Data center source-network-load-storage collaborative planning and scheduling methods, devices, equipment and media

Abstract

This invention relates to a data center source-grid-load-storage collaborative planning and scheduling method, apparatus, equipment, and medium, relating to the field of energy planning. The method includes: acquiring historical time-series monitoring data of a target area, including historical meteorological data and historical load data; determining a target scenario set for the target area based on the historical time-series monitoring data, Monte Carlo simulation technology, and clustering algorithms; determining the Pareto optimal solution set based on the target scenario set and a pre-constructed three-dimensional multi-objective bi-layer planning model, the three-dimensional multi-objective bi-layer planning model including an outer multi-objective capacity configuration model and an inner optimal operation scheduling model; and determining the optimal capacity configuration scheme as the planning result based on the optimal solution set and decision preference weights. This application achieves the technical effect of comprehensively considering factors such as cost, carbon emissions, and grid dependence to obtain a more reasonable capacity configuration scheme and optimize the collaborative planning and scheduling of data center source-grid-load-storage.

Description

Technical Field

[0001] This invention relates to the field of energy planning technology, and in particular to a method, apparatus, equipment and medium for coordinated planning and scheduling of data center sources, grids, loads and storage. Background Technology

[0002] With the rapid increase in energy consumption of data centers, the government has explicitly required newly built data centers to reduce power utilization efficiency (PUE) and significantly increase the utilization rate of renewable energy. Against this backdrop, constructing supporting wind, solar, and energy storage microgrids and adopting a "green electricity direct connection" model has become a key approach to achieving green computing power. Because data center loads have high value and high stability requirements, and are extremely sensitive to the "grid dependence" indicator, their planning process has become a complex systems engineering project involving the close coupling of capacity configuration and operation scheduling.

[0003] Currently, the mainstream implementation solutions in the industry are based on "single-layer optimization" or "simplified two-layer optimization".

[0004] In terms of scenario processing, K-means is typically used to cluster annual data. However, most clusters are performed separately for wind speed, sunlight, and load, and then randomly combined to generate typical days, or a single "annual average day" is used for calculation. This may miss atypical scenarios. Systems planned in this way may encounter insufficient energy storage during actual operation when encountering atypical weather, leading to computing power interruptions or the incurrence of high deviation assessment fees. Regarding the objective function, most solutions only aim at "minimizing total investment + operating cost," incorporating carbon emissions as a penalty item into the cost, without treating carbon emissions as an independent optimization dimension, and ignoring the key evaluation indicator of "grid dependence," resulting in poor performance in terms of environmental friendliness and energy independence. In terms of scheduling strategy, the inner layer adopts rule control, such as "charging when photovoltaic load is greater than load, discharging when load is less than load." Meanwhile, the inner-layer scheduling model does not incorporate battery throughput depreciation costs, relying solely on electricity price differences for scheduling. This leads to frequent battery scheduling to profit from small peak-valley price differences, resulting in battery life loss and inflated ROI calculated during the planning phase. In actual operation, premature battery failure leads to project losses. The energy storage model uses a simplified energy bucket model, only constraining the upper and lower limits of SOC without considering cycle life degradation. This may result in unrealistic capacity configurations that overly rely on energy storage for arbitrage, reducing the engineering guidance value of the planning results. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a data center source-network-load-storage collaborative planning and scheduling method, apparatus, equipment and medium, which aims to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, this application provides a data center source-network-load-storage collaborative planning and scheduling method, which adopts the following technical solution: A data center source-network-load-storage collaborative planning and scheduling method includes: Acquire historical time-series monitoring data of the target area, including historical meteorological data and historical load data of the computing center, wherein the historical meteorological data includes wind speed, irradiance and temperature; Based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm, a target scene set for the target area is determined. The target scene set includes multiple target scenes and the probability of occurrence of each target scene. Based on the target scenario set and the pre-constructed three-dimensional multi-objective bi-level programming model, the Pareto optimal solution set is determined. The three-dimensional multi-objective bi-level programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost, including battery aging costs. The Pareto optimal solution set represents the trade-off between cost, carbon emissions, and grid dependence. Based on the Pareto optimal solution set and the preset decision preference weights, the optimal capacity configuration scheme is determined as the planning result.

[0007] The beneficial effects of this invention are as follows: By employing joint clustering of source and load data, it ensures that the generated typical scenarios retain the true temporal coupling relationship between wind and solar resources and load. This forces the planning model to confront and resolve extreme operating conditions of "unfavorable conditions on both the source and load sides," thereby configuring a system capacity with stronger risk resistance and avoiding the risk of operational interruptions due to scenario distortion. By constructing a three-dimensional Pareto front with economic efficiency, environmental protection, and grid dependence as independent objectives, it abandons the traditional approach of a single cost objective or simply monetizing environmental indicators, providing decision-makers with a series of visualized optimal trade-off solutions. This resolves the core contradiction in planning green electricity direct-connection computing centers where "economy, low carbon, and independence" are difficult to balance. The inner scheduling model accurately incorporates battery aging costs that are positively correlated with charging and discharging energy, ensuring that the optimization algorithm does not overdraw the energy storage life for meager profits. Based on the Pareto optimal solution set and decision preference weights, the optimal capacity configuration scheme is determined, resulting in planning outcomes that meet the decision-maker's preferences.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the determination of the target scene set for the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm includes: Based on historical time-series monitoring data and a pre-set normal distribution error model, multiple daily operation scenarios are generated; For any of the daily operation scenarios, the load time series data, wind power output time series data and photovoltaic output time series data in the daily operation scenario are spliced ​​together to construct a high-dimensional feature vector; Clustering analysis is performed on the multiple high-dimensional feature vectors based on a clustering algorithm to extract a predetermined number of target scenes and determine the occurrence probability of each target scene, thereby obtaining a target scene set for the target region. The preset normal distribution error model is as follows: ; in, For simulation data, Pbase is the baseline value for typical midday wind and solar load output, δ is the random error, and σ is the preset volatility parameter. The wind and solar volatility σres is set to 10%~20%, and the dynamic composite volatility σload is set to 5%~10%.

[0010] The beneficial effects of adopting the above-mentioned further solutions are as follows: by using a preset normal distribution error model to generate multiple daily operation scenarios, the problem of single historical data samples is solved; the load time series data, wind power output time series data and photovoltaic output time series data in the daily operation scenarios are spliced ​​together to construct a high-dimensional feature vector, which forcibly preserves the coupling relationship between source and load on the time axis; cluster analysis of multiple high-dimensional feature vectors can extract target scenarios and their occurrence probabilities, including average operating conditions and unconventional operating conditions with extreme source-load mismatch, so that the planned system has stronger robustness in actual operation and is less prone to power outage accidents.

[0011] Furthermore, determining the Pareto optimal solution set based on the target scene set and the pre-constructed three-dimensional multi-objective bi-layer programming model includes: S31, obtain the equipment economic parameters, technical parameters and carbon emission factors of the target area; S32, based on the equipment economic parameters, technical parameters, carbon emission factors and target scenario set of the target area, determine the optimized input data; S33, the outer multi-objective capacity configuration model, based on the optimized input data and multi-objective optimization algorithm, takes the planned installed capacity of wind power, photovoltaic and energy storage as decision variables, and takes minimizing the daily average cost of the whole life cycle, minimizing the daily average carbon emissions and minimizing grid dependence as three independent optimization objectives to perform multi-objective optimization solution, generate the candidate capacity configuration scheme of the current iteration, and input the candidate capacity configuration scheme of the current iteration and the target scenario set into the inner optimal operation scheduling model; S34, the inner-layer optimal operation scheduling model performs operation simulation on the candidate capacity configuration scheme in each target scenario of the target scenario set, and obtains the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme in each target scenario. The daily operating cost includes the cost of purchasing electricity from the grid, the aging cost of the battery due to charging and discharging, and the penalty cost of abandoning new energy. S35, the outer multi-objective capacity configuration model, based on the probability of occurrence of all objective scenarios, performs a weighted summation of the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme under each objective scenario, to obtain the total daily cost, total daily carbon emissions and average grid dependence of the candidate capacity configuration scheme over its entire life cycle. S36, the outer multi-objective capacity configuration model updates the search state of the multi-objective optimization algorithm based on the candidate capacity configuration scheme and the corresponding total daily cost, total daily carbon emissions and average grid dependence over the entire life cycle, and generates the candidate capacity configuration scheme for the next iteration. S37. Repeat S34 to S36 until the termination condition of the multi-objective optimization algorithm is met, and obtain the Pareto optimal solution set consisting of multiple non-dominated solutions. The termination condition is that the current iteration number reaches the maximum iteration number or the Pareto solution set converges.

[0012] The beneficial effects of adopting the above-mentioned further scheme are as follows: Firstly, by obtaining relevant parameters of the target area, the optimized input data is determined, providing an accurate basis for subsequent calculations. Secondly, the outer-layer multi-objective capacity configuration model generates candidate capacity configuration schemes through multi-objective optimization, simultaneously considering three objectives: daily average cost over the entire lifecycle, daily average carbon emissions, and grid dependence. This avoids the limitations of traditional methods that only consider a single economic objective or simply convert carbon emissions, achieving true multi-dimensional balance. Thirdly, the inner-layer optimal operation scheduling model simulates the operation of candidate capacity configuration schemes, considering electricity purchase costs, battery aging costs, and renewable energy curtailment penalty costs. This makes the calculated daily operating costs more realistic, avoiding the problem of overestimating planned capacity due to neglecting battery life, and improving the accuracy of investment estimation. Fourthly, the outer-layer model obtains lifecycle-related indicators by weighted summation of daily data based on the probability of the target scenario, providing a more comprehensive evaluation of the schemes. Finally, the candidate capacity configuration schemes are continuously updated and iterated until the termination condition is met, resulting in the Pareto optimal solution set. This solution set reflects the trade-off between cost, carbon emissions, and grid dependence, providing multiple options for subsequent decision-making.

[0013] Furthermore, the inner-layer optimal operation scheduling model performs operational simulations on the candidate capacity configuration schemes under each target scenario of the target scenario set, obtaining the daily operating cost, daily carbon emissions, and daily grid dependence of the candidate capacity configuration schemes under each target scenario, including: The inner-layer optimal operation scheduling model optimizes each target scenario in the target scenario set with the objective function of minimizing the daily operating cost and with the constraints of power balance and energy storage operation as conditions. It optimizes and solves the candidate capacity configuration scheme to obtain the daily operating cost, daily carbon emissions and daily grid dependence of each candidate capacity configuration scheme in each target scenario. The objective function is as follows: The inner-layer optimal operation scheduling model takes minimizing the daily operating cost, which includes time-of-use electricity purchase cost, battery aging cost, and renewable energy curtailment penalty cost, as its objective function. Under the constraints of power balance and energy storage operation, it optimizes and solves the candidate capacity configuration schemes, which can obtain the daily operating cost, daily carbon emissions, and daily grid dependence under each target scenario. This can avoid the problem of over-planned capacity caused by the traditional model ignoring battery life, making the planned energy storage utilization rate and lifespan closer to the real physical characteristics. The calculated return on investment is no longer over-inflated, effectively avoiding investment risks. At the same time, through economic leverage and penalty mechanisms, it implicitly realizes the priority consumption of green electricity and the precise operation of energy storage.

[0014] ; Where Cgrid_buy is the time-of-use electricity purchase cost, and Caging is the battery aging cost. The punitive cost of abandoning renewable energy sources. The aging cost of the battery due to charging and discharging; in, ξ is the aging cost coefficient per unit throughput of the battery, P_chg,t and P_dis,t are the energy storage charging power and discharging power at time t, respectively, and Δt is the scheduling time interval.

[0015] The beneficial effects of adopting the above-mentioned further scheme are as follows: The inner-layer optimal operation scheduling model takes minimizing the daily operating cost, which includes time-of-use electricity purchase cost, battery aging cost, and renewable energy curtailment penalty cost, as the objective function. Under the constraints of power balance and energy storage operation, it optimizes and solves the candidate capacity configuration scheme, and obtains the daily operating cost, daily carbon emissions, and daily grid dependence under each target scenario. This avoids the problem of over-planned capacity caused by the traditional model ignoring battery life, and makes the planned energy storage utilization rate and lifespan closer to the real physical characteristics. The calculated return on investment is no longer over-inflated, effectively avoiding investment risks. At the same time, through economic leverage and penalty mechanisms, it implicitly realizes the priority consumption of green electricity and the precise operation of energy storage.

[0016] Furthermore, the constraint condition of the outer multi-objective capacity configuration model is the wind-solar co-location spatial reuse constraint, which is as follows: ; in, , These are the planned installed capacities for wind power and solar power, respectively. For wind power density, For photovoltaic power density, This refers to the area of ​​land available within the project's boundary line.

[0017] The beneficial effects of adopting the above-mentioned further scheme are as follows: by using the constraint of wind and solar co-location space reuse as the constraint condition of the outer multi-objective capacity configuration model, the same piece of land can simultaneously carry the installed capacity that originally required two pieces of land to accommodate, which greatly improves the green electricity production capacity per unit area of ​​the data center park, reduces the land amortization cost per unit watt-hour, solves the problem of land resource constraints and low utilization rate in the existing planning methods, increases the capacity of renewable energy under the condition of limited land resources, and increases the proportion of direct green electricity connection.

[0018] Furthermore, the optimization algorithm used in the outer multi-objective capacity configuration model is the NSGA-II algorithm; and / or, the inner optimal operation scheduling model is a convex optimization model.

[0019] The beneficial effects of adopting the above-mentioned further scheme are: the outer layer uses the NSGA-II algorithm, which has better distribution when dealing with three-objective problems, and can more effectively perform multi-objective optimization solutions to find better capacity configuration schemes; the inner layer uses a convex optimization model, which constructs a comprehensive objective function based on time-of-use electricity price, battery throughput depreciation cost, and power curtailment penalty, and automatically finds the optimal energy flow direction to achieve a dynamic optimal balance between economy and absorption rate.

[0020] Furthermore, each solution in the Pareto optimal solution set corresponds to a capacity configuration scheme that includes wind power planned installed capacity, photovoltaic planned installed capacity and energy storage planned installed capacity, as well as three objective function values ​​corresponding to the capacity configuration scheme: daily average cost over the entire life cycle, daily average carbon emissions and grid dependence. The process of determining the optimal capacity allocation scheme as the planning result based on the Pareto optimal solution set and preset decision preference weights includes: Based on preset decision preference weights, the TOPSIS comprehensive evaluation method is used to calculate the evaluation value of each capacity configuration scheme. Based on the evaluation value of each capacity configuration scheme, the capacity configuration scheme with the highest evaluation value is selected as the planning result.

[0021] The beneficial effects of adopting the above-mentioned further scheme are: by using the TOPSIS comprehensive evaluation method to calculate the evaluation value of the capacity configuration scheme in the Pareto optimal solution set according to the preset decision preference weights, the optimal capacity configuration scheme that meets the decision-maker's preferences is selected from many non-dominated solutions, and a dynamic balance is achieved between cost, carbon emissions and grid dependence, thus selecting the most suitable planning result.

[0022] Secondly, this application provides a data center source-network-load-storage collaborative planning and scheduling device, which adopts the following technical solution: A data center source-network-load-storage collaborative planning and scheduling device mainly includes: The acquisition module is used to acquire historical time-series monitoring data of the target area. The historical time-series monitoring data includes historical meteorological data and historical load data of the computing center. The historical meteorological data includes wind speed, irradiance and temperature. The simulation module is used to determine the target scene set of the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm. The target scene set includes multiple target scenes and the probability of occurrence of each target scene. The optimization module is used to determine the Pareto optimal solution set based on the target scenario set and a pre-built three-dimensional multi-objective bi-layer programming model. The three-dimensional multi-objective bi-layer programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost including battery aging costs. The Pareto optimal solution set represents the trade-off relationship between cost, carbon emissions, and grid dependence. The determination module is used to determine the optimal capacity configuration scheme as the planning result based on the Pareto optimal solution set and preset decision preference weights.

[0023] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the first aspects of the data center source-network-load-storage collaborative planning and scheduling method.

[0024] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the data center source-network-load-storage collaborative planning and scheduling method as described in any of the first aspects.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0026] Figure 1 A flowchart illustrating a data center source-network-load-storage collaborative planning and scheduling method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a data center source-network-load-storage collaborative planning and scheduling device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, 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.

[0028] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0029] This application provides a data center source-network-load-storage collaborative planning and scheduling method. This method can be executed by an electronic device, which can be a server or a mobile terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The mobile terminal device can be a laptop, a desktop computer, etc., but is not limited to these.

[0030] The following is an explanation of the terms used in the embodiments of this application: Green electricity direct connection refers to a power supply mode in which data centers are physically connected to new energy power plants (wind and solar), and the load is first consumed by local new energy sources, with the surplus or shortage of electricity only interacting with the public power grid.

[0031] Grid dependence is used to measure system independence. The formula is "electricity purchased from the grid / total electricity consumption of the data center". A lower dependence indicates a higher level of direct green power connection. The coordinated development of power generation, grid, load and energy storage refers to the deep integration of the four elements of power source (wind and solar), grid (interaction), load (data center computing power) and energy storage (batteries) during the planning and operation phases.

[0032] Two-layer planning: an optimization architecture where the upper layer is responsible for capacity configuration (capacity setting) and the lower layer is responsible for operation simulation (scheduling), with both layers iteratively solving the problem.

[0033] Source-load spatiotemporal joint clustering: This refers to the process of extracting typical scenarios without separating wind, light, and load, but instead treating them as a unified high-dimensional feature vector for clustering, in order to preserve their correlation in time and space.

[0034] like Figure 1 As shown, a data center source-network-load-storage collaborative planning and scheduling method mainly includes: S1, acquire historical time-series monitoring data of the target area, the historical time-series monitoring data includes historical meteorological data and historical load data of the computing center, wherein the historical meteorological data includes wind speed, irradiance and temperature; In this embodiment, meteorological sensors are used to collect hourly meteorological data such as wind speed, irradiance, and temperature in the target area, and load monitoring instruments, such as smart meters, are used to collect historical load data of the computing center to monitor the power load of the computing center in real time.

[0035] S2, based on historical time-series monitoring data, preset Monte Carlo simulation technology and preset clustering algorithm, determine the target scene set of the target area, the target scene set includes multiple target scenes and the occurrence probability of each target scene; In this embodiment of the application, determining the target scene set of the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm includes: Based on historical time-series monitoring data and a pre-set normal distribution error model, multiple daily operation scenarios are generated; For any of the daily operation scenarios, the load time series data, wind power output time series data and photovoltaic output time series data in the daily operation scenario are spliced ​​together to construct a high-dimensional feature vector; Clustering analysis is performed on the multiple high-dimensional feature vectors based on a clustering algorithm to extract a predetermined number of target scenes and determine the occurrence probability of each target scene, thereby obtaining a target scene set for the target region. The preset normal distribution error model is as follows: ; in, For simulation data, Pbase is the baseline value for typical midday wind and solar load output, δ is the random error, and σ is the preset volatility parameter. The wind and solar volatility σres is set to 10%~20%, and the dynamic composite volatility σload is set to 5%~10%.

[0036] In this embodiment of the application, for the i-th scenario, a high-dimensional feature vector Vi is constructed. ; By splicing rather than processing independently, the coupling relationship between the source and load on the time axis is forcibly preserved.

[0037] Clustering analysis is performed on the multiple high-dimensional feature vectors using a clustering algorithm. For example, 1000 vectors are clustered to extract K (e.g., 5) typical scenarios and their occurrence probabilities πk. These 5 scenarios not only include average operating conditions but also automatically capture unconventional operating conditions such as "extreme source-load mismatch".

[0038] The K-means algorithm can be used for clustering, as it is computationally efficient and easy to implement.

[0039] S3. Based on the target scenario set and the pre-constructed three-dimensional multi-objective bi-layer programming model, determine the Pareto optimal solution set. The three-dimensional multi-objective bi-layer programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost including battery aging costs. The Pareto optimal solution set represents the trade-off relationship between cost, carbon emissions, and grid dependence. In this embodiment of the application, the optimization algorithm used in the outer multi-objective capacity configuration model is the NSGA-II algorithm; and / or, the inner optimal operation scheduling model is a convex optimization model.

[0040] In this embodiment of the application, determining the Pareto optimal solution set based on the target scene set and the pre-constructed three-dimensional multi-objective bi-layer programming model includes: S31, obtain the equipment economic parameters, technical parameters and carbon emission factors of the target area; S32, based on the equipment economic parameters, technical parameters, carbon emission factors and target scenario set of the target area, determine the optimized input data; S33, the outer multi-objective capacity configuration model, based on the optimized input data and multi-objective optimization algorithm, takes the planned installed capacity of wind power, photovoltaic and energy storage as decision variables, and takes minimizing the daily average cost of the whole life cycle, minimizing the daily average carbon emissions and minimizing grid dependence as three independent optimization objectives to perform multi-objective optimization solution, generate the candidate capacity configuration scheme of the current iteration, and input the candidate capacity configuration scheme of the current iteration and the target scenario set into the inner optimal operation scheduling model; S34, the inner-layer optimal operation scheduling model performs operation simulation on the candidate capacity configuration scheme in each target scenario of the target scenario set, and obtains the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme in each target scenario. The daily operating cost includes the cost of purchasing electricity from the grid, the aging cost of the battery due to charging and discharging, and the penalty cost of abandoning new energy. S35, the outer multi-objective capacity configuration model, based on the probability of occurrence of all objective scenarios, performs a weighted summation of the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme under each objective scenario, to obtain the total daily cost, total daily carbon emissions and average grid dependence of the candidate capacity configuration scheme over its entire life cycle. S36, the outer multi-objective capacity configuration model updates the search state of the multi-objective optimization algorithm based on the candidate capacity configuration scheme and the corresponding total daily cost, total daily carbon emissions and average grid dependence over the entire life cycle, and generates the candidate capacity configuration scheme for the next iteration. S37. Repeat S34 to S36 until the termination condition of the multi-objective optimization algorithm is met, and obtain the Pareto optimal solution set consisting of multiple non-dominated solutions. The termination condition is that the current iteration number reaches the maximum iteration number or the Pareto solution set converges.

[0041] In this embodiment, although the inner layer only optimizes operating costs, the outer layer simultaneously introduces two non-economic objectives, "carbon emissions" and "grid dependence," through the NSGA-II algorithm.

[0042] When the outer layer pursues "minimum grid dependence", it will force the inner layer to configure larger photovoltaic and energy storage capacity during the planning stage, even if this may increase investment costs.

[0043] When the outer layer pursues "lowest cost", it may accept a higher dependence on the power grid.

[0044] The final scheduling strategy is the result of a dynamic game and balance among cost, carbon emissions, and dependence in a two-level iteration, rather than a single physical priority rule.

[0045] In this embodiment of the application, the decision variable can be represented as: ; The optimization objective (three dimensions) is: The economic objective is to minimize the average daily cost over the entire lifecycle. Environmental goals: minimize daily carbon emissions; The independence objective is to minimize grid dependence.

[0046] Among them, the economic objective is: ; OPEX is calculated and fed back from the inner layer, while CAPEX is the average daily capital expenditure, determined by the Capital Recovery Rate (CRF) and the Operations and Maintenance Rate (OM_Rate), as follows: ; Where r is the benchmark discount rate (taken as 6%), and n is the equipment lifespan.

[0047] ; Where i represents wind power, photovoltaic power, and energy storage, and OM_Rate is the annual operation and maintenance fee rate (taken as 1.5%).

[0048] Carbon emission targets: ; The study focuses on the indirect carbon emissions of different forms of electricity use, with ugrid representing the carbon emission factor and auren representing the carbon emission factor of renewable energy. The fair value of wind, solar and energy storage is taken.

[0049] Minimize grid dependency objective: ; The direct connection to green electricity aims to maximize the system's self-sufficiency.

[0050] In this embodiment of the application, the constraint condition of the outer multi-objective capacity configuration model is the wind and solar co-location spatial reuse constraint, which is as follows: ; in, , These are the planned installed capacities for wind power and solar power, respectively. For wind power density, For photovoltaic power density, This refers to the area of ​​land available within the project's boundary line.

[0051] Candidate capacity configuration schemes refer to system hardware planning schemes to be evaluated, generated by an outer multi-objective capacity configuration model (such as NSGA-II) in the current iteration.

[0052] In the above implementation, during each iteration of the evaluation process of the outer multi-objective capacity configuration model, after the optimization algorithm (such as NSGA-II) generates a candidate capacity configuration scheme, before or simultaneously evaluating its three objective function values ​​(cost, carbon emissions, and dependence), it is necessary to determine whether the scheme is physically feasible. The constraint of wind and solar co-location space reuse is one of the key conditions for determining feasibility.

[0053] If true, the solution satisfies the land constraints and is a feasible solution, which can be passed to the inner model for simulation and comprehensive evaluation.

[0054] If this condition is not met, the proposed solution is deemed infeasible because the required land area exceeds the designated red line area. In multi-objective optimization algorithms, such solutions are typically assigned extremely poor fitness or are directly eliminated during the selection and sorting process, thus guiding the algorithm's search direction away from those irrationally allocated areas.

[0055] In some embodiments, the application of the spatial reuse constraint for wind and light co-occurrence can be implemented in the optimization algorithm in various ways.

[0056] Optionally, it can be handled as a soft constraint using a penalty function method: Constraints This is transformed into an additional penalty term in the objective function.

[0057] For example, define the penalty function Penalty=α*max(0, )^2, where α is a large penalty coefficient.

[0058] When calculating the fitness of candidate solutions (for comparing their merits), a penalty term is added to the original objective function value. For solutions that satisfy the constraints, the penalty term is 0; for solutions that violate the constraints, the penalty term is a large positive number, causing their total "cost" to increase sharply and their fitness to deteriorate.

[0059] Driven by minimizing the overall objective, optimization algorithms automatically tend to search for regions that satisfy constraints or have minimal violations. This approach allows the search to briefly explore infeasible regions in the early stages of iteration, but eventually converges to the feasible region.

[0060] In this embodiment, the inner-layer optimal operation scheduling model performs operational simulations on the candidate capacity configuration scheme in each target scenario of the target scenario set, obtaining the daily operating cost, daily carbon emissions, and daily grid dependence of the candidate capacity configuration scheme in each target scenario, including: The inner-layer optimal operation scheduling model optimizes each target scenario in the target scenario set with the objective function of minimizing the daily operating cost and with the constraints of power balance and energy storage operation as conditions. It optimizes and solves the candidate capacity configuration scheme to obtain the daily operating cost, daily carbon emissions and daily grid dependence of each candidate capacity configuration scheme in each target scenario. The objective function is: ; Where Cgrid_buy is the time-of-use electricity purchase cost, and Caging is the battery aging cost. The punitive cost of abandoning renewable energy sources. The aging cost of the battery due to charging and discharging; in, ξ is the aging cost coefficient per unit throughput of the battery, P_chg,t and P_dis,t are the energy storage charging power and discharging power at time t, respectively, and Δt is the scheduling time interval.

[0061] In the above implementation, a global optimization strategy based on marginal value is adopted. Under the premise of satisfying power balance, the inner solver compares the numerical relationship between grid electricity price, battery aging cost, and curtailment penalty.

[0062] The inner-layer optimal operation scheduling model refers to the core optimization decision-making mechanism responsible for "optimal operation under given capacity" in this application.

[0063] Each target scenario includes a complete and relevant set of wind power output curves, photovoltaic power output curves, and data center load curves after wind speed / solar intensity / temperature conversion. Simulations are performed to evaluate the performance of candidate solutions under specific weather and load combinations.

[0064] Operation simulation refers to the process of using the inner optimal operation scheduling model as the computing engine, the candidate capacity configuration scheme as the system scale boundary, and the wind and solar load data of a target scenario as input, to deduce the 24-hour hourly scheduling plan with the lowest total operating cost under the scenario while satisfying all constraints by solving the optimization problem.

[0065] The power balance constraint characterizes the core physical constraint that ensures the instantaneous balance of supply and demand in the system at any given moment, and its mathematical expression is: ; Among them, Pgrid represents grid-purchased electricity, Pdis represents energy storage discharge, Pwind represents wind power generation, Ppv represents photovoltaic power generation, Pcurt represents abandoned electricity, Pload represents data center load, and Pchg represents energy storage charging. Through variable symbol constraints and penalty terms, it is ensured that the load prioritizes the consumption of wind and solar power.

[0066] Energy storage operation constraints are a set of mathematical inequalities and equations that describe the physical and operational limitations of an energy storage system. They include the energy storage SOC equation of state, the upper and lower limits of wind and solar energy storage power, and the consistency of the initial and final states of energy storage.

[0067] The above scheme provides the outer model with accurate, quantitative feedback based on economic scheduling principles, essential for evaluating candidate solutions. It avoids the inefficiency of simple rule-based control and, by introducing battery aging costs, prevents excessive use of energy storage for small electricity price differences, thus ensuring the authenticity and engineering guidance value of the evaluation results (especially operating costs). Simulation results from all scenarios are weighted by probability and used to calculate the expected performance of the candidate solution, thereby guiding the outer multi-objective optimization algorithm to search for a better capacity configuration.

[0068] In some embodiments, the construction and solution of the inner-layer optimal runtime scheduling model can be achieved in multiple ways: Optional, centralized solution methods based on linear programming / convex optimization: The objective function and all constraints (power balance, dynamic SOC of energy storage, upper and lower power limits, equality of initial and final SOC, wind and solar power output limits, etc.) are precisely expressed using linear equations or inequalities. The decision variables are the power purchased, the power charged and discharged by energy storage, and the power abandoned in a 24-hour time series.

[0069] The constructed linear programming model is then input into a professional mathematical optimization solver, such as the commercial solvers Gurobi and CPLEX, or open-source solvers like GLPK and CBC. In a Python environment, modeling languages ​​such as Pyomo and CVXPY can be used to easily describe the problem and call the solver.

[0070] After the solver completes its run, it returns to the optimal state. The optimal values ​​of all decision variables are extracted from the solver output, and based on these values, the daily operating cost, daily carbon emissions, and daily grid dependence are calculated and output.

[0071] S4. Based on the Pareto optimal solution set and the preset decision preference weights, the optimal capacity configuration scheme is determined as the planning result.

[0072] In this embodiment of the application, each solution in the Pareto optimal solution set corresponds to a capacity configuration scheme including wind power planned installed capacity, photovoltaic planned installed capacity and energy storage planned installed capacity, as well as three objective function values ​​corresponding to the capacity configuration scheme: daily average cost over the entire life cycle, daily average carbon emissions and grid dependence. The process of determining the optimal capacity allocation scheme as the planning result based on the Pareto optimal solution set and preset decision preference weights includes: Based on preset decision preference weights, the TOPSIS comprehensive evaluation method is used to calculate the evaluation value of each capacity configuration scheme. Based on the evaluation value of each capacity configuration scheme, the capacity configuration scheme with the highest evaluation value is selected as the planning result.

[0073] This method employs joint clustering of source and load data to ensure that the generated typical scenarios retain the true temporal coupling relationship between wind and solar resources and load. This forces the planning model to confront and resolve extreme operating conditions of "unfavorable conditions on both the source and load sides," thereby configuring a more resilient system capacity and avoiding operational interruptions due to scenario distortion. By constructing a three-dimensional Pareto front with economic efficiency, environmental friendliness, and grid dependence as independent objectives, it abandons the traditional approach of a single cost objective or simply monetizing environmental indicators. It provides decision-makers with a series of visualized optimal trade-offs, resolving the core contradiction in planning green power direct-connection computing centers where "economy, low carbon, and independence" are difficult to balance. The inner scheduling model accurately incorporates battery aging costs positively correlated with charging and discharging energy, ensuring that the optimization algorithm does not overextend energy storage lifespan for minimal profit. Based on the Pareto optimal solution set and decision preference weights, the optimal capacity configuration scheme is determined, yielding planning results that align with decision-makers' preferences.

[0074] Figure 2 A schematic diagram of a data center source-network-load-storage collaborative planning and scheduling device 200 is shown.

[0075] like Figure 2 As shown, a data center source-network-load-storage collaborative planning and scheduling device 200 mainly includes: The acquisition module 201 is used to acquire historical time-series monitoring data of the target area. The historical time-series monitoring data includes historical meteorological data and historical load data of the computing center. The historical meteorological data includes wind speed, irradiance and temperature. The simulation module 202 is used to determine the target scene set of the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology and preset clustering algorithm. The target scene set includes multiple target scenes and the probability of occurrence of each target scene. The optimization module 203 is used to determine the Pareto optimal solution set based on the target scenario set and the pre-built three-dimensional multi-objective bi-layer programming model. The three-dimensional multi-objective bi-layer programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost including battery aging costs. The Pareto optimal solution set represents the trade-off relationship between cost, carbon emissions, and grid dependence. The determination module 204 is used to determine the optimal capacity configuration scheme as the planning result based on the Pareto optimal solution set and the preset decision preference weights.

[0076] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0077] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0078] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0079] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of this application.

[0082] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0083] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the aforementioned data center source-network-load-storage collaborative planning and scheduling method. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0084] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0085] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0086] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the data center source-network-load-storage collaborative planning and scheduling method given in the above embodiments.

[0087] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium described below can be referred to in correspondence with the data center source-network-load-storage collaborative planning and scheduling method described above.

[0088] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described data center source-network-load-storage collaborative planning and scheduling method.

[0089] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] 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 process, method, article, or apparatus.

[0091] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A data center source-network-load-storage collaborative planning and scheduling method, characterized in that, include: Acquire historical time-series monitoring data of the target area, including historical meteorological data and historical load data of the computing center, wherein the historical meteorological data includes wind speed, irradiance and temperature; Based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm, a target scene set for the target area is determined. The target scene set includes multiple target scenes and the probability of occurrence of each target scene. Based on the target scenario set and the pre-constructed three-dimensional multi-objective bi-level programming model, the Pareto optimal solution set is determined. The three-dimensional multi-objective bi-level programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost, including battery aging costs. The Pareto optimal solution set represents the trade-off between cost, carbon emissions, and grid dependence. Based on the Pareto optimal solution set and the preset decision preference weights, the optimal capacity configuration scheme is determined as the planning result.

2. The data center source-network-load-storage collaborative planning and scheduling method according to claim 1, characterized in that, The determination of the target scene set for the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm includes: Based on historical time-series monitoring data and a pre-set normal distribution error model, multiple daily operation scenarios are generated; For any of the daily operation scenarios, the load time series data, wind power output time series data and photovoltaic output time series data in the daily operation scenario are spliced ​​together to construct a high-dimensional feature vector; Clustering analysis is performed on multiple high-dimensional feature vectors based on a clustering algorithm to extract a predetermined number of target scenes and determine the occurrence probability of each target scene, thereby obtaining a target scene set for the target region. The preset normal distribution error model is as follows: ； in, For simulation data, This is the baseline value for typical midday wind and solar power output, where δ represents random error. Set the wind and solar volatility to the preset volatility parameters. res = 10%~20%, dynamic composite volatility load = 5%~10%.

3. The data center source-network-load-storage collaborative planning and scheduling method according to claim 1, characterized in that, The process of determining the Pareto optimal solution set based on the target scene set and a pre-constructed three-dimensional multi-objective bi-layer programming model includes: S31, obtain the equipment economic parameters, technical parameters and carbon emission factors of the target area; S32, based on the equipment economic parameters, technical parameters, carbon emission factors and target scenario set of the target area, determine the optimized input data; S33, the outer multi-objective capacity configuration model, based on the optimized input data and multi-objective optimization algorithm, takes the planned installed capacity of wind power, photovoltaic and energy storage as decision variables, and takes minimizing the daily average cost of the whole life cycle, minimizing the daily average carbon emissions and minimizing grid dependence as three independent optimization objectives to perform multi-objective optimization solution, generate the candidate capacity configuration scheme of the current iteration, and input the candidate capacity configuration scheme of the current iteration and the target scenario set into the inner optimal operation scheduling model; S34, the inner-layer optimal operation scheduling model performs operation simulation on the candidate capacity configuration scheme in each target scenario of the target scenario set, and obtains the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme in each target scenario. The daily operating cost includes the cost of purchasing electricity from the grid, the aging cost of the battery due to charging and discharging, and the penalty cost of abandoning new energy. S35, the outer multi-objective capacity configuration model, based on the probability of occurrence of all objective scenarios, performs a weighted summation of the daily operating cost, daily carbon emissions and daily grid dependence of the candidate capacity configuration scheme under each objective scenario, to obtain the total daily cost, total daily carbon emissions and average grid dependence of the candidate capacity configuration scheme over its entire life cycle. S36, the outer multi-objective capacity configuration model updates the search state of the multi-objective optimization algorithm based on the candidate capacity configuration scheme and the corresponding total daily cost, total daily carbon emissions and average grid dependence over the entire life cycle, and generates the candidate capacity configuration scheme for the next iteration. S37. Repeat S34 to S36 until the termination condition of the multi-objective optimization algorithm is met, and obtain the Pareto optimal solution set consisting of multiple non-dominated solutions. The termination condition is that the current iteration number reaches the maximum iteration number or the Pareto solution set converges.

4. The data center source-network-load-storage collaborative planning and scheduling method according to claim 3, characterized in that, The inner-layer optimal operation scheduling model performs operational simulations on the candidate capacity configuration schemes under each target scenario in the target scenario set, obtaining the daily operating cost, daily carbon emissions, and daily grid dependence of the candidate capacity configuration schemes under each target scenario, including: The inner-layer optimal operation scheduling model optimizes each target scenario in the target scenario set with the objective function of minimizing the daily operating cost and with the constraints of power balance and energy storage operation as conditions. It optimizes and solves the candidate capacity configuration scheme to obtain the daily operating cost, daily carbon emissions and daily grid dependence of each candidate capacity configuration scheme in each target scenario. The objective function is: ； Where Cgrid_buy is the time-of-use electricity purchase cost, and Caging is the battery aging cost. The punitive cost of abandoning renewable energy sources. The aging cost of the battery due to charging and discharging; in, ξ is the aging cost coefficient per unit throughput of the battery, P_chg,t and P_dis,t are the energy storage charging power and discharging power at time t, respectively, and Δt is the scheduling time interval.

5. The data center source-network-load-storage collaborative planning and scheduling method according to claim 3, characterized in that, The constraint condition of the outer multi-objective capacity configuration model is the spatial reuse constraint of wind and solar co-location, which is as follows: ； in, , These are the planned installed capacities for wind power and solar power, respectively. For wind power density, For photovoltaic power density, This refers to the area of ​​land available within the project's boundary line.

6. The data center source-network-load-storage collaborative planning and scheduling method according to claim 3, characterized in that, The outer multi-objective capacity configuration model uses the NSGA-II algorithm for optimization; and / or, the inner optimal operation scheduling model is a convex optimization model.

7. The data center source-network-load-storage collaborative planning and scheduling method according to claim 1, characterized in that, Each solution in the Pareto optimal solution set corresponds to a capacity configuration scheme that includes wind power planned installed capacity, photovoltaic planned installed capacity and energy storage planned installed capacity, as well as three objective function values ​​corresponding to the capacity configuration scheme: daily average cost over the entire life cycle, daily average carbon emissions and grid dependence. The process of determining the optimal capacity allocation scheme as the planning result based on the Pareto optimal solution set and preset decision preference weights includes: Based on preset decision preference weights, the TOPSIS comprehensive evaluation method is used to calculate the evaluation value of each capacity configuration scheme. Based on the evaluation value of each capacity configuration scheme, the capacity configuration scheme with the highest evaluation value is selected as the planning result.

8. A data center source-network-load-storage collaborative planning and scheduling device, characterized in that, include: The acquisition module is used to acquire historical time-series monitoring data of the target area. The historical time-series monitoring data includes historical meteorological data and historical load data of the computing center. The historical meteorological data includes wind speed, irradiance and temperature. The simulation module is used to determine the target scene set of the target area based on historical time-series monitoring data, preset Monte Carlo simulation technology, and preset clustering algorithm. The target scene set includes multiple target scenes and the probability of occurrence of each target scene. The optimization module is used to determine the Pareto optimal solution set based on the target scenario set and a pre-built three-dimensional multi-objective bi-layer programming model. The three-dimensional multi-objective bi-layer programming model includes an outer multi-objective capacity configuration model and an inner optimal operation scheduling model. The outer multi-objective capacity configuration model represents an optimization model with the objectives of minimizing the average daily cost over the entire life cycle, minimizing the average daily carbon emissions, and minimizing grid dependence. The inner optimal operation scheduling model represents an optimization model with the objective of minimizing the daily operating cost, including battery aging costs. The Pareto optimal solution set represents the trade-off relationship between cost, carbon emissions, and grid dependence. The determination module is used to determine the optimal capacity configuration scheme as the planning result based on the Pareto optimal solution set and preset decision preference weights.

9. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1-7.

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