Method and device for determining electricity price combination, storage medium and computer program product

Abstract

The invention discloses an electricity price combination determination method and device, a storage medium and a computer program product, and relates to the field of electric power, and the method comprises the steps: encrypting an initial electricity price combination, and then obtaining a first ciphertext, and transmitting the first ciphertext to a server; a second ciphertext returned by the server is obtained, and first electricity price combination information is obtained through analysis according to the second ciphertext; the first electricity price combination and the reference information set are input into a target self-circulation network, target electricity price information is obtained, and the target electricity price information comprises at least one second generating capacity and clearing electricity price probability distribution matched with the at least one second generating capacity; a target electricity price combination is determined according to the target electricity price information, and the target electricity price combination comprises a plurality of target power generation amounts and target electricity prices matched with the target power generation amounts respectively. By adopting the technical scheme, the problem of inaccurate electricity price prediction in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of electricity, and more specifically, to a method and device for determining an electricity price combination, a storage medium, and a computer program product. Background Art

[0002] In the current electricity price forecasting process, it is necessary to fit the power generation quotation curve through power generation, power sales and other data, solve the market equilibrium point according to the predicted power generation quotation curve and the predicted value of system load, and then obtain the predicted value of time-of-use spot electricity price.

[0003] However, power generation and sales data are the core secrets of each company. It is very difficult to share and break down the barriers between data sources. For a long time, the data of each company has existed in the form of isolated islands. In this case, the electricity price prediction model can only predict electricity prices based on the company's data, but the company's data cannot cover all power generation, sales and other data in the current power market, so the predicted results lack accuracy. In other words, in the existing technology, there is a technical problem of inaccurate electricity price prediction.

[0004] Currently, no effective solution has been proposed for the above technical problems. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining an electricity price combination, a storage medium, and a computer program product to at least solve the problem of inaccurate electricity price prediction in the related art.

[0006] According to one aspect of an embodiment of the present application, a method for determining an electricity price combination is provided, comprising: sending a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes multiple initial power generation amounts and initial electricity prices that match each of the multiple initial power generation amounts; obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information based on the second ciphertext analysis, wherein the server is used to receive first ciphertexts sent by multiple nodes respectively, and to perform modeling based on the multiple first ciphertexts, the second ciphertext is determined by the server based on the modeling results, the first electricity price combination includes a first output curve, and first electricity prices corresponding to each of the multiple power generation amounts in the first output curve; inputting the first electricity price combination and a reference information set into a target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation amount and a probability distribution of clearing electricity prices that match each of the at least one second power generation amount; determining a target electricity price combination based on the target electricity price information, wherein the target electricity price combination includes multiple target power generation amounts and target electricity prices that match each of the multiple target power generation amounts.

[0007] In an exemplary embodiment, before the first ciphertext obtained after encrypting the initial electricity price combination is sent to the server, it also includes: determining the unit operating capacity curve of at least one generator set that matches the target period according to at least one generator set and the start and stop plan of at least one generator set; determining the reference electricity price information corresponding to the target period according to the historical electricity price combination corresponding to at least one generator set in at least one historical period, wherein the reference electricity price information includes reference electricity prices corresponding to multiple unit load rates respectively; determining the initial electricity price combination based on the unit operating capacity curve and the reference electricity price information.

[0008] In an exemplary embodiment, based on at least one generator set, before determining the reference electricity price information corresponding to the target period, the method further includes: obtaining a cycle description feature of the target period, wherein the cycle description feature is used to indicate the cycle description information of the target period, and the cycle description information includes at least one of the following: week type information, geographical environment information, meteorological information, lunar calendar information, and holiday information; when the feature similarity between the cycle description feature and the reference cycle description feature of at least one reference historical period satisfies a similarity condition, determining at least one reference historical period as at least one historical period.

[0009] In an exemplary embodiment, based on the historical electricity price combination corresponding to at least one generator set in at least one historical period, reference electricity price information corresponding to a target period is determined, including: obtaining the historical electricity price combination corresponding to at least one generator set in at least one historical period; determining the weighted summation result between at least one historical electricity price combination based on the reference weights matched by at least one historical period; and determining the weighted summation result as the reference electricity price information corresponding to the target period.

[0010] In an exemplary embodiment, the first ciphertext obtained by encrypting the initial electricity price combination is sent to the server, including: obtaining the second ciphertext returned by the server, and parsing the second ciphertext to obtain the first electricity price combination information, including:

[0011] In an exemplary embodiment, a first electricity price combination and a reference information set are input into a target self-circulating network to obtain target electricity price information, including: obtaining a reference information set, wherein the reference information set includes at least one of the following: reference historical electricity price data, historical disclosure data, weather information, holiday information, coal price information, historical unit operating capacity information of the current node, and supply and demand description information, wherein the supply and demand description information is used to indicate the expected tension of electricity supply; determining a target feature vector sequence according to the first electricity price combination and the reference information set; inputting the target feature vector sequence into a target self-circulating network, wherein the target self-circulating network includes a plurality of feature processing modules connected in sequence, a current feature processing module among the plurality of feature processing modules is used to receive a historical output result of a previous feature processing module as a first input, and to receive a current feature vector in the target feature vector sequence that matches the current time node as a second input, and the current feature processing module is also used to perform likelihood processing on a reference result determined based on the first input and the second input to obtain a current output result; determining the target electricity price information according to the output result of the last feature processing module among the plurality of feature processing modules.

[0012] In an exemplary embodiment, a target electricity price combination is determined based on target electricity price information, including: obtaining current power generation and a current electricity price probability distribution that matches the current power generation from at least one second power generation; determining a reference electricity price that matches the current power generation based on a distribution mean of the current electricity price probability distribution; and adjusting the reference electricity price based on a distribution characteristic of the current electricity price probability distribution to obtain a target electricity price in the target electricity price combination.

[0013] According to another aspect of an embodiment of the present application, a device for determining an electricity price combination is also provided, comprising: a sending unit, configured to send a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes multiple initial power generation amounts and initial electricity prices that match each of the multiple initial power generation amounts; a parsing unit, configured to obtain a second ciphertext returned by the server, and to obtain first electricity price combination information by parsing the second ciphertext, wherein the server is configured to receive first ciphertexts sent by multiple nodes respectively, and to perform modeling based on the multiple first ciphertexts, wherein the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to each of the multiple power generation amounts in the first output curve; an input unit, configured to input the first electricity price combination and a reference information set into a target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation amount and a probability distribution of clearing electricity prices that match each of the at least one second power generation amount; a determining unit, configured to determine a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation amounts and target electricity prices that match each of the multiple target power generation amounts. According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the electricity price combination when running.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein the program executes the steps of the method in each embodiment of the present application when it is run.

[0015] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the electricity price combination through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, which implements the steps of the method in each embodiment of the present application when the computer program is executed by a processor.

[0017] Through the present application, the first ciphertext obtained after encrypting the initial electricity price combination can be sent to the server, wherein the initial electricity price combination includes multiple initial power generation and initial electricity prices that match each of the multiple initial power generation; then the second ciphertext returned by the server is obtained, and the first electricity price combination information is obtained according to the second ciphertext analysis, wherein the server is used to receive the first ciphertexts sent by multiple nodes respectively, and to perform modeling based on the multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling results, the first electricity price combination includes a first output curve, and the first electricity prices corresponding to each of the multiple power generation in the first output curve; further, the first electricity price combination and the reference information set are input into the target self-circulating network to obtain the target electricity price information, wherein the target electricity price information includes at least one second power generation and a probability distribution of clearing electricity prices that match each of the at least one second power generation; then the target electricity price combination can be determined according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation and target electricity prices that match each of the multiple target power generation, thereby solving the problem of inaccurate electricity price prediction in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining an electricity price combination according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for determining an electricity price combination according to an embodiment of the present application;

[0022] Figure 3 is a flow chart of another method for determining an electricity price combination according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a method for determining an electricity price combination according to an embodiment of the present application;

[0024] Figure 5 is a flow chart of another method for determining an electricity price combination according to an embodiment of the present application;

[0025] Figure 6 is a flow chart of another method for determining an electricity price combination according to an embodiment of the present application;

[0026] Figure 7 is a schematic diagram of another method for determining an electricity price combination according to an embodiment of the present application;

[0027] Figure 8 It is a structural block diagram of a device for determining an electricity price combination according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal for determining a power price combination according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the electricity price combination in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a method for determining an electricity price combination is provided. Figure 2 is a flow chart of a method for determining an electricity price combination according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0034] S202, sending a first ciphertext obtained by encrypting the initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts;

[0035] S204, obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information according to the second ciphertext, wherein the server is used to receive first ciphertexts respectively sent by multiple nodes, and perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to multiple power generation amounts in the first output curve;

[0036] S206, inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation;

[0037] S208, determining a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes a plurality of target power generation amounts and target electricity prices that match the plurality of target power generation amounts.

[0038] It should be noted that in the above step S202, the above initial electricity price combination is a plurality of initial power generation and corresponding electricity prices. It can be understood that, for electricity volume and electricity price, different power generation corresponds to different prices. In the actual power generation process, the power generation power and power generation load of the generator set are usually kept unchanged within a period of time. Specifically, the maximum power generation power of a generator set is constant, but the generator set can work at different load rates. For example, when working at a load rate of 0.4, the actual power generation power of the unit is 40% of the maximum power generation power. For different load rates, there will be different electricity prices. The combination of initial electricity prices is a combination of multiple different load rates and corresponding electricity prices.

[0039] Optionally, the above initial electricity price combination can be obtained through the following steps S2001 to S2003.

[0040] S2001, determining a unit operation capacity curve of at least one generator set that matches a target cycle according to at least one generator set and a start / stop plan of at least one generator set;

[0041] S2002, determining reference electricity price information corresponding to a target period based on a combination of historical electricity prices corresponding to at least one generator set in at least one historical period, wherein the reference electricity price information includes reference electricity prices corresponding to load rates of multiple generator sets respectively;

[0042] S2003: Determine an initial electricity price combination based on the unit operation capacity curve and reference electricity price information.

[0043] It should be noted that in the above steps S2001 to S2003, the above unit operating capacity refers to the power generated by the unit during operation. For the above step S2001, in the power market, each electricity price forecast cycle contains multiple sub-cycles, each sub-cycle can have different expected power generation, and the specific expected power generation can be determined based on whether the generator set has a start-up and shutdown plan.

[0044] For example, a forecast day for an electricity price contains 96 sub-periods, each of which is 15 minutes. In this case, in the process of determining the initial electricity price combination, if the unit has a start-up and shutdown plan, the real-time power generation of the operating and standby units is set to increase by 1 / 8 of the adjustable total power every 15 minutes from the start-up time until the expected power generation reaches the maximum power generation of the unit. If the unit has no start-up and shutdown plan, the unit maintains the current operating power. Then, the unit operating capacity of the unit within 96 sub-periods can be determined, and the unit operating capacity of the 96 sub-periods and the 96 time sub-periods form the above-mentioned unit capacity operation curve. The unit capacity operation curve can be based on time as the horizontal coordinate and power as the vertical coordinate.

[0045] It is understandable that the determined unit operating capacity of the unit within the 96 sub-cycles is the maximum power generation of the unit within the 96 sub-cycles. Afterwards, after the unit operating capacity curve is obtained, the above step S2002 is executed to determine the reference electricity price information corresponding to the target cycle according to the historical electricity price combination corresponding to at least one generator set in at least one historical cycle, wherein the reference electricity price information includes reference electricity prices corresponding to the load rates of multiple units.

[0046] Specifically, the historical period in the above step S2002 can be determined through the following steps S3001 to S3002.

[0047] S3001, obtaining a cycle description feature of a target cycle, wherein the cycle description feature is used to indicate cycle description information of the target cycle, and the cycle description information includes at least one of the following: week type information, geographical environment information, meteorological information, lunar solar term information, and holiday information;

[0048] S3002: When the feature similarity between the period description feature and the reference period description feature of at least one reference historical period satisfies a similarity condition, determine at least one reference historical period as at least one historical period.

[0049] It can be understood that the above-mentioned target period is the time period of the electricity price to be predicted. For example, when the period of the electricity price to be predicted is from 0:00 to 24:00 tomorrow, then from 0:00 to 24:00 of each day in the past is a reference historical period. The period description features of the above-mentioned target period and the reference historical period are obtained, including week type information, geographical environment information, meteorological information, lunar calendar information, and holiday information. Different period description features correspond to a weight.

[0050] For example, for holiday information, if the reference historical period is the historical period of the same day as the target period last year, the weight is 1; if they are different, the weight is 0. Finally, the weights are added together, and the five reference historical periods with the highest total weights are determined as historical periods. It should be noted that this is only an example. In practice, more or fewer reference historical periods can be determined as historical periods.

[0051] Afterwards, the reference electricity price information may be determined by weighting the historical electricity price combinations corresponding to at least one determined historical period. For the specific weighting process, refer to steps S4001 to S4003.

[0052] S4001, obtaining a historical electricity price combination corresponding to at least one generating set in at least one historical period;

[0053] S4002, determining a weighted summation result between at least one historical electricity price combination according to the reference weights matched by each of at least one historical period;

[0054] S4003: Determine the weighted sum result as reference electricity price information corresponding to the target period.

[0055] For example, the reference electricity price information can be determined by an average weighted method. In this case, assuming that there are three historical periods, and the corresponding quotations for the three historical periods are 700 yuan, 800 yuan and 900 yuan respectively when the load factor is 0.4, the average weighted quotation corresponding to the load factor of 0.4 is 800 yuan. By calculating other load factors in an average weighted manner, the average weighted electricity prices under different load factors can be obtained, which are the reference electricity prices corresponding to the unit load factors in the above step S2002.

[0056] In addition, unequal weighting can also be performed. For example, the above three historical periods are sorted in ascending order according to similarity and assigned weights of 0.5, 0.8, and 1.0 respectively. When the load rate of the three historical periods is 0.4, the corresponding quotations are 700 yuan, 800 yuan, and 900 yuan respectively. Then, the unequal weighted quotation corresponding to the load rate of 0.4 is (700*0.5+800*0.8+900*1) / 3=630 yuan. Other load rates are also calculated according to the above unequal weighting method to obtain unequal weighted electricity prices under different load rates, that is, the reference electricity prices corresponding to the unit load rates in the above step S2002.

[0057] It is worth noting that in the reference electricity price information obtained at this time, multiple consecutive load rates have the same quotation. For example, when the load rate is 0.4, the quotation is 800. After that, the quotation of the load rate is still 800 until the load rate reaches 0.5. In other words, the correspondence between the load rate and the price increase is the correspondence between the load range and the specific price.

[0058] Then, step S2003 is executed to determine an initial electricity price combination based on the unit operation capacity curve and the reference electricity price information.

[0059] Specifically, based on the above-mentioned reference electricity price information, the correspondence between electricity price and load rate can be further refined. For example, in the load rate range of [0.4, 0.5), the corresponding electricity price is 800. Further, it is divided into two sub-ranges [0.4, 0.45) and [0.45, 0.5). In the sub-range [0.4, 0.45), the quotation is 800, and in the sub-range [0.4, 0.45), the quotation is increased to 820 yuan.

[0060] It should be noted that the above sub-interval division is only an example. Optionally, the number of load rate sub-intervals can be divided according to the load rate. For example, in the part with low load rate, such as the load rate interval [0.4, 0.5), the number of division blocks can be relatively small, such as dividing into two sub-intervals as mentioned above, while in the part with high load rate, such as the load rate interval [0.8, 0.9), a more refined division is required, such as dividing into ten load rate sub-intervals: [0.80, 0.81), [0.81, 0.82), ... [0.89, 0.9).

[0061] It can be understood that the product of the divided load factor and the rated capacity (maximum power) of the unit is the power increase based on the previous quotation segment. Each point of power increase corresponds to a new price increase. For example, the quotation for the sub-interval [0.45, 0.5) is 820, and the load factor of 0.4 in the previous quotation stage corresponds to an electricity price of 800. The product of the load factor value in the sub-interval [0.45, 0.5) and the corresponding rated capacity of the unit is the power increase corresponding to the load factor of 0.4. The price of 820 corresponding to the sub-interval [0.45, 0.5) is the price increase of 800 corresponding to the load factor of 0.4 in the previous quotation stage.

[0062] It should be pointed out that in the actual operation of the unit, it is not always running at full load, that is, the actual operating load rate is not always 1. When the load rate is not 1, the unit's generated power may reach the planned power (the unit capacity determined by the aforementioned steps S2001 to S2003). In this case, there is no need to consider the prices of all situations from the lowest load rate to the load rate of 1. Instead, it is only necessary to consider the lowest load rate to the highest load rate that can meet the planned power. Specifically, the highest load rate that can meet the planned power can be determined by the following mathematical model.

[0063] Operating capacity≤∑ i＝0 Load factor 最小出力点 * Rated capacity of the unit + load rate i * Rated capacity of the unit

[0064] Among them, the operating capacity in the above formula is the unit capacity determined by the aforementioned steps S2001 to S2003. When the right value in the above formula is basically equal to the left value, the load rate corresponding to the largest i at this time is determined as the maximum load rate, and the load rates from the minimum load rate to the maximum load rate and their respective corresponding prices are used as the initial electricity price combination in the aforementioned step S202.

[0065] It is worth noting that the above initial electricity price combination belongs to the core confidential data of each company and cannot be directly shared, but needs to be encrypted and the encrypted data (first ciphertext) is sent to the server for electricity price modeling. Specifically, the encryption process includes the following steps S5001 to S5004.

[0066] S5001, performing data conversion on an initial electricity price combination according to a first truth table to obtain a first ciphertext;

[0067] S5002, encrypt the first truth table according to the first secret key to obtain a third ciphertext;

[0068] S5003, performing a shuffle operation on the third ciphertext to obtain a fourth ciphertext;

[0069] S5004, sending the first ciphertext and the fourth ciphertext to the server;

[0070] The first truth table is used to perform data conversion on the initial electricity price combination to encrypt the initial electricity price combination. At the same time, the first truth table is encrypted according to the first secret key and the order is disrupted. The encrypted initial electricity price combination and the first truth table are sent to the server.

[0071] It should be noted that the server side has the same first secret key. The server side will decrypt the above-mentioned first truth table according to the first secret key, and parse out the initial electricity price combination according to the decrypted first truth table. It is worth noting that the initial electricity price combination parsed by the server side is only data used for calculation. The server side itself cannot parse out the specific meaning of the data. Therefore, the privacy of each company is protected, so that each company can also perform electricity price estimation modeling through the server without disclosing its core data.

[0072] Furthermore, after obtaining the initial electricity price combination, the server calculates the corresponding clearing price based on the initial electricity price combination. Specifically, after receiving the first ciphertext, the server accumulates the unit output (planned output power) according to the initial electricity price combination sent by all companies. Specifically, for the server, there is a total power generation demand of the power generation companies. In order to minimize the power generation cost, the server will look for the power generation load rate with the lowest price.

[0073] For example, assuming that Company A's initial electricity price combination is 700 yuan for a load factor of 0.4 and 800 yuan for a load factor of 0.5, and Company B's initial electricity price combination is 650 yuan for a load factor of 0.3 and 750 yuan for a load factor of 0.4, the server will give priority to finding the lowest price for Company B's 0.3 load factor. Assuming that at a load factor of 0.3, the generating capacity of Company B's unit is 3,000 megawatts.

[0074] It can be understood that the load rate and output power satisfy the following mathematical model:

[0075] Unit output = load factor * unit rated capacity

[0076] The above-mentioned unit output is the actual output power of the unit at the corresponding load rate, and the above-mentioned unit rated capacity is the maximum output power of the unit under full load.

[0077] Further assume that the total output power of all power generation companies is 5,000 megawatts. At this time, we find Company A with the second lowest price and a load rate of 0.4. Assuming that at a load rate of 0.4, the power generation power of Company A's units is 3,000 megawatts, the power generation conditions of Companies A and B can meet the demand. At this time, the power generation power of each unit is determined as the winning output. The output of each power generation company is determined in the above manner for the 96 15-minute power generation demands in a day, and the winning output curve corresponding to each company can be obtained. At the same time, the clearing price corresponding to each winning output in the winning output curve can be obtained according to the load rate corresponding to different winning bids, and then each winning output and the corresponding clearing price are used as the first electricity price combination.

[0078] It should be noted that the total power generation demand of the above power generation companies is calculated by the following formula:

[0079] Bidding space = direct load - total power generation of local power plants - interconnection line load - wind and solar output

[0080] -Nuclear power output-Self-owned unit output-Pumped storage output

[0081] The above direct regulation is in line with the total power generation demand, and the total thermal power generation demand of each power generation company is the total power generation demand minus the power generation capacity of each local power plant, external power, wind power generation capacity, standby unit power (self-provided unit output) and hydropower generation capacity.

[0082] Then, when the final winning output curves of each unit and the corresponding clearing prices are obtained, the winning output curves of each unit and the corresponding clearing prices are returned to the corresponding power generation company as the second ciphertext according to the sending ID of the first ciphertext.

[0083] It should be noted that the encryption process of the above-mentioned second ciphertext can refer to the aforementioned steps S5001 to S5004. The server will encrypt the winning output curve and the corresponding clearing price according to the second truth table. In addition, the second truth table is encrypted and scrambled using the second secret key. The above-mentioned second secret key and the above-mentioned first secret key can be the same key or different keys, which are not specifically limited here. Finally, the encrypted second truth table, the encrypted winning output curve and the corresponding clearing price are sent back to each power generation company respectively.

[0084] Then execute the above step S204, obtain the second ciphertext returned by the server, and parse the second ciphertext to obtain the first electricity price combination information, wherein the server is used to receive the first ciphertexts sent by multiple nodes respectively, and perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling results, and the first electricity price combination includes the first output curve, and the first electricity prices corresponding to the multiple power generation amounts in the first output curve.

[0085] Specifically, the parsing process may include the following steps S5005 to S5006.

[0086] S5005, obtaining a second ciphertext and a fifth ciphertext returned by the server, wherein the fifth ciphertext is an encrypted second truth table, and the second truth table is used to generate the second ciphertext;

[0087] S5006, decrypt the fifth ciphertext according to the second secret key to obtain a second truth table;

[0088] S5007, parse the second ciphertext according to the second truth table to obtain a first electricity price combination.

[0089] It should be noted that the power generation company itself holds the second secret key. The power generation company parses the second truth table according to the second secret key it holds, and parses out the first electricity price combination through the parsed second truth table.

[0090] Further, execute the above step S206, input the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation.

[0091] Specifically, the above step S206 includes the following steps S206-1 to S206-4:

[0092] S206-1, obtaining a reference information set, wherein the reference information set includes at least one of the following: reference historical electricity price data, historical disclosure data, weather information, holiday information, coal price information, historical unit operating capacity information of the current node, and supply and demand description information, wherein the supply and demand description information is used to indicate the expected tightness of power supply;

[0093] The simulated winning output curve and simulated clearing electricity price obtained in step S204 above, as well as real historical electricity price data, historical disclosure data (load, etc.), weather information, holiday information, coal price information, and the company's historical unit operating capacity and other data are used as a reference information set. In addition, the reference information set also includes supply and demand description information, which is used to reflect the expected tightness of power supply and demand, and the formula is as follows:

[0094]

[0095] S206-2, determining a target feature vector sequence according to the first electricity price combination and the reference information set;

[0096] Specifically, the reference set is cleaned and processed so that all data are presented as a characteristic sequence of 96 time points in a day.

[0097] S206-3, inputting the target feature vector sequence into the target self-circulating network, wherein the target self-circulating network includes a plurality of feature processing modules connected in sequence, and a current feature processing module among the plurality of feature processing modules is used to receive a historical output result of a previous feature processing module as a first input, and receive a current feature vector matching the current time node in the target feature vector sequence as a second input, and the current feature processing module is further used to perform likelihood processing on a reference result determined based on the first input and the second input to obtain a current output result;

[0098] The above target self-loop network is as follows Figure 7 As shown, Figure 7 In, z i，t-2and x i，t-1 is the input of the first feature processing module in the target self-circulatory network, and the output h is obtained through the first feature processing module. i，t-1 , then h i，t-1 , the result z output by the entire model at the last moment i，t-1 (the actual result of the previous moment), and the independent variable x of the next moment i，t As the input of the next feature processing module.

[0099] The hidden layer update process of the above target autoregressive network during the training phase is as follows:

[0100]

[0101] Above It refers to the given parameters of the LSTM cell state used in this autoregressive recurrent network. H represents an autoregressive recurrent network, and h is the output result of the hidden layer of the autoregressive recurrent network.

[0102] It is worth noting that during the training process, since all data are known, z i，t-1 The actual result of the last moment is used, and in the prediction part after the model training, z i，t-1 You need to use the value from the result of the previous moment.

[0103] like Figure 7 As shown, adding the likelihood function to the autoregressive recurrent network To make the output prediction value a probability distribution, Gaussian distribution (applicable to real continuous distribution data) or negative binomial distribution (applicable to integer data) can generally be selected, and no specific limitation is made here.

[0104] It should be noted that the above is the neural network model in the training process. Here, we take the likelihood function as Gaussian distribution as an example. It can be understood that the electricity price prediction result is the price, which is a continuously changing number, approximated to 3 decimal places. The Gaussian distribution formula is as follows:

[0105]

[0106] Among them, μ is the mathematical expectation, σ 2 is the variance, w is the weight obtained in the likelihood function training, and b is the error term.

[0107] It should be noted that since there is no negative variance, in order to ensure that the variance is positive, a softplus activation function is added. It can be understood that the softplus activation function is a smoothed version of the relu function, and the softplus activation function makes the variance change continuous and non-negative.

[0108] Then, the final DeepAR overall formula is as follows:

[0109]

[0110] in, is the probability product form of the joint probability distribution, To express the autoregression results in the form of probability using a likelihood function.

[0111] is the likelihood function. In addition, It has been explained in the above content and will not be repeated here. It is only emphasized again that H is an autoregressive recurrent network.

[0112] You can use the reference information of multiple historical days to Figure 7 The autoregressive recurrent network shown in FIG. 1 is trained, and the trained network is used as the target autoregressive network, and the reference information set updated daily is input into the trained target autoregressive network, and then step S206-4 is executed to determine the target electricity price information according to the output result of the last feature processing module among the multiple feature processing modules, so as to obtain the probability distribution of the optimized clearing electricity price.

[0113] After obtaining the above-mentioned target electricity price information, execute step S208 to determine a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation amounts and target electricity prices that match each of the multiple target power generation amounts. Specifically, step S208 may include the following steps S208-1 to S208-3.

[0114] S208-1, obtaining a current power generation and a probability distribution of a current electricity price matching the current power generation from at least one second power generation;

[0115] S208-2, determining a reference electricity price that matches the current power generation according to the distribution mean of the current electricity price probability distribution;

[0116] S208-3, adjusting the reference electricity price according to the distribution characteristics of the current electricity price probability distribution to obtain a target electricity price in the target electricity price combination.

[0117] It can be understood that the above step S208-1 is to obtain the target electricity price information through the above step S206, and then execute step S208-2 to determine the reference electricity price that matches the current power generation based on the distribution mean of the current electricity price probability distribution. Specifically, the mean of the predicted probability distribution is first calculated, and the result is determined as the clearing electricity price predicted by each node.

[0118] Afterwards, execute the above step S208-3, adjust the reference electricity price according to the distribution characteristics of the current electricity price probability distribution, and obtain a target electricity price in the target electricity price combination. Specifically, when the prediction certainty is relatively low, a conservative quotation strategy is adopted, and more prices lower than the predicted values ​​are set in the quotation table; when the certainty is higher, an aggressive strategy is adopted to increase the output points in the quotation that are close to the predicted values.

[0119] It should be noted that the above certainty can be determined by statistical characteristics or by shape. Furthermore, for the method of confirming certainty by statistical characteristics, it is necessary to calculate the standard deviation and 95% confidence interval of the predicted distribution. When the standard deviation is large and the confidence interval is wide, it is considered that the certainty is low.

[0120] Optionally, for certainty through shape, it is necessary to check the kurtosis and skewness of the forecast distribution. Lower kurtosis means that the forecast values ​​are more widely distributed. The skewness of the distribution may affect the risk assessment. A longer right tail of the distribution means that the possibility of higher extreme values ​​is increased, and there may be higher upside risk. A longer left tail means higher downside risk, that is, lower certainty.

[0121] Through the above-mentioned implementation mode of the present application, the first ciphertext obtained after encrypting the initial electricity price combination can be sent to the server, wherein the initial electricity price combination includes multiple initial power generation and initial electricity prices that match each of the multiple initial power generation; then the second ciphertext returned by the server is obtained, and the first electricity price combination information is obtained according to the second ciphertext analysis, wherein the server is used to receive the first ciphertexts sent by multiple nodes respectively, and to perform modeling based on the multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling results, the first electricity price combination includes the first output curve, and the first electricity prices corresponding to each of the multiple power generation in the first output curve; further, the first electricity price combination and the reference information set are input into the target self-circulating network to obtain the target electricity price information, wherein the target electricity price information includes at least one second power generation and a probability distribution of clearing electricity prices that match each of the at least one second power generation; then the target electricity price combination can be determined according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation and target electricity prices that match each of the multiple target power generation, thereby solving the problem of inaccurate electricity price prediction in the prior art.

[0122] The following combination Figure 3 A complete method for determining electricity price combinations is described.

[0123] S302, aligning the data used by each company for simulation.

[0124] It should be noted that, as mentioned above, power production and sales data are the core private data of each company and cannot be made public. In theory, only the power grid company can obtain all the information. In order to improve the prediction accuracy of each power generation company, it is necessary to use the private data of other companies under the condition of ensuring data security through federated learning (if the companies have reached an agreement), solve the problem of data isolation and ensure that data will not be leaked.

[0125] In the above step S302, N data owners {F1, ..., F N} and the data to be merged {D1,...,D N}, during model training F i No data will be exposed i To others.

[0126] It is worth noting that for shared data, which is similar information such as each company’s own quotations and unit operation modes, the feature space is the same and the business is similar, but the users of each company are their own power generation and sales organizations, and the user intersection is small, so horizontal federated learning can be chosen.

[0127] Specifically, the feature space is represented as X, the label space is represented as Y, and I is used to represent the sample ID space. The relationship between the data set features is as follows:

[0128]

[0129] like Figure 4 As shown, Figure 4 In the dataset, power quotes and unit operation modes are features, target power prices are labels, and each company’s own power sales company is the sample ID.

[0130] S304, calculate the winning bid power price pair based on the company's data (obtain the quotation table through similar days).

[0131] Specifically, a simulation clearing algorithm based on historical similar day information is used to obtain the winning output curve and clearing price of each unit based on simulation. The steps are as follows: Figure 5 As shown, the simulation process details of the local part refer to Figure 6 .

[0132] First, the unit operating capacity at 96 o'clock (96 15-minute intervals in a day) on the forecast day is updated according to the company's unit start-up and shutdown plan. The real-time power generation output of the operating and standby units is set to increase by 1 / 8 of the total adjustable capacity every 15 minutes from the start-up time until the bid output fully reaches the adjustable output; the shutdown is to reduce the current operating capacity by 1 / 4 every 15 minutes, and the output is reduced to 0 in one hour; the units without start-up and shutdown maintain the current operating capacity. The 96-point unit operating capacity obtained is the maximum output of the unit at each moment.

[0133] Afterwards, the weighted average historical quotations obtained by the similar day method are used as the preliminary quotation plan. The quotation is divided into several quotation segments according to the unit load rate, and the same price is quoted within a load rate range, as shown in Table 1.

[0134] Load factor 0.4 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 1.0 Quote 300 400 500 600 700 800 900 1000 1100 1300

[0135] Table 1

[0136] When using the quotation table shown in Table 1, when the output of the unit to be called is increased to the next stage, the higher quotation of the next stage is also used.

[0137] Furthermore, combined with the quotation sheet and the rated capacity of the unit, the unit can be divided into multiple segments of winning bid output and quotation according to different load rates. On the basis of each quotation segment in the quotation sheet, the quotation and load rate are further divided in the same proportion to refine the different output situations. In the part with low load rate, the number of division blocks can be relatively small, while the part with high load rate requires more refined division. The product of the divided load rate and the rated capacity of the unit is the increased output based on the previous quotation segment. Each additional output point corresponds to a new price increase. When the cumulative capacity is basically equal to the 96-point unit operating capacity, the matching based on the initial version of the quantity-price pair is completed. Figure 6 The initial version of the quantity-price pair determination formula is shown in .

[0138] Operating capacity≤∑ i＝0 Load factor 最小出力点 *Rated capacity of the unit + load rate i * Rated capacity of the unit

[0139] The above formula has been explained in the previous embodiment and will not be repeated here. Further, execute Figure 5 In step S503, the first edition quantity-price pair is masked through SMC, and the masked result is sent to the server.

[0140] S306, aggregate company data through federated learning, and simulate the unit's winning bid curve and clearing electricity price.

[0141] Specifically, after receiving the data sent by each company, the server executes Figure 5In step S505, all the participating companies' quantity-price pairs and the total thermal bidding space are compared to simulate the winning output curve and clearing price of each unit. Specifically, the server sets the clearing price to increase from low to high, and accumulates the unit output according to the initial quantity-price pairs of all units. When the quantity-price pairs are stacked until the planned cumulative output exceeds the thermal bidding space, the winning output curve and the corresponding clearing price of each unit are obtained.

[0142] Finally, execute Figure 5 In step S507, the server feeds back the results to each company according to the corresponding sample ID.

[0143] S308, based on the simulation results and other features, optimize the electricity price prediction results through DeepAR.

[0144] The specific process refers to the aforementioned steps S206-1 to S206-4, which will not be described in detail here.

[0145] S310, adjusting the quotation according to the prediction result.

[0146] First, the uncertainty of the forecast results needs to be evaluated. This can be done through statistical features or through shape. Specifically, for the evaluation through statistical features, the standard deviation and 95% confidence interval of the forecast distribution are calculated. When the standard deviation is large and the confidence interval is wide, it is considered to be a higher uncertainty. For the evaluation through shape, the kurtosis and skewness of the forecast distribution are checked. Lower kurtosis means that the forecast value distribution is wider; the degree of skewness of the distribution may affect the risk assessment. A longer right tail of the distribution means that the possibility of higher extreme values ​​increases, and there may be higher upside risks. A longer left tail means higher downside risks.

[0147] Then the mean of the predicted probability distribution is calculated, and the result can be considered as the predicted clearing electricity price of each node. When the prediction certainty is relatively low, a conservative bidding strategy is adopted, and the price in the quotation table is set lower than the predicted value; when the certainty is high, an aggressive strategy is adopted to increase the output points in the quotation that are close to the predicted value.

[0148] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0149] In this embodiment, a device for determining an electricity price combination is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0150] Figure 8 : is a structural block diagram of a device for determining an electricity price combination according to an embodiment of the present application, the device comprising:

[0151] A sending unit 92 is used to send a first ciphertext obtained by encrypting the initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts;

[0152] The parsing unit 94 is used to obtain the second ciphertext returned by the server, and parse the second ciphertext to obtain the first electricity price combination information, wherein the server is used to receive the first ciphertexts respectively sent by multiple nodes, and perform modeling based on the multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes the first output curve, and the first electricity prices corresponding to the multiple power generation amounts in the first output curve;

[0153] An input unit 96, configured to input the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation;

[0154] The determination unit 98 is used to determine a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation amounts and target electricity prices that match the multiple target power generation amounts.

[0155] Through the above-mentioned device, the first ciphertext obtained after encrypting the initial electricity price combination can be sent to the server, wherein the initial electricity price combination includes multiple initial power generation and initial electricity prices that match each of the multiple initial power generation; then the second ciphertext returned by the server is obtained, and the first electricity price combination information is obtained according to the second ciphertext analysis, wherein the server is used to receive the first ciphertexts sent by multiple nodes respectively, and modeling is performed based on the multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, the first electricity price combination includes the first output curve, and the first electricity prices corresponding to each of the multiple power generation in the first output curve; further, the first electricity price combination and the reference information set are input into the target self-circulating network to obtain the target electricity price information, wherein the target electricity price information includes at least one second power generation and the probability distribution of clearing electricity prices that match the at least one second power generation; then the target electricity price combination can be determined according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation and target electricity prices that match each of the multiple target power generation, thereby solving the problem of inaccurate electricity price prediction in the prior art.

[0156] In an exemplary embodiment, the above-mentioned electricity price combination determination device also includes an electricity price combination determination unit, which is used to determine the unit operating capacity curve of at least one generator set that matches the target cycle based on at least one generator set and the start and stop plan of at least one generator set; determine the reference electricity price information corresponding to the target cycle based on the historical electricity price combination corresponding to each of the at least one generator set in at least one historical cycle, wherein the reference electricity price information includes reference electricity prices corresponding to multiple unit load rates respectively; determine the initial electricity price combination based on the unit operating capacity curve and the reference electricity price information.

[0157] In an exemplary embodiment, the above-mentioned electricity price combination determination device also includes a historical cycle determination unit, which is used to obtain a cycle description feature of the target cycle, wherein the cycle description feature is used to indicate the cycle description information of the target cycle, and the cycle description information includes at least one of the following: week type information, geographical environment information, meteorological information, lunar calendar solar term information, holiday information; when the feature similarity between the cycle description feature and the reference cycle description feature of at least one reference historical cycle satisfies the similarity condition, at least one reference historical cycle is determined as at least one historical cycle.

[0158] In an exemplary embodiment, the above-mentioned electricity price combination determination device also includes an electricity price information determination unit, which is used to obtain a historical electricity price combination corresponding to at least one generator set in at least one historical period; determine a weighted summation result between at least one historical electricity price combination based on a reference weight matched by each of at least one historical period; and determine the weighted summation result as reference electricity price information corresponding to the target period.

[0159] In an exemplary embodiment, the input unit 96 is also used to obtain a reference information set, wherein the reference information set includes at least one of the following: reference historical electricity price data, historical disclosure data, weather information, holiday information, coal price information, current node historical unit operating capacity information, supply and demand description information, wherein the supply and demand description information is used to indicate the expected tightness of electricity supply; determine a target feature vector sequence based on the first electricity price combination and the reference information set; input the target feature vector sequence into a target self-circulating network, wherein the target self-circulating network includes a plurality of feature processing modules connected in sequence, a current feature processing module among the plurality of feature processing modules is used to receive a historical output result of a previous feature processing module as a first input, and receive a current feature vector in the target feature vector sequence that matches the current time node as a second input, and the current feature processing module is also used to perform likelihood processing on a reference result determined based on the first input and the second input to obtain a current output result; determine the target electricity price information based on the output result of the last feature processing module among the plurality of feature processing modules.

[0160] In an exemplary embodiment, the above-mentioned determination unit 98 is also used to obtain the current power generation from at least one second power generation, and the current electricity price probability distribution matching the current power generation; determine the reference electricity price matching the current power generation according to the distribution mean of the current electricity price probability distribution; adjust the reference electricity price according to the distribution characteristics of the current electricity price probability distribution to obtain a target electricity price in the target electricity price combination.

[0161] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0162] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0163] S1, sending a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts;

[0164] S2, obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information according to the second ciphertext, wherein the server is used to receive first ciphertexts respectively sent by multiple nodes, and to perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to multiple power generation amounts in the first output curve;

[0165] S3, inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation;

[0166] S4, determining a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes a plurality of target power generation amounts and target electricity prices that match the plurality of target power generation amounts.

[0167] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, 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.

[0168] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0169] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0170] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0171] S1, sending a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts;

[0172] S2, obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information according to the second ciphertext, wherein the server is used to receive first ciphertexts respectively sent by multiple nodes, and to perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to multiple power generation amounts in the first output curve;

[0173] S3, inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation;

[0174] S4, determining a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes a plurality of target power generation amounts and target electricity prices that match the plurality of target power generation amounts.

[0175] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0176] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores the computer program product, and when the computer program is executed by a processor, the steps of the method in each embodiment of the present application are implemented.

[0177] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0178] S1, sending a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts;

[0179] S2, obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information according to the second ciphertext, wherein the server is used to receive first ciphertexts respectively sent by multiple nodes, and to perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to multiple power generation amounts in the first output curve;

[0180] S3, inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation;

[0181] S4, determining a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes a plurality of target power generation amounts and target electricity prices that match the plurality of target power generation amounts.

[0182] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0183] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0184] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining an electricity price combination, characterized in that: include: Sending a first ciphertext obtained by encrypting the initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts; Obtaining a second ciphertext returned by the server, and obtaining first electricity price combination information according to the second ciphertext, wherein the server is used to receive the first ciphertexts respectively sent by multiple nodes, and to perform modeling based on multiple first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to multiple power generation amounts in the first output curve; Inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matching the at least one second power generation; A target electricity price combination is determined according to the target electricity price information, wherein the target electricity price combination includes a plurality of target power generation amounts and target electricity prices that match the plurality of target power generation amounts respectively.

2. The method according to claim 1, characterized in that Before the first ciphertext obtained by encrypting the initial electricity price combination is sent to the server, the method further includes: Determine, according to at least one generator set and the respective start-stop plan of at least one of the generator sets, a respective unit operating capacity curve of at least one of the generator sets that matches the target cycle; Determine reference electricity price information corresponding to the target period according to a combination of historical electricity prices corresponding to at least one of the generator sets in at least one historical period, wherein the reference electricity price information includes reference electricity prices corresponding to load rates of multiple generator sets respectively; The initial electricity price combination is determined based on the unit operation capacity curve and the reference electricity price information.

3. The method according to claim 2, characterized in that Before determining the reference electricity price information corresponding to the target period according to the historical electricity price combination corresponding to at least one historical period of at least one of the generator sets, the method further includes: Acquire a cycle description feature of the target cycle, wherein the cycle description feature is used to indicate cycle description information of the target cycle, and the cycle description information includes at least one of the following: week type information, geographical environment information, meteorological information, lunar solar term information, and holiday information; In the case where the feature similarity between the period description feature and the reference period description feature of at least one reference historical period satisfies a similarity condition, at least one of the reference historical periods is determined as at least one of the historical periods.

4. The method according to claim 2, characterized in that: The determining of reference electricity price information corresponding to the target period according to a combination of historical electricity prices corresponding to at least one of the generator sets in at least one historical period includes: Obtaining a historical electricity price combination corresponding to at least one of the generator sets in at least one of the historical periods; Determining a weighted summation result between at least one of the historical electricity price combinations according to the reference weights matched by at least one of the historical periods; The weighted sum result is determined as the reference electricity price information corresponding to the target period.

5. The method according to claim 2, characterized in that: The first ciphertext obtained by encrypting the initial electricity price combination is sent to the server, comprising: Performing data conversion on the initial electricity price combination according to a first truth table to obtain the first ciphertext; Encrypt the first truth table according to the first secret key to obtain a third ciphertext; Performing a shuffle operation on the third ciphertext to obtain a fourth ciphertext; sending the first ciphertext and the fourth ciphertext to the server; The obtaining of the second ciphertext returned by the server and obtaining the first electricity price combination information according to the second ciphertext parsing includes: Obtaining a second ciphertext and a fifth ciphertext returned by the server, wherein the fifth ciphertext is an encrypted second truth table, and the second truth table is used to generate the second ciphertext; decrypting the fifth ciphertext according to the second secret key to obtain a second truth table; The second ciphertext is parsed according to the second truth table to obtain the first electricity price combination.

6. The method according to any one of claims 1 to 5, characterized in that The step of inputting the first electricity price combination and the reference information set into the target self-circulating network to obtain target electricity price information includes: Acquire the reference information set, wherein the reference information set includes at least one of the following: reference historical electricity price data, historical disclosure data, weather information, holiday information, coal price information, historical unit operation capacity information of the current node, and supply and demand description information, wherein the supply and demand description information is used to indicate the expected tightness of power supply; Determine a target feature vector sequence according to the first electricity price combination and the reference information set; Inputting the target feature vector sequence into the target self-circulating network, wherein the target self-circulating network includes a plurality of feature processing modules connected in sequence, and a current feature processing module among the plurality of feature processing modules is used to receive a historical output result of a previous feature processing module as a first input, and receive a current feature vector matching a current time node in the target feature vector sequence as a second input, and the current feature processing module is further used to perform likelihood processing on a reference result determined based on the first input and the second input to obtain a current output result; The target electricity price information is determined according to the output result of the last feature processing module among the multiple feature processing modules.

7. The method according to claim 1, characterized in that The determining of a target electricity price combination according to the target electricity price information includes: Acquire a current power generation and a probability distribution of a current electricity price matched by the current power generation from at least one of the second power generation; Determining a reference electricity price that matches the current power generation according to the distribution mean of the current electricity price probability distribution; The reference electricity price is adjusted according to the distribution characteristics of the current electricity price probability distribution to obtain one of the target electricity prices in the target electricity price combination.

8. A device for determining an electricity price combination, characterized in that: include: A sending unit, configured to send a first ciphertext obtained by encrypting an initial electricity price combination to a server, wherein the initial electricity price combination includes a plurality of initial power generation amounts and initial electricity prices respectively matching the plurality of initial power generation amounts; a parsing unit, configured to obtain a second ciphertext returned by the server, and parse the second ciphertext to obtain first electricity price combination information, wherein the server is configured to receive the first ciphertexts respectively sent by a plurality of nodes, and perform modeling based on a plurality of the first ciphertexts, the second ciphertext is determined by the server according to the modeling result, and the first electricity price combination includes a first output curve, and first electricity prices corresponding to each of a plurality of power generation amounts in the first output curve; An input unit, configured to input the first electricity price combination and the reference information set into a target self-circulating network to obtain target electricity price information, wherein the target electricity price information includes at least one second power generation and a clearing electricity price probability distribution respectively matched with the at least one second power generation; A determination unit is used to determine a target electricity price combination according to the target electricity price information, wherein the target electricity price combination includes multiple target power generation amounts and target electricity prices that match the multiple target power generation amounts.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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