A multi-objective optimization charging station power supply management method and system

Abstract

The present invention relates to a multi-objective optimized charging station power supply management method, comprising the following steps: obtaining power demand and power management target in the charging station, performing multi-dimensional analysis on the configuration scheme of power supply equipment through a trained multi-dimensional analysis model to obtain multi-dimensional analysis results, and obtaining a corresponding adjustment strategy based on the multi-dimensional analysis results; modifying the configuration scheme of the power supply equipment based on the corresponding adjustment strategy to obtain a power supply equipment modification scheme; based on preset target power demand and power management target, continuously iteratively optimizing the power supply equipment modification scheme until a predetermined number of iterations is reached, and obtaining a multi-objective optimized power supply configuration scheme set; selecting the power supply configuration scheme with the highest efficiency score in the multi-objective optimized power supply configuration scheme set as a recommended scheme for power supply management of the charging station; and improving the stability and reliability of power supply through scientific configuration schemes and adjustment strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a multi-objective optimized charging station power supply management method, system, device and storage medium. Background Art

[0002] With the popularity of electric vehicles, the construction and management of charging stations have become an indispensable part of the power supply chain. Traditional charging station power supply management methods often only consider a single goal, such as minimizing costs or maximizing energy efficiency, and ignore other important factors in charging station operations, such as power supply reliability, user satisfaction, and environmental impact. In addition, traditional methods are often unable to flexibly adjust when faced with peak power demand, resulting in low charging efficiency and long user waiting times. Therefore, developing a charging station power supply management method that can comprehensively consider multiple objectives is of great significance to improving the operational efficiency and user experience of charging stations. Summary of the invention

[0003] The main purpose of the present invention is to provide a multi-objective optimization charging station power supply management method, system, device and storage medium, which takes environmental impact into consideration in power supply management, reduces energy waste by optimizing configuration schemes, helps to reduce the carbon footprint of charging stations and achieve green charging.

[0004] To achieve the above object, the present invention provides a multi-objective optimization charging station power supply management method, which is applied to the charging station power supply system; the method comprises the following steps:

[0005] Obtaining power demand and power management targets in the charging station, and constructing a configuration scheme for power supply equipment based on the power demand and power management targets; wherein the configuration scheme for power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment;

[0006] Perform multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain multi-dimensional analysis results, and obtain corresponding adjustment strategies based on the multi-dimensional analysis results;

[0007] Modifying the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes coexistence of multiple optimized power supply configurations;

[0008] Based on the preset target power demand and power management target, the power supply equipment modification plan is continuously iterated and optimized until the predetermined number of iterations is reached, and a set of multi-objective optimized power supply configuration plans is obtained;

[0009] The power supply configuration scheme with the highest efficiency score is selected from the set of multi-objective optimization power supply configuration schemes as the recommended scheme for charging station power supply management.

[0010] As a further solution of the present invention, the power supply equipment includes wind power generation equipment and solar power generation equipment.

[0011] As a further solution of the present invention, the number of charging piles, the output power and the operating time of the charging station are obtained, and the power demand in the charging station is obtained based on the number of charging piles, the output power and the operating time of the charging station;

[0012] Obtaining the peak power consumption of the charging station, and obtaining a power management target based on the peak power consumption and the power demand; wherein the peak power consumption is the peak power consumption within a time end;

[0013] Constructing a plurality of different preliminary configuration schemes for power supply equipment based on the power demand and power management objectives;

[0014] Simulating the plurality of different preliminary configuration schemes by using a preset simulation algorithm to obtain corresponding simulation results;

[0015] The cost-effectiveness and energy efficiency ratio corresponding to each simulation result are obtained, and a plurality of the simulation results are eliminated in multiple stages based on the cost-effectiveness and energy efficiency ratio corresponding to each simulation result to obtain a configuration plan for the power supply equipment.

[0016] As a further solution of the present invention, the following formula is used to calculate the power demand in the charging station:

[0017] ;

[0018] ;

[0019] ;

[0020] Among them, the Indicates the number of charging piles in the charging station; Indicates the basic output power of the charging pile; represents the weight parameter of the ambient temperature; A weight parameter representing the usage frequency of the charging pile; Indicates time Ambient temperature; Indicates the total operating time of the charging station; Indicates the total electricity demand of the charging station in one day; represents the time variable; Indicates the usage frequency of the charging pile; Indicates the charging pile at time output power.

[0021] As a further solution of the present invention, the multi-dimensional analysis includes performance dimensional analysis of power supply equipment, quality dimensional analysis of power supply equipment, cost dimensional analysis of power supply equipment and safety dimensional analysis of power supply equipment; wherein, the performance score is subjected to multi-dimensional analysis to obtain a multi-dimensional analysis result, and a corresponding adjustment strategy is obtained based on the multi-dimensional analysis result, including:

[0022] Performing a performance dimension analysis on the configuration scheme of the power supply equipment to obtain a performance dimension analysis result;

[0023] Performing a quality dimension analysis on the configuration scheme of the power supply equipment to obtain a quality dimension analysis result;

[0024] Performing a cost dimension analysis on the configuration scheme of the power supply equipment to obtain a cost dimension analysis result;

[0025] Performing a security dimension analysis on the configuration scheme of the power supply equipment to obtain a security dimension analysis result;

[0026] Based on the performance dimension analysis results, quality dimension analysis results, cost dimension analysis results and safety dimension analysis results, a corresponding adjustment strategy is obtained.

[0027] As a further solution of the present invention, the configuration scheme of the power supply device is modified based on the corresponding adjustment strategy to obtain a power supply device modification scheme, including:

[0028] Based on the corresponding adjustment strategy, the configuration scheme of the power supply device is modified in multiple dimensions to obtain a first modification scheme of the power supply device, a second modification scheme of the power supply device, and a third modification scheme of the power supply device; wherein the first modification scheme is a power supply capacity scheme of the power supply device, the second modification scheme is an efficiency scheme of the power supply device, and the third modification scheme is a failure rate scheme of the power supply device;

[0029] The first modification scheme and the second modification scheme are combined to obtain a first combination scheme, and the first combination scheme is encrypted by elliptic curve to obtain a first combination encryption scheme;

[0030] The first combined encryption scheme and the third modification scheme are combined to obtain a second combined scheme, and the second combined scheme is asymmetrically encrypted to obtain a power supply equipment modification scheme.

[0031] As a further solution of the present invention, the method of selecting the power supply configuration scheme with the highest efficiency score from the set of power supply configuration schemes for multi-objective optimization as a recommended scheme for power supply management of the charging station includes:

[0032] Constructing a metagraph based on the set of multi-objective optimized power supply configuration schemes; wherein the metagraph includes a plurality of nodes, one of the nodes is a multi-objective optimized power supply configuration scheme, and the attributes of the node include the cost of the configuration scheme, the resource consumption of the configuration scheme, and the score of the configuration scheme;

[0033] Calculate the cost similarity of each node in the meta-graph, generate a cost similarity value corresponding to each node, and sort each node based on the cost similarity value to obtain a cost sorting result;

[0034] Calculating resource consumption similarity for each node in the meta-graph, generating resource consumption similarity values ​​corresponding to each node, and sorting each node based on the resource consumption similarity values ​​to obtain a resource consumption sorting result;

[0035] Calculating the score similarity of each node in the meta-graph to generate a score similarity value corresponding to each node, and sorting each node based on the score similarity value to obtain a score sorting result;

[0036] Performing node search on the cost ranking results, resource consumption ranking results and score ranking results through a preset search model to obtain a recommended node; wherein the recommended node is the node that is at the front of the cost ranking results, resource consumption ranking results and score ranking results;

[0037] The recommended node is used as a recommended solution for power supply management of the charging station.

[0038] The present invention also provides a multi-objective optimized charging station power supply management system, comprising:

[0039] An acquisition module, used to acquire power demand and power management target in the charging station, and construct a configuration scheme of the power supply equipment based on the power demand and power management target; wherein the configuration scheme of the power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment;

[0040] An analysis module is used to perform a multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain a multi-dimensional analysis result, and obtain a corresponding adjustment strategy based on the multi-dimensional analysis result;

[0041] A modification module, used to modify the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes the coexistence of multiple optimized power supply configurations;

[0042] An optimization module is used to continuously iterate and optimize the power supply equipment modification plan based on the preset target power demand and power management goals until a predetermined number of iterations is reached to obtain a set of multi-objective optimized power supply configuration plans;

[0043] The recommendation module is used to select the power supply configuration scheme with the highest efficiency score from the set of multi-objective optimization power supply configuration schemes as the recommended scheme for power supply management of the charging station.

[0044] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0045] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0046] The multi-objective optimized charging station power supply management method, system, computer device and storage medium provided by the present invention include the following steps: obtaining power demand and power management target in the charging station, and constructing a configuration scheme of the power supply equipment based on the power demand and power management target; performing multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain the multi-dimensional analysis result, and obtaining the corresponding adjustment strategy based on the multi-dimensional analysis result; modifying the configuration scheme of the power supply equipment based on the corresponding adjustment strategy to obtain the power supply equipment modification scheme; based on the preset target power demand and power management target, continuously iteratively optimizing the power supply equipment modification scheme until a predetermined number of iterations is reached, and obtaining a multi-objective optimized power supply configuration scheme set; selecting the power supply configuration scheme with the highest efficiency score in the multi-objective optimized power supply configuration scheme set as the recommended scheme for power supply management of the charging station; by comprehensively considering multiple management targets and power demand, and through scientific configuration schemes and adjustment strategies, the technical problem that it is often impossible to flexibly adjust when facing power demand during peak hours, resulting in low charging efficiency and long user waiting time, is solved, the stability and reliability of power supply is improved, and energy distribution is optimized, thereby reducing the operating cost of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the steps of a multi-objective optimization charging station power supply management method in one embodiment of the present invention;

[0048] Figure 2 It is a structural block diagram of a multi-objective optimized charging station power supply management system in one embodiment of the present invention;

[0049] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a multi-objective optimization charging station power supply management method in one embodiment of the present invention;

[0053] In one embodiment of the present invention, a multi-objective optimization charging station power supply management method is provided, which is applied to a charging station power supply system; the method comprises the following steps:

[0054] Step S1, obtaining power demand and power management target in the charging station, and constructing a configuration scheme of the power supply equipment based on the power demand and power management target; wherein the configuration scheme of the power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment;

[0055] Specifically, demand analysis: First, the power demand of the charging station needs to be analyzed. This includes understanding the daily operating hours of the charging station, peak power consumption periods, and the power demand of different types of charging piles (such as fast charging and slow charging). In addition, future power demand growth should be predicted to facilitate long-term planning. Clarify the goals of power management. Including reducing energy consumption, reducing electricity bills, improving energy efficiency, ensuring power supply reliability, and supporting the integration of renewable energy. Evaluate the available power supply resources, including the power connected to the grid, renewable energy that may be connected (such as solar photovoltaic panels), and energy storage devices (such as battery energy storage systems). Based on the above analysis and evaluation, design a configuration plan for power supply equipment. This should include the allocated power of each power supply device (including charging piles, transformers, energy storage devices, etc.) and their scheduled power supply time. The configuration plan should optimize the utilization of equipment to achieve the power management goals. Use software tools to simulate the performance of the configuration plan, including its performance in different power consumption scenarios. Based on the simulation results, adjust the configuration plan to optimize performance. Develop a detailed implementation plan, including equipment procurement, installation and commissioning, and coordination with the power grid. After implementation, the performance of the power supply system is continuously monitored and adjustments are made based on actual power demand and changes in management objectives.

[0056] The above-mentioned technical features can achieve the following technical effects: Improve energy efficiency and reduce waste through refined power allocation and power supply time scheduling. Optimizing the configuration and operation of power supply equipment can significantly reduce energy costs. Supporting the integration of renewable energy and energy storage equipment helps reduce carbon emissions and achieve more environmentally friendly operations. Through reasonable configuration and management, ensure that charging stations can also provide stable power supply during high-demand periods and improve user satisfaction. Taking into account long-term needs and technological development, the designed configuration scheme has a certain degree of flexibility and can adapt to future changes. Through the above steps, a cost-effective and sustainable charging station power supply solution can be achieved that meets current needs while also adapting to future development.

[0057] Step S2, performing a multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain a multi-dimensional analysis result, and obtaining a corresponding adjustment strategy based on the multi-dimensional analysis result.

[0058] Specifically, first, a multidimensional analysis model needs to be constructed, which can consider multiple factors, such as energy costs, changes in electricity demand, environmental impact, power supply reliability, etc. The training data of the model should come from historical power supply equipment operation data, charging station electricity consumption records, weather conditions, electricity price changes, etc. In order to ensure the accuracy of the model analysis, the input data needs to be cleaned, standardized and normalized to eliminate noise and outliers and ensure the quality and consistency of the data. The trained model is used to analyze the current power supply equipment configuration scheme, considering factors such as electricity demand in different time periods, seasonal changes, and possible abnormal events. The analysis results will reveal the performance of the current configuration scheme in various dimensions considered, such as cost-effectiveness, environmental impact assessment, power supply reliability prediction, etc. According to the results of the multidimensional analysis, an adjustment strategy for the power supply equipment configuration is formulated. This may include adjusting the power distribution of the power supply equipment, optimizing the power supply schedule, increasing or reducing the use of energy storage equipment, etc. The potential impact of the adjustment strategy is evaluated in a simulation environment to ensure that the proposed strategy can achieve the expected effect in practice. After that, the adjustment strategy is implemented in actual operation based on the evaluation results.

[0059] The above-mentioned technical features can achieve the following technical effects: By comprehensively analyzing various factors, the formulated adjustment strategy can more effectively control costs and improve economic benefits. Fine-tune the management of power supply equipment, reduce energy waste, and improve overall energy utilization efficiency. Consider environmental factors, optimize power supply plans, and reduce carbon emissions and other environmental burdens. Through predictive analysis and timely adjustments, the stability and reliability of the power supply system can be improved, and the risks of power outages and power shortages can be reduced. Enable the power supply configuration plan to flexibly adapt to future changes, including demand growth, technological advances, or policy changes. Overall, step S2 uses advanced data analysis and model prediction to make the configuration of power supply equipment more scientific, economical, and sustainable, providing support for the long-term development of charging stations.

[0060] Step S3, modifying the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes coexistence of multiple optimized power supply configurations.

[0061] Specifically, review the current power supply equipment configuration scheme, including the allocated power, power supply time, location layout, etc. of each device. Based on the adjustment strategy proposed by the multi-dimensional analysis model, determine the specific modification items, such as increasing or decreasing the power of specific equipment, adjusting the power supply time, reconfiguring the equipment layout, etc. Create several different optimized power supply configuration schemes, each of which provides optimization in specific aspects (such as cost, energy efficiency, and environmental impact). The above schemes should be able to coexist and provide selection space for different management goals and operating conditions. Conduct a comprehensive evaluation of each optimized configuration scheme, considering its specific impact on cost, efficiency, reliability, and environmental impact. When selecting the final implementation plan, consider the actual operational needs and long-term development goals of the charging station. Refine the implementation details for the selected modification plan, including the required resources, budget, schedule, etc. Prepare the technical and personnel resources required for implementation. Modify the power supply equipment according to the plan, including the installation, adjustment, or replacement of the equipment. Ensure safety and quality control during the modification process. After the implementation of the modified configuration plan, test and evaluate it to ensure that all indicators achieve the expected results. If necessary, make further optimization adjustments based on the test results.

[0062] The above-mentioned technical features can achieve the following technical effects: through precise configuration adjustment, energy waste can be reduced and the overall energy efficiency of the power supply system can be improved. The optimized configuration scheme can reduce operating costs and improve economic benefits. By optimizing the power supply configuration, carbon emissions and other environmental burdens can be reduced, and sustainable development can be promoted. The optimized configuration scheme can improve the stability and reliability of the power supply system and reduce the failure rate. The coexistence of multiple optimized configuration schemes provides flexibility and selectivity for charging stations to cope with different operating conditions and future changes. Through a series of steps, the charging station can achieve scientific optimization of the configuration of the power supply equipment, which not only improves energy and economic efficiency, but also enhances the reliability and adaptability of the system, and supports sustainable development goals.

[0063] Step S4, based on the preset target power demand and power management target, continuously iteratively optimize the power supply equipment modification plan until a predetermined number of iterations is reached, and obtain a set of multi-objective optimized power supply configuration plans;

[0064] Specifically, clarify the electricity demand and electricity management goals, including but not limited to improving energy efficiency, reducing costs, minimizing environmental impact, etc. At the same time, determine the constraints that need to be followed during the optimization process, such as power supply capacity, equipment limitations, regulatory requirements, etc. Based on the existing power supply configuration scheme, design one or more preliminary modification schemes as the starting point of the optimization process. Use appropriate optimization algorithms (such as genetic algorithms, particle swarm optimization, simulated annealing, etc.) for iterative optimization. In each iteration, evaluate the performance of the current scheme according to the preset evaluation indicators and constraints, and then adjust the configuration scheme according to the evaluation results. After each round of iteration, use simulation or actual test data to evaluate the performance of the modified scheme, including energy efficiency, cost-effectiveness, environmental impact, etc. Check whether the iterative process converges, that is, whether the improvement of several consecutive iterations has become small and reached the predetermined number of iterations or optimization goals. During the iteration process, record all configuration schemes that meet the constraints and perform well to form the final multi-objective optimization power supply configuration scheme set. Select the configuration scheme that best meets the actual needs and goals from the scheme set for practical application and adjustment.

[0065] The above technical features can achieve the following technical effects: it can simultaneously meet multiple power management goals, such as cost optimization, maximum energy utilization and minimum environmental impact, and realize comprehensive optimization of power supply management. Through continuous iterative optimization, a more energy-efficient power supply configuration solution can be found to reduce energy waste. The optimized configuration solution can reduce operating costs and improve economic benefits. By optimizing the power supply configuration, carbon emissions and other environmental burdens can be reduced, and the use of green energy can be supported. The set of solutions formed in the iterative process provides charging stations with a variety of power supply configuration options, enhancing their adaptability and flexibility to future changes. It provides managers with scientific decision support tools to help them balance and choose between multiple goals to achieve the optimal power supply management strategy. Through the above steps, charging stations can continuously improve their power supply equipment configuration to achieve higher energy efficiency, lower costs and better environmental performance, while also improving the overall reliability and adaptability of the system.

[0066] Step S5, selecting the power supply configuration scheme with the highest efficiency score from the set of multi-objective optimized power supply configuration schemes as a recommended scheme for power supply management of the charging station.

[0067] Specifically, we first need to establish a comprehensive performance scoring system, which should cover all key dimensions of power supply configuration schemes, such as energy efficiency, operating costs, environmental impact, power supply reliability, etc. Set corresponding evaluation indicators and weights for each dimension to ensure that the scoring system can fully reflect the pros and cons of the scheme. Perform performance scoring on each scheme in the set of multi-objective optimized power supply configuration schemes. This includes a comprehensive evaluation of each scheme according to the established performance scoring system and calculating its total score. Compare and sort all power supply configuration schemes according to their performance scores to find the scheme with the highest score. Conduct in-depth analysis on the power supply configuration scheme with the highest performance score to verify whether its performance in each key dimension truly meets the optimization objectives and constraints of the charging station. Evaluate the feasibility of the recommended scheme in practical applications, including technical implementation difficulty, cost budget, expected operational improvements, etc. Based on the above analysis and evaluation, decide whether to use the scheme with the highest performance score as the recommended scheme for power supply management of charging stations, and formulate an implementation plan.

[0068] The above-mentioned technical features can achieve the following technical effects: by selecting the solution with the highest efficiency score, the power supply configuration of the charging station is ensured to be optimized in terms of energy efficiency and energy waste is reduced.

[0069] Solutions with high efficiency scores usually provide the greatest savings in operating costs, ensuring that the economic benefits of charging stations are maximized. The efficiency scoring system that takes environmental factors into account helps to select power supply configuration solutions with the least impact on the environment and supports green and sustainable operations. Solutions with high efficiency scores often perform well in terms of power supply reliability, which helps to improve the stability of charging station services and user satisfaction. It provides a scientific and systematic method to evaluate and select power supply configuration solutions, providing strong decision-making support for charging station managers. Through this series of steps, it can be ensured that charging stations adopt the power supply configuration solution that best suits their operating goals and conditions, and achieve efficient, economical and environmentally friendly power supply management.

[0070] In a specific embodiment, the power supply equipment includes wind power generation equipment and solar power generation equipment.

[0071] Specifically, resource availability assessment: Assess the energy availability of wind and solar power generation equipment in the area where the charging station is located, including wind speed, sunshine duration, seasonal changes, etc. Use advanced forecasting tools to predict the power output of wind and solar power generation to provide a basis for power scheduling and equipment configuration. Design a hybrid power supply system that combines wind and solar power generation with traditional grid power supply to ensure power supply stability. Consider introducing energy storage systems (such as battery energy storage) to store electricity generated by wind and solar power generation when there is excess capacity for power supply when there is insufficient capacity. Develop or adopt advanced energy management systems (EMS) to optimize the operation of wind and solar power generation equipment and their interaction with the grid. Design flexible power supply solutions to cope with the uncertainty and volatility of wind and solar power generation, and develop emergency plans to deal with extreme weather conditions or equipment failures. Implement a real-time monitoring system to track the performance of wind and solar power generation equipment and adjust power supply strategies based on real-time data.

[0072] The following technical effects can be achieved through the above technical features: The integration of wind and solar power generation equipment provides a diversified source of energy and reduces dependence on a single energy source. Through effective configuration and management, the utilization efficiency of wind and solar energy is maximized. Clean energy is used to reduce greenhouse gas emissions and promote the environmental sustainability of charging stations. Although the initial investment may be high, in the long run, operating costs can be significantly reduced due to the use of renewable energy. Through energy storage systems and intelligent management, the impact of the intermittent and uncertain nature of renewable energy on power supply stability is reduced. Through the above steps, the charging station can effectively integrate wind and solar power generation equipment, which not only improves energy efficiency and economic benefits, but also promotes environmental sustainability, while also improving the stability and reliability of the entire power supply system.

[0073] In a specific embodiment, the number of charging piles, the output power and the operating time of the charging station are obtained, and the power demand in the charging station is obtained based on the number of charging piles, the output power and the operating time of the charging station;

[0074] Obtaining the peak power consumption of the charging station, and obtaining a power management target based on the peak power consumption and the power demand; wherein the peak power consumption is the peak power consumption within a time end;

[0075] Constructing a plurality of different preliminary configuration schemes for power supply equipment based on the power demand and power management objectives;

[0076] Simulating the plurality of different preliminary configuration schemes by using a preset simulation algorithm to obtain corresponding simulation results;

[0077] The cost-effectiveness and energy efficiency ratio corresponding to each simulation result are obtained, and a plurality of the simulation results are eliminated in multiple stages based on the cost-effectiveness and energy efficiency ratio corresponding to each simulation result to obtain a configuration plan for the power supply equipment.

[0078] Specifically, collect basic operating data of charging stations, including the number of charging piles, the output power of each charging pile, and the operating hours of the charging station. Based on the above data, calculate the total power demand of the charging station, taking into account the changes in power consumption in different time periods. Determine the peak power consumption period, which is usually the time period with the highest power consumption in a day. Combine the peak power consumption and total power demand to set power management goals, such as peak reduction and energy efficiency optimization. Based on power demand and management goals, build multiple different power supply equipment configuration schemes. These schemes may include different types of power supply equipment, different energy combinations, different power allocations and power supply time arrangements. Use the preset simulation algorithm to simulate the preliminary configuration schemes and evaluate their performance in actual operation. The simulation results should include the energy efficiency of each scheme, the ability to meet power demand, the impact on peak power consumption, etc. Evaluate and compare the preliminary configuration schemes based on the cost-benefit analysis and energy efficiency ratio of the simulation results. Use a multi-level elimination mechanism to gradually eliminate the poorly performing schemes and finally determine the optimal power supply equipment configuration scheme.

[0079] The above-mentioned technical features can achieve the following technical effects: Through careful simulation and analysis, the most cost-effective power supply configuration scheme can be identified, thereby reducing operating costs while meeting electricity demand. Select the configuration scheme with the best energy efficiency ratio to improve the energy efficiency of the entire charging station and reduce energy waste. Through reasonable configuration and management of power supply equipment, effectively respond to the challenges of peak power consumption periods, optimize power supply arrangements, and reduce the burden on the power grid. Selecting the optimal configuration scheme helps support the sustainable operation of charging stations, including the use of renewable energy and reducing environmental impact. Based on actual operating data and advanced simulation algorithms, the selected configuration scheme can flexibly adapt to the actual needs and changes of the charging station to ensure long-term effectiveness.

[0080] Through this series of comprehensive steps, charging stations can develop an economical and efficient power supply equipment configuration plan to effectively address various operational challenges and improve overall performance and sustainability.

[0081] In a specific embodiment, the power demand in the charging station is calculated using the following formula:

[0082] ;

[0083] ;

[0084] ;

[0085] Among them, the Indicates the number of charging piles in the charging station; Indicates the basic output power of the charging pile; represents the weight parameter of the ambient temperature; A weight parameter representing the usage frequency of the charging pile; Indicates time Ambient temperature; Indicates the total operating time of the charging station; Indicates the total electricity demand of the charging station in one day; represents the time variable; Indicates the usage frequency of the charging pile; Indicates the charging pile at time output power.

[0086] Specifically,

[0087] ;

[0088] ;

[0089] ;

[0090] Among them, the Indicates the number of charging piles in the charging station; Indicates the basic output power of the charging pile; represents the weight parameter of the ambient temperature; A weight parameter representing the usage frequency of the charging pile; Indicates time Ambient temperature; Indicates the total operating time of the charging station; Indicates the total electricity demand of the charging station in one day; represents the time variable; Indicates the usage frequency of the charging pile; Indicates the charging pile at time output power.

[0091] The above-mentioned technical features can achieve the following technical effects: By accurately calculating the power demand of the charging pile, the energy distribution and use of the charging station can be optimized to reduce energy waste. Fine control of power output based on ambient temperature and frequency of use can reduce operating costs and improve economic benefits. Adapting to ambient temperature and adjusting frequency of use can prevent equipment from overheating or overloading, thereby extending the service life of the charging pile and related equipment. Ensure that the charging pile adjusts the output power according to actual needs and environmental conditions, improve the user's charging experience, and avoid insufficient power supply during peak charging hours. Through such steps, the refinement of power management of charging stations can be achieved, charging efficiency and service quality can be improved, while reducing maintenance costs and improving the overall reliability of the system.

[0092] In a specific embodiment, the multi-dimensional analysis includes performance dimension analysis of power supply equipment, quality dimension analysis of power supply equipment, cost dimension analysis of power supply equipment, and safety dimension analysis of power supply equipment; wherein, the performance score is subjected to multi-dimensional analysis to obtain a multi-dimensional analysis result, and a corresponding adjustment strategy is obtained based on the multi-dimensional analysis result, including:

[0093] Performing a performance dimension analysis on the configuration scheme of the power supply equipment to obtain a performance dimension analysis result;

[0094] Performing a quality dimension analysis on the configuration scheme of the power supply equipment to obtain a quality dimension analysis result;

[0095] Performing a cost dimension analysis on the configuration scheme of the power supply equipment to obtain a cost dimension analysis result;

[0096] Performing a security dimension analysis on the configuration scheme of the power supply equipment to obtain a security dimension analysis result;

[0097] Based on the performance dimension analysis results, quality dimension analysis results, cost dimension analysis results and safety dimension analysis results, a corresponding adjustment strategy is obtained.

[0098] Specifically, evaluate the performance indicators of power supply equipment (such as transformers, charging piles, wind turbines and solar panels, etc.), including but not limited to power output stability, energy conversion efficiency, equipment operation continuity and reliability, etc. Obtain the performance dimension analysis results and point out the strengths and weaknesses of each equipment. Investigate the manufacturing quality, durability, maintenance frequency and failure rate of power supply equipment. Obtain the quality dimension analysis results and identify possible quality problems and room for improvement. Analyze the total cost of ownership (TCO) of power supply equipment, including initial investment, operation and maintenance costs, possible failure repair costs, etc. Obtain the cost dimension analysis results and highlight the equipment configuration options with high cost-effectiveness. Evaluate the safety risks of power supply equipment during operation, including electrical safety, equipment stability, and the ability to cope with extreme weather. Obtain the safety dimension analysis results and identify potential safety hazards and improvement measures. Based on the analysis results of the above four dimensions, a comprehensive set of power supply equipment configuration adjustment strategies are formulated by comprehensively considering performance, quality, cost and safety. The above adjustment strategies include upgrading certain equipment, replacing inefficient or high failure rate equipment, and optimizing the configuration layout of power supply equipment.

[0099] The above-mentioned technical features can achieve the following technical effects: Through detailed performance analysis and corresponding adjustment strategies, the operating efficiency and reliability of the power supply system can be significantly improved. Quality dimension analysis helps to identify and solve quality problems of power supply equipment and improve the stability and durability of the entire power supply system. Cost dimension analysis makes cost control more accurate, ensures maximum return on investment, and reduces unnecessary expenses. Safety dimension analysis strengthens the safety management of the power supply system, reduces accident risks, and protects the safety of equipment and personnel. Comprehensively considering the analysis results and adjustment strategies of various dimensions, the comprehensive optimization of the power supply equipment configuration can be achieved, and the efficiency and sustainability of the overall power supply system can be improved. Through such a comprehensive and detailed multi-dimensional analysis and adjustment process, the power supply equipment configuration of the charging station will be more scientific and reasonable, and can efficiently and stably meet the charging needs, while achieving maximum cost-effectiveness and safety assurance.

[0100] In a specific embodiment, the configuration scheme of the power supply device is modified based on the corresponding adjustment strategy to obtain a power supply device modification scheme, including:

[0101] Based on the corresponding adjustment strategy, the configuration scheme of the power supply device is modified in multiple dimensions to obtain a first modification scheme of the power supply device, a second modification scheme of the power supply device, and a third modification scheme of the power supply device; wherein the first modification scheme is a power supply capacity scheme of the power supply device, the second modification scheme is an efficiency scheme of the power supply device, and the third modification scheme is a failure rate scheme of the power supply device;

[0102] The first modification scheme and the second modification scheme are combined to obtain a first combination scheme, and the first combination scheme is encrypted by elliptic curve to obtain a first combination encryption scheme;

[0103] The first combined encryption scheme and the third modification scheme are combined to obtain a second combined scheme, and the second combined scheme is asymmetrically encrypted to obtain a power supply equipment modification scheme.

[0104] Specifically, based on the previous multi-dimensional analysis results, three main modification plans are formulated: power supply capacity plan (first modification plan), efficiency plan (second modification plan), and failure rate plan (third modification plan). Each plan targets different optimization goals and requirements of power supply equipment, such as improving power supply capacity, improving operating efficiency, and reducing failure rate. The first modification plan (power supply capacity plan) is combined with the second modification plan (efficiency plan) to form a first combination plan. This step aims to comprehensively consider the needs of power supply capacity and efficiency improvement. Elliptic curve encryption technology is applied to the first combination plan to obtain the first combination encryption plan. This step increases the security of the plan and protects the configuration plan from unauthorized access or modification. The first combination encryption plan encrypted by elliptic curve encryption is combined with the third modification plan (failure rate plan) to obtain the second combination plan. This step further integrates the consideration of failure rate optimization. Asymmetric encryption technology is applied to the second combination plan to obtain the final power supply equipment modification plan. Asymmetric encryption ensures the transmission and storage security of the configuration plan.

[0105] The following technical effects can be achieved through the above steps: By making special modifications and combinations of the power supply capacity, efficiency and failure rate of the power supply equipment, the power supply system is fully optimized and the overall performance and reliability are improved. The security of the power supply equipment configuration scheme is protected by the application of elliptic curve encryption and asymmetric encryption technology, and the leakage and unauthorized access of key information are avoided. The needs of improving power supply efficiency and reducing failure rate are comprehensively considered to ensure that the power supply system operates efficiently while minimizing the need for maintenance and fault handling. This process provides a variety of modification and combination schemes, increases adaptability to different operating conditions and changes, and provides support for the long-term development of charging stations. The application of encryption technology ensures the information security of the scheme and provides strong data protection for the entire optimization and adjustment process. Through this series of professional and complex steps, the power supply equipment configuration scheme of the charging station is not only optimized in performance, but also fully considered and guaranteed in terms of information security. Such a process ensures the efficient, stable and safe operation of the power supply system, and also reflects the in-depth understanding and utilization of advanced technology applications.

[0106] In a specific embodiment, the step of selecting the power supply configuration scheme with the highest efficiency score from the set of power supply configuration schemes for multi-objective optimization as the recommended scheme for power supply management of the charging station includes:

[0107] Constructing a metagraph based on the set of multi-objective optimized power supply configuration schemes; wherein the metagraph includes a plurality of nodes, one of the nodes is a multi-objective optimized power supply configuration scheme, and the attributes of the node include the cost of the configuration scheme, the resource consumption of the configuration scheme, and the score of the configuration scheme;

[0108] Calculate the cost similarity of each node in the meta-graph, generate a cost similarity value corresponding to each node, and sort each node based on the cost similarity value to obtain a cost sorting result;

[0109] Calculating resource consumption similarity for each node in the meta-graph, generating resource consumption similarity values ​​corresponding to each node, and sorting each node based on the resource consumption similarity values ​​to obtain a resource consumption sorting result;

[0110] Calculating the score similarity of each node in the meta-graph to generate a score similarity value corresponding to each node, and sorting each node based on the score similarity value to obtain a score sorting result;

[0111] Performing node search on the cost ranking results, resource consumption ranking results and score ranking results through a preset search model to obtain a recommended node; wherein the recommended node is the node that is at the front of the cost ranking results, resource consumption ranking results and score ranking results;

[0112] The recommended node is used as a recommended solution for power supply management of the charging station.

[0113] Specifically, the set of power supply configuration schemes for multi-objective optimization is converted into a metagraph structure, in which each node represents a configuration scheme, and the attributes of the node include the cost, resource consumption and score of the scheme. Cost similarity calculation is performed on each node in the metagraph to generate a cost similarity value for each node. All nodes are sorted according to the cost similarity value to obtain a cost sorting result. Resource consumption similarity calculation is performed to generate a corresponding resource consumption similarity value for each node. Nodes are sorted based on the resource consumption similarity value to generate a resource consumption sorting result. Score similarity calculation is performed on the nodes to generate a score similarity value. Nodes are sorted using the score similarity value to obtain a score sorting result. A preset search model is applied to comprehensively analyze the cost sorting, resource consumption sorting and score sorting results, and the node that appears at the top position in each sorting result is determined as a recommended node. The power supply configuration scheme corresponding to the recommended node is used as the recommended scheme for power supply management of the charging station.

[0114] The above steps can achieve the following technical effects: Through multi-dimensional similarity calculation and sorting, the performance of each power supply configuration scheme in terms of cost, resource consumption and overall score can be comprehensively evaluated to ensure that the recommended scheme has advantages in many aspects. Metagraph and similarity calculation can accurately match the needs of charging stations in terms of cost control, resource utilization and performance scoring, and improve the applicability and effectiveness of recommended schemes. Provide a data-driven decision support tool for charging station management to help managers identify and select the best scheme from a large number of configuration schemes. By selecting the best configuration scheme, charging stations can improve operational efficiency, optimize resource utilization, and ensure maximum cost-effectiveness. In this way, charging stations can more scientifically and systematically select the power supply configuration scheme that best meets their operational goals and conditions, thereby optimizing cost and resource utilization while ensuring power supply reliability, and improving overall operational efficiency and economic benefits.

[0115] The above describes the multi-objective optimization charging station power supply management method in the embodiment of the present invention. The following describes the multi-objective optimization charging station power supply management system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a multi-objective optimized charging station power supply management system includes:

[0116] An acquisition module 21 is used to acquire power demand and power management targets in the charging station, and construct a configuration scheme of the power supply equipment based on the power demand and power management targets; wherein the configuration scheme of the power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment;

[0117] An analysis module 22 is used to perform a multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain a multi-dimensional analysis result, and obtain a corresponding adjustment strategy based on the multi-dimensional analysis result;

[0118] A modification module 23, configured to modify the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes the coexistence of multiple optimized power supply configurations;

[0119] The optimization module 24 is used to continuously iterate and optimize the power supply equipment modification plan based on the preset target power demand and power management target until a predetermined number of iterations is reached to obtain a set of multi-objective optimized power supply configuration plans;

[0120] The recommendation module 25 is used to select the power supply configuration scheme with the highest efficiency score from the set of power supply configuration schemes for multi-objective optimization as the recommended scheme for power supply management of the charging station.

[0121] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0122] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0123] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0124] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0126] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0127] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-objective optimization charging station power supply management method, applied to the charging station power supply system; characterized in that: The following steps are involved: Obtaining power demand and power management targets in the charging station, and constructing a configuration scheme for power supply equipment based on the power demand and power management targets; wherein the configuration scheme for power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment; Perform multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain multi-dimensional analysis results, and obtain corresponding adjustment strategies based on the multi-dimensional analysis results; Modifying the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes coexistence of multiple optimized power supply configurations; Based on the preset target power demand and power management target, the power supply equipment modification plan is continuously iterated and optimized until the predetermined number of iterations is reached, and a set of multi-objective optimized power supply configuration plans is obtained; Select the power supply configuration scheme with the highest efficiency score from the set of multi-objective optimization power supply configuration schemes as the recommended scheme for power supply management of charging stations; Obtaining the power demand and power management target in the charging station, and building a configuration plan for the power supply equipment based on the power demand and power management target, including: Obtaining the number of charging piles, output power, and operating hours of the charging station, and obtaining the power demand in the charging station based on the number of charging piles, output power, and operating hours of the charging station; Obtaining a peak power consumption of the charging station, and obtaining a power management target based on the peak power consumption and the power demand; wherein the peak power consumption is the peak power consumption of the charging station within a time period; Constructing a plurality of different preliminary configuration schemes for power supply equipment based on the power demand and power management objectives; Simulating the plurality of different preliminary configuration schemes by using a preset simulation algorithm to obtain corresponding simulation results; The cost-effectiveness and energy efficiency ratio corresponding to each simulation result are obtained, and a plurality of the simulation results are eliminated in multiple stages based on the cost-effectiveness and energy efficiency ratio corresponding to each simulation result to obtain a configuration plan for the power supply equipment.

2. The multi-objective optimization charging station power supply management method according to claim 1 is characterized in that: The power supply equipment includes wind power generation equipment and solar power generation equipment.

3. The multi-objective optimization charging station power supply management method according to claim 1, characterized in that: The following formula is used to calculate the power demand in the charging station: ； ； ； Among them, the Indicates the number of charging piles in the charging station; Indicates the basic output power of the charging pile; represents the weight parameter of the ambient temperature; A weight parameter representing the usage frequency of the charging pile; Indicates time Ambient temperature; Indicates the total operating time of the charging station; Indicates the total electricity demand of the charging station in one day; represents the time variable; Indicates the usage frequency of the charging pile; Indicates the charging pile at time output power.

4. The multi-objective optimization charging station power supply management method according to claim 1, characterized in that: The multi-dimensional analysis includes performance dimensional analysis of the power supply equipment, quality dimensional analysis of the power supply equipment, cost dimensional analysis of the power supply equipment, and safety dimensional analysis of the power supply equipment; wherein, a multi-dimensional analysis is performed on the configuration scheme of the power supply equipment to obtain a multi-dimensional analysis result, and a corresponding adjustment strategy is obtained based on the multi-dimensional analysis result, including: Performing a performance dimension analysis on the configuration scheme of the power supply equipment to obtain a performance dimension analysis result; Performing a quality dimension analysis on the configuration scheme of the power supply equipment to obtain a quality dimension analysis result; Performing a cost dimension analysis on the configuration scheme of the power supply equipment to obtain a cost dimension analysis result; Performing a security dimension analysis on the configuration scheme of the power supply equipment to obtain a security dimension analysis result; Based on the performance dimension analysis results, quality dimension analysis results, cost dimension analysis results and safety dimension analysis results, a corresponding adjustment strategy is obtained.

5. The multi-objective optimization charging station power supply management method according to claim 1, characterized in that: The configuration scheme of the power supply device is modified based on the corresponding adjustment strategy to obtain a power supply device modification scheme, including: Based on the corresponding adjustment strategy, the configuration scheme of the power supply device is modified in multiple dimensions to obtain a first modification scheme of the power supply device, a second modification scheme of the power supply device, and a third modification scheme of the power supply device; wherein the first modification scheme is a power supply capacity scheme of the power supply device, the second modification scheme is an efficiency scheme of the power supply device, and the third modification scheme is a failure rate scheme of the power supply device; The first modification scheme and the second modification scheme are combined to obtain a first combination scheme, and the first combination scheme is encrypted by elliptic curve to obtain a first combination encryption scheme; The first combined encryption scheme and the third modification scheme are combined to obtain a second combined scheme, and the second combined scheme is asymmetrically encrypted to obtain a power supply equipment modification scheme.

6. The multi-objective optimization charging station power supply management method according to claim 1, characterized in that: The step of selecting the power supply configuration scheme with the highest efficiency score from the set of power supply configuration schemes for multi-objective optimization as the recommended scheme for power supply management of the charging station includes: Constructing a metagraph based on the set of multi-objective optimized power supply configuration schemes; wherein the metagraph includes a plurality of nodes, one of the nodes is a multi-objective optimized power supply configuration scheme, and the attributes of the node include the cost of the configuration scheme, the resource consumption of the configuration scheme, and the score of the configuration scheme; Calculate the cost similarity of each node in the meta-graph, generate a cost similarity value corresponding to each node, and sort each node based on the cost similarity value to obtain a cost sorting result; Calculating resource consumption similarity for each node in the meta-graph, generating resource consumption similarity values ​​corresponding to each node, and sorting each node based on the resource consumption similarity values ​​to obtain a resource consumption sorting result; Calculating the score similarity of each node in the meta-graph to generate a score similarity value corresponding to each node, and sorting each node based on the score similarity value to obtain a score sorting result; Performing node search on the cost ranking results, resource consumption ranking results and score ranking results through a preset search model to obtain a recommended node; wherein the recommended node is the node that is at the front of the cost ranking results, resource consumption ranking results and score ranking results; The recommended node is used as a recommended solution for power supply management of the charging station.

7. A multi-objective optimization charging station power supply management system, characterized in that: include: An acquisition module, used to acquire power demand and power management target in the charging station, and construct a configuration scheme of the power supply equipment based on the power demand and power management target; wherein the configuration scheme of the power supply equipment includes the allocated power and the scheduled power supply time of the power supply equipment; An analysis module is used to perform a multi-dimensional analysis on the configuration scheme of the power supply equipment through the trained multi-dimensional analysis model to obtain a multi-dimensional analysis result, and obtain a corresponding adjustment strategy based on the multi-dimensional analysis result; A modification module, used to modify the configuration scheme of the power supply device based on the corresponding adjustment strategy to obtain a power supply device modification scheme; wherein the power supply device modification scheme includes the coexistence of multiple optimized power supply configurations; An optimization module is used to continuously iterate and optimize the power supply equipment modification plan based on the preset target power demand and power management goals until a predetermined number of iterations is reached to obtain a set of multi-objective optimized power supply configuration plans; A recommendation module is used to select the power supply configuration scheme with the highest efficiency score from the set of power supply configuration schemes for multi-objective optimization as the recommended scheme for power supply management of the charging station; Obtaining the power demand and power management target in the charging station, and building a configuration plan for the power supply equipment based on the power demand and power management target, including: Obtaining the number of charging piles, output power, and operating hours of the charging station, and obtaining the power demand in the charging station based on the number of charging piles, output power, and operating hours of the charging station; Obtaining a peak power consumption of the charging station, and obtaining a power management target based on the peak power consumption and the power demand; wherein the peak power consumption is the peak power consumption of the charging station within a time period; Constructing a plurality of different preliminary configuration schemes for power supply equipment based on the power demand and power management objectives; Simulating the plurality of different preliminary configuration schemes by using a preset simulation algorithm to obtain corresponding simulation results; The cost-effectiveness and energy efficiency ratio corresponding to each simulation result are obtained, and a plurality of the simulation results are eliminated in multiple stages based on the cost-effectiveness and energy efficiency ratio corresponding to each simulation result to obtain a configuration plan for the power supply equipment.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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