on each route can meet the transport demand of that route, which helps to avoid transport failures or additional transport costs due to insufficient transport vehicle capacity during transport, helps to improve transport efficiency, and reduces additional costs and time delays caused by improper configuration of transport vehicles.

[0052] (4) The total amount of retired photovoltaic modules collected from the multiple photovoltaic power stations is equal to the total amount of retired photovoltaic modules recovered from the multiple photovoltaic module recycling points. That is, the total amount of retired photovoltaic modules collected in the entire system is equal to the total amount of retired photovoltaic modules recovered.

[0053] In some embodiments, when setting up multiple transfer stations within a target area based on spatiotemporal distribution data and determining the geographical location information of each transfer station, a hotspot / coldspot analysis can be performed on the target area based on the spatiotemporal distribution data to identify one or more hotspot areas within the target area. These hotspot areas are regions where the concentration of photovoltaic power stations exceeds a preset threshold. Then, multiple transfer stations can be set up within the target area based on one or more hotspot areas, and the geographical location information of each transfer station can be determined. Each hotspot area contains one or more transfer stations. To more effectively and rationally distribute transfer stations and obtain a better transportation solution, when determining transfer stations, GIS tools or statistical software can be used to perform a hotspot / coldspot analysis on the target area based on the spatiotemporal distribution data to identify areas with a high concentration of photovoltaic power stations, i.e., hotspot areas. Then, one or more transfer stations can be set up within each identified hotspot area to ensure effective coverage of the photovoltaic power stations within that area. The number of transfer stations set up within each hotspot area can be positively correlated with the amount of photovoltaic modules decommissioned within that hotspot area; that is, the greater the amount of photovoltaic modules decommissioned within the hotspot area, the more transfer stations are needed. A rational layout of transfer stations helps to more effectively allocate transportation resources and improve overall operational efficiency.

[0054] In some embodiments, the optimization model for optimizing the transport volume of each path in the path set can be called a first optimization model. When setting up multiple transfer stations in the target area based on one or more hotspot areas and determining the geographical location information of each transfer station, multiple candidate points can be determined in the target area based on the one or more hotspot areas. Then, a second optimization model can be constructed. The decision variables of the second optimization model include whether to set up a transfer station for each candidate point and the allocation method for assigning multiple photovoltaic power stations to the set transfer stations. The optimization objective of the second optimization model includes minimizing the sum of the weighted distances corresponding to each of the multiple photovoltaic power stations. The weighted distance corresponding to each photovoltaic power station is the product of the distance between the photovoltaic power station and the transfer station assigned to it and the weight. The weight is positively correlated with the amount of decommissioned photovoltaic modules of the photovoltaic power station. Then, the second optimization model can be solved to obtain the geographical location information of each of the multiple transfer stations and the allocation method for assigning multiple photovoltaic power stations to the set transfer stations. For example, as shown in the figure... Figure 3 The diagram illustrates the correspondence between photovoltaic power plants and transfer stations. By constructing a second optimization model to optimize the location of transfer stations, it is possible to ensure that transfer stations can effectively cover hotspot areas and improve transportation efficiency. At the same time, by minimizing the sum of weighted distances, the overall transportation distance from photovoltaic power plants to transfer stations can be reduced, thereby lowering transportation costs. Furthermore, by setting weights based on the amount of retired photovoltaic modules from photovoltaic power plants, the loading rate of transportation vehicles can be increased, reducing empty runs and inefficient transportation, further reducing costs.

[0055] In some embodiments, when constructing a second optimization model to optimize the location of the transfer station and the allocation relationship between the photovoltaic power station and the transfer station, minimizing the carbon emissions of transporting decommissioned photovoltaic modules from the photovoltaic power station to the transfer station can also be used as an optimization objective to take into account environmental pollution.

[0056] In some embodiments, if the allocation method of assigning multiple photovoltaic power plants to transfer stations has been determined by the second optimization model, that is, the correspondence between photovoltaic power plants and transfer stations has been determined, then when solving the first optimization model to determine the transport volume of each path in the path set, the allocation method can be used as a constraint condition to solve the first optimization model.

[0057] In some embodiments, the basic information of a photovoltaic (PV) power plant may include one or more of the following: the type of PV modules in the PV power plant, the latitude and longitude information of the PV power plant, the installed capacity of the PV modules in the PV power plant, the installation time of the PV modules in the PV power plant, and the theoretical lifespan of the PV modules in the PV power plant. When determining the spatiotemporal distribution data of the decommissioned PV modules based on the basic information of multiple PV power plants within a target area, for any target time point (e.g., any year or month), the failure probability of the PV module before that target time point can be determined based on the type of PV module in the PV power plant, the theoretical lifespan of the PV modules, the installation time of the PV modules, and the latitude and longitude information of the PV power plant. Considering that different latitude and longitude information of the PV power plant leads to different climates, which can affect the lifespan of the PV modules, the failure probability of the PV modules can be predicted by combining latitude and longitude information, thus improving the accuracy of the prediction. Then, based on the failure probability and the installed capacity of the photovoltaic modules at each time point before the target time point, the number of photovoltaic modules to be decommissioned at the target time point can be predicted. The number of photovoltaic modules to be decommissioned at the target time point is the sum of the products of the installed capacity of photovoltaic modules N time periods prior to the target time point and the percentage of photovoltaic modules with a theoretical lifespan of N time periods. This percentage is determined based on the failure probability mentioned above, where N is a positive integer. When predicting the decommissioning amount, it is assumed that photovoltaic modules are not all discarded at once upon reaching their average lifespan, but rather gradually withdrawn from use around their average lifespan according to a certain proportion. Therefore, taking year i as the target time point as an example, the number of photovoltaic modules decommissioned in year i is the sum of the products of the installed capacity of photovoltaic modules i years prior to that year and the percentage of photovoltaic modules with a lifespan of i years. Then, the above spatiotemporal distribution data can be obtained based on the number of photovoltaic modules decommissioned at different target time points and the geographical location information of each photovoltaic power station.

[0058] This method, by comprehensively considering the type of photovoltaic modules, their theoretical lifespan, installation time, and geographical location information (latitude and longitude), can more accurately predict the failure probability and decommissioning volume of modules at any target time point for different photovoltaic power plants, thus obtaining accurate spatiotemporal distribution data. This method not only improves prediction accuracy, making the prediction results more consistent with reality, but also, by considering the gradual decommissioning of modules around their average lifespan, can more reasonably predict the decommissioning volume, providing a basis for offering more rational and efficient transportation solutions.

[0059] The method for determining the transportation scheme in the photovoltaic module recycling process of this application is described below with reference to a specific embodiment. A schematic diagram of the overall method is shown below. Figure 4 As shown, the method may include the following steps:

[0060] S1. Based on the type, installed capacity, and installation time of photovoltaic modules in centralized photovoltaic power plants, as well as the latitude and longitude information of the photovoltaic modules, predict the time distribution data of the amount of photovoltaic modules decommissioned, and collect the collection cost of photovoltaic modules.

[0061] The types of retired photovoltaic modules include: monocrystalline silicon, polycrystalline silicon, thin film, and bifacial modules. Based on the photovoltaic type, installed capacity, and corresponding installation time, the scale of retirement at different time points can be determined. Combined with the corresponding waste generation coefficient, the weight of the retired photovoltaic modules can be calculated as the retirement quantity.

[0062] When predicting the retirement time of photovoltaic modules, the failure probability of photovoltaic modules at different lifespans can be predicted based on the following methods:

[0063] The Weibull distribution, also known as the Weibull distribution, is used to predict failure probabilities and is the theoretical foundation for reliability analysis and life testing. Its mathematical expression is the Weibull function, which reflects the relationship between product failure time and failure probability. The probability density function and cumulative distribution function of the Weibull distribution are expressed as follows:

[0064]

[0065] Where α is the scale parameter, generally reflecting the theoretical average lifespan of the product, usually taken as 25; β is the shape parameter, used to describe the shape of the probability density distribution function, taken as 5.3759 for the normal decommissioning scenario and 2.4928 for the early decommissioning scenario. β can be set based on the latitude and longitude information (i.e., climate information) of the photovoltaic power station, and different latitudes and longitudes correspond to different β values.

[0066] Furthermore, the amount of decommissioned photovoltaic modules in a centralized photovoltaic power plant can be calculated based on the following methods:

[0067] The retirement volume is calculated using the market supply model A, assuming that electrical and electronic products are not all discarded at once upon reaching their average lifespan, but rather gradually phased out in proportions around their average lifespan. This assumption aligns with our general understanding and initial assumptions regarding the retirement of photovoltaic modules. The calculation formula for the market supply model A is as follows:

[0068]

[0069] Among them, Q wi Let S be the amount of photovoltaic modules scrapped in year i. i P represents the photovoltaic installation capacity i years from that year. i Let f(t) represent the percentage of photovoltaic modules with a lifespan of i years, which is represented here by the Weibull probability density function f(t).

[0070] Example: The Datang Alashan High-tech Industrial Development Zone Lanshan Phase II 200MW Photovoltaic Mining Project, with installation commencing in 2023, uses monocrystalline silicon photovoltaic modules and is located at 106.7061°E, 39.3953°N. The annual decommissioning volume is predicted based on the installation year, i.e., the decommissioning volume of photovoltaic modules in year i. = Summation Starting from that year, the photovoltaic installed capacity i years ago (s) i The percentage of photovoltaic modules with a lifespan of i years (p) × i ), here p i Using the Weibull probability density function (f i (replace)

[0071] Furthermore, the collection cost of centralized decommissioned photovoltaic modules can be obtained based on the following methods:

[0072] Based on the types of photovoltaic modules in centralized photovoltaic power plants and literature research, this study assesses the unit collection cost of decommissioned photovoltaic modules in different centralized photovoltaic power plants. Considering the actual conditions in different regions, the collection technology and costs may vary, and a specific analysis of the collection cost of decommissioned photovoltaic modules in different regions is conducted.

[0073] Specifically, collection costs are mainly affected by factors such as the structural complexity of the modules, the complexity of disassembly, and regional weather conditions. Collection costs vary depending on the type of retired photovoltaic modules, and are primarily based on literature research and existing projects. The types of retired photovoltaic modules considered in this implementation can be summarized in the following table:

[0074] Table 1

[0075] type Collection costs Monocrystalline silicon C1 Polycrystalline silicon C2 film C3 bifacial modules C4

[0076] S2. Based on geographic information software and the spatiotemporal distribution data of retired photovoltaic modules, determine the number and location of transfer stations for retired photovoltaic modules, and obtain the transfer transportation cost based on the location of the transfer stations.

[0077] For example, we can consider the impact of the latitude and longitude of centralized photovoltaic power stations, their concentration, and the amount of decommissioning on the selection of transfer station locations; conduct spatial analysis to uncover implicit spatial relationships, perform hotspot analysis to identify hotspot areas of centralized photovoltaic power stations to guide the selection of transfer station locations, determine the location of transfer stations, obtain the location allocation relationship between centralized photovoltaic power stations and transfer stations, quantify the transportation route (distance) from each centralized photovoltaic power station to the selected transfer station, and obtain the transfer transportation cost and the carbon emission factor of the transfer unit.

[0078] Specifically, based on the classic facility location problem (p-Median problem) in operations research, we can select p locations from existing centralized photovoltaic power plants to build transfer stations, minimizing the sum of weighted distances from all centralized photovoltaic power plants to the nearest transfer station. Based on this concept, we can construct the following objective function to optimize the transfer station location and the distribution of photovoltaic power plants among the transfer stations.

[0079] Objective function:

[0080]

[0081] Decision variables:

[0082]

[0083] Constraints:

[0084]

[0085] In the formula, I represents the set of locations of centralized photovoltaic power plants; h i The scale of decommissioned photovoltaic modules at point i in a centralized photovoltaic power station is represented by P; P represents the total number of transfer stations that need to be built; d ij This represents the distance between point i, the decommissioned centralized photovoltaic power station, and point j, the transfer station.

[0086] Specifically, the path distance from the centralized photovoltaic power station to the transfer station can be obtained by combining the latitude and longitude locations of the centralized photovoltaic power station and the pre-constructed transfer station with national road network data (road load and speed limit information, to obtain the type of transport vehicle, long-distance vehicle transport cost, and unit transport carbon emission factor). Based on the above algorithm, the location allocation information between each centralized photovoltaic power station and its corresponding transfer station is obtained, the transfer transport cost and unit transport carbon emission factor are determined, and the amount of decommissioned photovoltaic modules at the transfer station is obtained.

[0087] S3. Based on the type and location of the retired photovoltaic module recycling point, obtain the disposal cost and the revenue generated from recycling the retired photovoltaic modules.

[0088] Based on publicly available information from retired photovoltaic module recycling points (i.e., recycling companies), determine the spatial location information of the recycling points, including their latitude and longitude data, identify their recycling technology type and the corresponding recycling technology route's processing capacity, and obtain the processing cost and recycling revenue based on the corresponding recycling technology type and the processing capacity of the recycling technology route.

[0089] Example: The first pilot line for recycling crystalline silicon photovoltaic modules in China, built by State Power Investment Corporation Yellow River Upstream Hydropower Development Co., Ltd. in 2023, has an annual processing capacity of 4,000 tons and a comprehensive recycling efficiency of 92.23%. It is located at 101.7378°E, 36.6401°N, with a unit recycling cost of RMB 4,122 per ton and a unit profit of RMB 5,000 per ton.

[0090] S4. Based on the national road transport data obtained from the National Geomatics Center of China, determine the weight and speed limits and transport distance for long-distance road transport, and obtain the cost of long-distance road transport and the carbon emission factor per unit of transport.

[0091] For example, by obtaining national road transport data from the National Geomatics Center of China, we can obtain weight and speed limits for long-distance road transport, as well as transport distances. Based on these weight and speed limits and transport distances, we can determine the types of transport vehicles available and obtain the cost of long-distance road transport and the carbon emission factor per unit of transport.

[0092] Specifically, you can visit the official website of the National Geomatics Center of China to download the national road network vector data. This will provide information on road restrictions on vehicle load and speed. Based on this information, you can determine the appropriate vehicle type, and then calculate the transportation cost and carbon emissions per unit of transport. Example: On the Beijing-Tibet Expressway (G6) in Inner Mongolia, the weight limit is 49 tons and the speed limit is 120 km / h. A Jiefang J7 truck can be selected for transport. The long-distance transportation cost is 0.20 yuan / ton·km, and the carbon emissions per unit of transport are 0.313 kg CO2 / ton·km.

[0093] S5. Based on collection costs, transit transportation costs, long-distance road transportation costs, disposal costs, and recycling revenue, construct a transportation optimization model. With the optimization objectives of maximizing net revenue and minimizing total transportation carbon emissions, optimize the transportation plan to realize the transportation and utilization of retired photovoltaic modules.

[0094] For example, the following objective function can be constructed to determine the transportation plan.

[0095] Objective function:

[0096]

[0097] The total carbon emissions are minimized by minimizing the sum of carbon emissions from collection, transit, long-distance road transport, recycling, and utilization. The following objective function can be constructed to optimize the total carbon emissions.

[0098] Objective function:

[0099]

[0100] The constraints are as follows:

[0101] Collection and disposal constraints: the number of photovoltaic modules that any photovoltaic power station can collect shall not exceed its annual decommissioning volume, and the amount of decommissioned photovoltaic modules disposed of by any recycling company with disposal capacity during the planning period shall not exceed its total disposal capacity.

[0102]

[0103] Transportation constraints stipulate that the total transportation capacity of vehicles on any transportation route is not less than the transportation volume to be undertaken, and the input and output of any centralized photovoltaic power station's recycling site or disposal enterprise node maintain a zero balance.

[0104]

[0105] The target constraint for the number of retired photovoltaic modules collected and disposed of is that the total collection volume equals the total disposal volume target.

[0106]

[0107] Non-negativity constraints: the collection volume at any collection point is non-negative, the disposal volume at any disposal point is non-negative, and the transportation volume along any transportation route is non-negative.

[0108]

[0109] The parameters in the optimization model are divided into variables and parameters. Table 2 lists the model parameters and decision variables.

[0110] Table 2

[0111]

[0112]

[0113] Beneficial effects of this embodiment:

[0114] Accurate prediction and dynamic programming capabilities.

[0115] By introducing the Weibull distribution model to predict the failure probability and decommissioning scale of photovoltaic modules, and combining parameters such as installation time and module type, the prediction accuracy of the spatiotemporal distribution of decommissioning volume has been significantly improved. For example, the Datang Alashan photovoltaic project calculates the future decommissioning volume layer by layer based on the installation year and module type, providing a scientific basis for recycling network planning and avoiding the coarseness of traditional fixed recycling radius models.

[0116] Scientific site selection reduces logistics costs.

[0117] Hot and cold spot analysis and the P-median method are used to optimize the site selection of transfer stations. The location and allocation of transfer stations are dynamically determined by considering the concentration level and decommissioning scale of centralized photovoltaic power plants. For example... Figure 4As shown, by minimizing the weighted transport distance, the resource consumption of the transit transport links is reduced, the adaptability of the transit station layout is improved, and the average transport distance is expected to be shortened, significantly reducing transit costs.

[0118] Multi-objective optimization takes into account both economic and environmental protection, and constructs a transportation optimization model with the dual objectives of minimizing total cost and minimizing total carbon emissions. It comprehensively considers the costs and carbon emission factors of the entire process, including collection, transshipment, long-distance transportation, recycling and revenue, to achieve a win-win situation for both economic and environmental benefits.

[0119] A highly feasible solution based on the actual road network.

[0120] Based on the national highway network vector data (including weight limits, speed limits, mileage, etc.) from the National Geomatics Center of China, and combined with vehicle type and carbon emission factors, routes are optimized to fit actual transportation conditions. Route planning avoids overloaded sections and matches optimal vehicle speeds, thereby improving transportation efficiency.

[0121] Resource recycling efficiency has been significantly improved. By integrating the disposal capacity and revenue data of recycling companies, optimizing component allocation strategies, and promoting the large-scale development of resource utilization of retired components, the efficiency of resource recycling has been greatly improved.

[0122] In summary, this embodiment addresses the pain points of traditional recycling systems—low efficiency, high cost, and insufficient environmental protection—through accurate prediction, scientific site selection, multi-objective optimization, and data-driven approaches. It provides a systematic and innovative solution for the efficient recycling and low-carbon transportation of retired photovoltaic modules, demonstrating significant technological advancement and market application value.

[0123] The solutions in the above embodiments can be freely combined to obtain new solutions when there is no conflict. Due to space limitations, they will not be listed one by one here.

[0124] Furthermore, this application also provides a computer program product, which includes a computer program that, when executed by a processor, describes the method described in any of the above embodiments.

[0125] Furthermore, embodiments of this application also provide an electronic, such as Figure 5 As shown, the electronic device includes a processor 51, a memory 52, and computer instructions stored in the memory 52 that can be executed by the processor 51. When the processor 51 executes the computer instructions, it implements the method described in any of the above embodiments.

[0126] Accordingly, this application also provides a computer storage medium storing a program that, when executed by a processor, implements the method in any of the above embodiments.

[0127] The embodiments of this application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-usable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical discs, read-only memory (CD-ROM), digital versatile optical discs (DVD) or other optical storage, magnetic tape, disks or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0128] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0129] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0130] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0131] The methods and apparatus provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this application should not be construed as a limitation of this application.

Claims

1. A method for determining a transportation plan for decommissioned photovoltaic modules, characterized in that, The method includes: Based on the basic information of multiple photovoltaic power stations within the target area, the spatiotemporal distribution data of the decommissioned photovoltaic modules within the target area are determined; wherein, the basic information is related to the decommissioning time and / or decommissioning quantity of the photovoltaic modules; Based on the spatiotemporal distribution data, multiple transfer stations are set up within the target area, and the geographical location information of each transfer station is determined; Based on the geographical location information of the multiple photovoltaic power stations, the geographical location information of the multiple transfer stations, the geographical location information of the multiple photovoltaic module recycling points in the target area, and the road network data in the target area, a path set is constructed, and the attribute information of each path in the path set is determined; wherein, the paths in the path set include the path between any photovoltaic power station and any transfer station, and the path between any transfer station and any photovoltaic module recycling point; An optimization model is constructed based on the spatiotemporal distribution data and the attribute information of each path in the path set. The decision variables of the optimization model include at least the transportation volume of retired photovoltaic modules for each path in the path set. The optimization objective of the optimization model includes at least maximizing the net revenue from the recovery of the retired photovoltaic modules. The net revenue is determined based on the revenue generated from the recovery of the retired photovoltaic modules and the recovery cost of the retired photovoltaic modules. The recovery cost includes at least the transportation cost of the retired photovoltaic modules. Solve the optimization model to determine the transportation volume, and determine the transportation plan for the retired photovoltaic modules based on the transportation volume.

2. The method according to claim 1, characterized in that, The decision variables of the optimization model also include: the configuration scheme of transportation vehicles used for each path in the path set; wherein the configuration scheme includes at least the type and number of transportation vehicles; the optimization objective of the optimization model also includes: minimizing the total carbon emissions generated during the transportation of the decommissioned photovoltaic modules, wherein the total carbon emissions are determined based on the unit carbon emissions corresponding to different types of transportation vehicles.

3. The method according to claim 1 or 2, characterized in that, The decision variables of the optimization model also include: the amount of retired photovoltaic modules collected at each photovoltaic power station and the amount of retired photovoltaic modules recovered at each photovoltaic module recycling point; the recycling cost of the retired photovoltaic modules also includes: collection cost and disposal cost; wherein, the collection cost is determined based on the type of retired photovoltaic modules and the amount collected, and the disposal cost is determined based on the disposal capacity of each photovoltaic module recycling point and the amount of retired photovoltaic modules recovered at that photovoltaic module recycling point.

4. The method according to claim 3, characterized in that, In solving the optimization model, the optimization model is solved based on one or more of the following constraints: The amount of decommissioned photovoltaic modules collected from any single photovoltaic power station shall not exceed the total amount of decommissioned photovoltaic modules collected from that photovoltaic power station. The amount of decommissioned photovoltaic modules recycled at any single photovoltaic module recycling point shall not exceed the total amount disposed of at that point. The total transport capacity of the transport vehicles configured for any one of the routes in the set of routes shall not be less than the transport volume corresponding to that route; The total amount of retired photovoltaic modules collected from the multiple photovoltaic power plants is equal to the total amount of retired photovoltaic modules recovered from the multiple photovoltaic module recycling points.

5. The method according to claim 1, characterized in that, The process of setting up multiple transit stations within the target area based on the spatiotemporal distribution data and determining the geographical location information of each transit station includes: Based on the spatiotemporal distribution data, a hotspot analysis is performed on the target area to determine one or more hotspot areas from the target area, wherein the hotspot area is an area where the concentration of photovoltaic power stations is greater than a preset threshold; Based on the one or more hotspot areas, multiple transfer stations are set up in the target area, and the geographical location information of each transfer station is determined. Each hotspot area has one or more transfer stations, and the number of transfer stations set up in each hotspot area is positively correlated with the amount of retired photovoltaic modules in that hotspot area.

6. The method according to claim 5, characterized in that, The optimization model is a first optimization model. The step of setting up multiple transit stations within the target area based on the one or more hotspot areas and determining the geographical location information of each transit station includes: Based on the one or more hotspot areas, multiple candidate points are determined within the target area; A second optimization model is constructed. The decision variables of the second optimization model include whether to set up a transfer station for each candidate point and the allocation method of assigning the multiple photovoltaic power stations to the set transfer stations. The optimization objective of the second optimization model includes minimizing the sum of the weighted distances corresponding to the multiple photovoltaic power stations. The weighted distance corresponding to each photovoltaic power station is the product of the distance between the photovoltaic power station and the transfer station assigned to the photovoltaic power station and the weight. The weight is positively correlated with the amount of retired photovoltaic modules of the photovoltaic power station. Solve the second optimization model to obtain the geographical location information of each of the multiple transfer stations, as well as the allocation method.

7. The method according to claim 6, characterized in that, The optimization objective of the second optimization model also includes minimizing the carbon emissions from transporting the decommissioned photovoltaic modules from the multiple photovoltaic power plants to the transfer station; and / or Solving the first optimization model to determine the transportation volume includes: using the allocation method as a constraint to solve the first optimization model.

8. The method according to claim 1, characterized in that, The basic information includes one or more of the following: the type of photovoltaic modules in the photovoltaic power station, the latitude and longitude information of the photovoltaic power station, the installed capacity of the photovoltaic modules in the photovoltaic power station, the installation time of the photovoltaic modules in the photovoltaic power station, and the theoretical service life of the photovoltaic modules in the photovoltaic power station; Based on the basic information of multiple photovoltaic power plants within the target area, the spatiotemporal distribution data of the decommissioned photovoltaic modules were determined, including: For any target time point, based on the type of photovoltaic module in the photovoltaic power station, the theoretical service life of the photovoltaic module, the installation time of the photovoltaic module, and the latitude and longitude information of the photovoltaic power station, the failure probability of the photovoltaic module before the target time point is determined. Based on the failure probability and the installed capacity of photovoltaic modules at each time point before the target time point, the number of photovoltaic modules to be decommissioned at the target time point is predicted. The number of photovoltaic modules to be decommissioned at the target time point is the sum of the products of the installed capacity of photovoltaic modules N time periods before the target time point and the percentage of photovoltaic modules with a theoretical service life of N time periods. The percentage is determined based on the failure probability. N is a positive integer. The spatiotemporal distribution data is obtained based on the amount of photovoltaic modules decommissioned at different target time points and the geographical location information of each photovoltaic power station.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-8.

Images