A power grid clearing method, system, electronic equipment and medium

Abstract

The embodiment of the present application provides a method, system, electronic device and medium for clearing a power grid, wherein low-order data abstraction processing is performed according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics; a long-term transaction electricity price forecast is performed according to a preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack; a spot transaction stack is established based on the spot transaction electricity price of each of the associated power plants, and a first clearing function is determined through the long-term transaction stack and the spot transaction stack; according to the first clearing function, the power supply and demand balance state of the target power grid when clearing under the first clearing function is determined; according to the power supply and demand balance state, the first clearing function is adjusted to obtain a second clearing function, and power clearing is performed based on the second clearing function. In this way, the effect of improving the accuracy of power grid clearing is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a power grid clearing method, system, electronic equipment and medium. Background Art

[0002] In the current grid clearing process, it is often necessary to involve electricity price forecasts for different power plants and power distribution processes. However, in actual application scenarios, the prediction of power plant electricity prices and the power distribution process are often affected by many factors, resulting in poor accuracy of grid clearing.

[0003] Therefore, how to solve the current problem of poor grid clearing accuracy has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] Based on the above problems, in order to solve the current problem of poor accuracy of power grid clearing, the embodiments of the present application provide a power grid clearing method, system, electronic device and medium.

[0005] The embodiments of the present application disclose the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a power grid clearing method, which is applied to a target power grid, wherein the target power grid has a plurality of associated power plants, and the method includes:

[0007] Performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics;

[0008] Performing long-term transaction electricity price prediction according to a preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack;

[0009] Establishing a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determining a first clearing function through the long-term transaction stack and the spot transaction stack;

[0010] According to the first clearing function, determining the power supply and demand balance state of the target power grid when clearing under the first clearing function;

[0011] The first clearing function is adjusted according to the power supply and demand balance state to obtain a second clearing function, and power clearing is performed based on the second clearing function.

[0012] In a possible implementation, the power supply and demand balance state includes: an over-generation state; the first clearing function includes: a first long-term transaction volume;

[0013] The step of adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function includes:

[0014] When the power supply and demand balance state is the over-power generation state, high-order data abstract processing is performed on the characteristics of each power generation element through a preset deep learning model to obtain a second data vector of each associated power plant;

[0015] Determining a long-term transaction volume increase value according to each of the second data vectors and the preset long-term electricity price function library;

[0016] The first long-term transaction volume is adjusted according to the long-term transaction volume increase value to obtain the second clearing function.

[0017] In a possible implementation, the power supply and demand balance state includes: an under-generation state; the first clearing function includes: a first spot transaction volume;

[0018] The step of adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function includes:

[0019] When the power supply and demand balance state is the under-generation state, for a first power generation curve of the target power grid when the first clearing function is cleared, fitting the first power generation curve by a preset fitting function to obtain a fitting power generation curve;

[0020] Based on the fitted power generation curve and the preset planned power generation curve, determining whether the first spot transaction volume satisfies the preset planned power generation curve;

[0021] When the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value;

[0022] Adjusting the proportion of the spot trading volume in the first clearing function according to the long-term trading volume reduction value to obtain a spot trading volume increase value;

[0023] The first spot trading volume is adjusted according to the spot trading volume increase value to obtain the second clearing function.

[0024] In a possible implementation, when the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value, including:

[0025] Performing function screening on the preset long-term electricity price function library through a preset reinforcement learning model to obtain a first long-term transaction electricity price function;

[0026] An index adjustment is performed on the function index of the first long-term transaction electricity price function to determine the long-term transaction volume reduction value.

[0027] In a possible implementation, the determining, based on the fitted power generation curve and the preset planned power generation curve, whether the first spot transaction volume satisfies the preset planned power generation curve includes:

[0028] When the power generation of the fitting power generation curve is lower than the preset planned power generation curve, it is determined that the first spot transaction volume cannot satisfy the preset planned power generation curve.

[0029] In a possible implementation, the performing of long-term transaction electricity price prediction according to a preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack includes:

[0030] By using the first data vector, a data-driven adjustment is performed on the function index of the preset long-term electricity price function library to obtain a dynamic electricity price function library;

[0031] Performing a long-term transaction electricity price forecast based on the dynamic electricity price function library and the characteristics of each of the power generation elements to obtain a long-term transaction forecast electricity price for each of the associated power plants;

[0032] The long-term transaction stack is established by using the long-term transaction predicted electricity price of each of the associated power plants.

[0033] In a possible implementation, the step of establishing a spot transaction stack based on the spot transaction electricity price of each of the associated power plants includes:

[0034] Performing power forecasting according to the first data vector of each of the associated power plants to obtain the forecasted power of each of the associated power plants;

[0035] Establishing a preliminary spot transaction stack according to the predicted power output of each of the associated power plants;

[0036] Based on the spot transaction electricity price of the associated power plant, the preliminary spot transaction stack is adjusted to obtain the spot transaction stack.

[0037] In a possible implementation, performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics includes:

[0038] According to the characteristics of each power generation element, an energy consumption data model of each associated power plant is established; the energy consumption data model is used to evaluate the power generation performance of the associated power plant;

[0039] The energy consumption data model is used to perform low-order data abstraction processing on the characteristics of each power generation element to obtain the first data vector of the characteristics of each power generation element.

[0040] In a possible implementation, the spot transaction electricity price is obtained based on the spot transaction electricity price calculation formula corresponding to the associated power plant.

[0041] In a second aspect, an embodiment of the present application provides a power grid clearing system, which is applied to a target power grid, wherein the target power grid has a plurality of associated power plants, and the system includes:

[0042] A data abstraction module, used for performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics;

[0043] A first stack establishment module, used for predicting long-term transaction electricity prices according to a preset long-term electricity price function library and each of the first data vectors, so as to establish a long-term transaction stack;

[0044] A second stack establishment module, configured to establish a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determine a first clearing function through the long-term transaction stack and the spot transaction stack;

[0045] A balance state determination module, used to determine the power supply and demand balance state of the target power grid according to the first clearing function;

[0046] A clearing function adjustment module is used to adjust the first clearing function according to the power supply and demand balance state to obtain a second clearing function, and clear electricity based on the second clearing function.

[0047] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising: a processor, a memory, and a system bus;

[0048] The processor and the memory are connected via the system bus;

[0049] The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes any possible grid clearing method in the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any possible grid clearing method in the first aspect.

[0051] Compared with the prior art, the present application has the following beneficial effects: the embodiment of the present application provides a power grid clearing method, system, electronic device and medium, in which the method first performs low-order data abstraction processing according to the power generation element characteristics of each associated power plant, and quantifies various factors affecting the power grid clearing into a first data vector, thereby reducing the data complexity of the power generation element characteristics. Subsequently, a long-term transaction electricity price forecast is performed according to the first data vector and a preset long-term electricity price function library, so as to predict the corresponding long-term transaction electricity price according to the actual power generation element characteristics of each associated power plant, and establish a corresponding long-term transaction stack, so as to improve the accuracy of the power grid clearing by ensuring the accuracy of the long-term transaction electricity price. Further, a spot transaction stack is established based on the spot transaction electricity price of each associated power plant, and the first clearing function is determined by the long-term transaction stack and the spot transaction stack. Accordingly, according to the determined first clearing function, the power supply and demand balance state of the target power grid when clearing under the first clearing function is determined, and the first clearing function is adjusted in real time according to the actual power supply and demand balance state of the target power grid to obtain the second clearing function. In this way, the clearing function of the target power grid can be dynamically adjusted according to the supply and demand status of the target power grid, and a specific clearing function can be established through a transaction stack constructed in real time by long-term transaction electricity prices and spot transaction electricity prices. The clearing of the power grid can effectively consider the impact of the characteristics of power generation factors on the power generation of related power plants, as well as changes in spot transaction electricity prices of each related power plant, and then determine a more accurate clearing function, thereby achieving the effect of improving the accuracy of power grid clearing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A schematic diagram of a flow chart of a power grid clearing method provided in an embodiment of the present application;

[0054] Figure 2 A signaling interaction diagram of a power grid clearing method provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of a flow chart of another power grid clearing method provided in an embodiment of the present application;

[0056] Figure 4 A schematic diagram of a long-term transaction stack provided in an embodiment of the present application;

[0057] Figure 5A flowchart of another power grid clearing method provided in an embodiment of the present application;

[0058] Figure 6 A parameter diagram showing the thermal performance of a unit under full load conditions provided in an embodiment of the present application;

[0059] Figure 7 A schematic diagram of comprehensive consideration parameters for coal price and long-term transaction electricity price provided in an embodiment of the present application;

[0060] Figure 8 A schematic diagram of a stack structure of a spot transaction stack and a long-term transaction stack provided in an embodiment of the present application;

[0061] Fig. 9 A flowchart of a method for establishing a spot trading stack provided in an embodiment of the present application;

[0062] Fig.10 A schematic diagram of power prediction based on a wind power plant provided in an embodiment of the present application;

[0063] Fig.11 A schematic diagram of a spot transaction stack provided in an embodiment of the present application;

[0064] Fig.12 A schematic diagram of a curve of a power grid clearing function provided in an embodiment of the present application;

[0065] Fig.13 A flowchart of another power grid clearing method provided in an embodiment of the present application;

[0066] Fig.14 A flow chart of a first clearing function adjustment method when a target power grid is in an over-generation state provided in an embodiment of the present application;

[0067] Fig.15 A schematic diagram of calculating a second data vector through a deep learning model provided in an embodiment of the present application;

[0068] Fig.16 A flow chart of a first clearing function adjustment method when a target power grid is in an under-generation state provided in an embodiment of the present application;

[0069] Fig.17 A schematic diagram of a target power grid fitting power generation curve provided in an embodiment of the present application;

[0070] Fig.18 A schematic diagram of the structure of a power grid clearing system provided in an embodiment of the present application;

[0071] Fig.19 A schematic diagram of the structure of a power grid clearing electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of this application more clear, the following is a further detailed description of this application in combination with specific embodiments and with reference to the accompanying drawings. It should be noted that the embodiments described in the embodiments of this application are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0073] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "include" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0074] As described above, the current grid clearing process often requires electricity price forecasts for different power plants and power distribution processes. However, in actual application scenarios, the power plant electricity price forecasts and power distribution processes are often affected by many factors, resulting in poor accuracy in grid clearing.

[0075] In order to solve the above problems, the embodiment of the present application provides a power grid clearing method, system, electronic device and medium. In the method, firstly, low-order data abstraction processing is performed according to the power generation element characteristics of each associated power plant, and various factors affecting the power grid clearing are quantified into a first data vector, thereby reducing the data complexity of the power generation element characteristics. Subsequently, a long-term transaction electricity price forecast is performed according to the first data vector and a preset long-term electricity price function library, so as to predict the corresponding long-term transaction electricity price according to the actual power generation element characteristics of each associated power plant, and establish a corresponding long-term transaction stack, so as to improve the accuracy of the power grid clearing by ensuring the accuracy of the long-term transaction electricity price. Further, a spot transaction stack is established based on the spot transaction electricity price of each associated power plant, and a first clearing function is determined by the long-term transaction stack and the spot transaction stack. Accordingly, according to the determined first clearing function, the power supply and demand balance state of the target power grid when clearing under the first clearing function is determined, and the first clearing function is adjusted in real time according to the actual power supply and demand balance state of the target power grid to obtain a second clearing function. In this way, the clearing function of the target power grid can be dynamically adjusted according to the supply and demand status of the target power grid, and a specific clearing function can be established through a transaction stack constructed in real time by long-term transaction electricity prices and spot transaction electricity prices. The clearing of the power grid can effectively consider the impact of the characteristics of power generation factors on the power generation of related power plants, as well as changes in spot transaction electricity prices of each related power plant, and then determine a more accurate clearing function, thereby achieving the effect of improving the accuracy of power grid clearing.

[0076] 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 are within the scope of protection of this application.

[0077] Next, a power grid clearing method provided in an embodiment of the present application will be introduced in conjunction with specific embodiment drawings.

[0078] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of a flow chart of a power grid clearing method provided in an embodiment of the present application, Figure 2 A signaling interaction diagram of a power grid clearing method provided in an embodiment of the present application. Figure 1 In the example, the grid clearing method is implemented on the target grid side, while Figure 2 In the embodiment, the overall implementation of the solution is introduced by associating the interactive signaling between the power plant side and the target grid side.

[0079] like Figure 1As shown, the power grid clearing method specifically includes the following steps:

[0080] S101: Perform low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics.

[0081] The power generation factor characteristics of the associated power plant are the data of various natural factors and production factors that affect the power generation performance of the power plant, such as weather conditions (illuminance, temperature, wind speed, etc.), hydrological water flow, coal type and coal consumption, etc. In actual power generation scenarios, the power generation performance of the associated power plant is often affected by many factors such as weather environment, unit equipment, coal type and coal consumption.

[0082] Therefore, in order to determine the power generation performance of the associated power plants as accurately as possible, in the grid clearing method provided in the embodiment of the present application, it is first necessary to perform low-level data abstraction processing on the power generation factor characteristics of each associated power plant, so as to abstract the power generation factor characteristics that have a greater impact on the power generation performance of the power plant from the complex power generation factor characteristics, and integrate these power generation factor characteristics into a first data vector. At the same time, the quantization of the first data vector for the power generation factor characteristics can provide a data basis for the subsequent adjustment of the preset long-term electricity price function library. By presetting the long-term electricity price function library and the first data vector, the long-term transaction electricity price of the associated power plant can be more accurately predicted in the subsequent process, thereby improving the accuracy of grid clearing.

[0083] Specifically, taking a thermal power plant as an example of an associated power plant, after low-level data abstraction processing, the first data vector of the thermal power plant can be the data characteristics of its corresponding thermal performance, such as (heat consumption rate of the unit, coal consumption, and power consumption rate of the plant). Correspondingly, when the associated power plant is a photovoltaic power plant, its corresponding first data vector can also be a photoelectric conversion efficiency characteristic, such as (illuminance, power generation, and photoelectric conversion efficiency).

[0084] Next, the process of performing low-level data abstraction processing on the characteristics of power generation elements in this step will be introduced in conjunction with the drawings of specific embodiments.

[0085] See also Figure 3 , which is a flow chart of another power grid clearing method provided in an embodiment of the present application, specifically comprising the following steps:

[0086] S1101: establishing an energy consumption data model of each of the associated power plants according to the characteristics of each of the power generation elements; the energy consumption data model is used to evaluate the power generation performance of the associated power plants.

[0087] Specifically, in the process of low-level data abstraction based on the power generation factor characteristics of each related power plant, it is necessary to convert the collected various power generation factor characteristics into primary indicators (natural indicators), and based on the primary indicators, establish energy consumption data models of secondary and tertiary indicators related to the related power plants. Among them, these indicators include parameter indicators such as energy consumption and power generation efficiency. By constructing energy consumption data models of each related power plant, it can reflect the power generation performance of the related power plant under different conditions, so as to comprehensively evaluate the impact of various power generation factor characteristics on the power generation performance of its related power plant.

[0088] At the same time, by constructing an energy consumption data model based on the power generation element characteristics of each related power plant, a mapping of the corresponding data of each power generation element characteristic to the specific function in the preset long-term electricity price function library can be established, so that the association between data and function can be preliminarily constructed.

[0089] S1102: Perform low-order data abstraction processing on the characteristics of each power generation element through the energy consumption data model to obtain the first data vector of the characteristics of each power generation element.

[0090] After constructing the energy consumption data model of each associated power plant, low-level data abstraction processing is performed on the characteristics of each power generation element through the energy consumption data model, so as to extract the power generation element characteristics that can accurately characterize the power generation performance of the associated power plant from the complex power generation element characteristics, and integrate these power generation element characteristics into the first data vector corresponding to the associated power plant alone.

[0091] The first data vector thus generated can accurately describe the key characteristics of the associated power plant in the power generation process. At the same time, it will also be used to adjust the long-term electricity price prediction function in the preset long-term electricity price function library and be applied in the long-term electricity price forecast, thereby realizing dynamic and rapid long-term electricity price forecasting, so as to quickly adjust the clearing function of the target power grid.

[0092] In one possible implementation, the weights of various indicators can be set in the energy consumption data model to highlight the importance of different power generation elements in power output, thereby effectively improving the accuracy of subsequent function adjustments to the preset electricity price function library.

[0093] The above is the specific process of performing low-level data abstraction processing on the power generation element characteristics in step S101. Figure 1 The grid clearing method shown is introduced.

[0094] S102: performing long-term transaction electricity price prediction according to a preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack.

[0095] In the preset long-term electricity price function library, a plurality of operator functions for calculating the long-term transaction electricity prices of associated power plants are stored. By presetting the electricity price operator functions for different power plants in the long-term electricity price function library and combining the first data vector determined by the characteristics of each power generation element, the index parameters represented by the first data vector can be imported into the corresponding operator function, thereby calculating the long-term transaction electricity prices of each associated power plant, thereby completing the long-term transaction electricity price prediction for the associated power plants.

[0096] For example, taking the associated power plant as a hydropower plant as an example, the corresponding long-term electricity price operator function is:

[0097] PTOPE (long-term transaction electricity price) = A+B+C+D+E = Am+Bm+Cm+Dm+Em=(TOPEm-PCm)*CCRm+(TOPEm-PCm)*FOMRm+(TOPEm-PCm)*HFCm+NEOm*VOMRm+(TOPEm-PCm)*CCRTm=(TOPEm-PCm)*(CCRm+FOMRm+HFCm+CCRTm)+NEOm*VOMRm;

[0098] Where A represents the capital cost recovery factor, B represents the fixed operation and maintenance cost factor, C represents the hydropower facility factor, D represents the variable operation and maintenance cost factor, E represents the special facility factor, Am, Bm, Cm, Dm, Em represent the factors of the annual billing period (in rupees);

[0099] TOPEm represents the amount of electricity (kWh) taken or paid during the billing period, which is obtained through annual TOPE. TOPE needs to be confirmed by the target grid and the associated booking site.

[0100] CCRm represents the capital cost recovery charge rate applicable to the billing period (Rp / kWh);

[0101] PCm = Payment credit for the billing period (kWh). Payment credit PCm exists only at the end of each billing period and when the net output during the billing period is less than TOPEm;

[0102] Here: PCm=TOPEm-NEOm-DCm-WLCm;

[0103] Among them, NEOm represents the net output during the billing period; NEOm during the billing period should not exceed TOPEm;

[0104] DCm represents the transmission credit for the billing period, which should be equal to the amount of electricity (kWh) produced by non-power plants according to the dispatch instructions of the target grid plus the amount of electricity delivered or generated that is considered; if the power supply system of the target grid cannot accept all the electricity produced by the associated power plant, the target grid should issue appropriate dispatch instructions to the power plant;

[0105] FOMRm = Fixed Operation and Maintenance Cost Recovery Charge Rate (Rp / kWh) applicable during the billing period, HFCm represents allowances for (i) a portion of the operation and maintenance cost of hydropower facilities, (ii) basin coordination contribution, and (iii) water charges. All three are expressed in Rp / kWh.

[0106] VOMRm represents the variable operation and maintenance cost recovery charge rate (Rp / kWh) for the billing period, namely:

[0107] VOMRm=VOMRFm+VOMRLm;

[0108] Where VOMRFm represents the variable operation and maintenance cost (expressed in non-rupees) recovery charge rate applicable during the billing period (Rp / kWh), and VOMRLm represents the variable operation and maintenance cost (expressed in rupees) recovery charge rate applicable during the billing period (Rp / kWh);

[0109] CCRTm represents the capital cost recovery charge rate (Rp / kWh) for a particular facility applicable during the billing period.

[0110] As can be seen from the above formula, by importing the first data vector containing the characteristics of the power generation elements of the hydropower plant into its corresponding long-term electricity price operator function, the long-term transaction electricity price of the hydropower plant in a certain period of time in the future can be predicted. By presetting the long-term transaction electricity price operator function corresponding to each associated power plant in the long-term electricity price function library, the first data vector of each power plant is imported into it, and the long-term transaction electricity price of each power plant can be predicted.

[0111] It is understandable that in actual long-term transaction electricity price forecasting scenarios, there are differences in the characteristics of power generation factors of related power plants in different regions and countries, and their corresponding electricity price calculation formulas will also be different, which will not be elaborated in this embodiment.

[0112] Furthermore, by sorting the long-term transaction electricity prices of each power plant, the stack priority in the long-term transaction stack can be determined, thereby establishing a long-term transaction stack. For example, the long-term transaction stack can be found in Figure 4 A schematic diagram of a long-term transaction stack is shown. As can be seen from the figure, the long-term transaction stack is sorted based on the long-term transaction electricity prices predicted by each associated power plant.

[0113] It can be understood that in actual application scenarios, the long-term trading electricity between the power grid and its associated power plants is determined by the long-term trading contract signed in advance between the two parties. In the process of constructing a long-term trading stack, the long-term trading electricity indicated in the long-term trading contract can also be used as the weight of the stack. The more trading electricity, the lower the long-term trading electricity price, and the higher the priority of the stack. When the long-term trading electricity price is the same, in order to ensure the clearing stability of the power grid, the long-term trading electricity needs to have more weight than the long-term trading electricity price. The amount of trading electricity is used as the key factor in the construction of the long-term trading stack, which is not elaborated in this embodiment.

[0114] In this way, through the first data vector corresponding to each associated power plant and the preset long-term electricity price function library, the specific long-term transaction price of each associated power plant under the influence of the current power generation factor characteristics can be predicted, so as to establish a long-term transaction stack through the long-term transaction electricity prices of each associated power plant.

[0115] Next, the process of establishing a long-term transaction stack in step S102 will be introduced in conjunction with the accompanying drawings of the specific embodiment. Figure 5 , which is a flow chart of another grid clearing method provided in an embodiment of the present application, specifically comprising the following steps:

[0116] S2201: Using the first data vector, perform data-driven adjustment on the function indicators of the preset long-term electricity price function library to obtain a dynamic electricity price function library.

[0117] As can be seen from the foregoing, operator functions for calculating the long-term transaction electricity prices of each associated power plant are pre-stored in the preset electricity price function library. In actual application scenarios, the long-term transaction electricity prices of associated power plants are determined by their own power generation costs, and in different regions, different time periods, and even different seasons, the power generation costs of different associated power plants vary greatly. Therefore, in order to accurately predict the long-term transaction electricity prices of associated power plants, it is necessary to perform data-driven adjustments to the function indicators in the preset electricity price function library through the first data vector generated by the characteristics of the power generation elements, so as to adjust the function indicators in each operator function, thereby improving the prediction accuracy of the long-term transaction electricity price.

[0118] Here, a thermal power plant is taken as an example of an associated power plant. The first data vector of the thermal power plant is (unit heat rate, coal consumption, plant power rate, electricity price). Its first data vector is: (Pm, SHRW, GCVs, AUX, CERm), and its corresponding long-term transaction electricity price operator function is:

[0119] CERm=Pm*SHRW / GCVs(1-AUX)=0.031053242 USD / KWh(1)

[0120] In the formula, Pm represents the coal price in USD, SHRW represents the unit specific heat index in Kcal / KWh, GCVs represents the coal high calorific value in Kcal / Kg, AUX represents the plant power consumption rate in %, CERm represents the electricity price in USD / KWh, and SHRW=2138.4Kcal / KWh is set in formula* (1);

[0121] The electricity price operator function is adjusted by the first data vector, and the adjusted electricity price operator function is:

[0122] CERm=Pm*SHRW(1+AGE) / GCVs(1-AUX)=0.031947846 USD / KWh(2)

[0123] Wherein, AGE represents the unit loss, unit %, and the others are the same as above. When AGE is set to 5.4%, SHRW=2210.14Kcal / KWh;

[0124] For details, see Figure 6 and Figure 7 ,in, Figure 6 The table shown is a parameter indicating the thermal performance of the unit under full load conditions provided by the embodiment of the present application. Figure 7 The table shown is in Figure 6 Based on the table, a schematic diagram of comprehensive consideration parameters for coal prices and long-term trading electricity prices is provided. It can be seen that after the unit performance decay changes the specific heat index, the loss needs to be considered in the unit electricity price change formula, so that (2) replaces formula (1), so that the electricity price changes from 0.031USD / KWh to 0.032USD / KWh.

[0125] It can be seen that, through the first data vector determined in real time by each associated power plant, the long-term electricity price operator function for each associated power plant in the preset long-term electricity price function library can be adaptively adjusted, thereby ensuring the accuracy of the prediction of the long-term trading electricity price.

[0126] S2202: Perform long-term transaction electricity price forecasting based on the dynamic electricity price function library and the characteristics of each of the power generation elements to obtain the long-term transaction forecast electricity price of each of the associated power plants.

[0127] Correspondingly, after dynamically adjusting the preset long-term electricity price function library, the characteristics of each power generation factor are used as input data in the adjusted dynamic electricity price function library to calculate the long-term transaction forecast electricity price of each related power plant.

[0128] S2203: Establishing the long-term transaction stack through the long-term transaction predicted electricity price of each of the associated power plants.

[0129] It can be seen from this that the first data vector generated by the actual power generation element characteristics of each associated power plant can effectively adjust the function operator in the preset electricity price function library, so as to predict the actual long-term trading electricity price for the associated power plant through the adjusted dynamic electricity price function library, so as to ensure the prediction accuracy of the long-term trading electricity price and obtain the long-term trading stack.

[0130] S103: establishing a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determining a first clearing function through the long-term transaction stack and the spot transaction stack.

[0131] In the process of establishing the long-term transaction stack, the target power grid will synchronously receive the spot transaction electricity prices sent by the associated power plants. The target power grid can establish the corresponding spot transaction stack according to the size of the spot transaction electricity prices of each associated power plant. In this way, the first clearing function of the target power grid can be determined through the spot transaction stack and the long-term transaction stack.

[0132] For spot trading stack and long-term trading stack, please refer to Figure 8 , Figure 8 A schematic diagram of the stack structure of a spot trading stack and a long-term trading stack provided for an embodiment of the present application. As can be seen from the figure, the stack is divided into three levels according to the electricity price, and is divided into different levels of top, middle and bottom according to the gradually increasing electricity price. Among them, each layer considers different energy types (such as photovoltaics, wind power) and market types (spot trading, long-term trading). The adjustment mechanism for the stack is shown on the side of the stack, which optimizes the overall electricity price and electricity distribution by reducing or increasing the transaction volume and balancing between spot trading and long-term trading.

[0133] In actual application scenarios, the spot trading electricity prices of various related power plants often have various types, such as hourly quotations, block quotations, etc. The spot trading electricity price is calculated by the spot trading electricity price calculation formula set by the power plant itself. Taking the calculation of hourly quotations by hydropower plants as an example, the calculation formulas for hourly quotations are as follows:

[0134] Hydropower Plant:

[0135] (1.1)

[0136] (1.2)

[0137] In the formula, m is the mth hydropower station in the cascade hydropower station, i is the i-th unit in the mth hydropower station; m and i are both integers not less than 1; represents the actual benefit corresponding to the potential electric energy of the hydropower station at the current moment; Represents the actual power generation income corresponding to the actual power generation of the hydropower station at the current moment, represents the rated power of the i-th unit, represents the water level duration of the i-th unit, represents the derated power of the i-th unit, represents the duration of interruption during the water level period of the i-th unit, The power generation of the i-th unit during the water level period, Indicates the actual transaction electricity price of all units, The power generation of all units during the water level period.

[0138] As can be seen from the foregoing, operator functions for calculating the spot trading electricity prices of each associated power plant are pre-stored in the preset electricity price function library. In actual application scenarios, the spot trading electricity prices of associated power plants are determined by their own power generation costs, and in different regions, different time periods (unit performance decays with age), and even different seasons, there are large differences in the power generation costs of different associated power plants. Therefore, in order to accurately predict the long-term trading electricity prices of associated power plants, it is necessary to perform data-driven adjustments to the function indicators in the preset electricity price function library through the first data vector generated by the characteristics of the power generation elements, so as to adjust the function indicators in each operator function, thereby improving the prediction accuracy of the long-term trading electricity price.

[0139] In addition, in the process of establishing a spot transaction stack, it is necessary to construct it through the two dimensions of the spot transaction electricity price and the power generation of the associated power plant. Next, the process of establishing the spot transaction stack will be introduced in conjunction with the specific embodiment drawings. Fig. 9 , which is a flow chart of a method for establishing a spot transaction stack provided in an embodiment of the present application, and specifically includes the following steps:

[0140] S1031: Perform power forecasting according to the first data vector of each of the associated power plants to obtain the predicted power of each of the associated power plants.

[0141] As can be seen from the foregoing, in the first data vector corresponding to the associated power plant, multiple power generation factor characteristics that affect the power generation efficiency of the associated power plant are collected. By performing power forecasting based on the first data vector, the power generation of the associated power plant within a certain period of time can be predicted.

[0142] Specifically, in the process of predicting the power generation based on the first data vector, the neural network model can be used to predict the power generation of the associated power plant within a certain period of time. Here, a wind power plant is used as an example. For details, please refer to Fig.10 , Fig.10 A schematic diagram of power prediction based on a wind power plant provided in an embodiment of the present application.

[0143] As can be seen from the figure, the global forecast data, regional actual data and station observation data of the wind power plant in the historical time period are used as the training data of the model, so as to predict the power generation of the wind power plant through the trained neural network model. Correspondingly, the model can output the meteorological forecast results and the corresponding power forecast results of the wind power plant. Through the meteorological data and power forecast data within a certain period of time, the power generation of the wind power plant can be effectively predicted according to the main characteristics that affect the power generation performance of the wind power plant.

[0144] In addition, for the power forecast of the associated power plant, a time series forecasting model (ARIMA) or a machine learning method (such as random forest, XGBoost) can be used. For the first data vector with a complex data structure in this application, a deep learning model such as LSTM (Long Short-Term Memory) can be used to analyze the temporal relationship in the step sequence data.

[0145] LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture that aims to solve the gradient vanishing and gradient exploding problems faced by ordinary RNNs when processing long sequence data. LSTM can effectively capture long-term dependencies in sequences by introducing memory units and gating mechanisms.

[0146] S1032: Establishing a preliminary spot transaction stack according to the predicted power volume of each of the associated power plants.

[0147] After obtaining the predicted power for each associated power plant, the call priority of each associated power plant when calling for power can be determined through the size of the predicted power of each associated power plant, so as to establish a preliminary spot trading stack. For example, taking the associated power plants as wind power, photovoltaic power, hydropower, thermal power and nuclear power as an example, the predicted power of each power plant is (100, 50, 30, 60, 20) respectively. Through the predicted power of each associated power plant, a preliminary spot trading stack can be established based on the size of the predicted power, namely (wind power, thermal power, photovoltaic power, hydropower, nuclear power).

[0148] S1033: Based on the spot transaction electricity price of the associated power plant, the preliminary spot transaction stack is adjusted to obtain the spot transaction stack.

[0149] On the basis of establishing a preliminary spot trading stack based on the predicted power of each associated power plant, the preliminary spot trading stack is further adjusted through the spot trading electricity price calculated by the spot electricity price calculation formula inside each associated power plant, so that the two-dimensional preliminary spot trading stack is transformed into a three-dimensional spot trading stack containing spot trading electricity prices. In this way, when there is insufficient power generation or excessive power generation of an associated power plant, the method of taking out the upper stack of the stack and rebuilding the stack is followed, starting from the power generation of the associated power plant and the actual spot trading electricity price, the spot trading stack is dynamically adjusted, thereby improving the accuracy of power grid clearing.

[0150] For example, the spot trading stack can be found at Fig.11 Schematic diagram of the spot trading stack shown.

[0151] It should be noted that in the scenario of establishing a spot trading stack, the weight of the predicted power in establishing the stack is greater than the weight of the spot trading electricity price. That is, when the spot trading electricity prices of two associated power plants are the same, the associated power plant with a larger predicted power will be placed in a relatively higher priority position in the spot trading stack to ensure the power supply stability of the target power grid.

[0152] S104: Determine, according to the first clearing function, a power supply and demand balance state of the target power grid when clearing under the first clearing function.

[0153] Correspondingly, the target power grid is controlled to clear electricity based on the determined first clearing function, and the power supply and demand balance state of the target power grid can be determined according to the total power generation and total load of the target power grid when clearing based on the first clearing function. When the total power generation of the target power grid is greater than the total load, it indicates that the target power grid is in an over-power generation state, which means that the current power generation of the target power grid exceeds the actual power demand and there is a problem of excess power. When the total power generation of the target power grid is less than the total load, it indicates that the target power grid is in an under-power generation state, which means that the current power generation of the target power grid cannot meet the actual power demand and the power supply needs to be supplemented in time.

[0154] For details, please refer to Fig.12 The graph shows a curve diagram of a power grid clearing function, in which the horizontal axis represents the change in power supply, and the vertical axis represents the change in electricity price. The power dispatch priority shown in the figure is: wind power / photovoltaic power>hydropower with storage capacity>nuclear power>coal power>gas power.

[0155] When the power supply curve is higher than the power demand curve, it means that the power supply of the power grid exceeds the demand, which may lead to a rise in electricity prices. Correspondingly, when the power supply curve is lower than the power demand curve, the power demand exceeds the supply, which may lead to an increase in electricity prices.

[0156] By determining the power supply and demand balance state of the target power grid under the first clearing function, the undergeneration state or overgeneration state of the target power grid under the current first clearing function can be characterized, so as to control the long-term transaction stack and the spot transaction stack according to the actual power supply and demand balance state of the target power grid in the future, thereby realizing the clearing adjustment for the power grid and ensuring the clearing accuracy of the power grid.

[0157] S105: Adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function, and clearing power based on the second clearing function.

[0158] Finally, according to the overgeneration state or undergeneration state of the target power grid under the first clearing function, the first clearing function is adjusted, that is, the clearing function of the target power grid can be adjusted according to the actual power generation situation of the target power grid, thereby improving the accuracy of power grid clearing.

[0159] Next, the adjustment method for the first clearing function in different power supply and demand balance states will be introduced in conjunction with the specific embodiment drawings. Fig.13 and Fig.14 , Fig.13 A flowchart of another power grid clearing method provided in an embodiment of the present application is shown below. Fig.14 The flowchart of the first clearing function adjustment method when the target power grid is in an over-generation state is shown in FIG. 1 , which specifically includes the following steps:

[0160] S1051: When the power supply and demand balance state is the over-power generation state, a high-level abstract processing is performed on the characteristics of each power generation element through a preset deep learning model to obtain a second data vector of each associated power plant.

[0161] In actual application scenarios, long-term transaction electricity prices are often determined by long-term contracts between the target power grid and the associated power plants, while spot transaction electricity prices are often affected by the immediate supply and demand conditions. Therefore, long-term transaction electricity prices are often lower than spot transaction electricity prices. When the power supply and demand balance state of the target power grid is in an over-generation state, the total power generation of the target power grid is greater than the total load demand of the target power grid.

[0162] In order to reduce the cost of power dispatch as much as possible and improve the accuracy of power grid clearing, when the target power grid is in an overgeneration state, since the total power generation of the target power grid is greater than the total charge at this time, by increasing the first long-term transaction volume in the first clearing function, the proportion of long-term transaction volume in the clearing function can be increased, thereby effectively reducing the cost of power clearing and improving the accuracy of power clearing.

[0163] Therefore, in order to ensure the accuracy of adjustments for long-term trading volume, it is necessary to use a pre-set deep learning model to perform high-order data abstraction processing on the power generation factor characteristics of the associated power plants to obtain the second data vector that affects the power generation efficiency of each associated power plant.

[0164] Different from the low-order data abstraction processing of the characteristics of power generation factors through the energy consumption data model, which only involves the feature screening and combination of the characteristics of power generation factors, the high-order data abstraction processing involves the combination calculation between the characteristics of each power generation factor, so as to calculate the data vector that has a core influence on the power generation performance of the associated power plant.

[0165] For details, please refer to Fig.15 A schematic diagram of calculating a second data vector through a deep learning model is shown, in which a hydropower plant is used as an example of an associated power plant. Among them, parameters such as power generation and water consumption for power generation are the power generation factor characteristics of the hydropower plant, and the Top, Qm' and average power plant revenue of the hydropower plant are parameters calculated by power generation and water consumption for power generation. These need to be calculated through the power generation factor characteristics to obtain the relevant parameters, which is the above-mentioned process of performing high-level abstract processing on the power generation factor characteristics based on the preset reinforcement learning model to obtain the second data vector. In the preset reinforcement learning model provided in the embodiment of the present application, a plurality of different types of parameter calculation formulas are pre-set. When the power generation factor characteristics are subjected to high-level data abstract processing through the preset reinforcement learning model, the parameters are calculated through the preset calculation formula and the power generation factor characteristics, and the calculated results are integrated to obtain the second data vector.

[0166] Compared with linear models, deep learning has stronger nonlinear modeling capabilities, which can simulate the complex nonlinear relationship between the characteristics of various power generation elements in the associated power plant. Therefore, by presetting the high-order data abstraction processing of the deep learning model for the characteristics of the power generation elements, complex features can be further extracted from the original data to obtain a second data vector that can accurately characterize the power generation performance of the associated power plant.

[0167] S1052: Determine a long-term transaction volume increase value according to each of the second data vectors and the preset long-term electricity price function library.

[0168] S1053: Adjust the first long-term transaction volume according to the long-term transaction volume increase value to obtain the second clearing function.

[0169] Furthermore, after determining the second data vector of each associated power plant, the function index in the preset long-term electricity price function library is adjusted based on the second data vector, and the specific index value of the long-term electricity price function can be changed, thereby determining the new long-term transaction volume of the target power grid. By comparing this new long-term transaction volume with the long-term transaction volume in the first clearing function, the increase in the long-term transaction volume can be determined, and the corresponding second clearing function can be obtained.

[0170] Depend on Fig.13 It can be seen that when the target power grid is in an overgeneration state, the high-order data abstraction processing of the power generation element characteristics of the associated power plants is performed through the preset deep learning model, which can determine the added value of the long-term transaction volume, and then adjust the subsequent long-term transaction stack, thereby realizing the adjustment of the first clearing function and obtaining the second clearing function.

[0171] Next, the process of adjusting the first clearing function when the target power grid is in an under-generation state will be described in conjunction with the accompanying drawings of the specific embodiment. Fig.16 , which is a flow chart of a first clearing function adjustment method when a target power grid is in an under-generation state provided by an embodiment of the present application, specifically comprising the following steps:

[0172] S1054: When the power supply and demand balance state is the under-generation state, with respect to the first power generation curve of the target power grid when clearing based on the first clearing function, the first power generation curve is fitted by a preset fitting function to obtain a fitted power generation curve.

[0173] When the target power grid is in a state of under-generation, it indicates that the total power generation of the target power grid is lower than its total load. It is understandable that in the dual trading stack composed of the long-term trading stack and the spot trading stack, since the long-term trading electricity price in the long-term trading stack is often slightly lower than the spot trading electricity price in the spot trading stack, the long-term trading electricity in the long-term trading stack can be dispatched first. In this case, if the target power grid is still in a state of under-generation, it indicates that the target power grid still cannot meet the load demand when fully calling the long-term trading electricity in the long-term trading stack.

[0174] Therefore, in this case, it is necessary to meet the demand for power load by increasing the spot trading volume. In order to ensure the accuracy of the added value of the spot trading volume, before calculating the added value of the spot trading volume, it is first necessary to fit the first power generation curve of the target power grid based on the first clearing function based on the preset fitting function, so as to convert the complex and discrete power generation data into a continuous function model, thereby simplifying the data storage and analysis process and improving data processing efficiency. At the same time, through the fitted power generation curve obtained after fitting, the trends and patterns in the power generation data can be more clearly identified, so as to facilitate the subsequent optimization of the clearing function.

[0175] Specifically, the formula of the preset fitting function is as follows:

[0176] PR*S=P;

[0177] PR=A+BH T -CH T 2 ;

[0178] In the formula, PR represents the power conversion efficiency of the power plant, H T represents the power generation efficiency adjustment factor, S represents the primary indicator affecting the power generation efficiency of the associated power plant, P represents the fitting coefficient, A, B, C represent the characteristic constants calculated by the deep learning model, which are used to characterize the performance characteristics corresponding to different power plants.

[0179] In the above formula, H T The power of represents the influence of this feature on the power generation performance of the associated power plant. Correspondingly, the formula for H T The positive and negative signs of the preceding influences represent the positive or negative influence of the corresponding features on the power generation efficiency of the power plant. Thus, through the above formula, the fitting accuracy of the first power generation curve can be effectively improved, thereby ensuring the accuracy of the power grid clearing.

[0180] It is understandable that for different associated power plants, the data type corresponding to S will also be different. For example, when PR represents the power conversion efficiency of a photovoltaic power plant, S is related to the illuminance of the photovoltaic power plant. Similarly, when PR represents the power conversion efficiency of a thermal power plant, S is related to the coal quality of the thermal power plant.

[0181] By calculating the fitting coefficients P of the target power grid corresponding to all associated power plants, the first power generation curve of the target power grid can be effectively fitted, thereby improving the accuracy of the first power generation curve.

[0182] For details, please refer to Fig.17 , Fig.17 A schematic diagram of a target power grid fitting power generation curve provided for an embodiment of the present application, as shown in the figure, the top-most power generation curve in the figure is a preset power generation curve, and the curve that closely fits the preset power generation curve is the fitted power generation curve when the target power grid is cleared based on the first clearing function and after fitting processing. It can be seen that after fitting the first power generation curve with the preset fitting function, the fitted power generation curve is closer to the preset power generation curve, and by comparing the preset power generation curve with the fitted power generation curve, the spot transaction volume that needs to be supplemented can be accurately determined, thereby ensuring the stability of power grid clearing.

[0183] S1055: Based on the fitted power generation curve and the preset planned power generation curve, determine whether the first spot transaction volume satisfies the preset planned power generation curve.

[0184] Subsequently, the fitted power generation curve is compared with the preset planned power generation curve to determine whether the first spot transaction volume in the first clearing function can meet the preset planned power generation curve. The preset planned power generation curve is used to represent the power generation curve expected by the target power grid for the current power load. By comparing the two, it can be determined whether the first spot transaction volume of the target power grid in the first clearing function is sufficient.

[0185] See also Fig.17 As shown in the figure, the fitted power generation curve in the figure is lower than the preset power generation curve, that is, the power generation of the fitted power generation curve is lower than the power generation of the preset power generation curve. At this time, there is a certain gap between the fitted power generation curve and the preset power generation curve. It can be determined that the power generation curve of the target power grid under the first clearing function cannot meet the requirements of the preset power generation curve. Therefore, it can be determined that the first spot trading volume in the first clearing function cannot meet the preset planned power generation curve, and additional spot trading volume is needed.

[0186] S1056: When the first spot trading volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term trading volume reduction value.

[0187] Correspondingly, when the first spot trading volume does not meet the preset planned power generation curve, it indicates that the first spot trading volume of the target power grid at this time cannot meet the current power load. Therefore, in order to meet the current power load by increasing the spot trading volume, it is necessary to correct the preset long-term electricity price function based on the preset reinforcement learning model to obtain the long-term trading volume reduction value. In this way, the spot trading volume increase value can be determined by reducing the proportion of long-term trading volume, so as to ensure the smooth clearing of the power grid.

[0188] Among them, the preset reinforcement learning model can adopt MARL (Multi-agent Reinforcement Learning). MARL is a method for dealing with reinforcement learning problems in a multi-agent environment. Each participant (such as different associated power plants) can be regarded as an agent, and these agents need to adjust their bidding strategies based on real-time market data and the behavior of other agents. MARL can help these agents learn and adapt under complex and dynamically changing market conditions to improve the accuracy of power grid clearing.

[0189] Specifically, in the process of modifying the preset long-term electricity price function library, the first long-term trading electricity price function with the greatest impact on the overall long-term trading electricity price can be screened out from the preset long-term electricity price function library through the screening mechanism of the reinforcement learning model.

[0190] Subsequently, the function index in the first long-term transaction electricity price function is adjusted and increased to increase the long-term transaction electricity price, thereby achieving the effect of reducing the long-term transaction volume and obtaining the long-term transaction volume reduction value.

[0191] Here we take a thermal power plant as an example. As we can see from the previous article, the long-term transaction electricity price calculation function of a thermal power plant is:

[0192] By adjusting the function index in the long-term transaction electricity price calculation function and increasing the long-term transaction electricity price, the effect of reducing the long-term transaction volume can be achieved, and the long-term transaction volume reduction value can be obtained.

[0193] S1057: Adjust the proportion of the spot trading volume in the first clearing function according to the long-term trading volume reduction value to obtain the spot trading volume increase value.

[0194] S1058: Adjust the first spot trading volume according to the spot trading volume increase value to obtain the second clearing function.

[0195] On the basis of determining the long-term trading volume reduction value, the proportion of the spot trading volume in the first clearing function is increased to obtain the spot trading volume value. In this way, the first spot trading volume in the first clearing function can be adjusted by the spot trading volume increase value to obtain the second clearing function.

[0196] The embodiment of the present application provides a method for clearing a power grid, in which, first, low-order data abstraction processing is performed according to the power generation element characteristics of each associated power plant, and multiple factors affecting the clearing of the power grid are quantified into a first data vector, thereby reducing the data complexity of the power generation element characteristics. Subsequently, a long-term transaction electricity price forecast is performed according to the first data vector and a preset long-term electricity price function library, so as to predict the corresponding long-term transaction electricity price according to the actual power generation element characteristics of each associated power plant, and establish a corresponding long-term transaction stack, so as to improve the accuracy of the clearing of the power grid by ensuring the accuracy of the long-term transaction electricity price. Further, a spot transaction stack is established based on the spot transaction electricity price of each associated power plant, and a first clearing function is determined by the long-term transaction stack and the spot transaction stack. Accordingly, according to the determined first clearing function, the power supply and demand balance state of the target power grid when clearing under the first clearing function is determined, and the first clearing function is adjusted in real time according to the actual power supply and demand balance state of the target power grid to obtain a second clearing function. In this way, the clearing function of the target power grid can be dynamically adjusted according to the supply and demand status of the target power grid, and a specific clearing function can be established through a transaction stack constructed in real time by long-term transaction electricity prices and spot transaction electricity prices. The clearing of the power grid can effectively consider the impact of the characteristics of power generation factors on the power generation of related power plants, as well as changes in spot transaction electricity prices of each related power plant, and then determine a more accurate clearing function, thereby achieving the effect of improving the accuracy of power grid clearing.

[0197] A power grid clearing system provided in an embodiment of the present application is introduced below. The power grid clearing system described below and the power grid clearing method described above can refer to each other.

[0198] See also Fig.18 , which is a schematic diagram of the structure of a power grid clearing system provided in an embodiment of the present application, specifically including the following modules:

[0199] The data abstraction module 100 is used to perform low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics;

[0200] A first stack building module 200, configured to perform long-term transaction electricity price prediction according to a preset long-term electricity price function library and each of the first data vectors, so as to build a long-term transaction stack;

[0201] A second stack establishment module 300 is used to establish a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determine a first clearing function through the long-term transaction stack and the spot transaction stack;

[0202] A balance state determination module 400, configured to determine the power supply and demand balance state of the target power grid according to the first clearing function;

[0203] The clearing function adjustment module 500 is used to adjust the first clearing function according to the power supply and demand balance state to obtain a second clearing function, and perform power clearing based on the second clearing function.

[0204] In a possible implementation, the power supply and demand balance state includes: an over-generation state; the first clearing function includes: a first long-term transaction volume; the clearing function adjustment module 500 is specifically used to:

[0205] When the power supply and demand balance state is the over-power generation state, high-order data abstract processing is performed on the characteristics of each power generation element through a preset deep learning model to obtain a second data vector of each associated power plant;

[0206] Determining a long-term transaction volume increase value according to each of the second data vectors and the preset long-term electricity price function library;

[0207] The first long-term transaction volume is adjusted according to the long-term transaction volume increase value to obtain the second clearing function.

[0208] In a possible implementation, the power supply and demand balance state includes: an under-generation state; the first clearing function includes: a first spot transaction volume; the clearing function adjustment module 500 is further used to:

[0209] When the power supply and demand balance state is the under-generation state, for a first power generation curve of the target power grid when the first clearing function is cleared, fitting the first power generation curve by a preset fitting function to obtain a fitting power generation curve;

[0210] Based on the fitted power generation curve and the preset planned power generation curve, determining whether the first spot transaction volume satisfies the preset planned power generation curve;

[0211] When the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value;

[0212] Adjusting the proportion of the spot trading volume in the first clearing function according to the long-term trading volume reduction value to obtain a spot trading volume increase value;

[0213] The first spot trading volume is adjusted according to the spot trading volume increase value to obtain the second clearing function.

[0214] In a possible implementation, the first stack establishing module 200 is specifically configured to:

[0215] By using the first data vector, a data-driven adjustment is performed on the function index of the preset electricity price function library to obtain a dynamic electricity price function library;

[0216] Performing a long-term transaction electricity price forecast based on the dynamic electricity price function library and the characteristics of each of the power generation elements to obtain a long-term transaction forecast electricity price for each of the associated power plants;

[0217] The long-term transaction stack is established by using the long-term transaction predicted electricity price of each of the associated power plants.

[0218] In a possible implementation, the second stack establishing module 300 is specifically configured to:

[0219] Performing power forecasting according to the first data vector to obtain forecasted power of each of the associated power plants;

[0220] Establishing a preliminary spot transaction stack according to the predicted power output of each of the associated power plants;

[0221] Based on the spot transaction electricity price of the associated power plant, the preliminary spot transaction stack is adjusted to obtain the spot transaction stack.

[0222] In a possible implementation, the data abstraction module 100 is specifically used to:

[0223] According to the characteristics of each power generation element, an energy consumption data model of each associated power plant is established; the energy consumption data model is used to evaluate the power generation performance of the associated power plant;

[0224] The energy consumption data model is used to perform low-order data abstraction processing on the characteristics of each power generation element to obtain the first data vector of the characteristics of each power generation element.

[0225] In a possible implementation, the spot transaction electricity price is obtained based on the spot transaction electricity price calculation formula corresponding to the associated power plant.

[0226] See also Fig.19 , which is a schematic diagram of the structure of a power grid clearing electronic device provided in an embodiment of the present application, including:

[0227] A memory 11, used for storing computer programs;

[0228] The processor 12 is configured to implement the steps of a power grid clearing method described in any of the above method embodiments when executing the computer program.

[0229] In this embodiment, the device may be a vehicle-mounted computer, a PC (Personal Computer), or a terminal device such as a smart phone, a tablet computer, a PDA, or a portable computer.

[0230] The device may include a memory 11 , a processor 12 , and a bus 13 .

[0231] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the device, such as a hard disk of the device. In other embodiments, the memory 11 can also be an external storage device of the device, such as a plug-in hard disk equipped on the device, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 11 can also include both an internal storage unit of the device and an external storage device. The memory 11 can not only be used to store application software installed in the device and various types of data, such as program codes for executing fault prediction methods, etc., but can also be used to temporarily store data that has been output or is to be output. The processor 12 can be a central processing unit (CPU) in some embodiments.

[0232] In some embodiments, the processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, and is used to run program codes or process data stored in the memory 11, such as program codes for executing a grid clearing method.

[0233] The bus 13 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.19 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0234] Furthermore, the device may also include a network interface 14, which may optionally include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the device and other electronic devices.

[0235] Optionally, the device may further include a user interface 15, which may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 15 may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the device and to display a visual user interface.

[0236] Fig.19 Only the device with components 11-15 is shown, and it can be understood by those skilled in the art that Fig.19 The structure shown does not constitute a limitation of the device, and may include fewer or more components than shown, or combine certain components, or arrange the components differently.

[0237] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, an embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the power grid clearing method described in any of the above embodiments.

[0238] The computer-readable media of the embodiments of the present application include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0239] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the power grid clearing method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0240] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for methods, systems, electronic devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The methods, systems, electronic devices and media described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0241] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A power grid clearing method, characterized in that: Applied to a target power grid, the target power grid has a plurality of associated power plants, the method comprising: Performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of each of the power generation element characteristics; the power generation element characteristics are data of various natural elements and production elements that affect the power generation performance of the associated power plants; Performing long-term transaction electricity price prediction according to a preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack; Establishing a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determining a first clearing function through the long-term transaction stack and the spot transaction stack; According to the first clearing function, determining the power supply and demand balance state of the target power grid when clearing under the first clearing function; Adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function, and clearing power based on the second clearing function; The power supply and demand balance state includes: over-generation state; the first clearing function includes: first long-term transaction volume; The step of adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function includes: When the power supply and demand balance state is the over-power generation state, high-order data abstract processing is performed on the characteristics of each power generation element through a preset deep learning model to obtain a second data vector of each associated power plant; Determining a long-term transaction volume increase value according to each of the second data vectors and the preset long-term electricity price function library; Adjusting the first long-term transaction volume according to the long-term transaction volume increase value to obtain the second clearing function; The power supply and demand balance state includes: under-generation state; the first clearing function includes: first spot transaction volume; The step of adjusting the first clearing function according to the power supply and demand balance state to obtain a second clearing function includes: When the power supply and demand balance state is the under-generation state, for a first power generation curve of the target power grid when the first clearing function is cleared, fitting the first power generation curve by a preset fitting function to obtain a fitting power generation curve; Based on the fitted power generation curve and the preset planned power generation curve, determining whether the first spot transaction volume satisfies the preset planned power generation curve; When the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value; Adjusting the proportion of the spot trading volume in the first clearing function according to the long-term trading volume reduction value to obtain a spot trading volume increase value; adjusting the first spot trading volume according to the spot trading volume increase value to obtain the second clearing function; The step of performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics includes: According to the characteristics of each power generation element, an energy consumption data model of each associated power plant is established; the energy consumption data model is used to evaluate the power generation performance of the associated power plant; Performing low-order data abstraction processing on the characteristics of each power generation element through the energy consumption data model to obtain the first data vector of the characteristics of each power generation element; The step of establishing the energy consumption data model of each of the associated power plants according to the characteristics of each of the power generation elements comprises: The characteristics of each power generation element are converted into a primary indicator, and based on the primary indicator, an energy consumption data model of the secondary and tertiary indicators of each associated power plant is established; the indicators include: energy consumption and power generation efficiency.

2. The method according to claim 1, characterized in that When the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value, including: Performing function screening on the preset long-term electricity price function library through a preset reinforcement learning model to obtain a first long-term transaction electricity price function; An index adjustment is performed on the function index of the first long-term transaction electricity price function to determine the long-term transaction volume reduction value.

3. The method according to claim 1, characterized in that The determining, based on the fitted power generation curve and the preset planned power generation curve, whether the first spot transaction volume satisfies the preset planned power generation curve includes: When the power generation of the fitting power generation curve is lower than the preset planned power generation curve, it is determined that the first spot transaction volume cannot satisfy the preset planned power generation curve.

4. The method according to claim 1, characterized in that The long-term transaction electricity price prediction is performed according to the preset long-term electricity price function library and each of the first data vectors to establish a long-term transaction stack, including: By using the first data vector, a data-driven adjustment is performed on the function index of the preset long-term electricity price function library to obtain a dynamic electricity price function library; Performing a long-term transaction electricity price forecast based on the dynamic electricity price function library and the characteristics of each of the power generation elements to obtain a long-term transaction forecast electricity price for each of the associated power plants; The long-term transaction stack is established by using the long-term transaction predicted electricity price of each of the associated power plants.

5. The method according to claim 1, characterized in that The step of establishing a spot transaction stack based on the spot transaction electricity price of each of the associated power plants includes: Performing power forecasting according to the first data vector of each of the associated power plants to obtain the forecasted power of each of the associated power plants; Establishing a preliminary spot transaction stack according to the predicted power output of each of the associated power plants; Based on the spot transaction electricity price of the associated power plant, the preliminary spot transaction stack is adjusted to obtain the spot transaction stack.

6. The method according to claim 1, characterized in that The spot transaction electricity price is obtained based on the spot transaction electricity price calculation formula corresponding to the associated power plant.

7. A power grid clearing system, characterized in that: Applied to a target power grid, the target power grid has a plurality of associated power plants, the system comprises: A data abstraction module, used for performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of each of the power generation element characteristics; the power generation element characteristics are data of various natural elements and production elements that affect the power generation performance of the associated power plants; A first stack establishment module, used for predicting long-term transaction electricity prices according to a preset long-term electricity price function library and each of the first data vectors, so as to establish a long-term transaction stack; A second stack establishment module, configured to establish a spot transaction stack based on the spot transaction electricity price of each of the associated power plants, and determine a first clearing function through the long-term transaction stack and the spot transaction stack; A balance state determination module, used to determine the power supply and demand balance state of the target power grid according to the first clearing function; a clearing function adjustment module, configured to adjust the first clearing function according to the power supply and demand balance state to obtain a second clearing function, and perform power clearing based on the second clearing function; The power supply and demand balance state includes: over-generation state; the first clearing function includes: first long-term transaction volume; The clearing function adjustment module is specifically used for: When the power supply and demand balance state is the over-power generation state, high-order data abstract processing is performed on the characteristics of each power generation element through a preset deep learning model to obtain a second data vector of each associated power plant; Determining a long-term transaction volume increase value according to each of the second data vectors and the preset long-term electricity price function library; Adjusting the first long-term transaction volume according to the long-term transaction volume increase value to obtain the second clearing function; The power supply and demand balance state includes: under-generation state; the first clearing function includes: first spot transaction volume; The clearing function adjustment module is further used for: When the power supply and demand balance state is the under-generation state, for a first power generation curve of the target power grid when the first clearing function is cleared, fitting the first power generation curve by a preset fitting function to obtain a fitting power generation curve; Based on the fitted power generation curve and the preset planned power generation curve, determining whether the first spot transaction volume satisfies the preset planned power generation curve; When the first spot transaction volume does not satisfy the preset planned power generation curve, the preset long-term electricity price function library is modified by a preset reinforcement learning model to determine a long-term transaction volume reduction value; Adjusting the proportion of the spot trading volume in the first clearing function according to the long-term trading volume reduction value to obtain a spot trading volume increase value; adjusting the first spot trading volume according to the spot trading volume increase value to obtain the second clearing function; The step of performing low-order data abstraction processing according to the power generation element characteristics of each of the associated power plants to obtain a first data vector of the power generation element characteristics includes: According to the characteristics of each power generation element, an energy consumption data model of each associated power plant is established; the energy consumption data model is used to evaluate the power generation performance of the associated power plant; Performing low-order data abstraction processing on the characteristics of each power generation element through the energy consumption data model to obtain the first data vector of the characteristics of each power generation element; The step of establishing the energy consumption data model of each of the associated power plants according to the characteristics of each of the power generation elements comprises: The characteristics of each power generation element are converted into a primary indicator, and based on the primary indicator, an energy consumption data model of the secondary and tertiary indicators of each associated power plant is established; the indicators include: energy consumption and power generation efficiency.

8. An electronic device, characterized in that: The device comprises: a processor, a memory and a system bus; The processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the power grid clearing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the power grid clearing method described in any one of claims 1 to 6 is implemented.

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