Resource Allocation Method, Device, Electronic Device and Storage Medium

Abstract

The present invention discloses a resource allocation method, apparatus, electronic device and storage medium. The method includes: obtaining resource data of a target area within a preset time period, inputting the resource data into a preset resource allocation model to obtain n sets of resource allocation methods, determining the credibility values corresponding to each set of resource allocation methods among the n sets of resource allocation methods to obtain n credibility values, obtaining the cost values corresponding to each set of resource allocation methods among the n sets of resource allocation methods to obtain n cost values, determining the fitness values corresponding to each set of resource allocation methods among the n sets of resource allocation methods based on the n cost values and the n credibility values to obtain n fitness values, determining the maximum fitness value among the n fitness values, and determining the resource allocation method corresponding to the maximum fitness value to obtain the target resource allocation method. By adopting the implementation manner of the present application, the fitness of resource allocation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and particularly to a resource allocation method, device, electronic device and storage medium. Background Art

[0002] With the transformation of the energy structure and the rapid development of renewable energy, the power system faces greater volatility and uncertainty, bringing huge pressure to the power supply side. Most of the current resource allocation models are based on static historical data or simple empirical judgments, and it is difficult to capture the dynamic changes of the load in real time and accurately. In the process of formulating traditional resource allocation schemes, they often only focus on single-dimensional considerations, making the resource allocation schemes face many problems in actual implementation. Therefore, how to improve the adaptability of resource allocation is an urgent problem to be solved. Summary of the Invention

[0003] Embodiments of the present application provide a resource allocation method, device, electronic device and storage medium, which improve the adaptability of resource allocation.

[0004] In a first aspect, embodiments of the present application provide a resource allocation method, the method comprising:

[0005] Obtaining resource data of a target area within a preset time period;

[0006] Inputting the resource data into a preset resource allocation model to obtain n sets of resource allocation methods; n is an integer greater than 1, and each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values and energy storage load values;

[0007] Determining the credibility value corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n credibility values; the greater the credibility value of a resource allocation method, the more reliable the resource allocation method;

[0008] Obtaining the cost value corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n cost values;

[0009] Based on the n cost values and the n credibility values, determining the adaptability value corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n adaptability values;

[0010] Determining the maximum adaptability value among the n adaptability values;

[0011] Determining the resource allocation method corresponding to the maximum adaptability value to obtain the target resource allocation method.

[0012] Second aspect, an embodiment of the present application provides a resource allocation device, which includes: an acquisition unit and a processing unit;

[0013] The acquisition unit is configured to acquire resource data of a target area within a preset time period;

[0014] The processing unit is configured to input the resource data into a preset resource allocation model to obtain n sets of resource allocation methods; n is an integer greater than 1, and each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values;

[0015] Determine the credibility values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n credibility values; the greater the credibility value of a resource allocation method, the more reliable the resource allocation method;

[0016] Acquire the cost values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n cost values;

[0017] Based on the n cost values and the n credibility values, determine the fitness values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n fitness values;

[0018] Determine the maximum fitness value among the n fitness values;

[0019] Determine the resource allocation method corresponding to the maximum fitness value to obtain the target resource allocation method.

[0020] Third aspect, an embodiment of the present application provides an electronic device, which includes: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor so that the electronic device executes the method according to the first aspect.

[0021] Fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method according to the first aspect.

[0022] Fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that the computer executes the method according to the first aspect.

[0023] Implementing the embodiments of the present application has the following beneficial effects:

[0024] It can be seen that for the resource allocation method described in the embodiments of the present application, first, resource data of a target area within a preset time period is obtained, and then the resource data is input into a preset resource allocation model to obtain n sets of resource allocation methods. Each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values. Next, the credibility value corresponding to each set of resource allocation methods in the n sets of resource allocation methods is determined to obtain n credibility values, and the cost value corresponding to each set of resource allocation methods in the n sets of resource allocation methods is obtained to obtain n cost values. Then, based on the n cost values and the n credibility values, the fitness value corresponding to each set of resource allocation methods in the n sets of resource allocation methods is determined to obtain n fitness values. Finally, the maximum fitness value among the n fitness values is determined, and the resource allocation method corresponding to the maximum fitness value is determined to obtain the target resource allocation method, improving the fitness of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background art, the drawings required for use in the embodiments of the present application or the background art will be described below.

[0026] Figure 1 is a flowchart of a resource allocation method provided by an embodiment of the present application;

[0027] Figure 2 is a flowchart of determining the credibility value corresponding to the first set of resource allocation methods provided by an embodiment of the present application;

[0028] Figure 3 is a flowchart of determining the credibility value of the electric vehicle load provided by an embodiment of the present application;

[0029] Figure 4 is a flowchart of determining the credibility value of the electrolytic aluminum load provided by an embodiment of the present application;

[0030] Figure 5 is a schematic structural diagram of a precise coordination and optimization model for a multi-source resource aggregate provided by an embodiment of the present application;

[0031] Figure 6 is a flowchart of determining n fitness values provided by an embodiment of the present application;

[0032] Figure 7 is a schematic structural diagram of a resource allocation device provided by an embodiment of the application;

[0033] Figure 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application in combination with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of this application.

[0035] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0036] Referring to

[0037] Please refer to Figure 1 , Figure 1 which is a flowchart of a resource allocation method provided by an embodiment of this application, including but not limited to the following steps:

[0038] S101: Obtain resource data of the target area within a preset time period.

[0039] In this embodiment, the target area may be a specific geographical range such as a city, an industrial park, a power grid sub-region, etc., and the preset time period may be a time range such as one day, one month, one year, etc. Through various data collection means (such as sensor monitoring, data statistical reports, energy management system records, etc.), collect data related to resources in this area within the preset time period. These resource data may include but are not limited to the amount of energy generated (such as photovoltaic power generation), the amount of energy consumed (the electricity consumption of various loads), the amount of energy stored (the electricity of the energy storage system), etc.

[0040] S102: Input the resource data into a preset resource allocation model to obtain n sets of resource allocation methods.

[0041] In this embodiment, n is an integer greater than 1, and each of the n resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values.

[0042] In this embodiment, the preset resource model mainly includes a photovoltaic model, an electric vehicle model, an air conditioner model, an electrolytic aluminum load model, and an energy storage output model. Specifically, the photovoltaic output corresponding to the photovoltaic model satisfies the following function:

[0043]

[0044] Where, is the photovoltaic output, is the photovoltaic efficiency, is the area of the photovoltaic module, is the solar radiation, is the temperature attenuation coefficient, is the temperature corresponding to the current moment, is the reference temperature.

[0045] The upper and lower limits of the photovoltaic output satisfy the following function:

[0046]

[0047] Where, is the upper limit of the photovoltaic output.

[0048] The electric vehicle output corresponding to the electric vehicle model satisfies the following function:

[0049]

[0050] Where, is the electric vehicle output, represents the state of charge level desired by the vehicle owner, is the current state of charge level, is the average residence time of the electric vehicle at the charging station, is the increased charging time of the electric vehicle that the vehicle owner can tolerate, is the average capacity of the electric vehicle.

[0051] The electric vehicle constraint satisfies the following function:

[0052]

[0053] Where, is the equivalent load power of the electric vehicle at the current moment, is 0 or 1, is the current moment, is the charging power of the electric vehicle, is the discharge power of the electric vehicle, is the power of the electric vehicle at the current moment, is the upper limit of the electric vehicle power, is the lower limit of the electric vehicle power, the state of charge value of the electric vehicle at the current moment, is the minimum state of charge of the electric vehicle, is the maximum state of charge of the electric vehicle.

[0054] The air conditioner output corresponding to the air conditioner model satisfies the following function:

[0055]

[0056] is the cooling capacity of the air conditioner, , , , are all coefficients, is the electric power of the variable-frequency air conditioner.

[0057] The air conditioner output constraint satisfies the following function:

[0058]

[0059] Among them, is the temperature at the current moment, is the air conditioner output at the current moment, is the upper limit of the air conditioner temperature, is the lower limit of the air conditioner temperature, is the upper limit of the air conditioner output, is the lower limit of the air conditioner output.

[0060] The electrolytic aluminum load power corresponding to the electrolytic aluminum load model satisfies the following function:

[0061]

[0062] Among them, is the maximum electrolytic aluminum load power, is the voltage of the electrolytic cell, is the AC-side voltage, is the minimum on-load tap-changer ratio, is the minimum reactor voltage drop, is the expected voltage, is the resistance of the electrolytic cell, is the minimum electrolytic aluminum load power, is the maximum on-load tap-changer ratio, is the maximum reactor voltage drop.

[0063] The electrolytic aluminum constraint satisfies the following function:

[0064]

[0065] Among them, is the temperature of the electrolytic cell at the current moment, is the upper limit of the electrolytic cell temperature, is the lower limit of the electrolytic cell temperature, is the current of the electrolytic cell at the current moment, is the upper limit of the electrolytic cell current, is the lower limit of the electrolytic cell current, is the current of the electrolytic cell when not participating in regulation, , are both coefficients.

[0066] The energy storage output corresponding to the energy storage output model satisfies the following function:

[0067]

[0068] Among them, is the power of the battery energy storage at the current moment, is the self-discharge rate of the battery, is the power of the battery energy storage at the previous moment, is the charging power of the battery energy storage, is the discharging power of the battery energy storage, is the charging efficiency of the battery energy storage, is the discharging efficiency of the battery energy storage, is the time difference between the current moment and the previous moment.

[0069] The energy storage output constraint satisfies the following function:

[0070]

[0071] Among them, is 0 or 1, is the maximum charging power of the battery energy storage, is the minimum discharging power of the battery energy storage, is the state of charge value of the energy storage battery at the current moment, is the minimum state of charge of the energy storage battery, is the maximum state of charge of the energy storage battery.

[0072] Therefore, when the resource data is input into the preset resource allocation model, n sets of resource allocation methods are obtained, and each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values.

[0073] S103: Determine the credibility values corresponding to each of the n sets of resource allocation methods, obtaining n credibility values.

[0074] In this embodiment, the greater the credibility value of a resource allocation method, the more reliable the resource allocation method.

[0075] Please refer to Figure 2 , Figure 2 which is a flowchart for determining the credibility value corresponding to the first set of resource allocation methods provided by an embodiment of this application, including but not limited to the following steps:

[0076] S201: Determine the photovoltaic load credibility value, electric vehicle load credibility value, air conditioner load credibility value, and electrolytic aluminum load credibility value corresponding to the first set of resource allocation methods.

[0077] In this embodiment, the first set of resource allocation methods is any one of the n sets of resource allocation methods.

[0078] Exemplarily, determine the photovoltaic installed capacity and photovoltaic output power corresponding to the first set of resource allocation methods. Specifically, under the first set of resource allocation methods, it is necessary to clarify the installed capacity of the configured photovoltaic equipment, which refers to the rated power generation capacity of devices such as solar panels in a photovoltaic power station or photovoltaic system. At the same time, it is necessary to determine the power actually output by the photovoltaic system under this resource allocation method and current conditions such as illumination, that is, the photovoltaic output power. This power is affected by various factors such as illumination intensity, temperature, and the efficiency of photovoltaic cells. In actual operation, it is a dynamically changing value, and in actual operation, the power actually output by the photovoltaic system can be dynamically adjusted according to parameters such as illumination intensity and temperature.

[0079] Exemplarily, determine the photovoltaic load credibility value based on the photovoltaic installed capacity and the photovoltaic output power. Specifically, the photovoltaic load credibility value is used to measure the reliability of the power provided by the photovoltaic system to meet the load demand under a given resource allocation method. In this embodiment, the ratio of the photovoltaic output power to the installed capacity can be calculated. The closer this ratio is to 1, it indicates that the photovoltaic system can give full play to its installed capacity under the current conditions and provide stable and reliable power, and the photovoltaic load credibility value is higher. On the contrary, if the ratio is low, it may mean that the illumination conditions are poor or there are problems with the equipment, resulting in the photovoltaic system not being able to provide power as expected, and the photovoltaic load credibility value is low.

[0080] It should be noted that the PV credibility can also be determined by establishing a PV credibility model. Specifically, the operation data of a PV power station over a period of time is collected, including meteorological data such as actual output power, light intensity, temperature, and humidity, and equipment information such as the installed capacity of the PV power station, the type of PV modules, and the parameters of the inverters. A model is constructed based on the physical characteristics and power generation principle of PV cells, considering factors such as the photoelectric conversion efficiency of PV modules, temperature coefficient, and the relationship between light intensity and output power. Then, statistical analysis is performed using historical data to establish a statistical relationship between input variables (such as light intensity, temperature, etc.) and output variables (PV output power). Machine learning algorithms, such as neural networks and random forests, are used to learn and train a large amount of historical data to establish a more complex and accurate PV output prediction model. The collected historical data is divided into a training set and a test set. The constructed PV credibility model is trained using the training set data, and the parameters of the model are adjusted to enable it to better fit the training data. The validated model is used to predict the future output of the PV power station, and the predicted value is compared with the actual output value. Through multiple simulations or statistical analysis, the confidence interval of the PV output prediction value is determined. The higher the probability that the actual output value falls within the confidence interval, the higher the PV credibility.

[0081] Exemplarily, the average charging power and average charging duration of the electric vehicles corresponding to the first set of resource allocation methods are determined. Specifically, for the first set of resource allocation methods, to determine the average charging power of the electric vehicles during the charging process, it is considered that for different types of electric vehicles and different charging devices, the charging power may vary, and during the charging process, the charging power may also change due to factors such as battery state and charging strategy. Therefore, an average charging power needs to be calculated to represent the charging power level of the electric vehicles under this resource allocation method. At the same time, the average charging time of the electric vehicles needs to be determined, that is, the average time required from the start of charging to full charge or reaching a certain charging state. This time is also affected by various factors, such as battery capacity, initial battery charge, and charging power.

[0082] Exemplarily, based on the average charging power and average charging duration of the electric vehicles, the electric vehicle load credibility value is determined. Please refer to Figure 3 , Figure 3 which is a flowchart for determining the electric vehicle load credibility value provided by an embodiment of the present application, including but not limited to the following steps:

[0083] S301: Determine the difference between the average charging power of the electric vehicles and a preset charging power, and the difference between the average charging duration of the electric vehicles and a preset charging duration, to obtain a charging power difference and a charging duration difference.

[0084] In this embodiment, the preset charging power and the preset charging duration are standard values set in advance, which can be determined based on the design specifications of the electric vehicle and the rated power of the charging facility. Calculate the difference between the average charging power and the preset charging power, and the difference between the average charging duration and the preset charging duration respectively. These two differences respectively reflect the deviation degrees of the actual charging power and duration from the preset values, and the obtained results are respectively called the charging power difference and the charging duration difference.

[0085] S302: Determine the first deviation degree according to the charging power difference and the preset charging power.

[0086] In this embodiment, the charging power difference can be directly divided by the preset charging power, that is, the first deviation degree = (charging power difference ÷ preset charging power) × 100%; or the charging power difference and the preset charging power can be normalized first, and then the deviation degree is calculated; different weights can also be assigned to the charging power difference and the preset charging power, and then the first deviation degree is calculated; or it can be the relationship between the preset charging power difference and the deviation degree, and the first deviation degree can be determined based on this mapping relationship.

[0087] S303: Determine the second deviation degree according to the charging duration difference and the preset charging duration.

[0088] In this embodiment, the deviation degree can be presented in percentage form by calculating the ratio of the charging duration difference to the preset charging duration; or the weighted average method can be used to calculate the second deviation degree. Different weights are assigned to the charging duration difference and the preset charging duration respectively, and then the weighted deviation degree is calculated; the preset charging duration can also be divided into multiple intervals, and the second deviation degree is determined according to the interval where the charging duration difference is located; or it can be the mapping relationship between the preset charging duration difference and the deviation degree, and the second deviation degree can be determined based on this mapping relationship.

[0089] S304: Obtain the mapping relationship between the deviation degree and the credibility value to get the first mapping relationship.

[0090] In this embodiment, a large amount of data on electric vehicle charging needs to be collected, including the actual charging power, charging time, and the corresponding load credibility evaluation results under different scenarios. These data can be obtained through actual charging facility monitoring, user surveys, or simulation experiments. According to the results of data analysis, a mapping relationship between the deviation degree and the credibility value is established. If the data shows an approximately linear relationship between the deviation degree and the credibility value, a linear function can be used to establish the mapping. Alternatively, according to different ranges of the deviation degree, it can be divided into multiple intervals, each interval corresponding to a different range of credibility values or mapping functions. Machine learning algorithms, such as neural networks and decision trees, can also be used to train a large amount of data, enabling the model to automatically learn the complex mapping relationship between the deviation degree and the credibility value.

[0091] S305: Determine the first electric vehicle load credibility value corresponding to the first deviation degree and the second electric vehicle load credibility value corresponding to the second deviation degree based on the first mapping relationship.

[0092] In this embodiment, using the first mapping relationship obtained previously, substitute the calculated first deviation degree into it to find the corresponding first electric vehicle load credibility value. Similarly, substitute the second deviation degree into the first mapping relationship to determine the second electric vehicle load credibility value. These two credibility values respectively reflect the electric vehicle load credibility situation based on the deviation degrees of the charging power and charging duration.

[0093] S306: Determine the electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value.

[0094] In this embodiment, exemplarily, determine the reference electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value. Specifically, first determine the first weight corresponding to the first electric vehicle load credibility value and the second weight corresponding to the second electric vehicle load credibility value according to the mapping relationship between the preset electric vehicle load credibility value and the weight. Then, calculate the reference electric vehicle load credibility value according to the following formula:

[0095] Reference electric vehicle load credibility value = First electric vehicle load credibility value × First weight + Second electric vehicle load credibility value × Second weight;

[0096] According to the above formula, the reference electric vehicle load credibility value can be determined based on the first electric vehicle load credibility value and the second electric vehicle load credibility value.

[0097] Exemplarily, obtain the number of electric vehicle transactions in the target area during the historical time period, where the end time of the historical time period is earlier than the start time of the preset time period. Specifically, the number of electric vehicle transactions intuitively reflects the activity level of the electric vehicle market in the target area during the historical time period. A larger number of transactions means that more electric vehicles enter the usage link in the area, which may have a greater impact on the electric vehicle load in the area, and the usage and charging conditions of the vehicles may be more regular and predictable. On the contrary, a smaller number of transactions indicates a lower market activity level, a relatively smaller number of electric vehicles, and its load conditions may be more affected by other factors. By comparing with the preset number of transactions, it is decided whether to adjust the reference electric vehicle load credibility value based on the electric vehicle transaction rate or the charging pile usage frequency. When the number of transactions is greater than the preset number of transactions, it indicates that the market is active, and the transaction rate can better reflect the stability of the market and the usage conditions of electric vehicles. Then, the credibility value is adjusted through the first adjustment parameter. When the number of transactions is less than or equal to the preset number of transactions, the charging pile usage frequency becomes a more appropriate reference factor, and the credibility value is adjusted through the second adjustment parameter. In this way, according to different market activity levels, a more accurate adjustment method for reflecting the electric vehicle load credibility can be selected, making the finally determined electric vehicle load credibility value more in line with the actual situation.

[0098] Exemplarily, when the number of electric vehicle transactions is greater than the preset number of transactions, obtain the electric vehicle transaction rate in the target area during the historical time period. Specifically, the electric vehicle transaction rate refers to the ratio of the actual number of electric vehicle transactions to the total number of electric vehicle transaction intentions during the historical time period. The transaction rate can reflect the health level and smoothness of the electric vehicle market in this area.

[0099] Exemplarily, determine the first adjustment parameter corresponding to the electric vehicle transaction rate. Specifically, it can be a preset mapping relationship between the electric vehicle transaction rate and the adjustment parameter. Based on this mapping relationship, the first adjustment parameter corresponding to the electric vehicle transaction rate can be determined.

[0100] Exemplarily, adjust the reference electric vehicle load credibility value based on the first adjustment parameter to obtain the electric vehicle load credibility value. Specifically, calculate the electric vehicle load credibility value according to the following formula:

[0101] Electric vehicle load credibility value = reference electric vehicle load credibility value × (1 + first adjustment parameter);

[0102] According to the above formula, the reference electric vehicle load credibility value can be adjusted based on the first adjustment parameter to obtain the electric vehicle load credibility value.

[0103] Exemplarily, when the number of electric vehicle transactions is less than or equal to the preset number of transactions, the frequency of use of electric vehicle charging piles in the target area within the historical time period is obtained. Specifically, when the number of electric vehicle transactions is less than or equal to the preset number of transactions, it means that electric vehicle transactions in the area are not active enough. At this time, the reference electric vehicle load credibility value can be adjusted from another perspective, namely the frequency of use of electric vehicle charging piles.

[0104] Exemplarily, the second adjustment parameter corresponding to the usage frequency of the electric vehicle charging pile is determined. Specifically, it can be a mapping relationship between the preset usage frequency of the electric vehicle charging pile and the adjustment parameter. Based on the mapping relationship, the second adjustment parameter corresponding to the usage frequency of the electric vehicle charging pile can be determined.

[0105] Exemplarily, the reference electric vehicle load credibility value is adjusted based on the second adjustment parameter to obtain the electric vehicle load credibility value, and the electric vehicle load credibility value is specifically calculated according to the following formula:

[0106] Electric vehicle load credibility value = reference electric vehicle load credibility value × (1 + second adjustment parameter);

[0107] According to the above formula, the reference electric vehicle load credibility value can be adjusted based on the second adjustment parameter to obtain the electric vehicle load credibility value.

[0108] It can be seen that the first electric vehicle load credibility value is obtained based on the deviation of charging power, and the second electric vehicle load credibility value is obtained based on the deviation of charging time. Combining these two values ​​to determine the reference electric vehicle load credibility value can comprehensively consider the two key factors of power and time in the charging process, and more comprehensively reflect the difference between the actual charging behavior of electric vehicles and expectations, avoiding the one-sidedness caused by relying on a single factor evaluation. The number of electric vehicle transactions is used as the basis for judgment. According to the comparison results between the number of transactions and the preset number of transactions, different adjustment methods are used respectively. When the number of transactions is greater than the preset value, it means that the market is active, and the transaction rate can better reflect the market stability and vehicle usage. At this time, the credibility value is adjusted according to the transaction rate. When the number of transactions is less than or equal to the preset value, the frequency of charging pile use becomes a more appropriate reference factor. This flexible adjustment mechanism can adapt to the characteristics of electric vehicle loads under different market activity levels and improve the accuracy of the evaluation. Under different market activity levels, the use and charging modes of electric vehicles will be different. By distinguishing different situations for adjustment, these differences can be better captured, so that the final electric vehicle load credibility value can better reflect the actual operation and use of electric vehicles in the region, avoiding errors caused by unified standard evaluation.

[0109] It can be seen that by determining the difference between the average charging power of the electric vehicle and the preset charging power, and the difference between the average charging duration and the preset charging duration, two key factors of charging power and charging duration are comprehensively considered. These two factors can comprehensively reflect the actual situation of the electric vehicle charging process, avoiding the one-sidedness brought by evaluating based on a single factor. The first deviation degree and the second deviation degree are determined according to the power difference and the duration difference respectively, and the actual situation in the charging process is quantitatively compared with the preset standard. This quantitative method can accurately reflect the deviation degree of the electric vehicle charging behavior from the expectation in terms of power and duration, which helps to more accurately evaluate the uncertainty of the load, obtain the mapping relationship between the deviation degree and the credibility value, and determine the first electric vehicle load credibility value and the second electric vehicle load credibility value based on this. This mapping relationship converts the quantitative deviation degree into an intuitive credibility value, making the evaluation result more readable and understandable. Determining the final electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value integrates the information of both charging power and charging duration, providing a more comprehensive and accurate basis for decision-making such as power system planning and charging pile layout optimization.

[0110] Exemplarily, after determining the recovery duration required for the air-conditioning load response corresponding to the first set of resource allocation methods, the target recovery duration is obtained. Specifically, the recovery duration after the air-conditioning load response refers to the time required for the system to recover from the state of load change to the stable operation state when the load of the air-conditioning system changes. It is an important value to measure the performance of the air-conditioning system. The larger the air-conditioning load value, the greater the cooling or heating demand that the air-conditioning system needs to handle. Due to the large load it undertakes, the time required for the system to recover to the initial state or stable operation state may be longer. Under the first set of resource allocation methods, when the air-conditioning load responds, for example, due to reasons such as grid load adjustment, the air-conditioning system may take some measures to reduce the load, and then it is necessary to determine the time required for it to return to the normal operation state. This time is the target recovery duration, which reflects the recovery ability and characteristics of the air-conditioning load after being subjected to certain disturbances. By monitoring and analyzing the operation data of the air-conditioning system, or according to relevant technical parameters and empirical formulas, this target recovery duration can be determined.

[0111] Exemplarily, the air-conditioning load credibility value is determined based on the target recovery duration. Specifically, the shorter the target recovery duration, the faster the air-conditioning system can return to the normal operating state after being disturbed, indicating higher stability and reliability, and thus a higher air-conditioning load credibility value. Conversely, if the target recovery duration is longer, it may mean that there are some potential problems in the air-conditioning system, such as equipment aging, unreasonable control strategies, etc., resulting in poor recovery ability and thus a lower air-conditioning load credibility value. It can be a mapping relationship between the preset recovery duration and the air-conditioning load credibility value. Based on this mapping relationship, the air-conditioning load credibility value can be determined based on the target recovery duration.

[0112] Exemplarily, the electrolytic aluminum load response capacity and the electrolytic aluminum load response duration corresponding to the first set of resource allocation methods are determined. The electrolytic aluminum load response capacity refers to the magnitude of the load that can be adjusted according to the requirements of the power grid or production scheduling during the electrolytic aluminum production process. It reflects the adaptability of the electrolytic aluminum production system to load changes. The electrolytic aluminum load response duration refers to the time required from receiving the load adjustment instruction to actually completing the load adjustment. This time is very important for the stability of the power grid and the timeliness of scheduling. By monitoring the operating parameters of the electrolytic aluminum production equipment and analyzing the production process, these two key indicators can be determined.

[0113] Exemplarily, the electrolytic aluminum load credibility value is determined based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response duration. Please refer to Figure 4 , Figure 4 which is a flowchart for determining the electrolytic aluminum load credibility value provided by an embodiment of the present application, including but not limited to the following steps:

[0114] S401: Obtain the electrolytic aluminum load distribution data of the target area during the historical time period.

[0115] In this embodiment, relevant data on the load distribution during the electrolytic aluminum production process in the target area during a specific historical time period is collected. These data may include the magnitude of the electrolytic aluminum load at different historical time points during the historical time period, the distribution of the load among different production links or equipment, etc., which are the basis for subsequent analysis.

[0116] S402: Based on the electrolytic aluminum load distribution data, determine the maximum electrolytic aluminum load response capacity and the minimum electrolytic aluminum load response capacity, as well as the maximum electrolytic aluminum load response duration and the minimum electrolytic aluminum load response duration of the target area during the historical time period.

[0117] In this embodiment, by analyzing the collected electrolytic aluminum load distribution data, the maximum capacity and minimum capacity that the electrolytic aluminum load can respond to within the historical time period are found, as well as the longest time and shortest time required for load response. For example, in some special cases, the electrolytic aluminum production may need to quickly adjust the load to adapt to the changes in the power grid, and at this time, the maximum or minimum load response capacity and the corresponding response duration will appear.

[0118] S403: Determine the first electrolytic aluminum load credibility value based on the maximum electrolytic aluminum load response capacity, the minimum electrolytic aluminum load response capacity, and the electrolytic aluminum load response capacity.

[0119] In this embodiment, the calculation is specifically carried out according to the following formula:

[0120]

[0121] Wherein, is the first electrolytic aluminum load credibility value, is the electrolytic aluminum load response capacity, is the minimum electrolytic aluminum load response capacity, is the maximum electrolytic aluminum load response capacity.

[0122] S404: Determine the second electrolytic aluminum load credibility value based on the maximum electrolytic aluminum load response duration, the minimum electrolytic aluminum load response duration, and the electrolytic aluminum load response duration.

[0123] In this embodiment, the calculation is specifically carried out according to the following formula:

[0124]

[0125] Wherein, is the second electrolytic aluminum load credibility value, is the electrolytic aluminum load response duration, is the minimum electrolytic aluminum load response duration, is the maximum electrolytic aluminum load response duration.

[0126] S405: Determine the electrolytic aluminum load credibility value based on the first electrolytic aluminum load credibility value and the second electrolytic aluminum load credibility value.

[0127] In this embodiment, the mapping relationship between the electrolytic aluminum load credibility value and the weight can be obtained first. Based on this mapping relationship, the third weight corresponding to the first electrolytic aluminum load credibility value and the fourth weight corresponding to the second electrolytic aluminum load credibility value can be determined. Then, based on the third weight, the fourth weight, the first electrolytic aluminum load credibility value, and the second electrolytic aluminum load credibility value, the electrolytic aluminum load credibility value is determined. Then, the electrolytic aluminum load credibility value is specifically calculated according to the following formula:

[0128] Electrolytic aluminum load credibility value = First electrolytic aluminum load credibility value × Third weight + Second electrolytic aluminum load credibility value × Fourth weight;

[0129] According to the above formula, the electrolytic aluminum load credibility value can be determined based on the first electrolytic aluminum load credibility value and the second electrolytic aluminum load credibility value.

[0130] It can be seen that by obtaining the electrolytic aluminum load distribution data in the historical time period of the target area, the characteristics of the electrolytic aluminum load can be described from multiple aspects. It not only considers the change range of the load response capacity (the maximum and minimum electrolytic aluminum load response capacities), but also considers the change range of the load response duration (the maximum and minimum electrolytic aluminum load response durations), so as to more comprehensively understand the performance of the electrolytic aluminum load under different conditions, avoid the one-sidedness of evaluating the load credibility from a single dimension, and determine the final electrolytic aluminum load credibility value based on the credibility values of two dimensions, comprehensively considering the combined influence of the load response capacity and time on the electrolytic aluminum production process. This helps decision-makers more accurately grasp the actual situation of the electrolytic aluminum load, and thus make more reasonable and scientific decisions in aspects such as resource allocation, production plan adjustment, and power grid operation scheduling, improving the operation efficiency and stability of the entire system.

[0131] S202: Determine the first weight corresponding to the photovoltaic load credibility value, the second weight corresponding to the electric vehicle load credibility value, the third weight corresponding to the air conditioner load credibility value, and the fourth weight corresponding to the electrolytic aluminum load credibility value.

[0132] In this embodiment, the sum of the first weight, the second weight, the third weight, and the fourth weight is 1. The first weight corresponds to the credibility value of the photovoltaic load, the second weight corresponds to the credibility value of the electric vehicle load, the third weight corresponds to the credibility value of the air conditioner load, and the fourth weight corresponds to the credibility value of the electrolytic aluminum load. And the sum of these four weights is 1. This is to ensure that in the subsequent calculation of the overall credibility value, the influences of various loads can be reasonably integrated, and their weight distributions conform to the overall proportional relationship. The magnitudes of the weights can be determined according to actual situations and requirements. For example, if the importance of photovoltaic is relatively high in the current resource allocation scenario, then the first weight may be larger. It can be a preset mapping relationship between the credibility value and the weight. Based on this mapping relationship, the first weight corresponding to the credibility value of the photovoltaic load, the second weight corresponding to the credibility value of the electric vehicle load, the third weight corresponding to the credibility value of the air conditioner load, and the fourth weight corresponding to the credibility value of the electrolytic aluminum load can be determined.

[0133] S203: Determine the credibility value corresponding to the first set of resource allocation methods based on the first weight, the second weight, the third weight, the fourth weight, the credibility value of the photovoltaic load, the credibility value of the electric vehicle load, the credibility value of the air conditioner load, and the credibility value of the electrolytic aluminum load.

[0134] In this embodiment, the credibility value corresponding to the first set of resource allocation methods is specifically calculated according to the following formula:

[0135] Credibility value corresponding to the first set of resource allocation methods = Credibility value of photovoltaic load × First weight + Credibility value of electric vehicle load × Second weight + Credibility value of air conditioner load × Third weight + Credibility value of electrolytic aluminum load × Fourth weight;

[0136] According to the above formula, the credibility value corresponding to the first set of resource allocation methods can be determined based on the first weight, the second weight, the third weight, the fourth weight, the credibility value of the photovoltaic load, the credibility value of the electric vehicle load, the credibility value of the air conditioner load, and the credibility value of the electrolytic aluminum load.

[0137] It can be seen that different load types such as photovoltaic, electric vehicle, air conditioner, and electrolytic aluminum have different characteristics and influencing factors in the power system or resource allocation scenario. By separately determining their load credibility values, the impacts of various loads on the reliability of resource allocation methods can be comprehensively considered. Assigning corresponding weight values to each load credibility value can reflect the relative importance of different loads in the entire resource allocation. The credibility value corresponding to the first set of resource allocation methods finally obtained is a quantitative indicator that comprehensively considers various factors. This indicator can provide a clear reference basis for resource allocation decisions, helping decision-makers intuitively understand the reliability levels of different resource allocation methods, so as to compare and select among multiple options. The actual resource allocation scenario is often complex and changeable, and the characteristics and importance of different loads may change with factors such as time and environment. Through this flexible calculation method, the credibility values and weight values of various loads can be conveniently adjusted according to the actual situation to adapt to different scenarios and changes.

[0138] S104: Obtain the cost value corresponding to each set of resource allocation methods in the n sets of resource allocation methods, and obtain n cost values.

[0139] In this embodiment, the cost objective function satisfies the following function:

[0140]

[0141] Among them, is the cost value, is the initial moment of the current scheduling period, is the duration of the scheduling cycle, which can be 4h, is the selling price of electricity of the power grid, is the purchase price of electricity of the power grid, is the positive unbalance price coefficient, is the negative unbalance price coefficient, is the part where the actual interaction power with the power grid at the current moment is less than the reference value, and thus needs to be sold at a price lower than the selling electricity price, is the part where the actual interaction power with the power grid at the current moment is greater than the reference value, and thus needs to be purchased at a price higher than the purchase electricity price, is the cost of electric vehicles, is the cost of air conditioners, is the cost of energy storage, is the cost of electrolytic aluminum, is the cost of photovoltaic.

[0142] The photovoltaic cost satisfies the following function:

[0143]

[0144] Among them, is the fixed operation and maintenance cost coefficient, is the variable operation and maintenance cost coefficient, is the photovoltaic load value, is the photovoltaic output capacity.

[0145] The cost of electric vehicles satisfies the following function:

[0146]

[0147] where, is the energy consumption coefficient of electric vehicles, is the power of the electric vehicle at the current moment, positive for charging and negative for discharging.

[0148] The cost of air conditioners satisfies the following function:

[0149]

[0150] where, , , are the energy consumption cost coefficients of air conditioner loads, is the air conditioner load.

[0151] The cost of electrolytic aluminum satisfies the following function:

[0152]

[0153] where, , , are the energy consumption cost coefficients of electrolytic aluminum loads, is the electrolytic aluminum load.

[0154] The cost of energy storage satisfies the following function:

[0155]

[0156] where, is the unit price of operation and maintenance cost, is the charging power of battery energy storage, is the discharging power of battery energy storage.

[0157] According to the above cost objective function, the cost value corresponding to each group of resource allocation methods in the n groups of resource allocation methods can be determined, and n cost values are obtained.

[0158] It should be noted that in this embodiment, resource allocation can also be performed by determining a precise coordination and optimization model for a multi-resource aggregate. The precise coordination and optimization model for a multi-resource aggregate includes a resource credibility model and a resource cost model for resource allocation. Please refer to Figure 5 , Figure 5It is a schematic structural diagram of a precise coordination and optimization model for a multi - resource aggregate provided by an embodiment of the present application. The precise coordination and optimization model 500 of the multi - resource aggregate includes a resource credibility model 501 and a resource cost model 502. The resource credibility model 501 is used to evaluate the reliability of each resource to provide services or play a role as expected in a specific scenario, and the resource cost model 502 is used to measure the cost required to use each resource. By combining the resource credibility model and the resource cost model, the precise coordination and optimization model of the multi - resource aggregate can consider both the reliability of the resources and the cost of the resources when allocating resources.

[0159] S105: Determine the fitness value corresponding to each group of resource allocation methods among the n groups of resource allocation methods based on the n cost values and the n credibility values, and obtain n fitness values.

[0160] In this embodiment, please refer to Figure 6 , Figure 6 It is a flowchart for determining n fitness values provided by an embodiment of the present application, including but not limited to the following steps:

[0161] S601: Determine the cost value and the credibility value corresponding to the first group of resource allocation methods to obtain a first cost value and a first credibility value.

[0162] In this embodiment, by respectively determining the cost value and the credibility value corresponding to the first group of resource allocation methods, the first cost value and the first credibility value corresponding to the first group of resource allocation methods can be obtained.

[0163] S602: Determine the fifth weight value corresponding to the first cost value and the sixth weight value corresponding to the first credibility.

[0164] In this embodiment, it can be a preset mapping relationship between the cost value and the weight value. Based on this mapping relationship, the fifth weight value corresponding to the first cost value and the sixth weight value corresponding to the first credibility can be determined.

[0165] S603: Determine the reciprocal of the first cost value to obtain a first cost reference value.

[0166] In this embodiment, a mathematical operation is performed on the first cost value to take its reciprocal to obtain a new value, which is defined as the first cost reference value. The purpose of taking the reciprocal may be to convert the magnitude relationship of the cost value. Because when allocating resources, the smaller the cost and the larger the credibility, the better. For example, the larger the cost value originally represents the higher the cost, after taking the reciprocal, the first cost reference value will be smaller, so that the impact of the cost on the final result can be reflected in an inverse relationship in subsequent calculations.

[0167] S604: Calculate based on the first cost reference value, the first credibility value, the fifth weight, and the sixth weight to obtain a reference fitness value.

[0168] In this embodiment, specifically calculate the reference fitness value according to the following formula:

[0169] Reference fitness value = First cost reference value × Fifth weight + First credibility value × Sixth weight;

[0170] According to the above formula, it is possible to calculate based on the first cost reference value, the first credibility value, the fifth weight, and the sixth weight to obtain a reference fitness value.

[0171] Through this calculation, the two factors of cost and credibility are combined to initially obtain an index for measuring the adaptation degree of this resource allocation method.

[0172] S605: Obtain the energy storage load value corresponding to the first set of resource allocation methods to obtain a first energy storage load value.

[0173] In this embodiment, in the first set of resource allocation methods, for the part related to energy storage resources, through corresponding records or calculations, obtain the value corresponding to the energy storage load under this resource allocation method, that is, the first energy storage load value. This value reflects the load situation of the energy storage device under this resource allocation method, and may include relevant information such as the charge and discharge amount and remaining capacity of the energy storage device.

[0174] S606: Determine the target optimization factor corresponding to the first energy storage load value.

[0175] In this embodiment, it can be a preset mapping relationship between the energy storage load value and the optimization factor. Based on this mapping relationship, the target optimization factor corresponding to the first energy storage load value can be determined.

[0176] S607: Optimize the reference fitness value based on the target optimization factor to obtain the fitness value corresponding to the first set of resource allocation methods.

[0177] In this embodiment, specifically calculate the fitness value corresponding to the first set of resource allocation methods according to the following formula:

[0178] Fitness value corresponding to the first set of resource allocation methods = Reference fitness value × (1 + Target optimization factor);

[0179] According to the above formula, it is possible to optimize the reference fitness value based on the target optimization factor to obtain the fitness value corresponding to the first set of resource allocation methods.

[0180] It should be noted that the first set of resource allocation methods is any one of the n sets of resource allocation methods. Therefore, the fitness values corresponding to each set of the n sets of resource allocation methods can be determined according to the determination method of the fitness value corresponding to the first set of resource allocation methods, and n fitness values are obtained.

[0181] S106: Determine the maximum fitness value among the n fitness values.

[0182] In this embodiment, the n fitness values obtained previously are compared to find the one with the largest value. This maximum fitness value represents the relatively best performance considering both cost and credibility.

[0183] S107: Determine the resource allocation method corresponding to the maximum fitness value to obtain the target resource allocation method.

[0184] In this embodiment, since each fitness value corresponds to a set of resource allocation methods, after finding the maximum fitness value, the corresponding set of resource allocation methods is determined. This set of resource allocation methods is the most suitable solution for resource allocation in the target area within the preset time period considering both cost and credibility, and it is determined as the target resource allocation method to be used as the final basis for resource allocation decision-making.

[0185] It can be seen that different allocation methods can adapt to different requirements and scenario changes. By determining the credibility value for each set of resource allocation methods and quantifying the reliability of the resource allocation methods, the larger the credibility value, the more reliable it is. Through this quantitative evaluation, a solution with high reliability can be preferentially selected. By obtaining the cost value of each set of resource allocation methods and considering the economic factors in the resource allocation process, cost is one of the important consideration indicators in resource allocation decision-making. By comprehensively considering the cost value and the credibility value to determine the fitness value, a balance can be found between reliability and economy, avoiding the situation of only pursuing high reliability while ignoring cost, or only considering cost while sacrificing reliability, and maximizing the economic benefits of resource allocation. Based on the cost value and the credibility value to determine the fitness value, and finding the resource allocation method corresponding to the maximum fitness value as the target resource allocation method, a comprehensive evaluation of multiple resource allocation methods is realized. This comprehensive evaluation considers multiple key factors, can more comprehensively reflect the advantages and disadvantages of resource allocation methods, and the finally selected target resource allocation method is optimal in comprehensive performance, which helps to improve the efficiency and effect of resource allocation and make resources more reasonably and efficiently utilized.

[0186] In summary, implementing the embodiments of the present application has the following beneficial effects:

[0187] It can be seen that for the resource allocation method described in the embodiments of the present application, first, resource data of a target area within a preset time period is obtained, and then the resource data is input into a preset resource allocation model to obtain n sets of resource allocation methods. Each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values. Next, credibility values corresponding to each set of resource allocation methods in the n sets of resource allocation methods are determined to obtain n credibility values, and cost values corresponding to each set of resource allocation methods in the n sets of resource allocation methods are obtained to obtain n cost values. Then, based on the n cost values and the n credibility values, fitness values corresponding to each set of resource allocation methods in the n sets of resource allocation methods are determined to obtain n fitness values. Finally, the maximum fitness value among the n fitness values is determined, and the resource allocation method corresponding to the maximum fitness value is determined to obtain the target resource allocation method, improving the fitness of resource allocation.

[0188] Please refer to Figure 7 , Figure 7 FIG. is a schematic structural diagram of a resource allocation device provided by an embodiment of the present application. The resource allocation device 700 includes: an acquisition unit 701 and a processing unit 702;

[0189] The acquisition unit 701 is configured to acquire resource data of a target area within a preset time period;

[0190] The processing unit 702 is configured to input the resource data into a preset resource allocation model to obtain n sets of resource allocation methods; n is an integer greater than 1, and each resource allocation method in the n sets of resource allocation methods corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values;

[0191] Determine credibility values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n credibility values; the greater the credibility value of a resource allocation method, the more reliable the resource allocation method;

[0192] Obtain cost values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n cost values;

[0193] Based on the n cost values and the n credibility values, determine fitness values corresponding to each set of resource allocation methods in the n sets of resource allocation methods to obtain n fitness values;

[0194] Determine the maximum fitness value among the n fitness values;

[0195] Determine the resource allocation method corresponding to the maximum fitness value to obtain the target resource allocation method.

[0196] In some possible implementation manners, in terms of determining the credibility values corresponding to each group of resource allocation manners in the n groups of resource allocation manners, obtaining n credibility values, the processing unit 702 is specifically configured to:

[0197] Determine the photovoltaic load credibility value, electric vehicle load credibility value, air conditioner load credibility value, and electrolytic aluminum load credibility value corresponding to the first group of resource allocation manners; the first group of resource allocation manners is any group of resource allocation manners in the n groups of resource allocation manners;

[0198] Determine the first weight corresponding to the photovoltaic load credibility value, the second weight corresponding to the electric vehicle load credibility value, the third weight corresponding to the air conditioner load credibility value, and the fourth weight corresponding to the electrolytic aluminum load credibility value; the sum of the first weight, the second weight, the third weight, and the fourth weight is 1;

[0199] Based on the first weight, the second weight, the third weight, the fourth weight, the photovoltaic load credibility value, the electric vehicle load credibility value, the air conditioner load credibility value, and the electrolytic aluminum load credibility value, determine the credibility value corresponding to the first group of resource allocation manners.

[0200] In some possible implementation manners, in terms of determining the photovoltaic load credibility value, electric vehicle load credibility value, air conditioner load credibility value, and electrolytic aluminum load credibility value corresponding to the first group of resource allocation manners, the processing unit 702 is specifically configured to:

[0201] Determine the photovoltaic installed capacity and photovoltaic output power corresponding to the first group of resource allocation manners;

[0202] Based on the photovoltaic installed capacity and the photovoltaic output power, determine the photovoltaic load credibility value;

[0203] Determine the average charging power and average charging duration of electric vehicles corresponding to the first group of resource allocation manners;

[0204] Based on the average charging power of electric vehicles and the average charging duration of electric vehicles, determine the electric vehicle load credibility value;

[0205] Determine the recovery duration required after the response of the air conditioner load corresponding to the first group of resource allocation manners to obtain the target recovery duration;

[0206] Based on the target recovery duration, determine the air conditioner load credibility value;

[0207] Determine the electrolytic aluminum load response capacity and electrolytic aluminum load response duration corresponding to the first group of resource allocation manners;

[0208] The electrolytic aluminum load credibility value is determined based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response time.

[0209] In some possible implementations, in determining the electric vehicle load credibility value based on the average charging power of the electric vehicle and the average charging time of the electric vehicle, the processing unit 702 is specifically configured to:

[0210] Determine the difference between the average charging power of the electric vehicle and the preset charging power, and the difference between the average charging time of the electric vehicle and the preset charging time, to obtain the charging power difference and the charging time difference;

[0211] Determining a first deviation according to the charging power difference and the preset charging power;

[0212] Determining a second deviation according to the charging time difference and the preset charging time;

[0213] Acquire a mapping relationship between the deviation and the credibility value to obtain a first mapping relationship;

[0214] Determine a first electric vehicle load credibility value corresponding to the first deviation and a second electric vehicle load credibility value corresponding to the second deviation based on the first mapping relationship;

[0215] The electric vehicle load credibility value is determined based on the first electric vehicle load credibility value and the second electric vehicle load credibility value.

[0216] In some possible implementations, in determining the electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value, the processing unit 702 is specifically configured to:

[0217] Determining a reference electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value;

[0218] Acquire the number of electric vehicle transactions in the target area within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0219] When the number of electric vehicle transactions is greater than a preset number of transactions, obtaining the electric vehicle transaction rate of the target area within the historical time period;

[0220] Determining a first adjustment parameter corresponding to the electric vehicle transaction rate;

[0221] Adjusting the reference electric vehicle load credibility value based on the first adjustment parameter to obtain the electric vehicle load credibility value;

[0222] When the number of electric vehicle transactions is less than or equal to the preset number of transactions, obtain the usage frequency of electric vehicle charging piles in the target area during the historical time period;

[0223] Determine a second adjustment parameter corresponding to the usage frequency of the electric vehicle charging pile;

[0224] Adjust the reference electric vehicle load credibility value based on the second adjustment parameter to obtain the electric vehicle load credibility value.

[0225] In some possible implementation manners, in terms of determining the electrolytic aluminum load credibility value based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response duration, the processing unit 702 is specifically configured to:

[0226] Obtain the electrolytic aluminum load distribution data in the target area during the historical time period;

[0227] Based on the electrolytic aluminum load distribution data, determine the maximum electrolytic aluminum load response capacity and the minimum electrolytic aluminum load response capacity, as well as the maximum electrolytic aluminum load response duration and the minimum electrolytic aluminum load response duration in the target area during the historical time period;

[0228] Determine a first electrolytic aluminum load credibility value based on the maximum electrolytic aluminum load response capacity, the minimum electrolytic aluminum load response capacity, and the electrolytic aluminum load response capacity, and specifically calculate according to the following formula:

[0229]

[0230] where, is the first electrolytic aluminum load credibility value, is the electrolytic aluminum load response capacity, is the minimum electrolytic aluminum load response capacity, is the maximum electrolytic aluminum load response capacity;

[0231] Determine a second electrolytic aluminum load credibility value based on the maximum electrolytic aluminum load response duration, the minimum electrolytic aluminum load response duration, and the electrolytic aluminum load response duration, and specifically calculate according to the following formula:

[0232]

[0233] where, is the second electrolytic aluminum load credibility value, is the electrolytic aluminum load response duration, is the minimum electrolytic aluminum load response duration, is the maximum electrolytic aluminum load response duration;

[0234] Determine the electrolytic aluminum load credibility value based on the first electrolytic aluminum load credibility value and the second electrolytic aluminum load credibility value.

[0235] In some possible implementation manners, in terms of determining the fitness value corresponding to each group of resource allocation manners in the n groups of resource allocation manners based on the n cost values and the n credibility values, to obtain n fitness values, the processing unit 702 is specifically configured to:

[0236] Determine the cost value and the credibility value corresponding to the first group of resource allocation manners to obtain a first cost value and a first credibility value;

[0237] Determine a fifth weight value corresponding to the first cost value and a sixth weight value corresponding to the first credibility;

[0238] Determine the reciprocal of the first cost value to obtain a first cost reference value;

[0239] Perform calculations based on the first cost reference value, the first credibility value, the fifth weight value, and the sixth weight value to obtain a reference fitness value;

[0240] Obtain the energy storage load value corresponding to the first group of resource allocation manners to obtain a first energy storage load value;

[0241] Determine a target optimization factor corresponding to the first energy storage load value;

[0242] Optimize the reference fitness value based on the target optimization factor to obtain the fitness value corresponding to the first group of resource allocation manners.

[0243] Please refer to Figure 8 , Figure 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 8 shown, the electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. They are connected through a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for performing the following steps:

[0244] Obtain the resource data of the target area within a preset time period;

[0245] Input the resource data into a preset resource allocation model to obtain n groups of resource allocation manners; n is an integer greater than 1, and each resource allocation manner in the n groups of resource allocation manners corresponds to different photovoltaic load values, electric vehicle load values, air conditioner load values, electrolytic aluminum load values, and energy storage load values;

[0246] Determine the credibility values corresponding to each of the n sets of resource allocation methods, obtaining n credibility values; the greater the credibility value of a resource allocation method, the more reliable the resource allocation method.

[0247] Obtain the cost values corresponding to each of the n sets of resource allocation methods, obtaining n cost values.

[0248] Based on the n cost values and the n credibility values, determine the fitness values corresponding to each of the n sets of resource allocation methods, obtaining n fitness values.

[0249] Determine the maximum fitness value among the n fitness values.

[0250] Determine the resource allocation method corresponding to the maximum fitness value, obtaining the target resource allocation method.

[0251] In some possible implementation manners, in terms of determining the credibility values corresponding to each of the n sets of resource allocation methods, obtaining n credibility values, the above program includes instructions for performing the following steps:

[0252] Determine the photovoltaic load credibility value, electric vehicle load credibility value, air conditioner load credibility value, and electrolytic aluminum load credibility value corresponding to the first set of resource allocation methods; the first set of resource allocation methods is any one of the n sets of resource allocation methods.

[0253] Determine the first weight corresponding to the photovoltaic load credibility value, the second weight corresponding to the electric vehicle load credibility value, the third weight corresponding to the air conditioner load credibility value, and the fourth weight corresponding to the electrolytic aluminum load credibility value; the sum of the first weight, the second weight, the third weight, and the fourth weight is 1.

[0254] Based on the first weight, the second weight, the third weight, the fourth weight, the photovoltaic load credibility value, the electric vehicle load credibility value, the air conditioner load credibility value, and the electrolytic aluminum load credibility value, determine the credibility value corresponding to the first set of resource allocation methods.

[0255] In some possible implementation manners, in terms of determining the photovoltaic load credibility value, electric vehicle load credibility value, air conditioner load credibility value, and electrolytic aluminum load credibility value corresponding to the first set of resource allocation methods, the above program includes instructions for performing the following steps:

[0256] Determine the photovoltaic installed capacity and photovoltaic output power corresponding to the first set of resource allocation methods.

[0257] Determine the photovoltaic load credibility value based on the photovoltaic installed capacity and the photovoltaic output power;

[0258] Determine the average charging power and the average charging duration of the electric vehicles corresponding to the first set of resource allocation methods;

[0259] Determine the electric vehicle load credibility value based on the average charging power and the average charging duration of the electric vehicles;

[0260] Determine the required recovery duration after the air-conditioning load responds corresponding to the first set of resource allocation methods to obtain the target recovery duration;

[0261] Determine the air-conditioning load credibility value based on the target recovery duration;

[0262] Determine the load response capacity and the load response duration of the electrolytic aluminum corresponding to the first set of resource allocation methods;

[0263] Determine the electrolytic aluminum load credibility value based on the load response capacity and the load response duration of the electrolytic aluminum.

[0264] In some possible implementation manners, in terms of determining the electric vehicle load credibility value based on the average charging power and the average charging duration of the electric vehicles, the above program includes instructions for performing the following steps:

[0265] Determine the difference between the average charging power of the electric vehicles and the preset charging power, and the difference between the average charging duration of the electric vehicles and the preset charging duration to obtain the charging power difference and the charging duration difference;

[0266] Determine the first deviation degree according to the charging power difference and the preset charging power;

[0267] Determine the second deviation degree according to the charging duration difference and the preset charging duration;

[0268] Obtain the mapping relationship between the deviation degree and the credibility value to get the first mapping relationship;

[0269] Determine the first electric vehicle load credibility value corresponding to the first deviation degree and the second electric vehicle load credibility value corresponding to the second deviation degree based on the first mapping relationship;

[0270] Determine the electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value.

[0271] In some possible embodiments, in determining the electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value, the above program includes instructions for performing the following steps:

[0272] Determine a reference electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value;

[0273] Obtain the number of electric vehicle transactions in the target area during the historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0274] When the number of electric vehicle transactions is greater than the preset number of transactions, obtain the electric vehicle transaction success rate in the target area during the historical time period;

[0275] Determine a first adjustment parameter corresponding to the electric vehicle transaction success rate;

[0276] Adjust the reference electric vehicle load credibility value based on the first adjustment parameter to obtain the electric vehicle load credibility value;

[0277] When the number of electric vehicle transactions is less than or equal to the preset number of transactions, obtain the usage frequency of electric vehicle charging piles in the target area during the historical time period;

[0278] Determine a second adjustment parameter corresponding to the usage frequency of electric vehicle charging piles;

[0279] Adjust the reference electric vehicle load credibility value based on the second adjustment parameter to obtain the electric vehicle load credibility value.

[0280] In some possible embodiments, in determining the electrolytic aluminum load credibility value based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response duration, the above program includes instructions for performing the following steps:

[0281] Obtain the electrolytic aluminum load distribution data in the target area during the historical time period;

[0282] Based on the electrolytic aluminum load distribution data, determine the maximum electrolytic aluminum load response capacity and the minimum electrolytic aluminum load response capacity, as well as the maximum electrolytic aluminum load response duration and the minimum electrolytic aluminum load response duration in the target area during the historical time period;

[0283] Determine a first electrolytic aluminum load credibility value based on the maximum electrolytic aluminum load response capacity, the minimum electrolytic aluminum load response capacity, and the electrolytic aluminum load response capacity, specifically calculated according to the following formula:

[0284]

[0285] Among them, is the credibility value of the first electrolytic aluminum load, is the response capacity of the electrolytic aluminum load, is the minimum response capacity of the electrolytic aluminum load, is the maximum response capacity of the electrolytic aluminum load;

[0286] Based on the maximum electrolytic aluminum load response duration, the minimum electrolytic aluminum load response duration, and the electrolytic aluminum load response duration, a second electrolytic aluminum load credibility value is determined, and the specific calculation is carried out according to the following formula:

[0287]

[0288] Among them, is the second electrolytic aluminum load credibility value, is the electrolytic aluminum load response duration, is the minimum electrolytic aluminum load response duration, is the maximum electrolytic aluminum load response duration;

[0289] Based on the first electrolytic aluminum load credibility value and the second electrolytic aluminum load credibility value, the electrolytic aluminum load credibility value is determined.

[0290] In some possible implementation manners, in terms of determining the fitness value corresponding to each group of resource allocation manners in the n groups of resource allocation manners based on the n cost values and the n credibility values, the above program includes instructions for performing the following steps:

[0291] Determine the cost value and credibility value corresponding to the first group of resource allocation manners to obtain a first cost value and a first credibility value;

[0292] Determine the fifth weight value corresponding to the first cost value and the sixth weight value corresponding to the first credibility;

[0293] Determine the reciprocal of the first cost value to obtain a first cost reference value;

[0294] Based on the first cost reference value, the first credibility value, the fifth weight value, and the sixth weight value, perform calculations to obtain a reference fitness value;

[0295] Obtain the energy storage load value corresponding to the first group of resource allocation manners to obtain a first energy storage load value;

[0296] Determine the target optimization factor corresponding to the first energy storage load value;

[0297] Optimize the reference fitness value based on the target optimization factor to obtain the fitness value corresponding to the first set of resource allocation manners.

[0298] It should be understood that the electronic devices in the present application may include a resource allocation device, a smart phone (such as an Android phone, an iOS phone, a Windows Phone, etc.), a tablet computer, a handheld computer, a laptop computer, a mobile Internet device MID (Mobile Internet Devices), a wearable device, or a server, an edge computing node, etc. The above-mentioned electronic devices are only examples, not an exhaustive list, including but not limited to the above-mentioned electronic devices.

[0299] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement some or all of the steps of any one of the resource allocation methods described in the above method embodiments.

[0300] An embodiment of the present application further provides a computer program product including a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any one of the resource allocation methods described in the above method embodiments.

[0301] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps may be in other sequences or performed simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0302] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0303] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the device or unit may be in an electrical or other form.

[0304] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0305] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software program modules.

[0306] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of this application. The aforementioned memory includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other media that can store program codes.

[0307] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory. The memory can include: flash drives, read-only memories (abbreviation: ROM), random access memories (abbreviation: RAM), magnetic disks, or optical discs, etc.

[0308] The above has introduced the embodiments of this application in detail. Specific examples are used in this article to elaborate on the principles and embodiments of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific embodiments and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A resource allocation method, characterized in that: include: Obtain resource data of the target area within a preset time period; Input the resource data into a preset resource allocation model to obtain n groups of resource allocation methods; n is an integer greater than 1, and each resource allocation method in the n groups of resource allocation methods corresponds to a different photovoltaic load value, electric vehicle load value, air conditioning load value, electrolytic aluminum load value, and energy storage load value; Determine the credibility value corresponding to each of the n groups of resource allocation modes to obtain n credibility values; the greater the credibility value of a resource allocation mode, the more reliable the resource allocation mode; Obtaining a cost value corresponding to each of the n groups of resource allocation modes to obtain n cost values; Determine the fitness value corresponding to each of the n groups of resource allocation modes based on the n cost values ​​and the n credibility values, and obtain n fitness values; Determining the maximum fitness value among the n fitness values; Determine the resource allocation mode corresponding to the maximum fitness value to obtain a target resource allocation mode; The step of determining the credibility value corresponding to each of the n groups of resource allocation modes to obtain n credibility values ​​includes: Determine the photovoltaic load credibility value, electric vehicle load credibility value, air conditioning load credibility value and electrolytic aluminum load credibility value corresponding to the first group of resource allocation methods; the first group of resource allocation methods is any one of the n groups of resource allocation methods; Determine a first weight corresponding to the photovoltaic load credibility value, a second weight corresponding to the electric vehicle load credibility value, a third weight corresponding to the air conditioning load credibility value, and a fourth weight corresponding to the electrolytic aluminum load credibility value; the sum of the first weight, the second weight, the third weight, and the fourth weight is 1; Determine the credibility value corresponding to the first group of resource allocation methods based on the first weight, the second weight, the third weight, the fourth weight, the photovoltaic load credibility value, the electric vehicle load credibility value, the air conditioning load credibility value, and the electrolytic aluminum load credibility value; The step of determining the fitness value corresponding to each of the n groups of resource allocation modes based on the n cost values ​​and the n credibility values ​​to obtain n fitness values ​​includes: Determine a cost value and a credibility value corresponding to the first group of resource allocation modes to obtain a first cost value and a first credibility value; Determine a fifth weight corresponding to the first cost value and a sixth weight corresponding to the first credibility value; determining the reciprocal of the first cost value to obtain a first cost reference value; Calculate based on the first cost reference value, the first credibility value, the fifth weight and the sixth weight to obtain a reference fitness value; Obtaining an energy storage load value corresponding to the first group of resource allocation methods to obtain a first energy storage load value; Determining a target optimization factor corresponding to the first energy storage load value; The reference fitness value is optimized based on the target optimization factor to obtain the fitness value corresponding to the first group of resource allocation methods.

2. The method according to claim 1, characterized in that The step of determining the photovoltaic load credibility value, the electric vehicle load credibility value, the air conditioning load credibility value, and the electrolytic aluminum load credibility value corresponding to the first group of resource allocation methods includes: Determine the photovoltaic installed capacity and photovoltaic output power corresponding to the first group of resource allocation methods; Determining the photovoltaic load credibility value based on the photovoltaic installed capacity and the photovoltaic output power; Determine an average charging power of electric vehicles and an average charging time of electric vehicles corresponding to the first group of resource allocation methods; Determining the electric vehicle load credibility value based on the average charging power of the electric vehicle and the average charging time of the electric vehicle; Determine the required recovery time after the air conditioning load corresponding to the first group of resource allocation modes responds, and obtain a target recovery time; Determining the air conditioning load credibility value based on the target recovery time; Determine the electrolytic aluminum load response capacity and electrolytic aluminum load response duration corresponding to the first group of resource allocation methods; The electrolytic aluminum load credibility value is determined based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response time.

3. The method according to claim 2, characterized in that The determining the electric vehicle load credibility value based on the average charging power of the electric vehicle and the average charging time of the electric vehicle includes: Determine the difference between the average charging power of the electric vehicle and the preset charging power, and the difference between the average charging time of the electric vehicle and the preset charging time, to obtain the charging power difference and the charging time difference; Determining a first deviation according to the charging power difference and the preset charging power; Determining a second deviation according to the charging time difference and the preset charging time; Acquire a mapping relationship between the deviation and the credibility value to obtain a first mapping relationship; Determine a first electric vehicle load credibility value corresponding to the first deviation and a second electric vehicle load credibility value corresponding to the second deviation based on the first mapping relationship; The electric vehicle load credibility value is determined based on the first electric vehicle load credibility value and the second electric vehicle load credibility value.

4. The method according to claim 3, characterized in that The determining the electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value includes: Determining a reference electric vehicle load credibility value based on the first electric vehicle load credibility value and the second electric vehicle load credibility value; Acquire the number of electric vehicle transactions in the target area within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period; When the number of electric vehicle transactions is greater than a preset number of transactions, obtaining the electric vehicle transaction rate of the target area within the historical time period; Determining a first adjustment parameter corresponding to the electric vehicle transaction rate; Adjusting the reference electric vehicle load credibility value based on the first adjustment parameter to obtain the electric vehicle load credibility value; When the number of electric vehicle transactions is less than or equal to the preset number of transactions, obtaining the usage frequency of the electric vehicle charging piles in the target area within the historical time period; Determine a second adjustment parameter corresponding to the usage frequency of the electric vehicle charging pile; The reference electric vehicle load credibility value is adjusted based on the second adjustment parameter to obtain the electric vehicle load credibility value.

5. The method according to claim 4, characterized in that The determining the electrolytic aluminum load credibility value based on the electrolytic aluminum load response capacity and the electrolytic aluminum load response duration includes: Acquire the electrolytic aluminum load distribution data of the target area within the historical time period; Determine the maximum and minimum electrolytic aluminum load response capacity, as well as the maximum and minimum electrolytic aluminum load response time of the target area within the historical time period based on the electrolytic aluminum load distribution data; The first electrolytic aluminum load credibility value is determined based on the maximum electrolytic aluminum load response capacity, the minimum electrolytic aluminum load response capacity and the electrolytic aluminum load response capacity, and is specifically calculated according to the following formula: in, is the first electrolytic aluminum load credibility value, is the electrolytic aluminum load response capacity, is the minimum electrolytic aluminum load response capacity, is the maximum electrolytic aluminum load response capacity; The second electrolytic aluminum load credibility value is determined based on the maximum electrolytic aluminum load response time, the minimum electrolytic aluminum load response time and the electrolytic aluminum load response time, and is specifically calculated according to the following formula: in, is the second electrolytic aluminum load credibility value, is the electrolytic aluminum load response time, is the minimum electrolytic aluminum load response time, is the maximum electrolytic aluminum load response time; The electrolytic aluminum load credibility value is determined based on the first electrolytic aluminum load credibility value and the second electrolytic aluminum load credibility value.

6. A resource allocation device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire resource data of the target area within a preset time period; The processing unit is used to input the resource data into a preset resource allocation model to obtain n groups of resource allocation methods; n is an integer greater than 1, and each resource allocation method in the n groups of resource allocation methods corresponds to a different photovoltaic load value, electric vehicle load value, air conditioning load value, electrolytic aluminum load value and energy storage load value; Determine the credibility value corresponding to each of the n groups of resource allocation modes to obtain n credibility values; the greater the credibility value of a resource allocation mode, the more reliable the resource allocation mode; Obtaining a cost value corresponding to each of the n groups of resource allocation modes to obtain n cost values; Determine the fitness value corresponding to each of the n groups of resource allocation modes based on the n cost values ​​and the n credibility values, and obtain n fitness values; Determining the maximum fitness value among the n fitness values; Determine the resource allocation mode corresponding to the maximum fitness value to obtain a target resource allocation mode; The step of determining the credibility value corresponding to each of the n groups of resource allocation modes to obtain n credibility values ​​includes: Determine the photovoltaic load credibility value, electric vehicle load credibility value, air conditioning load credibility value and electrolytic aluminum load credibility value corresponding to the first group of resource allocation methods; the first group of resource allocation methods is any one of the n groups of resource allocation methods; Determine a first weight corresponding to the photovoltaic load credibility value, a second weight corresponding to the electric vehicle load credibility value, a third weight corresponding to the air conditioning load credibility value, and a fourth weight corresponding to the electrolytic aluminum load credibility value; the sum of the first weight, the second weight, the third weight, and the fourth weight is 1; Determine the credibility value corresponding to the first group of resource allocation methods based on the first weight, the second weight, the third weight, the fourth weight, the photovoltaic load credibility value, the electric vehicle load credibility value, the air conditioning load credibility value, and the electrolytic aluminum load credibility value; The step of determining the fitness value corresponding to each of the n groups of resource allocation modes based on the n cost values ​​and the n credibility values ​​to obtain n fitness values ​​includes: Determine a cost value and a credibility value corresponding to the first group of resource allocation modes to obtain a first cost value and a first credibility value; Determine a fifth weight corresponding to the first cost value and a sixth weight corresponding to the first credibility value; determining the reciprocal of the first cost value to obtain a first cost reference value; Calculate based on the first cost reference value, the first credibility value, the fifth weight and the sixth weight to obtain a reference fitness value; Obtaining an energy storage load value corresponding to the first group of resource allocation methods to obtain a first energy storage load value; Determining a target optimization factor corresponding to the first energy storage load value; The reference fitness value is optimized based on the target optimization factor to obtain the fitness value corresponding to the first group of resource allocation methods.

7. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

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