Operation parameter determination method and device of power proxy object and electronic equipment

Abstract

The invention discloses an operation parameter determination method and device of a power proxy object and electronic equipment. The method comprises the following steps: obtaining region feature factors of a first preset region, to be subjected to power operation, of a power proxy object, and obtaining a first feature factor set; predicting the user power consumption data of the first preset area based on the first feature factor set to obtain first power consumption data; a revenue objective function of the electric power proxy object for performing electric power operation on the first preset area is constructed, and the revenue objective function is used for representing a function relationship established by taking the revenue of the electric power proxy object as a target; based on the first power consumption data and the revenue objective function, operation parameters of the power proxy object are determined, the operation parameters at least comprise the electricity purchasing quantity and the electricity selling price, and the electricity purchasing quantity is used for representing the electricity purchasing quantity of the power proxy object from the power market. According to the method and the device, the technical problem of relatively low efficiency of determining the operation parameters in the operation process of the power proxy object in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of power operation management, and in particular to a method, device and electronic equipment for determining operation parameters of a power proxy object. Background Art

[0002] Determine the operating parameters during the operation of the power proxy object. These parameters may include but are not limited to electricity prices, market size, market demand, market competition, etc., which have a relatively important impact on the operation of the power proxy object. By determining these operating parameters, the power proxy object can better understand the market environment, improve market competitiveness and power service level.

[0003] At present, the relevant technology usually requires the help of relevant technical personnel or expert experience to determine the operating parameters of the power proxy object, and is more dependent on human factors. The determined operating parameters of the power proxy object have a low match with the area to be operated, resulting in low efficiency in determining the operating parameters during the operation of the power proxy object.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] Embodiments of the present invention provide a method, device and electronic device for determining operating parameters of a power proxy object, so as to at least solve the technical problem of low efficiency in determining operating parameters during the operation of a power proxy object in the related art.

[0006] According to one aspect of an embodiment of the present invention, there is provided a method for determining operating parameters of an electric power proxy object, comprising: obtaining regional characteristic factors of a first preset area where the electric power proxy object is to perform electric power operation, and obtaining a first characteristic factor set; predicting user electricity consumption data of the first preset area based on the first characteristic factor set, and obtaining first electricity consumption data; constructing a profit objective function for the electric power proxy object to perform electric power operation in the first preset area, wherein the profit objective function is used to represent a functional relationship established with the profit of the electric power proxy object as the target; determining the operating parameters of the electric power proxy object based on the first electricity consumption data and the profit objective function, wherein the operating parameters include at least an amount of purchased electricity and an amount of electricity selling price, wherein the amount of purchased electricity is used to represent the amount of electricity purchased by the electric power proxy object from the electric power market, and the amount of electricity selling price is used to represent the price at which the electric power proxy object sells electricity to the first preset area.

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

[0008] Optionally, based on the first feature factor set and the second feature factor set, at least one target area is screened out from multiple second preset areas, including: constructing a first feature matrix based on the first feature factor set, and constructing a second feature matrix based on the second feature factor set; using a preset area similarity evaluation model, based on the first feature matrix and the second feature matrix, to screen out the target area from the multiple second preset areas, wherein the preset area similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between areas.

[0009] Optionally, based on the target power consumption data of the target area, the user power consumption data of the first preset area is predicted to obtain the first power consumption data, including: obtaining the regional characteristic difference coefficient and the user characteristic difference coefficient between the first preset area and the target area, wherein the regional characteristic 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 characteristic 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 characteristic difference coefficient and the user characteristic difference coefficient, the target power consumption data is corrected to obtain the corrected target power consumption data; based on the corrected target power consumption data, the user power consumption data of the first preset area is predicted to obtain the first power consumption data.

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

[0011] Optionally, based on the first electricity consumption data and the profit objective function, the operating parameters of the power proxy object are determined, including: obtaining a set of constraints for the power proxy object to perform power operation in a first preset area, wherein the constraint set includes at least one of the following: new energy output constraints, monthly profit constraints of the proxy object, and monthly power balance constraints; based on the first electricity consumption data and the constraint set, the profit objective function is solved to obtain the operating parameters.

[0012] According to another aspect of an embodiment of the present invention, there is also provided an operating parameter determination device for an electric power proxy object, comprising: an acquisition module, used to acquire regional characteristic factors of a first preset area where the electric power proxy object is to perform electric power operation, and obtain a first characteristic factor set; a prediction module, used to predict user electricity consumption data of the first preset area based on the first characteristic factor set, and obtain first electricity consumption data; a construction module, used to construct a profit objective function for the electric power proxy object to perform electric power operation in the first preset area, wherein the profit objective function is used to represent a functional relationship established with the profit of the electric power proxy object as the target; a determination module, used to determine the operating parameters of the electric power proxy object based on the first electricity consumption data and the profit objective function, wherein the operating parameters include at least a purchased electricity quantity and a sold electricity price, wherein the purchased electricity quantity is used to represent the quantity of electricity purchased by the electric power proxy object from the electric power market, and the sold electricity price is used to represent the price at which the electric power proxy object sells electricity to the first preset area.

[0013] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0017] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0018] In an embodiment of the present invention, a method for determining operating parameters of an electric power proxy object is provided, comprising: obtaining regional characteristic factors of a first preset area where the electric power proxy object is to perform electric power operation, and obtaining a first characteristic factor set; predicting user electricity consumption data of the first preset area based on the first characteristic factor set, and obtaining first electricity consumption data; constructing a profit target function for the electric power proxy object to perform electric power operation in the first preset area, wherein the profit target function is used to represent a functional relationship established with the profit of the electric power proxy object as the target; determining the operating parameters of the electric power proxy object based on the first electricity consumption data and the profit target function, wherein the operating parameters include at least a purchased electricity amount and a sold electricity price, wherein the purchased electricity amount is used to represent the amount of electricity purchased by the electric power proxy object from the electric power market, and the sold electricity price is used to represent the price at which the electric power proxy object sells electricity to the first preset area. It is easy to notice that this application obtains the regional characteristic factors and user electricity consumption data of the first preset area, constructs the profit target function of the power proxy object for power operation in the area, and determines the operating parameters of the power proxy object based on the first electricity consumption data and the profit target function, so as to have a more comprehensive understanding and prediction of the power consumption in the area, and can comprehensively consider the power purchase cost, electricity sales income and other related costs in the power market, so as to determine the profit target of the power proxy object. By comprehensively analyzing and predicting the characteristic factors and user electricity consumption data of the first preset area, and combining the profit target function to determine the optimal operating parameters, the operating parameters can be better formulated, resource allocation can be optimized, operating efficiency can be improved, and sustainable development can be achieved. At the same time, through the optimization solution of the mathematical model, the optimal solution can be quickly found, manual intervention can be reduced, and work efficiency can be improved, thereby solving the technical problem of low efficiency in determining the operating parameters of the power proxy object in the operation process in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of a method for determining an operation parameter of a power proxy object according to an embodiment of the present invention;

[0021] Figure 2 is a flow chart of an optional method for determining operating parameters of a power proxy object according to an embodiment of the present invention;

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

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

[0024] Figure 5 is a schematic diagram of a device for determining operating parameters of a power proxy object according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

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

[0028] Figure 1 is a flow chart of a method for determining an operation parameter of a power proxy object according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0029] Step S102: Acquire regional characteristic factors of a first preset area where the power proxy object is to perform power operation, and obtain a first characteristic factor set.

[0030] The above-mentioned power agency object may refer to an enterprise or organization that acts as an agent or sells electricity, which may be called a power agency. The power agency object cooperates with power plants or power suppliers to provide electricity to users.

[0031] The above-mentioned 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 first preset area may be determined according to actual needs and is not limited here.

[0032] The above-mentioned regional characteristic factors may refer to various factors that affect the electricity market in the first preset area. The regional characteristic factors may include characteristic factors such as the community area, floor area ratio, decoration conditions, number of parking spaces, greening rate, or local population size, industrial structure, electricity demand, and power supply stability. By analyzing and evaluating these factors, the power agency can better understand the market environment of the region and formulate corresponding operating parameters.

[0033] In an optional embodiment, the regional characteristic factors of the first preset area where the power proxy object is to carry out power operation can be obtained to obtain a first characteristic factor set. Specifically, the specific preset area where the power proxy object is to carry out power operation can be determined, and then various characteristic data of the preset area can be collected, such as climate conditions, power industry structure, power load curve, distribution of power generation facilities, etc., which can be obtained through power companies and other institutions. Then, the collected regional characteristic data can be analyzed to determine the characteristic factors that have a greater impact on the operation of the power proxy object, such as the peak-to-valley difference in power load, the development potential of renewable energy, and the stability of the power grid, to form a first characteristic factor set, which can be used as a data reference for the power proxy object when it carries out power operation in the preset area. Through the implementation of the above steps, detailed characteristic information about the preset area where the power proxy object is to carry out power operation can be provided to the power proxy object to help it better formulate operating parameters, resource allocation, etc.

[0034] In the above steps, by analyzing and extracting the characteristic factors of the preset area, it can help the power agency object to more accurately evaluate the market demand, competition situation and development potential, thereby reducing business risks. Under the guidance of the characteristic factor set, the power agency object can formulate operation parameters and resource allocation plans more targeted, improve operation efficiency and service quality, and by obtaining the regional characteristic factors of the preset area and forming a characteristic factor set, it can provide a scientific basis for the operation determination of the power agency object and promote it to achieve better results and performance in market services.

[0035] Step S104: predicting the power consumption data of users in the first preset area based on the first characteristic factor set to obtain first power consumption data.

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

[0037] In an optional embodiment, in the process of determining the operating parameters of the power proxy object, the user power consumption data of the first preset area is predicted based on the first characteristic factor set, which can help the proxy object to more accurately understand the user's power consumption, so as to better formulate the operating parameters. Specifically, the key characteristic factors affecting the user's power consumption can be determined, which may include the user's power consumption habits, weather conditions, seasonal changes, etc., and through data analysis and statistical methods, representative and influential characteristic factors can be determined. Machine learning, data mining and other technologies can be used to build a prediction model for power consumption data, and regression analysis, time series analysis and other methods can be used in combination with the first characteristic factor set to predict the user's power consumption data. The prediction model constructed by the above steps can predict the user's power consumption data in the first preset area to obtain the first power consumption data. The determination method of the first power consumption data can be determined according to actual needs, which is not limited here. The first power consumption data can help the proxy object to more accurately understand the user's power consumption and provide a reference for formulating operating parameters. By predicting based on the characteristic factor set, the law and change trend of user power consumption can be more accurately captured, thereby improving the accuracy of the prediction.

[0038] Step S106: constructing a profit objective function for the power proxy object to perform power operation in the first preset area.

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

[0040] The above-mentioned profit objective function may refer to a mathematical function relationship established with the annual profit or monthly profit of the power agency object as the optimization target, which may be determined according to actual needs and is not limited here.

[0041] In an optional embodiment, during the process of determining the operating parameters of the power proxy object, a revenue objective function for the power proxy object to operate the power in the first preset area can be constructed to help the proxy object improve its revenue during the operation process. The revenue objective function can be composed of multiple variables, which can include factors such as the price of selling power, operating costs, and market demand. The operating costs of the proxy object can be determined, including the cost of purchasing power, the cost of transmission, and the cost of distribution. Other variables of the objective function can also be determined, such as market demand, power supply capacity, power load, etc. These variables can be determined based on historical data and market forecasts. Then, the selling power price, operating costs and other variables can be combined to establish a mathematical model to describe the revenue of the proxy object, so that the revenue objective function can be obtained. Finally, mathematical tools and optimization algorithms can be used to solve this objective function to find the appropriate selling power price and operating parameters to improve the revenue and operating service level of the power proxy object. By constructing the revenue objective function, the proxy object can better formulate operating parameters, optimize resource allocation, improve operating efficiency, and achieve sustainable development. At the same time, through the optimization solution of the mathematical model, the optimal solution can be quickly found, manual intervention can be reduced, and work efficiency can be improved.

[0042] Step S108: determining the operation parameters of the power proxy object based on the first power consumption data and the profit objective function.

[0043] The operation parameters include at least a purchase amount and a selling price of electricity. The purchase amount is used to indicate the amount of electricity purchased by the power proxy from the power market, and the selling price of electricity is used to indicate the price at which the power proxy sells electricity to the first preset area.

[0044] The above-mentioned operating parameters may refer to the parameters that the power agency needs to take to perform power operations in the first preset area, and may include but are not limited to the amount of electricity purchased by the power agency from the power market, the power purchased by the power agency from the power market, the price of electricity sold by the power agency to the first preset area, etc., which can be determined according to actual needs and are not limited here. The operating parameters that need to be determined in this application are data results obtained by scientific data processing that conforms to the laws of nature, and do not belong to management methods and systems in production, commercial implementation and economy.

[0045] In an optional embodiment, when determining the operating parameters of the power proxy object, analysis and optimization can be performed based on the first power consumption data and the revenue objective function. The power purchase amount and the power selling price can be used as operating parameters to establish a mathematical model to describe the operation process of the power proxy object. The model may include constraints and objective functions to ensure that the power proxy object can achieve the expected revenue target during the operation process. Then, by optimizing the established operating parameter model, a suitable power purchase amount and power selling price can be obtained. The optimization goal can enable the power proxy object to increase profits or reduce operating costs while meeting user needs. Through the implementation of the above steps, it can help the power proxy object to better formulate the power purchase amount and power selling price, improve operational efficiency, optimize resource allocation, and improve market competitiveness, etc., which will help the power proxy object to achieve better performance and development in the market.

[0046] In an embodiment of the present invention, a method for determining operating parameters of an electric power proxy object is provided, comprising: obtaining regional characteristic factors of a first preset area where the electric power proxy object is to perform electric power operation, and obtaining a first characteristic factor set; predicting user electricity consumption data of the first preset area based on the first characteristic factor set, and obtaining first electricity consumption data; constructing a profit target function for the electric power proxy object to perform electric power operation in the first preset area, wherein the profit target function is used to represent a functional relationship established with the profit of the electric power proxy object as the target; determining the operating parameters of the electric power proxy object based on the first electricity consumption data and the profit target function, wherein the operating parameters include at least a purchased electricity amount and a sold electricity price, wherein the purchased electricity amount is used to represent the amount of electricity purchased by the electric power proxy object from the electric power market, and the sold electricity price is used to represent the price at which the electric power proxy object sells electricity to the first preset area. It is easy to notice that this application obtains the regional characteristic factors and user electricity consumption data of the first preset area, constructs the profit target function of the power proxy object for power operation in the area, and determines the operating parameters of the power proxy object based on the first electricity consumption data and the profit target function, so as to have a more comprehensive understanding and prediction of the power consumption in the area, and can comprehensively consider the power purchase cost, electricity sales income and other related costs in the power market, so as to determine the profit target of the power proxy object. By comprehensively analyzing and predicting the characteristic factors and user electricity consumption data of the first preset area, and combining the profit target function to determine the optimal operating parameters, the operating parameters can be better formulated, resource allocation can be optimized, operating efficiency can be improved, and sustainable development can be achieved. At the same time, through the optimization solution of the mathematical model, the optimal solution can be quickly found, manual intervention can be reduced, and work efficiency can be improved, thereby solving the technical problem of low efficiency in determining the operating parameters of the power proxy object in the operation process in the related technology.

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

[0048] The above-mentioned second 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 old residential areas, old communities or old streets, etc. The richness of the historical electricity consumption data of the second preset area is greater than the richness of the historical electricity consumption data of the first preset area. The second preset area can be determined according to actual needs and is not limited here.

[0049] The target area mentioned above may refer to an area screened from a plurality of second preset areas and having a high similarity to the first preset area. It may be determined according to actual needs and is not limited here.

[0050] In an optional embodiment, regional characteristic factors of multiple second preset areas, including information such as power operation time, can be obtained to form a second characteristic factor set, and the power operation time of any one of the multiple second preset areas is greater than the power operation time of the first preset area. Then, based on the first characteristic factor set and the second characteristic factor set, at least one target area can be screened out from the multiple second preset areas, and data mining or machine learning algorithms can be used for screening and matching. Finally, based on the target power consumption data of the target area, the user power consumption data of the first preset area can be predicted using a prediction model to obtain the first power consumption data. In the above steps, by introducing the data and characteristic factors of the second preset area, the accuracy and reliability of the user power consumption data of the first preset area can be improved. At the same time, the screened target area can provide more reference data, further improve the accuracy and credibility of the prediction model, and effectively improve the prediction effect of user power consumption data in the field of determining the operating parameters of the power proxy object.

[0051] Optionally, based on the first feature factor set and the second feature factor set, at least one target area is screened out from multiple second preset areas, including: constructing a first feature matrix based on the first feature factor set, and constructing a second feature matrix based on the second feature factor set; using a preset area similarity evaluation model, based on the first feature matrix and the second feature matrix, to screen out the target area from the multiple second preset areas, wherein the preset area similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between areas.

[0052] In an optional embodiment, the relevant characteristics of each preset area can be quantified according to the first characteristic factor set, and a first characteristic matrix can be constructed, which can include characteristic information such as power demand, power consumption characteristics, and industry type of each area. Then, the relevant characteristics of each preset area can be quantified according to the second characteristic factor set, and a second characteristic matrix can be constructed, which can include characteristic information such as power supply, power price, and renewable energy utilization of each area. Then, based on the first characteristic matrix and the second characteristic matrix, the similarity between each preset area can be calculated using a preset area similarity evaluation model. The preset area similarity evaluation model can use various similarity measurement methods, such as Euclidean distance, cosine similarity, etc., to evaluate the similarity between different areas. Furthermore, based on the calculated similarity results, at least one target area can be screened out from the multiple second preset areas, and the area with higher similarity can be selected as the target area to ensure that the target area has similar characteristics and environment as the current operating area. Through such steps, it is possible to screen out the target area from the multiple second preset areas. In the above steps, by considering different sets of characteristic factors, the situation of each preset area can be more comprehensively evaluated, so as to more accurately select the target area. By calculating the similarity evaluation model, it can be ensured that the target area has similar characteristics to the current operating area, thereby reducing the risk of operating in a new area. By automatically calculating the similarity and screening the target area, it is possible to reduce manual intervention and determination time, thereby improving operational efficiency and rapidity of determination.

[0053] Optionally, based on the target power consumption data of the target area, the user power consumption data of the first preset area is predicted to obtain the first power consumption data, including: obtaining the regional characteristic difference coefficient and the user characteristic difference coefficient between the first preset area and the target area, wherein the regional characteristic 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 characteristic 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 characteristic difference coefficient and the user characteristic difference coefficient, the target power consumption data is corrected to obtain the corrected target power consumption data; based on the corrected target power consumption data, the user power consumption data of the first preset area is predicted to obtain the first power consumption data.

[0054] In an optional embodiment, the regional characteristic difference coefficient and the user characteristic difference coefficient between the first preset area and the target area can be obtained, and the relevant data of the first preset area and the target area can be collected, including regional characteristics, such as climate, population density, economic conditions, etc. and user characteristics, such as electricity usage habits, electricity equipment, etc. Then, through data analysis and modeling methods, the regional characteristic difference coefficient and the user characteristic difference coefficient are calculated to quantitatively represent the characteristic difference between the first preset area and the target area. Then, the target electricity consumption data can be corrected based on the regional characteristic difference coefficient and the user characteristic difference coefficient. According to the calculated difference coefficient, the electricity consumption data of the target area is corrected, and the difference between the regional and user characteristics is taken into account to obtain more accurate target electricity consumption data. Then, based on the corrected target electricity consumption data, the electricity consumption data of the users in the first preset area can be predicted, and the corrected target electricity consumption data can be applied to the users in the first preset area to predict the electricity consumption data. By taking into account the difference between the regional and user characteristics, more accurate first electricity consumption data is obtained. In the above steps, by taking into account the characteristic differences of different regions and users, correcting the target electricity consumption data and predicting the first electricity consumption data, the actual situation can be reflected more accurately and prediction deviation can be avoided. The electricity consumption data prediction method based on regional and user characteristic differences can effectively improve the prediction accuracy, optimize resource allocation, improve user satisfaction, and provide more effective data support for the determination of the operating parameters of the power proxy object.

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

[0056] The above-mentioned corrected target electricity consumption data may refer to data obtained after correction and adjustment of the target electricity consumption data, and may be obtained by processing the original data considering various factors such as weather, seasonality, etc. The corrected target electricity consumption data is closer to the actual situation and can improve the accuracy of the prediction.

[0057] In an optional embodiment, the preset smoothing algorithm can be a probabilistic calculation model, which is used to smooth the corrected target power consumption data, reduce the volatility of the data, and make the data more stable and predictable. The preset smoothing algorithm can use moving average method, exponential smoothing method and other methods, and obtain smoothed data by weighted averaging the historical values ​​of the original data. The corrected target power consumption data is the data after correction and adjustment, which is closer to the actual situation. Combining the corrected target power consumption data with the data obtained by the preset smoothing algorithm can more accurately predict the user power consumption data, and obtain the prediction result of the user power consumption data in the first preset area, that is, the first power consumption data. In the above steps, by using the corrected target power consumption data and the preset smoothing algorithm, the volatility of the data can be reduced, the accuracy of the prediction can be improved, and the prediction result can be closer to the actual situation; the corrected target power consumption data can better reflect the actual situation, and combined with the preset smoothing algorithm, the prediction error can be reduced and the reliability of the prediction can be improved.

[0058] Optionally, based on the first electricity consumption data and the profit objective function, the operating parameters of the power proxy object are determined, including: obtaining a set of constraints for the power proxy object to perform power operation in a first preset area, wherein the constraint set includes at least one of the following: new energy output constraints, monthly profit constraints of the proxy object, and monthly power balance constraints; based on the first electricity consumption data and the constraint set, the profit objective function is solved to obtain the operating parameters.

[0059] In an optional embodiment, according to the demand and restriction of the power proxy object for power operation in the first preset area, a set of constraints can be determined, wherein the new energy output constraint condition can limit the proportion or quantity of the proxy object using new energy; the monthly income constraint condition of the proxy object can limit the total income of the proxy object to not exceed the preset value; the monthly power balance constraint can require the proxy object to maintain the balance of power supply and demand in each month. Then, the first power consumption data and the set of constraints can be substituted into the income objective function, and the income objective function can be solved using mathematical optimization methods such as linear programming and integer programming to obtain appropriate operating parameters, including power purchase volume, new energy utilization ratio, sales electricity price, etc. Through the implementation of the above steps, it can help the power proxy object to formulate operating parameters more scientifically during the operation process, effectively manage power supply and demand, and improve operational efficiency; at the same time, through the setting of constraints and the solution of the income objective function, it can increase profits or achieve other operating goals under the premise of ensuring the stability of the proxy object's operation, improve the adaptability of the proxy object to market changes, optimize the power operation plan, and provide effective support for the sustainable development of the power proxy object.

[0060] The technical solution proposed in this application is explained below in combination with an optional application scenario. With the marketization of electricity and the connection of large-scale new energy sources to the grid, the relationship between electricity supply and demand is becoming increasingly complex, especially the volatility and uncertainty of the medium- and long-term electricity market are becoming increasingly significant. At present, residential users still find it difficult to directly participate in the medium- and long-term electricity market due to their small electricity demand, and need to rely on electricity proxy objects to participate in the electricity market for electricity purchase and sale and pricing operations. However, most of the existing pricing methods for electricity proxy objects explore the impact of new energy uncertainty on electricity prices from a short-term time scale, ignoring the uncertainty of the load of residential users in newly built communities and the output of new energy on a medium- and long-term time scale, which will cause residential users to bear greater risks of electricity price fluctuations in the medium and long term. Therefore, how to formulate reasonable and stable electricity prices for the electricity consumption of users in newly built communities through proxy objects while taking into account the uncertainty of the load of residential users in newly built communities and the output of new energy sources is a problem that needs to be solved urgently.

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

[0062] The present application provides an optimal pricing and operation method for proxy objects of newly built communities taking into account the medium- and long-term electricity market, in order to support the construction of the electricity market trading mechanism and the planning and operation of the new power distribution and utilization system. The specific steps are as follows: First, considering the characteristic factors such as the area and volume ratio of the community, a community similarity evaluation 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 of the community and the householder, an uncertainty model for electricity consumption of users in newly built communities within the medium- and long-term time scale is established; secondly, based on the uncertainty model for electricity consumption of users in newly built communities, a two-layer interval number-master-slave game pricing and operation model for proxy objects taking into account the medium- and long-term electricity market is established; finally, the optimal conditions of the convex optimization problem (Karush-Kuhn-Tucker, abbreviated as KKT) are used to transform the two-layer game model into a single layer, and the interval linear programming algorithm is used to solve the single-layer linear model to obtain the optimal power purchase and pricing parameters for the proxy objects in the medium- and long-term electricity market. This application starts from the perspective of the agent, takes into account the uncertainty of electricity consumption in newly built communities, and provides an economical and reliable operation plan for the newly built community agents to participate in the medium and long-term electricity market; it promotes users' on-site consumption of new energy and improves the safety, reliability and low-carbon nature of the operation of new power distribution systems.

[0063] The purpose of this application is to construct a two-layer interval number-master-slave game optimal pricing model for new community proxy objects considering the medium- and long-term electricity market based on the uncertainty of electricity consumption of new community users, and provide an economical and reliable operation plan for new community proxy objects to participate in the medium- and long-term electricity market. At the same time, it promotes users to consume new energy locally and improves the safety, reliability and low-carbon nature of the operation of new power distribution and utilization systems. The following steps may be included:

[0064] Considering the characteristic factors such as the area, plot ratio, decoration, number of parking spaces, and greening rate of the community, a community similarity evaluation model is constructed based on the cosine distance method, and the load curve of users in the old community with high similarity to the new community is obtained. The differences in community characteristics and household characteristics between the new community and the old community are quantified, and the corresponding characteristic difference correction coefficients are generated. The electricity consumption data of users in the old community with high similarity are corrected based on the correction coefficients of community and householder characteristics. The Laplace smoothing method is used to establish an uncertainty model of electricity consumption for users in the new community in the medium and long-term time scale. Based on the uncertainty model of electricity consumption for users in the new community, a two-layer interval number-master-slave game pricing operation model for proxy objects considering the medium and long-term electricity market is established. The Lagrangian function is constructed for the lower layer of the two-layer model of proxy object pricing operation, and the KKT optimal condition of the lower model is obtained, which is substituted into the upper model to obtain the single-layer model of the optimal pricing of the proxy object. The interval linear programming algorithm is used to solve the single-layer linear model of the optimal pricing of the proxy object, and the optimal power purchase and pricing parameters of the proxy object in the medium and long-term electricity market are obtained.

[0065] Furthermore, considering the characteristic factors such as the area, plot ratio, decoration, number of parking spaces, and greening rate of the community, a community similarity evaluation model is constructed based on the cosine distance method to obtain the user load curve of the old community with high similarity to the new community. The specific method is as follows:

[0066] Considering the characteristic factors such as the area, plot ratio, decoration, number of parking spaces, and greening rate of the community, that is, the first characteristic factor set and the second characteristic factor set, the characteristic matrix α of the i-th old community is constructed i , which is the second characteristic matrix, and the characteristic matrix β of the newly built cell, which is the first characteristic matrix, are as follows:

[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 are the area, plot ratio, decoration, number of parking spaces and greening rate of the i-th old residential area; I is the total number of old residential areas; a, b, c, d, e are the area, plot ratio, decoration, number of parking spaces and greening rate of the newly built residential areas.

[0070] A cell similarity evaluation model is constructed based on the cosine distance method, that is, a preset area similarity evaluation model, as shown in the following formula:

[0071]

[0072] Among them, π i is the similarity between the new community and the i-th old community; cosθ is the similarity between the new community feature matrix β and the i-th old community feature matrix α i The cosine value of .

[0073] Compare the similarity π between the newly built community and the i-th old community i The size of the old community with the highest similarity to the new community is obtained, that is, the target area; then the power load curve P of the old community with the highest similarity to the new community in time period m can be obtained old,L (m), namely the target electricity consumption data and the corresponding user characteristics such as user check-in time and number of occupants.

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

[0075] There are differences in community characteristics such as floor area and plot ratio, as well as household characteristics such as user move-in time and number of residents between the newly built communities and the highly similar old communities. It is necessary to quantify the above-mentioned characteristic differences and generate corresponding correction coefficients. The specific analysis is as follows:

[0076] Differences in community characteristics: quantify differences in floor area, plot ratio, decoration, number of parking spaces, greening rate, etc. between new communities and highly similar old communities, and generate a weight correction coefficient δ k , to correct the electricity consumption data of high similarity cells, as follows:

[0077]

[0078] Among them, δ k is the weight correction coefficient of the kth cell characteristic difference, that is, the regional characteristic difference coefficient; α old It is the feature matrix of old neighborhoods with high similarity.

[0079] Differences in householder characteristics: For differences in householder characteristics such as user check-in time and number of residents between new communities and highly similar old communities, the characteristic correction coefficient γ is used toh Quantify it to adjust the forecast results of electricity load for users in the newly built community, as shown in the following formula:

[0080]

[0081] Among them, γ h is the characteristic correction coefficient of the hth householder characteristic difference, that is, the user characteristic difference coefficient; X is the total number of users in the newly built community; x represents the xth user in the newly built community; μ is the householder characteristic matrix of the newly built community; Y is the total number of users in the old community with high similarity; y represents the yth user in the old community with high similarity; ν old It is the household head feature matrix of high-similarity old communities.

[0082] Furthermore, based on the correction coefficient of the difference in characteristics of the community and the householder, the electricity consumption data of users in the old community with high similarity is corrected. The specific method is as follows:

[0083] According to the weight coefficient of community feature difference and the correction coefficient of household feature difference, in order to improve the prediction accuracy of electricity consumption data of users in newly built communities, it is necessary to correct the electricity consumption data of users in old communities with high similarity, as shown in the following formula:

[0084]

[0085] in, is the corrected electricity consumption data of users in old communities with high similarity, that is, the corrected target electricity consumption data; K is the total number of community features; H is the total number of householder features.

[0086] Furthermore, the Laplace smoothing method is used to establish the uncertainty model of electricity consumption of users in the newly built community in the medium and long term time scale. The specific method is as follows:

[0087] Since there is no actual historical electricity consumption data for newly built communities, directly relying on the electricity consumption data of old communities may be sparse or insufficient in certain periods of time. In order to avoid zero probability or data deviation in the prediction process, the Laplace smoothing method, which can be a preset smoothing algorithm, is used to predict the electricity consumption data of users in newly built communities, as shown in the following formula:

[0088]

[0089] in, is the predicted electricity consumption data of users in the newly built community, that is, the first electricity consumption data; κ is the smoothing parameter.

[0090] Due to the uncertainty of factors such as the decoration of the newly built community, the time when the household owner moved in, and the number of residents, in order to make the predicted electricity consumption data of the newly built community users more accurate and reliable, it is necessary to use the standard deviation σ(m) of the historical electricity consumption data of users in the old community with high similarity to measure the fluctuation range of the electricity load of the newly built community users, so as to obtain its uncertainty range. The specific formula is as follows:

[0091]

[0092] in, are the minimum and maximum power loads of users in the newly built community in the mth month, respectively; r is the prediction accuracy parameter; σ(m) is the standard deviation of the historical power loads of users in the old community with high similarity.

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

[0094] The upper layer takes the agent as the leader and takes the maximization of the agent's annual income as the objective function to build the agent's electricity purchase and pricing operation model. Its objective function, that is, the income objective function, is as follows:

[0095]

[0096] Among them, F agent is the annual income of the agent; Z is the total number of months in a year; The unit power price of electricity sold by the agent to the user in the mth month, that is, the unit power price of electricity purchased by the user from the agent in the mth month; is the power sold by the agent to the user in month m, that is, the power purchased by the user from the agent in month m; Δt is the time interval; The unit electricity price that the agent purchases from the medium- and long-term electricity market in month m; is the power purchased by the agent from the medium- and long-term power market in month m; c PV The unit operation and maintenance cost required for the agent to manage distributed photovoltaics; is the output of distributed photovoltaic in the mth month.

[0097] In addition, the model also needs to consider constraints such as new energy output constraints, monthly revenue constraints of agents, medium- and long-term contract constraints, and monthly power balance constraints, that is, the constraint set, which can be specifically as follows:

[0098] New energy output constraints. In response to the trend of clean energy, the agent builds distributed photovoltaic devices in new communities. Since photovoltaic output is greatly affected by factors such as temperature and light, and has great randomness and uncertainty, the output of distributed photovoltaics will fluctuate within a certain range, as shown in the following formula:

[0099]

[0100] in, They are the minimum and maximum output of distributed photovoltaic in the mth month respectively.

[0101] Monthly revenue constraints for agents: In order to enhance the vitality of agents in participating in the medium- and long-term electricity market, the monthly revenue of agents in the process of participating in the medium- and long-term electricity market should be greater than their monthly operating costs, as shown in the following formula:

[0102]

[0103] in, is the operating cost of the agent in month m.

[0104] The medium- and long-term contract constraints mean that the agent signs a power purchase contract with the power generator during the process of participating in the medium- and long-term power market. Therefore, the power purchase price and power purchase power reported by the agent to the medium- and long-term power market should be limited to a certain range, as shown in the following formula:

[0105]

[0106] in, They are the upper and lower limits of the electricity purchase price declared by the agent of the newly built community in the mth month of the medium and long-term market; They are respectively the upper and lower limits of the power purchase power declared by the agent in the mth month in the medium and long-term market.

[0107] Monthly power balance constraints, during the operation of the new community power distribution system, should meet the monthly power balance constraints, as follows:

[0108]

[0109] The lower layer takes users as followers and takes the maximization of user annual benefits and comfort as the objective function to establish a user electricity operation model. Since user comfort is mainly related to user electricity consumption and its electricity price, the greater the user's electricity consumption, the less electricity bills paid, and the higher the user's comfort, the objective function of the model is as follows:

[0110]

[0111] Among them, F user Annual benefits for users.

[0112] In addition, in this model, the user's monthly expected benefit constraints must also be considered, as follows:

[0113]

[0114] in, The monthly expected benefits for users.

[0115] Furthermore, a Lagrangian function is constructed for the lower layer of the two-layer model of proxy object pricing operation to obtain the KKT optimal condition of the lower layer model, and it is substituted into the upper layer model to obtain the single-layer model of optimal pricing of proxy objects. The specific method is as follows:

[0116] For the lower user electricity operation model of the two-layer model of proxy object pricing operation, a Lagrangian function is constructed as follows:

[0117]

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

[0119] Because the model is ultimately solved using the interval linear programming algorithm, the operating variables of the model are the power purchased by the agent from the medium- and long-term market and the electricity price of the user. Therefore, the optimal condition of the lower model KKT can be obtained as follows:

[0120]

[0121] The optimal conditions of the lower model KKT are substituted as constraints into the upper layer optimal electricity purchase and pricing operation model for the agent objects of the newly built community, and the single-layer model of the agent object optimal pricing is obtained.

[0122] Furthermore, the interval linear programming algorithm is used to solve the single-layer linear model of the optimal pricing of the agent object, and the optimal power purchase and pricing parameters of the agent object in the medium and long-term power market are obtained. The specific method is as follows:

[0123] According to the annual income function of the agent object, when the user's electricity load is the maximum value of the interval, the price of electricity purchased by the agent from the medium- and long-term electricity market is the minimum value of the interval, and the distributed photovoltaic output of the new community is the maximum value of the interval, the annual income of the agent object of the new community is the maximum value; and when the user's electricity load is the minimum value of the interval, the price of electricity purchased by the agent from the medium- and long-term electricity market is the maximum value of the interval, and the distributed photovoltaic output of the new community is the minimum value of the interval, the annual income of the agent object of the new community is the minimum value; in order to ensure that the agent object directly selects the optimal operation plan from the annual income interval, the pessimism of the agent object to uncertainty is introduced into the single-layer linear model of the optimal pricing of the agent object, so the objective function of the model is as follows:

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

[0125] Among them, F agent is the annual return range of the agent; ω is the pessimism of the agent to uncertainty, ranging from 0 to 1. The larger the value within this range, the better the agent's ability to accept future uncertainty; e(F agent )、f(F agent ) are respectively the middle term and width term of the annual return range of the proxy object, and their specific expressions are as follows:

[0126]

[0127] in, They are respectively the maximum and minimum annual income of the agent.

[0128] The optimal pricing single-layer linear model of the proxy object of the newly built community is solved on the solver (YALMIP / CPLEX) of the software (MATLAB) or other similar software and solvers, and the optimal power purchasing and pricing parameters of the proxy object of the newly built community in the medium and long-term electricity market are obtained.

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

[0130] Figure 2 is a flow chart of an optional method for determining operating parameters of a power proxy object according to an embodiment of the present invention. Figure 2 As shown, including:

[0131] S202: Acquire regional characteristic factors of a first preset area where the power proxy object is to perform power operation, and obtain a first characteristic factor set.

[0132] S204: Acquire regional characteristic factors of a plurality of second preset regions to obtain a second characteristic factor set.

[0133] S206, constructing a first feature matrix based on the first feature factor set, and constructing a second feature matrix based on the second feature factor set; using a preset region similarity evaluation model, based on the first feature matrix and the second feature matrix, screening out a target region from a plurality of second preset regions.

[0134] S208, obtaining the regional characteristic difference coefficient and the user characteristic difference coefficient between the first preset area and the target area; based on the regional characteristic difference coefficient and the user characteristic difference coefficient, correcting the target power consumption data of the target area to obtain the corrected target power consumption data; based on the corrected target power consumption data, predicting the user power consumption data of the first preset area to obtain the first power consumption data.

[0135] S210, constructing a profit objective function for the power proxy object to perform power operation in a first preset area.

[0136] S212: Determine the operation parameters of the power proxy object based on the first power consumption data and the profit objective function.

[0137] The following is a calculation example of the method of the present application. Taking a newly built community in a certain place as an example, based on the above steps, the uncertainty model of electricity consumption of users in the newly built community can be obtained, that is, the first electricity consumption data. Figure 3 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, the horizontal axis in the figure represents the month, the vertical axis represents the power consumption, and the figure includes the maximum value curve ① of the user load curve in the predicted first power consumption data, the predicted value curve ② of the user load curve in the predicted first power consumption data, and the minimum value curve ③ of the user load curve in the predicted first power consumption data.

[0138] At the same time, the cost of setting the new community agent to manage distributed photovoltaics is 0.0125 yuan / (kWh·month), the agent's pessimism about uncertainty is 0.8, and the rated capacity of distributed photovoltaics is 1.5MW. Based on the above steps, the optimal power purchasing parameters and pricing parameters of the agent in the medium- and long-term electricity market can be obtained. Figure 4 is a schematic diagram of the power purchase amount in an optional operation parameter obtained according to an embodiment of the present invention, such as Figure 4 As shown, the horizontal axis in the figure represents the month, the vertical axis represents the amount of electricity purchased, and the figure includes the maximum value curve ① of the amount of electricity purchased in the obtained operating parameters, the optimal value curve ② of the amount of electricity purchased in the obtained operating parameters, and the minimum value curve ③ of the amount of electricity purchased in the obtained operating parameters. Table 1 below is an optional electricity selling price in an 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 optimal pricing single-layer linear model of the agent object respectively, and the annual income that the agent object can obtain when accepting different degrees of uncertainty can be obtained. The following Table 2 is an optional summary table of the annual income of the agent object under different pessimism obtained according to an embodiment of the present invention.

[0142] Table 2

[0143]

[0144] According to the above results, when the pessimism is 0, it means that the agent does not accept any uncertainties, its operation is the most conservative, and its return is the lowest; when the pessimism is 1, it means that the agent accepts all uncertainties, its operation is the most risky, but its return is the highest. Therefore, the greater the agent's pessimism about uncertainty, the higher the return and the greater the uncertainty.

[0145] According to another aspect of an embodiment of the present invention, a device for determining operating parameters of an electric power proxy object is also provided. The device can execute the method for determining operating parameters of an electric power proxy object of the above embodiment. The specific implementation method and preferred application scenario are the same as those of the above embodiment and will not be repeated here.

[0146] Figure 5 is a schematic diagram of an operating parameter determination device for a power proxy object according to an embodiment of the present 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] Among them, the acquisition module is used to obtain the regional characteristic factors of the first preset area where the power proxy object is to carry out power operation, and obtain the first characteristic factor set; the prediction module is used to predict the user power consumption data of the first preset area based on the first characteristic factor set, and obtain the first power consumption data; the construction module is used to construct the profit target function of the power proxy object for power operation in the first preset area, wherein the profit target function is used to represent the functional relationship established with the profit of the power proxy object as the goal; the determination module is used to determine the operating parameters of the power proxy object based on the first power consumption data and the profit target function, wherein the operating parameters include at least the purchase quantity and the sales price of electricity, the purchase quantity is used to represent the amount of electricity purchased by the power proxy object from the power market, and the sales price of electricity is used to represent the price of electricity sold by the power proxy object to the first preset area.

[0148] Among them, the prediction module is also used to obtain regional characteristic factors of multiple second preset areas to obtain a second characteristic factor set, wherein the power operation time of any area among the multiple second preset areas is greater than the power operation time of the first preset area; based on the first characteristic factor set and the second characteristic factor set, at least one target area is screened out from the multiple second preset areas; based on the target power consumption data of the target area, the user power consumption data of the first preset area is predicted to obtain the first power consumption data.

[0149] Among them, the prediction module is also used to construct a first feature matrix based on the first feature factor set, and to construct a second feature matrix based on the second feature factor set; using a preset regional similarity evaluation model, based on the first feature matrix and the second feature matrix, a target area is screened out from multiple second preset areas, wherein the preset regional similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between regions.

[0150] Among them, the prediction module is also used to obtain the regional characteristic difference coefficient and the user characteristic difference coefficient between the first preset area and the target area, wherein the regional characteristic 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 characteristic 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 characteristic difference coefficient and the user characteristic difference coefficient, the target power consumption data is corrected to obtain the corrected target power consumption data; based on the corrected target power consumption data, the user power consumption data of the first preset area is predicted to obtain the first power consumption data.

[0151] Among them, the prediction module is also used to use a preset smoothing algorithm and the corrected target power consumption data to predict the user power consumption data in the first preset area to obtain the first power consumption data, wherein the preset smoothing algorithm is used to represent a preset probabilistic calculation model.

[0152] Among them, the determination module is also used to obtain a set of constraints for the power agent object to perform power operation in the first preset area, wherein the constraint set includes at least one of the following: new energy output constraints, agent object monthly revenue constraints and monthly power balance constraints; based on the first electricity consumption data and the constraint set, the revenue objective function is solved to obtain the operating parameters.

[0153] An embodiment of the present application further provides an electronic device, comprising: 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 when running.

[0154] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0155] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0156] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0157] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0158] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0159] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0160] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0161] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0162] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0163] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made 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. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0166] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated unit is implemented in the form of 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0168] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining operating parameters of a power proxy object, characterized in that: include: Acquire regional characteristic factors of a first preset area where the power proxy object is to perform power operation, and obtain a first characteristic factor set; Predicting the power consumption data of users in the first preset area based on the first characteristic factor set to obtain first power consumption data; Constructing a profit objective function of the power proxy object performing power operation on the first preset area, wherein the profit objective function is used to represent a functional relationship established with the profit of the power proxy object as the target; Based on the first electricity consumption data and the profit objective function, the operating parameters of the power proxy object are determined, wherein the operating parameters include at least a purchased electricity amount and a sold electricity price, the purchased electricity amount is used to represent the amount of electricity purchased by the power proxy object from the power market, and the sold electricity price is used to represent the price at which the power proxy object sells electricity to the first preset area.

2. The method for determining the operation parameters of the power proxy object according to claim 1, characterized in that: Predicting the power consumption data of users in the first preset area based on the first characteristic factor set to obtain first power consumption data includes: Acquire regional characteristic factors of multiple second preset areas to obtain a second characteristic factor set, wherein the power operation duration of any area of ​​the multiple second preset areas is greater than the power operation duration of the first preset area; Based on the first feature factor set and the second feature factor set, screening out at least one target area from the plurality of second preset areas; Based on the target power consumption data of the target area, the power consumption data of users in the first preset area is predicted to obtain the first power consumption data.

3. The method for determining the operation parameters of the power proxy object according to claim 2, characterized in that: Based on the first feature factor set and the second feature factor set, selecting at least one target area from the plurality of second preset areas comprises: constructing a first feature matrix based on the first feature factor set, and constructing a second feature matrix based on the second feature factor set; The target area is screened out from the plurality of second preset areas by using a preset area similarity evaluation model based on the first feature matrix and the second feature matrix, wherein the preset area similarity evaluation model is used to represent a pre-constructed calculation model for determining the similarity between areas.

4. The method for determining the operation parameters of the power proxy object according to claim 2, characterized in that: Predicting the user power consumption data of the first preset area based on the target power consumption data of the target area to obtain the first power consumption data includes: Obtaining a regional characteristic difference coefficient and a user characteristic difference coefficient between the first preset area and the target area, wherein the regional characteristic difference coefficient is used to represent a weight correction coefficient obtained by quantifying the regional characteristic difference between the first preset area and the target area, and the user characteristic difference coefficient is used to represent a weight correction coefficient obtained by quantifying the user characteristic difference between the first preset area and the target area; Based on the regional characteristic difference coefficient and the user characteristic difference coefficient, the target power consumption data is corrected to obtain corrected target power consumption data; Based on the corrected target power consumption data, the power consumption data of users in the first preset area is predicted to obtain the first power consumption data.

5. The method for determining the operation parameters of the power proxy object according to claim 4, characterized in that: Predicting the user power consumption data of the first preset area based on the corrected target power consumption data to obtain the first power consumption data includes: The user power consumption data of the first preset area is predicted by using a preset smoothing algorithm and the corrected target power consumption data to obtain the first power consumption data, wherein the preset smoothing algorithm is used to represent a preset probabilistic calculation model.

6. The method for determining the operation parameters of a power proxy object according to claim 1, characterized in that: Determining the operation parameters of the power proxy object based on the first power consumption data and the profit objective function includes: Obtaining a set of constraints for the power proxy object to perform power operation on the first preset area, wherein the set of constraints includes at least one of the following: a new energy output constraint, a proxy object monthly revenue constraint, and a monthly power balance constraint; The profit objective function is solved based on the first electricity consumption data and the constraint condition set to obtain the operation parameters.

7. A device for determining operating parameters of a power proxy object, characterized in that: include: An acquisition module, used to acquire regional characteristic factors of a first preset area where the power proxy object is to perform power operation, and obtain a first characteristic factor set; A prediction module, configured to predict the power consumption data of users in the first preset area based on the first characteristic factor set to obtain first power consumption data; A construction module, used to construct a profit objective function of the power proxy object performing power operation on the first preset area, wherein the profit objective function is used to represent a functional relationship established with the profit of the power proxy object as the target; A determination module is used to determine the operating parameters of the power proxy object based on the first electricity consumption data and the profit objective function, wherein the operating parameters include at least a purchased electricity amount and a sold electricity price, the purchased electricity amount is used to represent the amount of electricity purchased by the power proxy object from the power market, and the sold electricity price is used to represent the price at which the power proxy object sells electricity to the first preset area.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for determining the operating parameters of the power proxy object as described in any one of claims 1 to 6.

9. 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, the device where the storage medium is located is controlled to execute the method for determining the operating parameters of the power proxy object according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for determining an operation parameter of an electric power proxy object according to any one of claims 1 to 6.

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