Electric power market bidding quotation optimization method and system

Abstract

The invention provides an electricity market bidding quotation optimization method and system, and is used for the technical field of electricity information, and the method comprises the steps: collecting the historical quotation data of a competitor, and calculating the probability distribution of each section of capacity quotation in the bidding of the competitor according to the historical quotation data; and according to the probability distribution of the quotation of the competitor, obtaining an optimal quotation solution in each sample simulation through a random simulation experiment, and calculating an average value of the optimal quotation solutions to obtain a final bidding quotation. According to the scheme, the bidding strategy can be efficiently generated, user quotation optimization is guaranteed, and user economic benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power information technology, and in particular relates to a method and system for optimizing bidding and quotation in an electric power market. Background Art

[0002] Electricity market transactions are completed by supply and demand parties through voluntary negotiation or centralized bidding. During the centralized bidding process, it is usually necessary to analyze the opponent's possible trading strategies based on the opponent's medium- and long-term positions, disclose spot information, and calculate the opponent's trading strategies to maximize their own interests.

[0003] However, current bidding strategies usually require professionals to formulate them based on experience-based analysis of opponent transaction information, bidding habits, etc. This requires accurate cost accounting and reliable price negotiation, which is not only inefficient but also difficult to ensure the optimal bid. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a method and system for optimizing bidding and quotation in an electricity market, which is used to solve the problem that current bidding and quotation efficiency is low and it is difficult to ensure the optimal bidding.

[0005] In a first aspect of an embodiment of the present invention, a method for optimizing bidding in an electricity market is provided, comprising: Collect historical bid data from competitors and calculate the probability distribution of bids for each capacity segment based on the historical bid data; Based on the probability distribution of competitors' quotations, random simulation experiments are conducted to obtain the optimal quotation solution in each sample simulation, and the final bid price is obtained by calculating the average value of the optimal quotation solution.

[0006] In a second aspect of an embodiment of the present invention, a system for optimizing bidding and quotation in a power market is provided, comprising: The distribution statistics module is used to collect historical bidding data of competitors and calculate the probability distribution of the bidding prices of each capacity segment in the competitors' bids based on the historical bidding data; The random simulation module is used to obtain the optimal quotation solution in each sample simulation through random simulation experiments based on the probability distribution of competitors' quotations, and obtain the final bidding quotation by calculating the average value of the optimal quotation solution.

[0007] In a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the steps of the method described in the first aspect of the embodiment of the present invention when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiment of the present invention are implemented.

[0009] In an embodiment of the present invention, by collecting historical data of competitors, the price probability distribution of competitors in the bidding process is calculated based on statistical methods, and the optimal bidding price is solved through random optimization based on the probability distribution, thereby realizing computer-based bidding and quotation. This not only improves the efficiency of bidding plan formulation, but also ensures the optimal bidding and maximizes its own profits. At the same time, the bidding strategy is stable and reliable, and has high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a method for optimizing bidding and quotation in a power market provided by one embodiment of the present invention; Figure 2 A schematic diagram of the structure of a power market bidding and quotation optimization system provided by one embodiment of the present invention; Figure 3 The present invention provides a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below 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 making creative work are within the scope of protection of the present invention.

[0013] It should be understood that the terms "including" and similar expressions in the specification, claims, and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, or apparatus comprising a series of steps or units is not limited to the listed steps or units. Furthermore, the terms "first" and "second" are used to distinguish between different objects and are not intended to describe a specific order.

[0014] See also Figure 1, a flow chart of a method for optimizing bidding and quotation in a power market provided by an embodiment of the present invention includes: S101. Collect historical bid data of competitors and calculate the probability distribution of bids for each capacity segment in competitors' bids based on the historical bid data. In electricity market transactions, a segment capacity refers to the power output of a power plant within a specific range, and a segment quote is the bid price corresponding to each capacity segment. When a power plant submits multiple segment quotes, each segment quote includes the starting and ending points of the output range and the price for that range. For example, a unit with a rated capacity of 100 megawatts may submit 5 to 10 segment quotes, with each segment typically covering a capacity range of 5% to 20% of the unit's rated capacity, with a price difference of between 20 and 100 yuan per megawatt-hour.

[0015] Assume that there are N+1 power plants in the electricity market, and each power plant has N competitors. We can study power plant A. Assume that the maximum output of the capacity segment quoted by power generation group A is Q, and the capacity segment quotation is O. Then the other competitors n=1, 2,…,n, and the corresponding quotation segment capacity , the price of the segment is , the system forecast load is D. We do not consider unit startup and shutdown, network constraints, etc. For power plants, the goal of formulating a bidding strategy is to determine the bid that maximizes their profit.

[0016] For N competitors, by collecting and analyzing their historical data, we can use statistical methods to determine the probability distribution of their prices during the bidding process. A probability distribution describes the probabilistic patterns in the values ​​of random variables. Based on the mean and variance of competitors' historical bids, we can determine the probability distribution of their bids.

[0017] Among them, the capacity of the nth competitor in segment i is set to , bid price The mean is , the standard deviation is The normal distribution of , the probability density function is expressed as: ; Where, represents the probability density of the nth competitor in the i-th segment capacity, represents the standard deviation of bid prices, represents the mean of the bid prices.

[0018] S102. Based on the probability distribution of competitors' quotations, obtain the optimal quotation solution in each sample simulation through random simulation experiments, and obtain the final bidding quotation by calculating the average value of the optimal quotation solutions.

[0019] Stochastic simulation constructs a mathematical model similar to the real system and uses random sampling techniques to simulate the opponent's behavior and predict the optimal solution. Specifically, it generates a random number sequence that conforms to a specific probability distribution, conducts a large number of repeated experiments on the system, and obtains an approximate solution through statistical analysis.

[0020] Through random simulation combined with optimization algorithm, the optimal quotation combination corresponding to each capacity segment is obtained when the objective function is maximized.

[0021] Among them, the objective function of random simulation is set as: ; ; The constraints are: ; ; in, , ; Where E represents the profit of the power plant, λ is the market clearing price during the bidding period, represents the power generation cost, q represents the total amount of electricity won in the bid, represents the winning bid electricity of the i-th section of the power plant, Q is the maximum dispatchable capacity of the i-th section of the power plant, represents the bid of the power plant in the i-th section, Indicates the lower limit of the bid price, represents the upper limit of the bidding price, a, b, d are the cost coefficients of the generator set, and n represents the total segment capacity.

[0022] Specifically, the Monte Carlo method is used to simulate the quotation combinations of each competitor, and the segmented optimal quotation combination corresponding to the maximum objective function is obtained based on the genetic algorithm; the average value of each segmented optimal quotation combination is calculated to obtain the final bid price.

[0023] In this embodiment, the probability distribution of competitors' historical bidding information is obtained by statistical analysis, and competitors' bids are estimated through random simulation to obtain the optimal solution for the bids in the sample simulation. This not only enables efficient generation of bidding strategies, but also ensures that the bidding strategies are optimal, thereby maximizing one's own profits.

[0024] It should be understood that the sequence numbers of the steps in the above embodiments do not imply a specific order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0025] Figure 2 A schematic diagram of a system for optimizing bidding and quotation in a power market according to an embodiment of the present invention is provided. The system includes: Distribution statistics module 210, used to collect historical bid data of competitors and calculate the probability distribution of bids for each capacity segment in competitors' bids based on the historical bid data; The calculation of the probability distribution of bids for each capacity segment in competitors' bids based on historical bid data includes: Assume that the capacity of the nth competitor in segment i is , bid price The mean is , the standard deviation is The normal distribution of , the probability density function is expressed as:

[0026] Where, represents the probability density of the nth competitor in the i-th segment capacity, represents the standard deviation of bid prices, represents the mean of the bid prices.

[0027] The random simulation module 220 is used to obtain the optimal solution of the quotation in each sample simulation through random simulation experiments according to the probability distribution of the competitor's quotation, and obtain the final bidding quotation by calculating the average value of the optimal quotation solution.

[0028] The method of obtaining the optimal solution for each sample simulation through random simulation experiments based on the probability distribution of competitors' quotations includes: The objective function of the random simulation is set as: ; ; The constraints are: ; ; in, , ; Where E represents the profit of the power plant, λ is the market clearing price during the bidding period, represents the power generation cost, q represents the total amount of electricity won in the bid, represents the winning bid electricity of the i-th section of the power plant, Q is the maximum dispatchable capacity of the i-th section of the power plant, represents the bid of the power plant in the i-th section, Indicates the lower limit of the bid price, represents the upper limit of the bidding price, a, b, d are the cost coefficients of the generator set, and n represents the total segment capacity.

[0029] Optionally, the random simulation module 220 includes: The simulation optimization unit is used to simulate the quotation combinations of each competitor through the Monte Carlo method and obtain the segmented optimal quotation combination corresponding to the maximum objective function based on the genetic algorithm; The average calculation unit is used to calculate the average value of the optimal bid combination in each segment to obtain the final bidding price.

[0030] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0031] Figure 3 This is a schematic diagram of the structure of an electronic device provided by one embodiment of the present invention. The electronic device is used for bidding in the electricity market. Figure 3 As shown, the electronic device 3 of this embodiment includes: a memory 310, a processor 320 and a system bus 330, and the memory 310 includes an executable program 3101 stored thereon. It can be understood by those skilled in the art that Figure 3 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0032] The following combination Figure 3 A detailed introduction to the various components of electronic equipment: Memory 310 can be used to store software programs and modules. Processor 320 executes the software programs and modules stored in memory 310 to perform various functional applications and data processing of the electronic device. Memory 310 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback). The data storage area may store data generated based on the use of the electronic device (such as cached data). Memory 310 may also include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state memory device.

[0033] The memory 310 includes an executable program 3101 for the interface generation method. The executable program 3101 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 310 and executed by the processor 320 to implement bidding strategy generation, etc. The one or more modules / units can be a series of computer program instruction segments that can perform specific functions. The instruction segments are used to describe the execution process of the executable program 3101 in the electronic device 3. For example, the executable program 3101 can be divided into functional modules such as a distribution statistics module and a random simulation module.

[0034] The processor 320 is the control center of the electronic device. It connects all parts of the electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 310 and accessing data stored in the memory 310, it performs various functions of the electronic device and processes data, thereby monitoring the overall status of the electronic device. Optionally, the processor 320 may include one or more processing units; preferably, the processor 320 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, application programs, etc., and the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 320.

[0035] The system bus 330 connects the various functional components within the computer and can transmit data, address information, and control information. It can be a PCI bus, ISA bus, or CAN bus, for example. Instructions from the processor 320 are transmitted to the memory 310 via the bus, and the memory 310 feeds data back to the processor 320. The system bus 330 is responsible for the exchange of data and instructions between the processor 320 and the memory 310. Of course, the system bus 330 can also connect to other devices, such as network interfaces and display devices.

[0036] In an embodiment of the present invention, the executable program executed by the processing 320 included in the electronic device includes: Collect historical bid data from competitors and calculate the probability distribution of bids for each capacity segment based on the historical bid data; Based on the probability distribution of competitors' quotations, random simulation experiments are conducted to obtain the optimal quotation solution in each sample simulation, and the final bid price is obtained by calculating the average value of the optimal quotation solution.

[0037] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0039] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing bidding in the power market, characterized in that: include: Collect historical bid data from competitors and calculate the probability distribution of bids for each capacity segment based on the historical bid data; Based on the probability distribution of competitors' quotations, random simulation experiments are conducted to obtain the optimal quotation solution in each sample simulation, and the final bid price is obtained by calculating the average value of the optimal quotation solution.

2. The method according to claim 1, characterized in that Calculating the probability distribution of bids for each capacity segment in competitors' bids based on historical bid data includes: Assume that the capacity of the nth competitor in segment i is , bid price The mean is , the standard deviation is The normal distribution of , the probability density function is expressed as: ； Where, represents the probability density of the nth competitor in the i-th segment capacity, represents the standard deviation of bid prices, represents the mean of the bid prices.

3. The method according to claim 1, characterized in that The method of obtaining the optimal solution for each sample simulation through random simulation experiments based on the probability distribution of competitor quotations includes: The objective function of the random simulation is set as: ； ； The constraints are: ； ； in, , ; Where E represents the profit of the power plant, λ is the market clearing price during the bidding period, represents the power generation cost, q represents the total amount of electricity won in the bid, represents the winning bid electricity of the i-th section of the power plant, Q is the maximum dispatchable capacity of the i-th section of the power plant, represents the bid of the power plant in the i-th section, Indicates the lower limit of the bid price, represents the upper limit of the bidding price, a, b, d are the cost coefficients of the generator set, and n represents the total section capacity.

4. The method according to claim 1, wherein The method of obtaining the optimal bid price in each sample simulation through random simulation experiments and obtaining the final bid price by calculating the average of the optimal bid price includes: The Monte Carlo method is used to simulate the quotation combinations of each competitor, and the genetic algorithm is used to obtain the segmented optimal quotation combination corresponding to the maximum objective function. The final bidding price is obtained by averaging the best bid combinations in each segment.

5. A power market bidding and quotation optimization system, characterized in that: include: The distribution statistics module is used to collect historical bidding data of competitors and calculate the probability distribution of the bidding prices of each capacity segment in the competitors' bids based on the historical bidding data; The random simulation module is used to obtain the optimal quotation solution in each sample simulation through random simulation experiments based on the probability distribution of competitors' quotations, and obtain the final bidding quotation by calculating the average value of the optimal quotation solution.

6. The system according to claim 5, characterized in that Calculating the probability distribution of bids for each capacity segment in competitors' bids based on historical bid data includes: Assume that the capacity of the nth competitor in segment i is , bid price Obey the mean , the standard deviation is The normal distribution of , the probability density function is expressed as: Where, represents the probability density of the nth competitor in the i-th segment capacity, represents the standard deviation of bid prices, represents the mean of the bid prices.

7. The system according to claim 5, characterized in that The method of obtaining the optimal solution for each sample simulation through random simulation experiments based on the probability distribution of competitor quotations includes: The objective function of the random simulation is set as: ； ； The constraints are: ； ； in, , ; Where E represents the profit of the power plant, λ is the market clearing price during the bidding period, represents the power generation cost, q represents the total amount of electricity won in the bid, represents the winning bid electricity of the i-th section of the power plant, Q is the maximum dispatchable capacity of the i-th section of the power plant, represents the bid of the power plant in the i-th section, Indicates the lower limit of the bid price, represents the upper limit of the bidding price, a, b, d are the cost coefficients of the generator set, and n represents the total section capacity.

8. The system according to claim 5, characterized in that The random simulation module includes: The simulation optimization unit is used to simulate the quotation combinations of each competitor through the Monte Carlo method and obtain the segmented optimal quotation combination corresponding to the maximum objective function based on the genetic algorithm; The average calculation unit is used to calculate the average value of the optimal bid combination in each segment to obtain the final bidding price.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the power market bidding quotation optimization method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of the power market bidding quotation optimization method according to any one of claims 1 to 4 are implemented.

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