CN111340568A - Electricity selling price simulation method, system, readable storage medium and device - Google Patents
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Abstract
The invention belongs to the field of power selling and electricity price simulation, and provides a power selling and electricity price simulation method, a power selling and electricity price simulation system, a readable storage medium and a device. The power selling price simulation method comprises the steps of receiving power utilization information corresponding to all users in a preset time period in a preset area, and storing the power utilization information in a partition mode according to types of the users; calling pre-stored preset electricity prices matched with different user types; constructing a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price, solving the multiple linear regression relation model to obtain a regression coefficient, and using the regression coefficient as a demand price elastic value of the corresponding user type to obtain a value range of the demand price elastic value; and combining the demand price elasticity value with the Lameqi pricing formula to obtain the fixed cost of the corresponding user type, and simulating and outputting the power price curve of each user type by taking the power selling profit rate as an independent variable and taking the optimized power price as a dependent variable. Which provides an intuitive decision selection for the electricity selling price scheme.
Description
Technical Field
The invention belongs to the field of power selling and electricity price scheme generation, and particularly relates to a power selling and electricity price simulation method, a power selling and electricity price simulation system, a readable storage medium and readable storage equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Due to the special nature of the power industry, governments are still in charge of this even if effective competition is introduced on the generation and sales sides. The government not only needs to strengthen the supervision of the links of power transmission and distribution with nature monopoly, but also needs to carry out price supervision, namely, the regulation and pricing, on the links of power generation and power selling which are gradually released.
The inventor finds that the current electricity selling price scheme does not consider electricity consumption of different user types, and can not scientifically reflect the commodity characteristics of electric power and the difference of power supply cost under different conditions, so that the electricity selling price scheme can not be accurately matched with the user types; in addition, the power selling price decision maker cannot visually check the corresponding relation change trend between the power price and the related parameters thereof, and can only search the power prices matched with different user types by means of historical experience or a manual trial and adjustment method, so that the working efficiency of the power selling price decision maker is influenced.
Disclosure of Invention
In order to solve the technical problems, the invention provides a power selling and electricity price simulation method, a system, a readable storage medium and equipment, which consider the power consumption corresponding to different user types, construct a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price, solve the model to obtain a regression coefficient and serve as a required price elastic value of the corresponding user type, combine the regression coefficient with a Lamm uniform pricing formula to obtain an optimized electricity price, simulate and output an electricity price curve which takes the electricity selling profit rate as an independent variable and takes the optimized electricity price of each user type as a dependent variable, provide an intuitive electricity selling decision basis for an electricity selling decision maker, and improve the working efficiency of the electricity selling decision maker.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the invention provides a power selling price simulation method in a first aspect.
A power selling price simulation method comprises the following steps:
receiving power utilization information corresponding to all users in a preset time period of a preset area, and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises an electricity utilization time period and electricity consumption in the time period;
calling pre-stored preset electricity prices matched with different user types;
constructing a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price, and solving the multiple linear regression relation model to obtain a regression coefficient which is used as a demand price elastic value of the corresponding user type;
combining the demand price elasticity value with a Lameqi pricing formula to obtain fixed cost of the corresponding user type, and simulating and outputting an electricity price curve of each user type with the electricity selling profit rate as an independent variable and the optimized electricity price as a dependent variable; and after the electricity price is optimized, the electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
A second aspect of the present invention provides a power selling price simulation system.
A power selling price simulation system comprising:
the power utilization information receiving module is used for receiving power utilization information corresponding to all users in a preset time period in a preset area and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises an electricity utilization time period and electricity consumption in the time period;
the classified storage module is used for calling pre-stored preset electricity prices matched with different user types;
the regression relation model building and solving module is used for building a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset power price, solving the multiple linear regression relation model to obtain a regression coefficient, and using the regression coefficient as a required price elastic value of the corresponding user type;
the electricity price curve drawing module is used for combining the demand price elasticity value with the Lameqi pricing formula to obtain the fixed cost of the corresponding user type, and simulating and outputting the electricity price curve of each user type by taking the electricity selling profit rate as an independent variable and taking the optimized electricity price as a dependent variable; and after the electricity price is optimized, the electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the electricity selling price simulation method as described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the electricity selling price simulation method as described above when executing the program.
The invention has the beneficial effects that:
according to the method, the power consumption corresponding to different user types is considered, and a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price is constructed, so that a data base is laid for matching the electricity selling price scheme with the user types;
the invention solves the model to obtain the regression coefficient and takes the regression coefficient as the elastic value of the required price of the corresponding user type, and then combines the regression coefficient with the Lamez pricing formula to obtain the fixed cost, the simulation outputs the electricity price curve which takes the profit rate of electricity selling as the independent variable and takes the optimized electricity price of each user type as the dependent variable, thereby providing the decision basis for directly viewing electricity selling for the electricity selling decision maker, improving the working efficiency of the electricity selling decision maker and being capable of quickly and accurately simulating the electricity prices corresponding to different user types.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for simulating electricity selling price according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electricity selling price system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The electricity rate referred to in the present invention means a unit price per degree of electricity.
Example one
Fig. 1 shows a flow chart of a power selling price simulation method according to the present embodiment. The following describes in detail the specific implementation process of the electricity selling price simulation method according to this embodiment with reference to fig. 1.
As shown in fig. 1, the method for simulating electricity selling price of the present embodiment includes:
step 1: receiving power utilization information corresponding to all users in a preset time period of a preset area, and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises electricity utilization time periods and electricity consumption in the time periods.
In specific implementation, the electricity utilization information can be collected by adopting an electric energy metering device, wherein the electric energy metering device meters the electricity consumption of corresponding users, adds a timestamp and then sends the timestamp to a preset storage area for classified storage. This is beneficial to the accuracy and convenience of data retrieval.
Specifically, the information stored in the storage area is stored with the user type as a key value. Wherein the user types include residential users, industrial users, commercial users, and office building users.
For example:
| type of user | Residential user | Industrial user | Business user | Office building user |
| 08:00-12:00 | Used electric quantity Q11 | Used electric quantity Q12 | Used electric quantity Q13 | Used electric quantity Q14 |
| 12:00-16:00 | Used electric quantity Q21 | Used electric quantity Q22 | Used electric quantity Q23 | Used electric quantity Q24 |
| 16:00-20:00 | Used electric quantity Q31 | Used electric quantity Q32 | Used electric quantity Q33 | Used electric quantity Q34 |
| 20:00-24:00 | Used electric quantity Q41 | Used electric quantity Q42 | Used electric quantity Q43 | Used electric quantity Q44 |
Step 2: and calling pre-stored preset electricity prices matched with different user types.
In specific implementation, different user types are matched and provided with corresponding electricity prices in advance according to historical experience; for example: the residential user presets a first electricity price, the industrial user presets a second electricity price, the commercial user presets a third electricity price, and the office building user presets a fourth electricity price.
Wherein the first electricity price, the second electricity price, the third electricity price and the fourth electricity price are all preset according to historical experience.
It should be noted that the first electricity price, the second electricity price, the third electricity price, and the fourth electricity price may be fixed values or may be a piecewise function value.
And step 3: and constructing a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price, and solving the multiple linear regression relation model to obtain a regression coefficient which is used as the demand price elastic value of the corresponding user type.
Specifically, a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset power price is lnQ- α lnP + β lnV + gamma lnG + C, wherein Q is the power consumption corresponding to the corresponding user type, P is the corresponding preset power price, V is an industry added value and is a constant, G is a purchase price index of an industrial producer and is a constant, C is a constant, α, β and gamma are required price elastic values, and the order difference among the variables is eliminated in a logarithmic mode, so that a data base is laid for matching the power selling price scheme with the user type.
And 4, step 4: combining the demand price elasticity value with a Lameqi pricing formula to obtain fixed cost of the corresponding user type, and simulating and outputting an electricity price curve of each user type with the electricity selling profit rate as an independent variable and the optimized electricity price as a dependent variable; and after the electricity price is optimized, the electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
The lamuzi pricing formula is:
fixed cost F:
Wherein, MCiThe marginal cost of power supply for the ith class of users is constant; deltaiThe user demand price elasticity of the ith class of users, α Lagrange multiplier which is a constant coefficient, R α/(1+ α) which is a Lambert index, n which is the total number of user types, qiOptimized electricity prices for class i users; p is a radical ofiAnd the preset electricity price of the ith type user.
The process of combining the demand price elasticity value with the lameqi pricing formula to obtain the fixed cost of the corresponding user type is the existing process. The remainders of the consumers and the remainders of the producers are considered by the Lamziq pricing formula to be maximized, the fixed cost is calculated by combining the demand price elasticity of different users, the regression coefficient is obtained by the solving model and is used as the demand price elasticity value of the corresponding user type, the fixed cost is obtained by combining the Lamziq pricing formula, the electricity price curve with the electricity selling profit rate as an independent variable and the optimized electricity price of each user type as a dependent variable is output in a simulation mode, the electricity selling decision basis is provided for the electricity selling decision maker, the working efficiency of the electricity selling decision maker is improved, and the electricity prices corresponding to different user types can be simulated quickly and accurately.
Example two
Fig. 2 shows a schematic structural diagram of a power selling price simulation system according to this embodiment. The structure of the electricity selling price simulation system of the present embodiment will be described in detail with reference to fig. 2.
As shown in fig. 2, the power selling price simulation system of the present embodiment includes:
(1) the power utilization information receiving module is used for receiving power utilization information corresponding to all users in a preset time period in a preset area and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises electricity utilization time periods and electricity consumption in the time periods.
In specific implementation, the electricity utilization information can be collected by adopting an electric energy metering device, wherein the electric energy metering device meters the electricity consumption of corresponding users, adds a timestamp and then sends the timestamp to a preset storage area for classified storage. This is beneficial to the accuracy and convenience of data retrieval.
Specifically, the information stored in the storage area is stored with the user type as a key value. Wherein the user types include residential users, industrial users, commercial users, and office building users.
For example:
| type of user | Residential user | Industrial user | Business user | Office building user |
| 08:00-12:00 | Used electric quantity Q11 | Used electric quantity Q12 | Used electric quantity Q13 | Used electric quantity Q14 |
| 12:00-16:00 | Used electric quantity Q21 | Used electric quantity Q22 | Used electric quantity Q23 | Used electric quantity Q24 |
| 16:00-20:00 | Used electric quantity Q31 | Used electric quantity Q32 | Used electric quantity Q33 | Used electric quantity Q34 |
| 20:00-24:00 | Used electric quantity Q41 | Used electric quantity Q42 | Used electric quantity Q43 | Used electric quantity Q44 |
(2) And the classified storage module is used for calling pre-stored preset electricity prices matched with different user types.
In specific implementation, different user types are matched and provided with corresponding electricity prices in advance according to historical experience; for example: the residential user presets a first electricity price, the industrial user presets a second electricity price, the commercial user presets a third electricity price, and the office building user presets a fourth electricity price.
Wherein the first electricity price, the second electricity price, the third electricity price and the fourth electricity price are all preset according to historical experience.
(3) And the regression relation model building and solving module is used for building a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset power price, and solving the multiple linear regression relation model to obtain a regression coefficient which is used as the demand price elasticity value of the corresponding user type.
Specifically, a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset power price is lnQ- α lnP + β lnV + gamma lnG + C, wherein Q is the power consumption corresponding to the corresponding user type, P is the corresponding preset power price, V is an industry added value and is a constant, G is a purchase price index of an industrial producer and is a constant, C is a constant, α, β and gamma are required price elastic values, and the order difference among the variables is eliminated in a logarithmic mode, so that a data base is laid for matching the power selling price scheme with the user type.
(4) The electricity price curve drawing module is used for combining the demand price elasticity value with the Lameqi pricing formula to obtain the fixed cost of the corresponding user type, and simulating and outputting the electricity price curve of each user type by taking the electricity selling profit rate as an independent variable and taking the optimized electricity price as a dependent variable; and after the electricity price is optimized, the electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
Wherein, the lamb pricing formula is as follows:
fixed cost F:
MCiThe marginal cost of power supply for the ith class of users is constant; deltaiThe user demand price elasticity of the ith class of users, α Lagrange multiplier which is a constant coefficient, R α/(1+ α) which is a Lambert index, n which is the total number of user types, qiOptimized electricity prices for class i users; p is a radical ofiAnd the preset electricity price of the ith type user.
The Lamziq pricing formula considers the maximization of the sum of the consumer surplus and the producer surplus, the fixed cost is calculated by combining the demand price elasticity of different users, the solution model of the embodiment obtains a regression coefficient and is used as the demand price elasticity value of the corresponding user type, the fixed cost is obtained by combining the Lamziq pricing formula, the electricity price curve with the electricity selling profit rate as an independent variable and the optimized electricity price of each user type as a dependent variable is output in a simulation mode, a visual electricity selling decision basis is provided for electricity selling decision makers, the working efficiency of the electricity selling decision makers is improved, and the electricity prices corresponding to different user types can be simulated quickly and accurately.
EXAMPLE III
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps in the electricity selling price simulation method according to the first embodiment.
Example four
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the electricity selling price simulation method according to the embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A power selling price simulation method is characterized by comprising the following steps:
receiving power utilization information corresponding to all users in a preset time period of a preset area, and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises an electricity utilization time period and electricity consumption in the time period;
calling pre-stored preset electricity prices matched with different user types;
constructing a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset electricity price, and solving the multiple linear regression relation model to obtain a regression coefficient which is used as a demand price elastic value of the corresponding user type;
combining the demand price elasticity value with a Lameqi pricing formula to obtain fixed cost of the corresponding user type, and simulating and outputting an electricity price curve of each user type with the electricity selling profit rate as an independent variable and the optimized electricity price as a dependent variable; and the optimized electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
2. The power selling price simulation method according to claim 1, wherein the user types include residential users, industrial users, commercial users, and office building users.
3. The power selling price simulation method according to claim 1, wherein the power consumption of different user types are classified and stored into a preset storage area after being added with the time stamp.
4. The power selling price simulation method of claim 1, wherein the multiple linear regression relationship model between the power consumption corresponding to each user type and the corresponding preset power price is lnQ- α lnP + β lnV + γ lnG + C, wherein Q is the power consumption corresponding to the corresponding user type, P is the corresponding preset power price, V is an industry added value and is a constant, G is an industry producer purchase price index and is a constant, C is a constant, and α, β and γ are required price elastic values.
5. An electricity selling price simulation system, characterized by comprising:
the power utilization information receiving module is used for receiving power utilization information corresponding to all users in a preset time period in a preset area and storing the power utilization information in a partition mode according to the types of the users; the electricity utilization information comprises an electricity utilization time period and electricity consumption in the time period;
the classified storage module is used for calling pre-stored preset electricity prices matched with different user types;
the regression relation model building and solving module is used for building a multiple linear regression relation model between the power consumption corresponding to each user type and the corresponding preset power price, solving the multiple linear regression relation model to obtain a regression coefficient, and using the regression coefficient as a required price elastic value of the corresponding user type;
the electricity price curve drawing module is used for combining the demand price elasticity value with the Lameqi pricing formula to obtain the fixed cost of the corresponding user type, and simulating and outputting the electricity price curve of each user type by taking the electricity selling profit rate as an independent variable and taking the optimized electricity price as a dependent variable; and the optimized electricity price is equal to the sum of the fixed cost and the electricity selling profit rate after being multiplied by the fixed cost.
6. The electricity selling price simulation system according to claim 5, wherein in the electricity consumption information receiving module, the user types include residential users, industrial users, commercial users, and office building users.
7. The power selling price simulation system according to claim 5, wherein in the power consumption information receiving module, power consumptions of different user types are classified and stored into a preset storage area after being added with time stamps.
8. The power selling price simulation system of claim 5 wherein in the regression relationship model building and solving module, the multiple linear regression relationship model between the power consumption corresponding to each user type and the corresponding preset price is lnQ- α lnP + β lnV + γ lnG + C, wherein Q is the power consumption corresponding to the corresponding user type, P is the corresponding preset price, V is an industry added value which is a constant, G is an industry producer purchase price index which is a constant, C is a constant, α, β and γ are required price elastic values.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the electricity selling price simulation method according to any one of claims 1 to 4.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the electricity selling price simulation method according to any one of claims 1 to 4 when executing the program.
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| CN112801513A (en) * | 2021-01-29 | 2021-05-14 | 山东大学 | Collaborative optimization method and system for comprehensive energy system |
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