Pricing method, system and equipment of electric power product and medium

Abstract

The invention provides an electric power product pricing method, system and device, and a medium. The pricing method comprises the steps of obtaining a standard parameter set and a target influence parameter set of an electric power product; the standard parameter set comprises previous kilowatt-hour fuel cost, electric energy quality coefficient and kilowatt-hour cost profit rate; the target influence parameter set comprises target influence parameters; inputting the target influence parameter set into a factor determination model to obtain a target factor of each target influence parameter; the target factor is used for representing the influence degree of each target influence parameter on the price upper limit data of the power product; and constructing a price upper limit model based on the target influence parameter and the target factor to obtain price upper limit data of the power product.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a pricing method, system, equipment and medium for electric power products. Background Art

[0002] The price volatility of power products has a profound impact on the stability of the market and the interests of participants. Especially in the context of large-scale entry of new energy into the market, the price of power products is more susceptible to external influencing parameters. The existing price cap method does not fully consider the impact of external influencing parameters on prices, making it difficult to ensure the market stability of power products, which in turn affects the safe operation of the power system. Summary of the invention

[0003] Aiming at the problem that the price cap method of power products in the prior art does not take into account the impact of external parameters on the safe operation of the power system, the present invention proposes a pricing method, system, device and medium for power products.

[0004] In a first aspect, an embodiment of the present invention provides a pricing method for an electric power product, the pricing method comprising:

[0005] Obtain the standard parameter set and target impact parameter set of the power product; the standard parameter set includes the previous period's kilowatt-hour fuel cost, power quality coefficient and kilowatt-hour cost profit rate; the target impact parameter set includes the target impact parameters;

[0006] Input the target impact parameter set into the factor determination model to obtain the target factor of each target impact parameter; the target factor is used to characterize the degree of influence of each target impact parameter on the price ceiling data of the power product;

[0007] A price ceiling model is constructed based on the target impact parameters and target factors to obtain the price ceiling data of power products.

[0008] In one embodiment, the price cap model includes:

[0009]

[0010] Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh in the previous period, is the power quality coefficient, r is the cost-profit ratio of electricity, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, W i is the target factor of the i-th target influencing parameter.

[0011] In one embodiment, the factor determination model is trained based on historical price data and historical retail price data.

[0012] In one embodiment, the factor determination model includes:

[0013] P t =P t-1 ×(1+RPI-X);

[0014] Among them, P t is the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, and X is the efficiency factor.

[0015] In one embodiment, P t-1 Determined based on ROR, which includes:

[0016] R(p,q)=C+S(RB);

[0017] Among them, R(p,q) is the function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base;

[0018] P t-1 include:

[0019]

[0020] The factor determination model after sorting includes:

[0021] P t =C t-1 (1+r)(1+RPI-X).

[0022] In one embodiment, the factor determination model introduces a price cap association layer for solving;

[0023] The price cap association layers include:

[0024]

[0025] Among them, W1 is the target factor of the first target influencing parameter, W2 is the target factor of the second target influencing parameter; X1 is the first target influencing parameter, X2 is the second target influencing parameter, Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply period, P e is the price of primary energy in period t, P b is the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out It is the peak and valley load value.

[0026] In one embodiment, the factor determination model is fitted according to the least squares method.

[0027] In one embodiment, the target impact parameter is determined by establishing a regression model between the price data and the impact parameter, and the regression model is used to determine the association between the impact parameter and the price cap data;

[0028] And / or, the target influencing parameter includes at least one of primary energy price, new energy power generation, and load demand increment.

[0029] In a second aspect, an embodiment of the present invention provides a pricing system for power products, the pricing system comprising:

[0030] An acquisition module is used to acquire a standard parameter set and a target impact parameter set of a power product; the standard parameter set includes the previous period's kilowatt-hour fuel cost, power quality coefficient, and kilowatt-hour cost profit rate; the target impact parameter set includes target impact parameters;

[0031] A factor determination module is used to input the target impact parameter set into the factor determination model to obtain the target factor of each target impact parameter; the target factor is used to characterize the degree of influence of each target impact parameter on the price ceiling data of the power product;

[0032] The price cap module is used to build a price cap model based on target influencing parameters and target factors to obtain price cap data for power products.

[0033] In one embodiment, the price cap model includes:

[0034]

[0035] Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh in the previous period, is the power quality coefficient, r is the cost-profit ratio of electricity, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, W i is the target factor of the i-th target influencing parameter.

[0036] In one embodiment, the factor determination model is trained based on historical price data and historical retail price data.

[0037] In one embodiment, the factor determination model includes:

[0038] P t =P t-1 ×(1+RPI-X);

[0039] Among them, P tis the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, and X is the efficiency factor.

[0040] In one embodiment, P t-1 Determined based on ROR, which includes:

[0041] R(p,q)=C+S(RB);

[0042] Among them, R(p,q) is the function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base;

[0043] P t-1 include:

[0044]

[0045] The factor determination model after sorting includes:

[0046] P t =C t-1 (1+r)(1+RPI-X).

[0047] In one embodiment, the factor determination model introduces a price cap association layer for solving;

[0048] The price cap association layers include:

[0049]

[0050] Among them, W1 is the target factor of the first target influencing parameter, W2 is the target factor of the second target influencing parameter; X1 is the first target influencing parameter, X2 is the second target influencing parameter, Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply period, P e is the price of primary energy in period t, P b is the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out It is the peak and valley load value.

[0051] In one embodiment, the factor determination model is fitted according to the least squares method.

[0052] In one embodiment, the target impact parameter is determined by establishing a regression model between the price data and the impact parameter, and the regression model is used to determine the association between the impact parameter and the price cap data;

[0053] And / or, the target influencing parameter includes at least one of primary energy price, new energy power generation, and load demand increment.

[0054] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement a pricing method for electric power products as described in any one of the first aspects.

[0055] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement a pricing method for electricity products as described in any one of the first aspects.

[0056] The present invention provides a method, system, device and medium for pricing electric power products, which have the following technical effects: constructing a price ceiling model based on a target influencing parameter set and a standard parameter set can fully consider the impact of multiple influencing parameters on the price of electric power products. Before constructing the price ceiling model, the target influencing parameters are analyzed to obtain target factors that characterize the degree of influence of the target influencing parameters and price data, so that the final pricing can fully consider the impact of each different target influencing parameter, so that it can be more flexible in addition to being able to better meet market laws. In addition, after obtaining price ceiling data that can better meet market laws, the stability of the market is guaranteed, and the safe operation of the power system depends more on the external market, thereby ensuring the safe operation of the power system.

[0057] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. 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 accompanying drawings:

[0059] Figure 1 A flowchart of a pricing method for power products provided by an exemplary embodiment of the present invention;

[0060] Figure 2 A structural diagram of a pricing system for power products provided by an exemplary embodiment of the present invention;

[0061] Figure 3 A structural diagram of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0063] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0064] The embodiment of the present invention provides a pricing method for power products, see Figure 1 , pricing methods include:

[0065] S11. Obtain standard parameter sets and target impact parameter sets for power products.

[0066] Among them, power products include but are not limited to basic power products, peak-valley power products, green power, load management service products, energy storage service products, power contracts, etc. The standard parameter set includes but is not limited to data such as the previous period's fuel cost per kWh, power quality coefficient, and cost-profit ratio per kWh. The standard parameters in the standard parameter set can be the influencing parameters on the power generation side. The target influencing parameter set includes the target influencing parameters, which can be the influencing parameters that take into account both the power generation side and the user side.

[0067] Regarding the target influencing parameters, since there are many influencing parameters on the price of power products in the power product market, and the correlations between them and the price data of power products are complex and intertwined, in order to ensure the accuracy and adaptability of the price data of power products, it is necessary to find the key target influencing parameters from the numerous influencing parameters, so that the price data of power products obtained according to the target influencing parameters can meet both the needs of the power generation side and the needs of the user side.

[0068] Methods for determining target impact parameters include but are not limited to regression analysis methods, sensitivity analysis methods, scenario analysis methods, machine learning methods, market simulation methods, etc.

[0069] For the regression analysis method, a regression model can be established based on the establishment of price data and influencing parameters. The regression model is used to determine the association between the influencing parameters and the price data. Regarding the sensitivity analysis method, by changing the value of one or more influencing parameters, the changes in price data can be observed to evaluate which parameters are most sensitive to changes in price data. Regarding the scenario analysis method, by creating different scenario influencing parameters (such as different policy environments, fuel price fluctuations, etc.), the changes in the price data of power products under these scenario influencing parameters can be analyzed to help identify the target influencing parameters. Regarding the machine learning method, regression trees and random forests can be used. These algorithms can handle nonlinear relationships and help identify target influencing parameters; principal component analysis (PCA) can also be used to reduce data dimensions and extract target influencing parameters. Regarding the duration simulation method, the supply and demand model can be used to simulate the supply and demand relationship in the electricity market and predict the impact of different influencing parameters on prices; an optimization model can also be used to optimize electricity production and prices while considering multiple parameters such as cost, demand, and policy.

[0070] In this embodiment, a regression model is preferably used to determine the association between the price data of power products and the influencing parameters, but it is not limited to this, and the specific selection can be made according to the actual situation. The influencing factors determined according to any of the above methods are determined as target influencing factors. In this embodiment, the following three influencing parameters are preferably used as target influencing parameters for explanation, the first target influencing parameter is the primary energy price X1, the second target influencing parameter is the new energy generation X2, and the third target influencing parameter is the load demand increment η.

[0071] The price of primary energy is an important factor affecting the price limit level of the electricity market. Primary energy includes coal, natural gas, oil, etc. Their price fluctuations directly affect the cost of power generation, thereby affecting the price limit level and method of the electricity market.

[0072] The contribution coefficient of new energy power generation, with the acceleration of the construction of new power systems, has promoted the large-scale entry of new energy into the market, but the substantial increase in the proportion of new energy grid connection has brought challenges to the safety, stability and economic operation of the power system. Due to the large differences in the proportion of new energy installed capacity in various places and the unstable output, the contribution rate of new energy power generation is different, that is, the net load curve exceeds the average value, which means that the contribution rate of new energy power generation is low, and the contribution rate of new energy power generation is high when it does not reach the average value. Obviously, the contribution rate of new energy power generation directly affects the power generation of conventional units and has an impact on the price cost of the electricity market.

[0073] Load demand increase: During the peak load periods in summer and winter, extreme weather causes a significant increase in electricity demand, and the seasonal differences in the output of new energy sources make the supply and demand situation even more tense. Therefore, the price limit levels in different regions need to be dynamically adjusted based on the output of new energy sources over the years and the demand for high temperatures in summer and heating in winter, in order to meet the challenges of balancing supply and demand in various regions.

[0074] S12. Input the target influencing parameter set into the factor determination model to obtain the target factor of each target influencing parameter.

[0075] In order to obtain price data that can better meet the needs of the power generation side and the user side, in addition to obtaining the target impact parameters, it is also necessary to obtain the target factors between each target impact parameter and the price data of the power product. The target factor is used to characterize the degree of influence of each target impact parameter on the price data of the power product.

[0076] In one embodiment, a factor determination model is described, and the factor determination model is trained based on historical price data and historical retail price data.

[0077] Specifically, the factor determination model includes:

[0078] P t =P t-1 ×(1+RPI-X) (1);

[0079] Among them, P t is the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, X is the efficiency factor. PRI represents inflation, and X represents the expected rate of technological progress.

[0080] The RPI-X in this embodiment is a price adjustment model, where "RPI" stands for "Zero-Based Price Adjustment Model" (Retail Price Index) and "X" represents an adjustment factor. This model adjusts prices according to the inflation rate (RPI) over a certain period of time by setting a price adjustment factor (X). It is mainly used to regulate monopolistic industries such as electricity and natural gas to ensure that prices are fair to consumers, while encouraging companies to improve efficiency and reduce costs.

[0081] The RPI-X model may cause power generation companies to reduce power quality under the condition of the maximum price level limit, which will harm the interests of users and fail to clearly reflect the profitability principle of power generation companies. Therefore, this scheme introduces ROR, P t-1Determined based on ROR, which stands for "Rate of Return". In the pricing and regulation of the power industry, ROR is a common revenue regulation method used to ensure that power generation companies can obtain fair returns while protecting the interests of users. Under the ROR model, the revenue of power generation companies is determined based on the rate of return on their capital investment, and power generation companies can make a profit within this rate of return. The goal of this method is to incentivize companies to improve efficiency and service quality while ensuring that they receive an appropriate return on reasonable capital investment.

[0082] Specifically, ROR includes:

[0083] R(p,q)=C+S(RB) (2);

[0084] Among them, R(p,q) is a function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base. S can be determined according to national energy policies.

[0085] For P in formula (2), t-1 To sort out, P t-1 Specifically include:

[0086]

[0087] Substituting formula (3) into formula (1), the factor determination model after sorting includes:

[0088] P t =C t-1 (1+r)(1+RPI-X) (4);

[0089] At the same time, since there are multiple target influencing parameters, in order to accurately and fully consider the mutual influence between the multiple target influencing parameters, this embodiment introduces a price cap association layer into the factor determination model for solution;

[0090] The price cap association layers include:

[0091]

[0092] Wherein, W1 is the target factor of the first target influencing parameter (i.e., the price of primary energy in the above embodiment), W2 is the target factor of the second target influencing parameter (i.e., the contribution coefficient of new energy power generation in the above embodiment); X1 is the first target influencing parameter (i.e., the price of primary energy in the above embodiment), X2 is the second target influencing parameter (i.e., the price of primary energy in the above embodiment), Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply guarantee period, P e is the price of primary energy in period t, P bis the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out It should be noted that X2 can represent the load demand increment index in special periods (winter, summer and safety supply period), and it is 0 at other times.

[0093] The factor determination model is fitted according to the least squares method. Specifically, the historical price data and the historical retail price data are filtered, and the actual data and abnormal data are ignored. The historical price data and the historical retail data are input into the factor determination model. The factor determination model is fitted using the least squares method, and the residual sum of squares between the estimated value and the actual value is minimized to evaluate the quality of the model fit. The target factors W1 and W2 are iterated until the model is fitted to the best, and the values ​​of the target factors W1 and W2 are obtained.

[0094] S13. Construct a price ceiling model based on the target influencing parameters and target factors to obtain price ceiling data for power products.

[0095] Among them, the price ceiling data is the maximum price when pricing electricity products. The price data can be the same as the price ceiling data, but cannot be greater than the price ceiling data. It can be selected by the power generation enterprise.

[0096] Price The price cap model includes:

[0097]

[0098] Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh in the previous period, is the power quality coefficient, r is the cost-profit ratio of electricity, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, W i is the target factor of the i-th target influencing parameter (due to the setting of the target influencing parameter in this embodiment, i=2), η is the i+1-th target influencing parameter (ie, load demand), and in this embodiment, the target factor of the load demand is set to -1.

[0099] For the power quality factor From the perspective of safe and reliable power supply and promoting technological progress, power quality should be considered as a factor in the model. At present, the comprehensive evaluation indicators of power quality are generally divided into service and technical indicators. This paper sets the limit values ​​of various power quality parameters specified in the power quality standard. If the maximum value of the power quality coefficient is set to 1, the price limit level will be higher when the power quality is high than when the power quality is low, and the profit margin of the power generation side will increase. Incorporating price limit model can effectively promote the enthusiasm of power generation side to improve power quality and safeguard the interests of users.

[0100] In this embodiment, constructing a price ceiling model based on the target impact parameter set and the standard parameter set can fully consider the impact of multiple impact parameters on the price of power products. Before constructing the price ceiling model, the target impact parameters are analyzed to obtain target factors that characterize the impact of the target impact parameters and price data, so that the final pricing can fully consider the impact of each different target impact parameter, so that it can be more flexible in addition to meeting market rules. In addition, after obtaining price ceiling data that can better meet market rules, the stability of the market is guaranteed, and the safe operation of the power system depends more on the external market, which can ensure the safe operation of the power system.

[0101] An exemplary embodiment of the present invention provides a pricing system for power products, see Figure 2 , the pricing system includes:

[0102] The acquisition module 21 is used to acquire the standard parameter set and the target impact parameter set of the power product; the standard parameter set includes the last period's kilowatt-hour fuel cost, the power quality coefficient and the kilowatt-hour cost profit rate; the target impact parameter set includes the target impact parameter;

[0103] The factor determination module 22 is used to input the target impact parameter set into the factor determination model to obtain the target factor of each target impact parameter; the target factor is used to characterize the influence degree of each target impact parameter on the price ceiling data of the power product;

[0104] The price cap module 23 is used to construct a price cap model based on the target influencing parameters and target factors to obtain price cap data for power products.

[0105] In one embodiment, the price cap model includes:

[0106]

[0107] Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh in the previous period, is the power quality coefficient, r is the cost-profit ratio of electricity, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, W i is the target factor of the i-th target influencing parameter, and η is the i+1-th target influencing parameter (i.e., load demand).

[0108] In one embodiment, the factor determination model is trained based on historical price data and historical retail price data.

[0109] In one embodiment, the factor determination model includes:

[0110] P t =P t-1 ×(1+RPI-X);

[0111] Among them, P t is the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, and X is the efficiency factor.

[0112] In one embodiment, P t-1 Determined based on ROR, which includes:

[0113] R(p,q)=C+S(RB);

[0114] Among them, R(p,q) is the function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base;

[0115] P t-1 include:

[0116]

[0117] The factor determination model after sorting includes:

[0118] P t =C t-1 (1+r)(1+RPI-X).

[0119] In one embodiment, the factor determination model introduces a price cap association layer for solving;

[0120] The price cap association layers include:

[0121]

[0122] Among them, W1 is the target factor of the first target influencing parameter, W2 is the target factor of the second target influencing parameter; X1 is the first target influencing parameter, X2 is the second target influencing parameter, Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply period, P e is the price of primary energy in period t, P b is the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out It is the peak and valley load value.

[0123] In one embodiment, the factor determination model is fitted according to the least squares method.

[0124] In one embodiment, the target impact parameter is determined by establishing a regression model between the price data and the impact parameter, and the regression model is used to determine the association between the impact parameter and the price cap data;

[0125] And / or, the target influencing parameter includes at least one of primary energy price, new energy power generation, and load demand increment.

[0126] The system and method embodiments in the embodiments of the present invention are based on the same inventive concept.

[0127] An exemplary embodiment of the present invention further provides an electronic device, see Figure 3 The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 310 (the processor 310 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic battery FPGA), a memory 330 for storing data, and one or more storage media 320 (such as one or more mass storage devices) for storing application programs 323 or data 322. Among them, the memory 330 and the storage medium 320 can be short-term storage or permanent storage. The storage medium 320 stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the above method.

[0128] Furthermore, the CPU 310 may be configured to communicate with the storage medium 320 to execute a series of instruction operations in the storage medium 320 on the electronic device 300. The electronic device 300 may also include one or more power supplies 360, one or more wired or wireless network interfaces 30, one or more input and output interfaces 340, and / or one or more operating systems 321, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, FreeBSD TM, etc.

[0129] The input / output interface 340 may be used to receive or send data via a network. The specific example of the network may include a wireless network provided by a communication provider of the electronic device 300. In one example, the input / output interface 340 includes a network adapter (Network Interface Controller, NIC), which may be connected to other network devices via a base station so as to communicate with the Internet. In one example, the input / output interface 340 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0130] It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown.

[0131] An embodiment of the present invention also provides a computer-readable storage medium, which can be set in a server to store at least one instruction, at least one program, code set or instruction set related to implementing a method in a method embodiment. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement the above method.

[0132] Optionally, in this embodiment, the storage medium may be located in a network server among multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0133] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the embodiments of the above invention are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A pricing method for power products, characterized in that: The pricing methods include: Obtaining a standard parameter set and a target impact parameter set for power products; the standard parameter set includes the previous period's kilowatt-hour fuel cost, power quality coefficient, and kilowatt-hour cost profit rate; the target impact parameter set includes target impact parameters; Inputting the target impact parameter set into the factor determination model to obtain the target factor of each target impact parameter; the target factor is used to characterize the degree of influence of each target impact parameter on the price ceiling data of the power product; A price ceiling model is constructed based on the target impact parameters and target factors to obtain price ceiling data for the power product.

2. The pricing method for electric power products according to claim 1, characterized in that: The price cap model includes: Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh of electricity in the previous period, is the power quality coefficient, r is the profit rate of the electricity cost, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, and η is the i+1-th target influence parameter W i is the target factor of the i-th target influencing parameter.

3. The pricing method for electric power products according to claim 1, characterized in that: The factor determination model is trained based on historical price data and historical retail price data.

4. The pricing method for electric power products according to claim 3, characterized in that: The factor determination model includes: P t =P t-1 ×(1+RPI-X); Among them, P t is the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, and X is the efficiency factor.

5. The pricing method for electric power products according to claim 4, characterized in that: The P t-1 The determination is made based on the ROR, which includes: R(p,q)=C+S(RB); Among them, R(p,q) is the function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base; The P t-1 include: The factor determination model after arrangement includes: P t =C t-1 (1+r)(1+RPI-X)。 6. The pricing method for electric power products according to claim 4, characterized in that: The factor determination model introduces a price cap correlation layer for solution; The price cap association layer includes: Among them, W1 is the target factor of the first target influencing parameter, W2 is the target factor of the second target influencing parameter; X1 is the first target influencing parameter, X2 is the second target influencing parameter, Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply period, P e is the price of primary energy in period t, P b is the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out is the peak and valley load value; And / or, the factor determination model is obtained by fitting according to the least squares method.

7. The pricing method for electric power products according to claim 1, characterized in that: The target influencing parameter is determined by establishing a regression model based on the price data and the influencing parameter, and the regression model is used to determine the association between the influencing parameter and the price cap data; And / or, the target influencing parameter includes at least one of primary energy price, new energy power generation, and load demand increment.

8. A pricing system for power products, characterized in that: The pricing system includes: An acquisition module, used to acquire a standard parameter set and a target impact parameter set of a power product; the standard parameter set includes the previous period's kilowatt-hour fuel cost, power quality coefficient, and kilowatt-hour cost profit rate; the target impact parameter set includes target impact parameters; A factor determination module, used for inputting the target impact parameter set into a factor determination model to obtain a target factor for each target impact parameter; the target factor is used to characterize the degree of influence of each target impact parameter on the price ceiling data of the power product; The price ceiling module is used to construct a price ceiling model based on the target impact parameters and target factors to obtain the price ceiling data of the power product.

9. The pricing system for electric power products according to claim 8, characterized in that: The price cap model includes: Among them, P max is the price ceiling data, C t-1 is the fuel cost per kWh in the previous period, is the power quality coefficient, r is the cost-profit ratio of electricity, λ p-f is the association layer of the target influence parameter, X i is the i-th target influence parameter in the target influence parameter set, W i is the target factor of the i-th target influencing parameter, and η is the i+1-th target influencing parameter (i.e., load demand).

10. The pricing system for electric power products according to claim 8, characterized in that: The factor determination model is trained based on historical price data and historical retail price data.

11. The pricing system for electric power products according to claim 10, characterized in that: The factor determination model includes: P t =P t-1 ×(1+RPI-X); Among them, P t is the historical price data of the tth period; P t-1 is the historical price data of the t-1 period; RPI is the historical retail price data of the t period, and X is the efficiency factor.

12. The pricing system for electric power products according to claim 11, characterized in that: The P t-1 The determination is made based on the ROR, which includes: R(p,q)=C+S(RB); Among them, R(p,q) is the function of historical price data p and power generation q; C is the power generation cost; S is the return on investment, and RB is the return on investment base; P t-1 include: The factor determination model after arrangement includes: P t =C t-1 (1+r)(1+RPI-X)。 13. The pricing system for electric power products according to claim 11, characterized in that: The factor determination model introduces a price cap correlation layer for solution; The price cap association layers include: Among them, W1 is the target factor of the first target influencing parameter, W2 is the target factor of the second target influencing parameter; X1 is the first target influencing parameter, X2 is the second target influencing parameter, Q1 is the system load value in summer and winter, Q2 is the load increment value during the power supply period, P e is the price of primary energy in period t, P b is the basic energy price in period t, P0 is the average load level in the flat section of the net load curve, P out is the peak and valley load value; And / or, the factor determination model is obtained by fitting according to the least squares method.

14. The pricing system for electric power products according to claim 8, characterized in that: The target impact parameter is determined by establishing a regression model based on the price data and the impact parameter, and the regression model is used to determine the association between the impact parameter and the price cap data; And / or, the target influencing parameter includes at least one of primary energy price, new energy power generation, and load demand increment.

15. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the pricing method for electric power products as described in any one of claims 1-7.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the pricing method for power products according to any one of claims 1-7.

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