Method and apparatus for determining operation parameters of power proxy object, and electronic device

Abstract

The application discloses a kind of electric power agent object's operation parameter determination method, device and electronic equipment.Therein, the method includes: obtaining the area characteristic factor of the first preset area of electric power agent object to be carried out electric power operation, obtain first characteristic factor set;First characteristic factor set is based on the user electricity consumption data of first preset area is predicted, obtain first electricity consumption data;The revenue target function of electric power agent object is constructed to the electric power operation of first preset area, wherein, revenue target function is used to indicate the function relationship established with the income of electric power agent object as target;Based on first electricity consumption data and revenue target function, the operation parameter of electric power agent object is determined, wherein, operation parameter at least includes purchase power and selling price, purchase power is used to indicate the purchase quantity of electric power agent object to power market.The present application solves the technical problem that the efficiency of determining the operation parameter in the operation process of electric power agent object in the related art is relatively low.

Description

Technical Field

[0001] This invention relates to the field of power operation and management, and more specifically, to a method, apparatus, and electronic device for determining the operating parameters of a power agency object. Background Technology

[0002] Determining the operational parameters of the electricity agency is crucial. These parameters may include, but are not limited to, electricity prices, market size, market demand, and market competition. They have a significant impact on the operation of the electricity agency. By determining these operational parameters, the electricity agency can better understand the market environment and improve its market competitiveness and electricity service level.

[0003] Currently, determining the operational parameters of power agency objects in related technologies usually requires the help of relevant technical personnel or experts, which is highly dependent on human factors. The determined operational parameters of power agency objects have low matching with the area to be operated, resulting in low efficiency in determining the operational parameters of power agency objects during operation.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for determining the operating parameters of a power agent object, in order to at least solve the technical problem of low efficiency in determining the operating parameters of a power agent object during its operation in related technologies.

[0006] According to one aspect of the present invention, a method for determining the operating parameters of an electricity agent is provided, comprising: acquiring regional characteristic factors of a first preset area where the electricity agent is to conduct electricity operation, thereby obtaining a first set of characteristic factors; predicting user electricity consumption data of the first preset area based on the first set of characteristic factors, thereby obtaining first electricity consumption data; constructing a revenue objective function for the electricity agent's operation of electricity in the first preset area, wherein the revenue objective function represents a functional relationship established with the revenue of the electricity agent as the objective; and determining the operating parameters of the electricity agent based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, wherein the amount of electricity purchased represents the quantity of electricity purchased by the electricity agent from the electricity market, and the electricity selling price represents the price at which the electricity agent sells electricity to the first preset area.

[0007] Optionally, predicting user electricity consumption data in a first preset area based on a first set of feature factors to obtain first electricity consumption data includes: acquiring regional feature factors of multiple second preset areas to obtain a second set of feature factors, wherein the power operation time of any region in the multiple second preset areas is longer than the power operation time of the first preset area; selecting at least one target area from the multiple second preset areas based on the first set of feature factors and the second set of feature factors; and predicting user electricity consumption data in the first preset area based on the target electricity consumption data of the target area to obtain first electricity consumption data.

[0008] Optionally, selecting at least one target region from multiple second preset regions based on a first feature set and a second feature set includes: constructing a first feature matrix based on the first feature set and constructing a second feature matrix based on the second feature set; and selecting the target region from multiple second preset regions based on the first feature matrix and the second feature matrix using a preset region similarity evaluation model, wherein the preset region similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between regions.

[0009] Optionally, based on the target electricity consumption data of the target area, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data, including: obtaining the regional feature difference coefficient and the user feature difference coefficient between the first preset area and the target area, wherein the regional feature difference coefficient is used to represent the weight correction coefficient obtained by quantifying the regional characteristic difference between the first preset area and the target area, and the user feature difference coefficient is used to represent the weight correction coefficient obtained by quantifying the user characteristic difference between the first preset area and the target area; based on the regional feature difference coefficient and the user feature difference coefficient, the target electricity consumption data is corrected to obtain the corrected target electricity consumption data; based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data.

[0010] Optionally, based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data, including: using a preset smoothing algorithm and the corrected target electricity consumption data to predict the user electricity consumption data of the first preset area to obtain the first electricity consumption data, wherein the preset smoothing algorithm is used to represent a pre-set probabilistic calculation model.

[0011] Optionally, based on the first electricity consumption data and the revenue objective function, the operating parameters of the electricity agent are determined, including: obtaining a set of constraints for the electricity agent to operate electricity in the first preset area, wherein the set of constraints includes at least one of the following: new energy output constraints, monthly revenue constraints of the agent, and monthly power balance constraints; and solving the revenue objective function based on the first electricity consumption data and the set of constraints to obtain the operating parameters.

[0012] According to another aspect of the present invention, an apparatus for determining the operating parameters of an electricity agent is also provided, comprising: an acquisition module, configured to acquire regional characteristic factors of a first preset area in which the electricity agent is to conduct electricity operation, thereby obtaining a first set of characteristic factors; a prediction module, configured to predict user electricity consumption data of the first preset area based on the first set of characteristic factors, thereby obtaining first electricity consumption data; a construction module, configured to construct a revenue objective function for the electricity agent's operation of electricity in the first preset area, wherein the revenue objective function represents a functional relationship established with the revenue of the electricity agent as the objective; and a determination module, configured to determine the operating parameters of the electricity agent based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, the amount of electricity purchased representing the quantity of electricity purchased by the electricity agent from the electricity market, and the electricity selling price representing the price at which the electricity agent sells electricity to the first preset area.

[0013] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0018] In this embodiment of the invention, a method for determining the operating parameters of an electricity agent is provided, comprising: acquiring regional characteristic factors of a first preset area where the electricity agent is to conduct electricity operation, thereby obtaining a first set of characteristic factors; predicting user electricity consumption data in the first preset area based on the first set of characteristic factors, thereby obtaining first electricity consumption data; constructing a revenue objective function for the electricity agent's operation of electricity in the first preset area, wherein the revenue objective function represents a functional relationship established with the revenue of the electricity agent as the objective; and determining the operating parameters of the electricity agent based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, wherein the amount of electricity purchased represents the quantity of electricity purchased by the electricity agent from the electricity market, and the electricity selling price represents the price at which the electricity agent sells electricity to the first preset area. It is noteworthy that this application constructs a revenue objective function for the power agency's operation of electricity in a first preset area by acquiring regional characteristic factors and user electricity consumption data. Based on the first electricity consumption data and the revenue objective function, it determines the operating parameters of the power agency. This allows for a more comprehensive understanding and prediction of the electricity consumption in the area, taking into account the electricity market's purchase cost, sales revenue, and other related costs to determine the power agency's revenue objective. By comprehensively analyzing and predicting the characteristic factors and user electricity consumption data of the first preset area, and combining this with the revenue objective function, the optimal operating parameters can be determined. This allows for better formulation of operating parameters, optimization of resource allocation, improvement of operational efficiency, and achievement of sustainable development. Furthermore, through the optimization and solution of the mathematical model, the optimal solution can be quickly found, reducing manual intervention and improving work efficiency. This solves the technical problem of low efficiency in determining operating parameters during the operation of power agencies in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of a method for determining the operating parameters of an electricity agent object according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of an optional method for determining the operating parameters of an electricity agent object according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of an optional first power consumption data obtained according to an embodiment of the present invention;

[0023] Figure 4This is a schematic diagram of the purchased electricity volume in an optional operating parameter obtained according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of an operating parameter determination device for an electric power agent according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] According to one aspect of the present invention, a method for determining the operating parameters of an electric power agent object is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a method for determining the operating parameters of an electricity agent object according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Obtain the regional characteristic factors of the first preset area where the power agent object is to carry out power operation, and obtain the first characteristic factor set.

[0030] The aforementioned electricity agents can refer to enterprises or organizations that act as agents or sell electricity, and can be called electricity agents. Electricity agents cooperate with power plants or electricity suppliers to provide electricity to users.

[0031] The aforementioned first preset area may refer to the region or area selected by the power agent during power operation. Preferably, in this application, it may refer to a newly built residential area, a newly built community, or a newly built street, etc. The specific first preset area can be determined according to actual needs, and is not limited here.

[0032] The aforementioned regional characteristic factors can refer to various factors that affect the electricity market in the first preset region. These regional characteristic factors may include features such as the land area, plot ratio, decoration status, number of parking spaces, and greening rate of the residential area, or local population, industrial structure, electricity demand, and stability of electricity supply. By analyzing and evaluating these factors, the electricity agent can better understand the market environment of the region and formulate corresponding operating parameters.

[0033] In one optional embodiment, regional characteristic factors of a first preset area where the power agency will conduct power operations can be obtained, resulting in a first set of characteristic factors. Specifically, the specific preset area where the power agency will conduct power operations can be determined. Then, various characteristic data of this preset area can be collected, such as climate conditions, electricity industry structure, electricity load curves, and distribution of power generation facilities. This data can be obtained through power companies or other institutions. Next, the collected regional characteristic data can be analyzed to identify characteristic factors that have a significant impact on the operation of the power agency, such as peak-valley differences in electricity load, the development potential of renewable energy, and grid stability, forming the first set of characteristic factors. This set of characteristic factors can serve as a data reference for the power agency when conducting power operations in this preset area. Through the implementation of the above steps, detailed characteristic information about the preset area where power operations will be conducted can be provided to the power agency, helping it to better formulate operating parameters and resource allocation.

[0034] In the above steps, by analyzing and extracting the characteristic factors of the preset area, power agents can more accurately assess market demand, competition, and development potential, thereby reducing operational risks. Guided by the set of characteristic factors, power agents can more effectively formulate operational parameters and resource allocation plans, improving operational efficiency and service quality. By acquiring the regional characteristic factors of the preset area and forming a set of characteristic factors, a scientific basis can be provided for determining the operation of power agents, promoting better results and performance in market services.

[0035] Step S104: Based on the first set of feature factors, predict the user electricity consumption data of the first preset area to obtain the first electricity consumption data.

[0036] The aforementioned first electricity consumption data may refer to the predicted electricity demand data or electricity load data of the first preset area in the future, etc., which can be determined according to actual needs, and is not limited here.

[0037] In one optional embodiment, during the determination of operational parameters for the power agent, predicting user electricity consumption data in a first preset area based on a first set of feature factors can help the agent more accurately understand user electricity consumption, thereby better formulating operational parameters. Specifically, key feature factors affecting user electricity consumption can be identified. These factors may include user electricity consumption habits, weather conditions, seasonal changes, etc. Representative and influential feature factors can be identified through data analysis and statistical methods. Machine learning, data mining, and other technologies can be used to construct a predictive model for electricity consumption data. Regression analysis, time series analysis, and other methods, combined with the first set of feature factors, can be used to predict user electricity consumption data. The predictive model constructed through the above steps can predict user electricity consumption data in the first preset area, obtaining first electricity consumption data. The method for determining the first electricity consumption data can be determined according to actual needs and is not limited here. The first electricity consumption data can help the agent more accurately understand user electricity consumption and provide a reference for formulating operational parameters. By predicting based on the feature factor set, the patterns and trends of user electricity consumption can be captured more accurately, thereby improving the accuracy of prediction.

[0038] Step S106: Construct the revenue objective function for the power agent object to perform power operation in the first preset area.

[0039] Among them, the profit objective function is used to represent the functional relationship established with the profit of the power agency object as the objective.

[0040] The aforementioned profit objective function can refer to a mathematical function relationship established with the annual or monthly profit of the power agency object as the optimization objective. It can be determined according to actual needs and is not limited here.

[0041] In one optional embodiment, during the determination of the operating parameters of the power agency, a revenue objective function for the power agency's operation in a first preset area can be constructed to help the agency improve its revenue during operation. The revenue objective function can consist of multiple variables, including factors such as the price of electricity sold, operating costs, and market demand. The agency's operating costs can be determined, including electricity purchase costs, transmission costs, and distribution costs. Other variables of the objective function can also be determined, such as market demand, power supply capacity, and power load, which can be determined based on historical data and market forecasts. Next, the electricity price, operating costs, and other variables can be combined to establish a mathematical model to describe the agency's revenue, thus obtaining the revenue objective function. Finally, mathematical tools and optimization algorithms can be used to solve this objective function to find suitable electricity prices and operating parameters to improve the power agency's revenue and operational service level. By constructing the revenue objective function, the agency can better formulate operating parameters, optimize resource allocation, improve operational efficiency, and achieve sustainable development. Simultaneously, through the optimization solution of the mathematical model, an optimal solution can be quickly found, reducing manual intervention and improving work efficiency.

[0042] Step S108: Based on the first electricity consumption data and the revenue objective function, determine the operating parameters of the electricity agency object.

[0043] The operating parameters include at least the amount of electricity purchased and the price of electricity sold. The amount of electricity purchased represents the quantity of electricity purchased by the electricity agent from the electricity market, and the price of electricity sold represents the price at which the electricity agent sells electricity to the first preset area.

[0044] The aforementioned operating parameters may refer to the parameters required for the power agent to conduct power operations in the first preset area. These parameters may include, but are not limited to, the quantity of electricity purchased by the power agent from the power market, the power capacity purchased by the power agent from the power market, and the price at which the power agent sells electricity to the first preset area. They can be determined according to actual needs and are not limited here. The operating parameters to be determined in this application are data results obtained from scientific data processing that conforms to natural laws and do not belong to management methods and systems in production, commercial implementation, and economic aspects.

[0045] In one optional embodiment, when determining the operating parameters of the electricity agency, analysis and optimization can be performed based on first electricity consumption data and a revenue objective function. The purchased electricity volume and the selling electricity price can be used as operating parameters to establish a mathematical model describing the operation process of the electricity agency. The model can include constraints and an objective function to ensure that the electricity agency achieves the expected revenue target during operation. Then, by optimizing the established operating parameter model, suitable purchased electricity volume and selling electricity price can be obtained. The optimization objective allows the electricity agency to increase profits or reduce operating costs while meeting user needs. Through the implementation of the above steps, the electricity agency can better determine purchased electricity volume and selling electricity prices, improve operational efficiency, optimize resource allocation, and enhance market competitiveness, thus contributing to better performance and development in the market.

[0046] In this embodiment of the invention, a method for determining the operating parameters of an electricity agent is provided, comprising: acquiring regional characteristic factors of a first preset area where the electricity agent is to conduct electricity operation, thereby obtaining a first set of characteristic factors; predicting user electricity consumption data in the first preset area based on the first set of characteristic factors, thereby obtaining first electricity consumption data; constructing a revenue objective function for the electricity agent's operation of electricity in the first preset area, wherein the revenue objective function represents a functional relationship established with the revenue of the electricity agent as the objective; and determining the operating parameters of the electricity agent based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, wherein the amount of electricity purchased represents the quantity of electricity purchased by the electricity agent from the electricity market, and the electricity selling price represents the price at which the electricity agent sells electricity to the first preset area. It is noteworthy that this application constructs a revenue objective function for the power agency's operation of electricity in a first preset area by acquiring regional characteristic factors and user electricity consumption data. Based on the first electricity consumption data and the revenue objective function, it determines the operating parameters of the power agency. This allows for a more comprehensive understanding and prediction of the electricity consumption in the area, taking into account the electricity market's purchase cost, sales revenue, and other related costs to determine the power agency's revenue objective. By comprehensively analyzing and predicting the characteristic factors and user electricity consumption data of the first preset area, and combining this with the revenue objective function, the optimal operating parameters can be determined. This allows for better formulation of operating parameters, optimization of resource allocation, improvement of operational efficiency, and achievement of sustainable development. Furthermore, through the optimization and solution of the mathematical model, the optimal solution can be quickly found, reducing manual intervention and improving work efficiency. This solves the technical problem of low efficiency in determining operating parameters during the operation of power agencies in related technologies.

[0047] Optionally, predicting user electricity consumption data in a first preset area based on a first set of feature factors to obtain first electricity consumption data includes: acquiring regional feature factors of multiple second preset areas to obtain a second set of feature factors, wherein the power operation time of any region in the multiple second preset areas is longer than the power operation time of the first preset area; selecting at least one target area from the multiple second preset areas based on the first set of feature factors and the second set of feature factors; and predicting user electricity consumption data in the first preset area based on the target electricity consumption data of the target area to obtain first electricity consumption data.

[0048] The aforementioned second preset area can refer to the region or area selected by the power agent during power operation. Preferably, in this application, it can refer to old residential areas, old communities, or old streets, etc. The richness of historical electricity consumption data in the second preset area is greater than that in the first preset area. The specific second preset area can be determined according to actual needs, and is not limited here.

[0049] The target area mentioned above can refer to the area that is highly similar to the first preset area and is selected from multiple second preset areas. It can be determined according to actual needs and is not limited here.

[0050] In one optional embodiment, regional characteristic factors of multiple second preset regions, including information such as power operation duration, can be acquired to form a second characteristic factor set. The power operation duration of any one of the multiple second preset regions is longer than that of the first preset region. Next, based on the first and second characteristic factor sets, at least one target region can be selected from the multiple second preset regions. Data mining or machine learning algorithms can be used for selection and matching. Finally, based on the target electricity consumption data of the target region, a prediction model can be used to predict the user electricity consumption data of the first preset region to obtain the first electricity consumption data. In the above steps, by introducing data and characteristic factors from the second preset regions, the accuracy and reliability of the user electricity consumption data of the first preset region can be improved. Simultaneously, the selected target regions can provide more meaningful data, further improving the accuracy and credibility of the prediction model, and effectively improving the user electricity consumption data prediction effect in the field of determining the operating parameters of power agency objects.

[0051] Optionally, selecting at least one target region from multiple second preset regions based on a first feature set and a second feature set includes: constructing a first feature matrix based on the first feature set and constructing a second feature matrix based on the second feature set; and selecting the target region from multiple second preset regions based on the first feature matrix and the second feature matrix using a preset region similarity evaluation model, wherein the preset region similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between regions.

[0052] In one optional embodiment, the relevant features of each preset region can be quantified according to a first set of feature factors, and a first feature matrix can be constructed, which may include feature information such as electricity demand, electricity consumption characteristics, and industry type of each region. Next, the relevant features of each preset region can be quantified according to a second set of feature factors, and a second feature matrix can be constructed, which may include feature information such as electricity supply, electricity price, and renewable energy utilization of each region. Then, based on the first and second feature matrices, a preset region similarity assessment model can be used to calculate the similarity between the preset regions. The preset region similarity assessment model can employ various similarity measurement methods, such as Euclidean distance and cosine similarity, to evaluate the degree of similarity between different regions. Furthermore, based on the calculated similarity results, at least one target region can be selected from multiple second-preset regions. The region with higher similarity is selected as the target region to ensure that the target region has similar characteristics and environment to the current operating region. Through this step, the target region can be selected from multiple second-preset regions. In the above steps, by considering different sets of feature factors, the situation of each preset region can be evaluated more comprehensively, thereby selecting the target region more accurately. Through the calculation of the similarity evaluation model, it can be ensured that the target region has similar characteristics to the current operating region, reducing the risk of operating in a new region. By automating the calculation of similarity and the selection of target regions, manual intervention and determination time can be reduced, improving operational efficiency and the speed of determination.

[0053] Optionally, based on the target electricity consumption data of the target area, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data, including: obtaining the regional feature difference coefficient and the user feature difference coefficient between the first preset area and the target area, wherein the regional feature difference coefficient is used to represent the weight correction coefficient obtained by quantifying the regional characteristic difference between the first preset area and the target area, and the user feature difference coefficient is used to represent the weight correction coefficient obtained by quantifying the user characteristic difference between the first preset area and the target area; based on the regional feature difference coefficient and the user feature difference coefficient, the target electricity consumption data is corrected to obtain the corrected target electricity consumption data; based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data.

[0054] In one optional embodiment, regional characteristic difference coefficients and user characteristic difference coefficients between a first preset area and a target area can be obtained. Relevant data on the first preset area and the target area can be collected, including regional characteristics such as climate, population density, and economic conditions, and user characteristics such as electricity usage habits and electrical equipment. Then, through data analysis and modeling methods, the regional characteristic difference coefficients and user characteristic difference coefficients are calculated to quantify the characteristic differences between the first preset area and the target area. Next, the target electricity consumption data can be corrected based on the regional characteristic difference coefficients and user characteristic difference coefficients. According to the calculated difference coefficients, the electricity consumption data of the target area is corrected, taking into account the differences between regional and user characteristics, resulting in more accurate target electricity consumption data. Then, based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area can be predicted. The corrected target electricity consumption data is applied to the users in the first preset area to predict electricity consumption data. By taking into account the differences between regional and user characteristics, more accurate first electricity consumption data is obtained. In the above steps, by taking into account the differences in characteristics of different regions and users, the target electricity consumption data is corrected and the first electricity consumption data is predicted. This can more accurately reflect the actual situation and avoid prediction bias. By using the electricity consumption data prediction method based on regional and user characteristic differences, the prediction accuracy can be effectively improved, resource allocation can be optimized, user satisfaction can be enhanced, and more effective data support can be provided for determining the operating parameters of the power agency object.

[0055] Optionally, based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data, including: using a preset smoothing algorithm and the corrected target electricity consumption data to predict the user electricity consumption data of the first preset area to obtain the first electricity consumption data, wherein the preset smoothing algorithm is used to represent a pre-set probabilistic calculation model.

[0056] The aforementioned corrected target electricity consumption data can refer to the data obtained after correcting and adjusting the target electricity consumption data. It can be obtained by processing the original data by considering various factors such as weather and seasonality. The corrected target electricity consumption data is closer to the actual situation and can improve the accuracy of prediction.

[0057] In one optional embodiment, the preset smoothing algorithm can be a probabilistic calculation model used to smooth the corrected target electricity consumption data, reducing data volatility and making the data more stable and predictable. The preset smoothing algorithm can employ methods such as moving averages or exponential smoothing, obtaining smoothed data by weighted averaging of historical values ​​of the original data. The corrected target electricity consumption data, having undergone correction and adjustment, is closer to the actual situation. Combining the corrected target electricity consumption data with the data obtained from the preset smoothing algorithm allows for more accurate prediction of user electricity consumption data, yielding the predicted user electricity consumption data for the first preset area, i.e., the first electricity consumption data. In the above steps, by utilizing the corrected target electricity consumption data and the preset smoothing algorithm, data volatility can be reduced, prediction accuracy improved, and the prediction results made closer to the actual situation. The corrected target electricity consumption data better reflects the actual situation, and combining it with the preset smoothing algorithm can reduce prediction errors and improve prediction reliability.

[0058] Optionally, based on the first electricity consumption data and the revenue objective function, the operating parameters of the electricity agent are determined, including: obtaining a set of constraints for the electricity agent to operate electricity in the first preset area, wherein the set of constraints includes at least one of the following: new energy output constraints, monthly revenue constraints of the agent, and monthly power balance constraints; and solving the revenue objective function based on the first electricity consumption data and the set of constraints to obtain the operating parameters.

[0059] In one optional embodiment, based on the electricity agency's needs and constraints for electricity operation in the first preset area, a set of constraints can be determined. These constraints include: a renewable energy output constraint limiting the proportion or quantity of renewable energy used by the agency; a monthly revenue constraint limiting the agency's total revenue to a preset value; and a monthly power balance constraint requiring the agency to maintain a balance between power supply and demand each month. Next, the first electricity consumption data and the set of constraints can be substituted into the revenue objective function. Mathematical optimization methods, such as linear programming and integer programming, can be used to solve the revenue objective function to obtain suitable operating parameters, including electricity purchase volume, renewable energy utilization ratio, and electricity sales price. By implementing these steps, the electricity agency can more scientifically formulate operating parameters, effectively manage power supply and demand, and improve operational efficiency. Simultaneously, by setting constraints and solving the revenue objective function, profits can be increased or other business objectives achieved while ensuring the agency's operational stability. This enhances the agency's adaptability to market changes, optimizes electricity operation plans, and provides effective support for the sustainable development of the electricity agency.

[0060] The technical solution proposed in this application is described below in conjunction with an optional application scenario. With the marketization of electricity and the large-scale grid connection of renewable energy, the relationship between electricity supply and demand is becoming increasingly complex, especially the volatility and uncertainty of the medium- and long-term electricity market. Currently, residential users, due to their relatively small electricity demand, still find it difficult to directly participate in the medium- and long-term electricity market and need to rely on electricity brokers to participate in the purchase, sale, and pricing of electricity. However, existing pricing methods of electricity brokers mostly explore the impact of renewable energy uncertainty on electricity prices from a short-term time scale, ignoring the uncertainty of the load of newly built residential users and the output of renewable energy in the medium- and long-term time scale. This will lead to residential users potentially bearing significant risks of electricity price fluctuations in the medium and long term. Therefore, how to formulate reasonable and stable electricity prices for users in newly built residential areas in the medium and long term, taking into account the uncertainty of the load of newly built residential users and the output of renewable energy, through brokers, is an urgent problem to be solved.

[0061] In order to formulate reasonable and stable electricity prices for users in newly built residential communities over a medium- to long-term timescale, this application proposes an optimal pricing and operation method based on the uncertainty model of electricity consumption for users in newly built residential communities and considering the number of agent intervals in the medium- to long-term electricity market and master-slave game theory.

[0062] This application provides an optimal pricing and operation method for newly built residential communities considering the medium- and long-term electricity market, to support the construction of the electricity market trading mechanism and the planning and operation of new power distribution systems. The specific steps are as follows: First, considering the characteristics of the community, such as its land area and floor area ratio, a community similarity assessment model is constructed based on the cosine distance method to obtain the electricity load of users in highly similar communities; based on the characteristic differences between communities and households, an uncertainty model of electricity consumption for users in newly built communities within a medium- and long-term time scale is established; second, based on the uncertainty model of electricity consumption for users in newly built communities, a two-layer interval number-master-slave game pricing and operation model for agents considering the medium- and long-term electricity market is established; finally, the two-layer game model is transformed into a single-layer model using the optimal conditions of a convex optimization problem (Karush-Kuhn-Tucker, KKT), and the single-layer linear model is solved using an interval linear programming algorithm to obtain the optimal electricity purchase and pricing parameters for agents under the medium- and long-term electricity market. This application, from the perspective of the agent, takes into account the uncertainty of electricity consumption in newly built residential areas and provides an economical and reliable operation plan for agents of newly built residential areas to participate in the medium and long-term electricity market; it promotes the local consumption of new energy by users and improves the safety, reliability and low carbon emissions of the operation of the new power distribution system.

[0063] The purpose of this application is to construct a two-layer interval number-master-slave game optimal pricing model for newly built residential communities, considering the uncertainty of electricity consumption by users, and taking into account the medium- and long-term electricity market. This model provides an economical and reliable operating solution for newly built residential community agents to participate in the medium- and long-term electricity market, while promoting the local consumption of renewable energy by users and improving the safety, reliability, and low-carbon operation of the new power distribution system. The model may include the following steps:

[0064] Considering factors such as land area, plot ratio, decoration status, number of parking spaces, and greening rate, a similarity assessment model for residential communities is constructed based on the cosine distance method, yielding user load curves for older communities with high similarity to newly built communities. The differences in community and household characteristics between newly built and older communities are quantified, and corresponding feature difference correction coefficients are generated. Electricity consumption data for users in highly similar older communities are corrected based on these correction coefficients. A Laplace smoothing method is used to establish an uncertainty model for electricity consumption in newly built communities over a medium- to long-term timescale. Based on this uncertainty model, a two-layer interval number-master-slave game pricing and operation model considering the medium- to long-term electricity market is established. A Lagrangian function is constructed for the lower layer of the two-layer pricing and operation model to obtain the KKT optimal conditions for the lower-layer model, which are then substituted into the upper-layer model to obtain the single-layer model for optimal pricing of the agent. An interval linear programming algorithm is used to solve the single-layer linear model for optimal pricing of the agent, yielding the optimal electricity purchase and pricing parameters for the agent in the medium- to long-term electricity market.

[0065] Furthermore, considering factors such as the community's land area, plot ratio, decoration status, number of parking spaces, and greening rate, a community similarity assessment model is constructed based on the cosine distance method to obtain the user load curve of an older community with high similarity to a newly built community. The specific method is as follows:

[0066] Considering the characteristics of the community, such as land area, plot ratio, decoration status, number of parking spaces, and greening rate, i.e., the first set of characteristic factors and the second set of characteristic factors, we construct the feature matrix α of the i-th old community. i The second characteristic matrix, and the characteristic matrix β of the newly built cell, which is also the first characteristic matrix, are shown in the following formula:

[0067] α i =[a i ,b i ,c i ,d i ,e i ], i∈{1,...,I};

[0068] β = [a, b, c, d, e];

[0069] Where a i b i c i di e i Let be the land area, plot ratio, decoration status, number of parking spaces, and greening rate of the i-th old community; I is the total number of old communities; and a, b, c, d, and e are the land area, plot ratio, decoration status, number of parking spaces, and greening rate of the newly built communities, respectively.

[0070] A cell similarity assessment model, or a pre-defined regional similarity assessment model, is constructed based on the cosine distance method, as shown in the following formula:

[0071]

[0072] Where, π i Let β be the similarity between the newly built community and the i-th old community; cosθ is the similarity between the feature matrix β of the newly built community and the feature matrix α of the i-th old community. i The cosine value.

[0073] Compare the similarity π between the newly built residential area and the i-th old residential area. i The size of the value is used to identify the old residential area with the highest similarity to the newly built residential area, which is the target area. Then, the electricity load curve P of users in the old residential area with the highest similarity to the newly built residential area during time period m can be obtained. old,L (m), which is the target electricity consumption data, and the corresponding user characteristics such as the user check-in time and the number of occupants.

[0074] Furthermore, the differences in community characteristics and household head characteristics between newly built and older communities are quantified, and corresponding characteristic difference correction coefficients are generated. The specific method is as follows:

[0075] Newly built residential communities differ from highly similar older communities in terms of community characteristics such as land area and plot ratio, as well as household owner characteristics such as move-in time and number of residents. These differences need to be quantified and corresponding correction coefficients generated. A detailed analysis follows:

[0076] The differences in community characteristics are quantified between newly built communities and highly similar older communities in terms of land area, plot ratio, decoration status, number of parking spaces, and greening rate, and a weighting correction coefficient δ is generated. k To correct the electricity consumption data of highly similar communities, the following formula is used:

[0077]

[0078] Where, δ k α is the weighting correction coefficient for the feature differences of the k-th cell, also known as the regional feature difference coefficient; old This is the feature matrix of old residential communities with high similarity.

[0079] Differences in household head characteristics: This involves addressing differences in household head characteristics, such as move-in time and number of residents, between newly built residential areas and older, highly similar communities. A feature correction coefficient γ is used to address these differences.h The results are quantified to adjust the predicted electricity load for newly built residential areas, as shown in the following formula:

[0080]

[0081] Where, γ h ν is the feature correction coefficient for the h-th household head feature difference, also known as the user feature difference coefficient; X is the total number of users in the newly built community; x represents the x-th user in the newly built community; μ is the household head feature matrix of the newly built community; Y is the total number of users in the highly similar old community; y represents the y-th user in the highly similar old community; ν old This is the feature matrix of household owners in old residential communities with high similarity.

[0082] Furthermore, the electricity consumption data of users in highly similar old residential communities are corrected based on the correction coefficient for differences in community and household characteristics. The specific method is as follows:

[0083] Based on the weighting coefficients for differences in community characteristics and the correction coefficients for differences in household head characteristics, in order to improve the accuracy of electricity consumption data prediction for users in newly built communities, it is necessary to correct the electricity consumption data of users in older communities with high similarity, as shown in the following formula:

[0084]

[0085] in, The corrected electricity consumption data for users in older residential communities with high similarity is also known as the corrected target electricity consumption data; K represents the total number of community features; and H represents the total number of household features.

[0086] Furthermore, a model for the uncertainty of electricity consumption by users in newly built residential areas over a medium- to long-term timescale is established using the Laplace smoothing method. The specific method is as follows:

[0087] Since newly built residential areas lack actual historical electricity consumption data, directly relying on the electricity consumption data of older residential areas may result in sparse or insufficient data at certain times. To avoid zero probability or data bias during the prediction process, the Laplace smoothing method, or a pre-set smoothing algorithm, is used to predict the electricity consumption data of users in newly built residential areas, as shown in the following formula:

[0088]

[0089] in, κ represents the predicted electricity consumption data for newly built residential communities, also known as the first electricity consumption data; κ is the smoothing parameter.

[0090] Due to uncertainties arising from factors such as the condition of newly built residential communities, the move-in time of residents, and the number of residents, to ensure the accuracy and reliability of the predicted electricity consumption data for these communities, it is necessary to use the standard deviation σ(m) of historical electricity consumption data from highly similar older residential communities to measure the fluctuation range of electricity load in the newly built communities, thereby obtaining the range of uncertainty. The specific formula is as follows:

[0091]

[0092] in, , respectively, represent the minimum and maximum electricity load of users in the newly built community in month m; r is the prediction accuracy parameter; σ(m) is the standard deviation of historical electricity load of users in the older, highly similar communities.

[0093] Furthermore, based on the uncertainty model of electricity consumption for users in newly built residential communities, a two-layer interval number-master-slave game pricing and operation model considering the agent objects in the medium- and long-term electricity market is established. The specific method is as follows:

[0094] The upper layer uses the proxy object as the leader and maximizing the annual revenue of the proxy object as the objective function to construct the proxy object's electricity purchase and pricing operation model. Its objective function, that is, the revenue objective function, is as follows:

[0095]

[0096] Among them, F agent Z represents the annual revenue of the proxy object; Z is the total number of months in a year. The unit power price at which the agent sells electricity to the user in month m, which is also the unit electricity price at which the user purchases electricity from the agent in month m. Δt represents the power of electricity sold by the agent to the user in month m, which is also the power of electricity purchased by the user from the agent in month m; Δt is the time interval. The unit electricity price at which the agent purchases electricity from the medium- and long-term electricity market in month m; c represents the power of electricity purchased by the agent from the medium- to long-term electricity market in month m; PV The unit operation and maintenance cost required to manage distributed photovoltaic power generation for proxy objects; The output of distributed photovoltaic power in month m.

[0097] In addition, the model also needs to consider constraints such as renewable energy output constraints, monthly revenue constraints of the proxy object, medium- and long-term contract constraints, and monthly power balance constraints, which can be specifically described as follows:

[0098] Due to constraints on renewable energy output, and in response to the clean energy trend, clients are constructing distributed photovoltaic (PV) systems in newly built residential areas. However, PV output is significantly affected by factors such as temperature and sunlight, exhibiting considerable randomness and uncertainty. Therefore, the output of distributed PV systems will fluctuate within a certain range, as shown in the following formula:

[0099]

[0100] in, These represent the minimum and maximum output of distributed photovoltaic power in month m, respectively.

[0101] To enhance the activity of agents participating in the medium- and long-term electricity market, the monthly revenue of agents participating in the medium- and long-term electricity market should exceed their monthly operating costs, as shown in the following formula:

[0102]

[0103] in, The operating cost of the proxy object in month m.

[0104] Due to the constraints of medium- and long-term contracts, when participating in the medium- and long-term electricity market, the agent enters into power purchase agreements with power generators. Therefore, the power purchase price and power capacity declared by the agent to the medium- and long-term electricity market should be limited to a certain range, as shown in the following formula:

[0105]

[0106] in, These are the upper and lower limits of the electricity purchase price declared by the agent for the newly built residential community in the medium- and long-term market in month m; These represent the upper and lower limits of the electricity purchase capacity declared by the agent in the m-th month of the medium- and long-term market.

[0107] The monthly power balance constraint should be met during the operation of the power distribution system in a newly built residential area, as shown in the following formula:

[0108]

[0109] The lower layer adopts users as followers and establishes a user electricity operation model with the objective function of maximizing user annual benefits and comfort. Since user comfort is mainly related to user electricity consumption and electricity prices, the higher the user's electricity consumption, the lower the electricity bill and the higher the user comfort. Therefore, the objective function of this model is as follows:

[0110]

[0111] Among them, F user For the user's annual benefits.

[0112] In addition, the model also needs to consider the user's expected monthly benefits constraint, as follows:

[0113]

[0114] in, The user's expected monthly benefits.

[0115] Furthermore, a Lagrangian function is constructed for the lower layer of the two-layer model of agent pricing operations to obtain the KKT optimal conditions of the lower-layer model. These conditions are then substituted into the upper-layer model to obtain the single-layer model of agent optimal pricing. The specific method is as follows:

[0116] For the lower-level user electricity consumption operation model in the two-tier pricing operation model for agent objects, a Lagrangian function is constructed as follows:

[0117]

[0118] Where L is the Lagrange function; τ is the Lagrange multiplier;

[0119] Since this model ultimately uses an interval linear programming algorithm to solve it, the operational variables of this model are the power purchased by the agent from the medium- and long-term market and the electricity price paid by the user. Therefore, the optimal conditions of the lower-level model KKT can be obtained, as follows:

[0120]

[0121] Substituting the KKT optimal conditions of the lower-level model as constraints into the upper-level model of optimal electricity purchase and pricing operation for newly built community agents, we obtain a single-level model for optimal pricing of agents.

[0122] Furthermore, the optimal pricing single-layer linear model for the proxy object is solved using an interval linear programming algorithm to obtain the optimal power purchase and pricing parameters for the proxy object under the medium- and long-term electricity market. The specific method is as follows:

[0123] According to the annual revenue function of the agent, the annual revenue of the agent in the new residential area is at its maximum when the user's electricity load is at its maximum value in the interval, the agent's purchase price from the medium- and long-term electricity market is at its minimum value in the interval, and the output of the distributed photovoltaic power generation in the new residential area is at its maximum value in the interval. Conversely, the annual revenue of the agent in the new residential area is at its minimum when the user's electricity load is at its minimum value in the interval, the agent's purchase price from the medium- and long-term electricity market is at its maximum value in the interval, and the output of the distributed photovoltaic power generation in the new residential area is at its minimum value in the interval. To ensure that the agent directly selects the optimal operating plan from the annual revenue interval, the agent's pessimism regarding uncertainty is introduced into the single-layer linear model of the agent's optimal pricing. Therefore, the objective function of this model is as follows:

[0124] max[e(F agent)+(ω-1)·f(F agent )];

[0125] Among them, F agent ω represents the annual return range of the proxy object; ω is the pessimism of the proxy object regarding uncertainty, ranging from 0 to 1. A higher value within this range indicates a better tolerance for future uncertainty; e(F agent ), f(F agent The terms ) represent the middle and width terms of the annual revenue range of the proxy object, respectively, and their specific expressions are as follows:

[0126]

[0127] in, These represent the maximum and minimum annual earnings of the proxy object, respectively.

[0128] Solve the single-layer linear model of optimal pricing for new community agents using the solver (YALMIP / CPLEX) in software (MATLAB) or other similar software and solvers to obtain the optimal electricity purchase and pricing parameters for new community agents under the medium- and long-term electricity market.

[0129] The specific implementation of this application will be further explained below with reference to the accompanying drawings and examples.

[0130] Figure 2 This is a flowchart of an optional method for determining the operating parameters of a power agent object according to an embodiment of the present invention, such as... Figure 2 As shown, it includes:

[0131] S202, obtain the regional characteristic factors of the first preset area where the power agent object is to carry out power operation, and obtain the first characteristic factor set.

[0132] S204, obtain the regional feature factors of multiple second preset regions to obtain the second feature factor set.

[0133] S206, construct a first feature matrix based on the first feature factor set, and construct a second feature matrix based on the second feature factor set; using a preset region similarity evaluation model, select the target region from multiple second preset regions based on the first feature matrix and the second feature matrix.

[0134] S208, obtain the regional feature difference coefficient and user feature difference coefficient between the first preset area and the target area; based on the regional feature difference coefficient and user feature difference coefficient, correct the target electricity consumption data of the target area to obtain the corrected target electricity consumption data; based on the corrected target electricity consumption data, predict the user electricity consumption data of the first preset area to obtain the first electricity consumption data.

[0135] S210, Construct the revenue objective function for the power agent object to perform power operation in the first preset area.

[0136] S212, based on the first electricity consumption data and the revenue objective function, determine the operating parameters of the electricity agency object.

[0137] The following is a calculation example of the method in this application. Taking a newly built residential community in a certain area as an example, based on the above steps, the uncertainty model of electricity consumption of users in the newly built residential community can be obtained, that is, the first electricity consumption data. Figure 3 This is a schematic diagram of an optional first power consumption data obtained according to an embodiment of the present invention, such as... Figure 3 As shown in the figure, the horizontal axis represents the month, and the vertical axis represents the electricity consumption. The figure includes the maximum value curve ① of the user load curve in the first predicted electricity consumption data, the predicted value curve ② of the user load curve in the first predicted electricity consumption data, and the minimum value curve ③ of the user load curve in the first predicted electricity consumption data.

[0138] Meanwhile, the cost of managing distributed photovoltaic power by the agent in the newly built community is set at 0.0125 yuan / (kWh·month), the agent's pessimism about uncertainty is 0.8, and the rated capacity of distributed photovoltaic power is 1.5MW. Based on the above steps, the optimal power purchase parameters and pricing parameters for the agent under the medium- and long-term electricity market can be obtained. Figure 4 This is a schematic diagram of the purchased electricity volume in an optional operating parameter obtained according to an embodiment of the present invention, such as... Figure 4 As shown in the figure, the horizontal axis represents the month, and the vertical axis represents the electricity purchased. The figure includes curves ① (maximum value of electricity purchased), ② (optimal value of electricity purchased), and ③ (minimum value of electricity purchased) among the obtained operating parameters. Table 1 below shows the electricity sales price in an optional operating parameter obtained according to an embodiment of the present invention.

[0139] Table 1

[0140]

[0141] In addition, different pessimism levels can be set to solve the single-layer linear model of the optimal pricing of the proxy object, and the annual returns that the proxy object can obtain when accepting different degrees of uncertainty can be obtained. Table 2 below is a summary table of the annual returns of the proxy object under different optional pessimism levels obtained according to an embodiment of the present invention.

[0142] Table 2

[0143]

[0144] Based on the above results, when the pessimism level is 0, it indicates that the proxy does not accept any uncertainties, its operation is the most conservative, and its returns are correspondingly the lowest. When the pessimism level is 1, it indicates that the proxy accepts all uncertainties, its operation is the most risky, but its returns are correspondingly the highest. Therefore, the greater the pessimism of the proxy regarding uncertainty, the higher the returns and the greater the uncertainty it will face.

[0145] According to another aspect of the present invention, an operating parameter determination device for an electricity proxy object is also provided. This device can execute the operating parameter determination method for the electricity proxy object described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0146] Figure 5 This is a schematic diagram of an operating parameter determination device for an electricity agent according to an embodiment of this application, such as... Figure 5 As shown, the device includes the following: an acquisition module 502, a prediction module 504, a construction module 506, and a determination module 508.

[0147] The system comprises the following modules: an acquisition module for acquiring regional characteristic factors of a first preset area where the power agent will conduct power operations, resulting in a first set of characteristic factors; a prediction module for predicting user electricity consumption data in the first preset area based on the first set of characteristic factors, resulting in first electricity consumption data; a construction module for constructing a revenue objective function for the power agent's power operations in the first preset area, wherein the revenue objective function represents a functional relationship established with the power agent's revenue as the objective; and a determination module for determining the power agent's operating parameters based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, where the amount of electricity purchased represents the quantity of electricity purchased by the power agent from the electricity market, and the electricity selling price represents the price at which the power agent sells electricity to the first preset area.

[0148] The prediction module is further used to obtain regional characteristic factors of multiple second preset areas to obtain a second set of characteristic factors, wherein the power operation time of any region in the multiple second preset areas is greater than the power operation time of the first preset area; based on the first set of characteristic factors and the second set of characteristic factors, at least one target area is selected from the multiple second preset areas; based on the target electricity consumption data of the target area, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data.

[0149] The prediction module is further used to construct a first feature matrix based on a first feature factor set and a second feature matrix based on a second feature factor set; and to select target regions from multiple second preset regions based on the first feature matrix and the second feature matrix using a preset region similarity evaluation model. The preset region similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between regions.

[0150] The prediction module is further configured to obtain the regional feature difference coefficient and the user feature difference coefficient between the first preset area and the target area. The regional feature difference coefficient represents the weight correction coefficient obtained by quantifying the regional characteristic difference between the first preset area and the target area, and the user feature difference coefficient represents the weight correction coefficient obtained by quantifying the user characteristic difference between the first preset area and the target area. Based on the regional feature difference coefficient and the user feature difference coefficient, the target electricity consumption data is corrected to obtain the corrected target electricity consumption data. Based on the corrected target electricity consumption data, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data.

[0151] The prediction module is also used to predict the user electricity consumption data of the first preset area using a preset smoothing algorithm and the corrected target electricity consumption data to obtain the first electricity consumption data. The preset smoothing algorithm is used to represent a pre-set probabilistic calculation model.

[0152] The determination module is further used to obtain the set of constraints for the power agency object to operate the power in the first preset area. The set of constraints includes at least one of the following: new energy output constraints, monthly revenue constraints for the agency object, and monthly power balance constraints. Based on the first electricity consumption data and the set of constraints, the revenue objective function is solved to obtain the operating parameters.

[0153] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0154] The aforementioned memory can refer to devices inside a computer used to store data and programs, including RAM, hard disks, etc. RAM can be used to temporarily store running programs and data, while hard disks can be used to store programs and data long-term. Memory enables the computer to read and write data and execute programs. The aforementioned processor is responsible for executing instructions in computer programs and performing data processing. It can also be responsible for controlling and executing various operations, including arithmetic operations, logical operations, and data transmission.

[0155] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0156] The aforementioned computer storage media can refer to the media used in computer memory to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser discs, etc. Computer-readable storage media include stored programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer to meet certain information needs.

[0157] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0158] The aforementioned computer program products can refer to software programs that have been written, tested, and released, and can run on computers or other devices. Computer program products can include application programs, operating systems, utility software, etc., used to achieve specific functions or solve specific problems.

[0159] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0160] The aforementioned non-volatile computer-readable storage medium can refer to a medium for storing data. Non-volatile computer-readable storage media can retain data without loss when power is off and can be used to store long-term data, such as operating systems, applications, and user files. Non-volatile storage media can include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0161] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0162] The aforementioned computer program can refer to a set of instructions used to tell the computer to perform specific tasks or operations. Computer programs can be written by programmers using specific programming languages ​​and can include algorithms, data structures, logic, and control flow. Computer programs can be used for a variety of purposes, including application software, operating systems, etc.

[0163] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0165] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0167] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0168] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the operating parameters of an electricity agency object, characterized in that, include: Obtain the regional characteristic factors of the first preset area where the power agent object is to carry out power operation, and obtain the first characteristic factor set; Based on the first set of feature factors, the user electricity consumption data of the first preset area is predicted to obtain the first electricity consumption data, wherein the electricity consumption data is used to represent electricity demand data or electricity load data. Construct a revenue objective function for the power agent object to perform power operation in the first preset area, wherein the revenue objective function is used to represent the functional relationship established with the revenue of the power agent object as the objective; Based on the first electricity consumption data and the revenue objective function, the operating parameters of the electricity agent are determined, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price. The amount of electricity purchased represents the quantity of electricity purchased by the electricity agent from the electricity market, and the electricity selling price represents the price at which the electricity agent sells electricity to the first preset area. The method for predicting user electricity consumption data in the first preset region based on the first feature factor set to obtain first electricity consumption data includes: acquiring regional feature factors of multiple second preset regions to obtain a second feature factor set, wherein the power operation time of any region in the multiple second preset regions is greater than the power operation time of the first preset region; selecting at least one target region from the multiple second preset regions based on the first feature factor set and the second feature factor set, and obtaining target electricity consumption data for the target region; acquiring regional feature difference coefficients and user feature difference coefficients between the first preset region and the target region, wherein the regional feature difference coefficients represent weight correction coefficients obtained by quantifying the regional characteristic differences between the first preset region and the target region, and the user feature difference coefficients represent weight correction coefficients obtained by quantifying the user characteristic differences between the first preset region and the target region; correcting the target electricity consumption data based on the regional feature difference coefficients and the user feature difference coefficients to obtain corrected target electricity consumption data; and predicting user electricity consumption data in the first preset region using a preset smoothing algorithm and the corrected target electricity consumption data to obtain the first electricity consumption data, wherein the preset smoothing algorithm represents a pre-set probabilistic calculation model.

2. The method for determining the operating parameters of a power agency object according to claim 1, characterized in that, Based on the first feature set and the second feature set, at least one target region is selected from the plurality of second preset regions, including: A first feature matrix is ​​constructed based on the first feature factor set, and a second feature matrix is ​​constructed based on the second feature factor set; Using a preset region similarity evaluation model, the target region is selected from the plurality of second preset regions based on the first feature matrix and the second feature matrix. The preset region similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between regions.

3. The method for determining the operating parameters of an electricity agency object according to claim 1, characterized in that, Based on the first electricity consumption data and the revenue objective function, the operating parameters of the electricity agency object are determined, including: Obtain the set of constraints for the power agent to operate the power in the first preset area, wherein the set of constraints includes at least one of the following: new energy output constraints, monthly revenue constraints of the agent, and monthly power balance constraints. Based on the first electricity consumption data and the set of constraints, the revenue objective function is solved to obtain the operating parameters.

4. A device for determining the operating parameters of an electricity agency object, characterized in that, include: The acquisition module is used to acquire the regional characteristic factors of the first preset area where the power agent object is to carry out power operation, and obtain the first characteristic factor set; The prediction module is used to predict the user electricity consumption data of the first preset area based on the first feature factor set to obtain the first electricity consumption data, wherein the electricity consumption data is used to represent electricity demand data or electricity load data. A construction module is used to construct a revenue objective function for the power agent object to perform power operation in the first preset area, wherein the revenue objective function is used to represent a functional relationship established with the revenue of the power agent object as the objective; The determining module is used to determine the operating parameters of the power agent object based on the first electricity consumption data and the revenue objective function, wherein the operating parameters include at least the amount of electricity purchased and the electricity selling price, the amount of electricity purchased is used to represent the quantity of electricity purchased by the power agent object from the electricity market, and the electricity selling price is used to represent the price at which the power agent object sells electricity to the first preset area; The prediction module is further configured to: acquire regional characteristic factors of multiple second preset regions to obtain a second set of characteristic factors, wherein the power operation time of any region in the multiple second preset regions is greater than the power operation time of the first preset region; based on the first set of characteristic factors and the second set of characteristic factors, select at least one target region from the multiple second preset regions and obtain target electricity consumption data for the target region; acquire regional characteristic difference coefficients and user characteristic difference coefficients between the first preset region and the target region, wherein the regional characteristic difference coefficients represent weight correction coefficients obtained by quantifying the regional characteristic differences between the first preset region and the target region, and the user characteristic difference coefficients represent weight correction coefficients obtained by quantifying the user characteristic differences between the first preset region and the target region; correct the target electricity consumption data based on the regional characteristic difference coefficients and the user characteristic difference coefficients to obtain corrected target electricity consumption data; and predict user electricity consumption data of the first preset region using a preset smoothing algorithm and the corrected target electricity consumption data to obtain the first electricity consumption data, wherein the preset smoothing algorithm represents a pre-set probabilistic calculation model.

5. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for determining the operating parameters of the power agent object as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method for determining the operating parameters of the power agent object as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method for determining the operating parameters of an electricity agent object according to any one of claims 1 to 3.

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